모델 박물관2024년 개관

기준

Debug the architecture: 답변 250개

“Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for sta”

보관된 답변, 최신순, 2026년 2월부터 2026년 10월까지. 각 답변은 기록된 프롬프트와 함께 보관됩니다.

  1. Step 5 Preview StepFun, 2026년 10월 9일
    Here is a systematic breakdown of the failure modes, race conditions, and bottlenecks, grouped by domain. Each includes a concrete remediation and its…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  2. Claude Haiku 5.5 Anthropic, 2026년 10월 9일
    Architecture Review: Real-Time Collaborative Editor The most serious problems are in the core sync model. Last-write-wins on client clocks, 2-second p…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  3. Mistral Large 4 Mistral, 2026년 10월 6일
    I'll analyze this architecture systematically, identifying issues across each layer. This is a thoughtful exercise because many of these issues are su…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  4. Ling 3.1 Flash inclusionAI, 2026년 10월 6일
    Architecture Review: Real-Time Collaborative Document Editor 🔴 Critical Issues --- 1. Cross-Server Real-Time Sync is Fundamentally Broken Problem: The…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  5. GPT-6.1 Sol OpenAI, 2026년 9월 30일
    The main problem is that this design treats collaborative editing as ordinary database updates plus broadcasting. Collaboration needs a convergence pr…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  6. Qwen3.8 Omni Flash Qwen, 2026년 9월 28일
    This architecture has several launch-blocking correctness, security, and scaling problems. The most severe are: 1. Last-write-wins using client clocks…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  7. Qwen3.8 Flash Qwen, 2026년 9월 28일
    This architecture has several fundamental problems for a Google Docs-style real-time editor. The biggest issue is that it treats collaborative editing…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  8. MiMo-V2.6-Pro Xiaomi, 2026년 9월 28일
    Architecture Review: Real-Time Collaborative Document Editor I've identified 22 distinct issues across six categories, ordered by severity. Each inclu…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  9. MiMo-V2.6-Flash Xiaomi, 2026년 9월 28일
    Comprehensive Architecture Failure Mode Analysis Issue 1: CloudFront Caching API Responses (5 minutes) — CRITICAL Failure Mode: A user saves a documen…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  10. Laguna XS 2.1 Poolside, 2026년 9월 28일
    Real-Time Collaborative Editor Architecture Analysis This architecture has several critical issues that would cause data loss, conflicts, and poor use…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  11. DeepSeek V4.1 Flash DeepSeek, 2026년 9월 28일
    This architecture has several fundamental correctness, durability, and scaling problems. The biggest risks are client-clock last-write-wins, cross-ser…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  12. Claude Sonnet 5.5 Anthropic, 2026년 9월 28일
    Architecture Review: Real-Time Collaborative Editor Issues are grouped by category and ordered roughly by severity within each group. --- 1. Consisten…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  13. Solar Mini 4 Upstage, 2026년 9월 25일
    Here’s a systematic breakdown of the architectural weaknesses, failure modes, and scaling bottlenecks in your design, along with specific solutions an…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  14. Qwen3.8 Max Prime Qwen, 2026년 9월 25일
    Architecture Analysis: Failure Modes, Race Conditions & Scaling Bottlenecks --- 1. SYNC & CONSISTENCY LAYER 1.1 Last-Write-Wins with Client Clocks (Cr…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  15. GPT-6 Sol Pro OpenAI, 2026년 9월 25일
    The central problem is that this design treats concurrent edits as independent database writes, then tries to reconcile them with client timestamps. T…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  16. GPT-6 Sol OpenAI, 2026년 9월 25일
    The central flaw is that the system treats collaborative edits as independent writes to a document. They are not: edits need a shared ordering and a m…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  17. GPT-6 Luna Pro OpenAI, 2026년 9월 25일
    The main architectural risk is that this design treats collaborative editing as ordinary “last write wins” data storage. It is not: edits need a well-…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  18. GPT-6 Luna OpenAI, 2026년 9월 25일
    The main architectural risk is that the system treats collaborative editing as “write a value, then broadcast it.” That works poorly under concurrency…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  19. GLM 5.3 Prime Zhipu, 2026년 9월 25일
    Architecture Review: Real-Time Collaborative Editor Critical Issues 1. Client-clock timestamps for last-write-wins (LWR) Problem: Client clocks are un…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  20. Command A+ Cohere, 2026년 9월 25일
    This architecture, while functional for a prototype, contains several critical architectural flaws regarding consistency, availability, and scalabilit…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  21. Claude Opus 5.5 Anthropic, 2026년 9월 25일
    Architecture Review: Real-Time Collaborative Editor The most serious problems cluster in three areas: the sync model (client-clock LWW), cross-server …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  22. Solar Pro 4 Upstage, 2026년 9월 22일
    This architecture contains several fundamental mismatches between real-time collaborative editing requirements and the chosen patterns. Below is a sys…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  23. Qwen3.8 Max (0902) Qwen, 2026년 9월 22일
    Architecture Failure Analysis: Real-Time Collaborative Editor Critical Issues (Data Loss / Correctness) --- 1. Last-Write-Wins with Client Clocks — Fu…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  24. Qwen3.7 Flash Qwen, 2026년 9월 22일
    Here’s a structured breakdown of the critical failure modes, race conditions, and scaling bottlenecks in the proposed architecture, along with targete…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  25. Nex-N2.5-Pro Nex AGI, 2026년 9월 퇴역, 2026년 9월 22일
    Executive summary The biggest correctness risks are: 1. Client-clock last-write-wins is unsafe: clock skew, malicious clients, ties, and out-of-order …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  26. Nex-N2.5-Mini Nex AGI, 2026년 9월 퇴역, 2026년 9월 22일
    The biggest red flags are: client-clock LWW for document content, polling PostgreSQL for real-time fanout, CDN caching mutable authenticated APIs, per…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  27. Nemotron 3 Nano Omni NVIDIA, 2026년 9월 22일
    We need to list potential failure modes, race conditions, scaling bottlenecks, and propose specific solutions with trade-offs. Must be thorough. Consi…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  28. Muse Glimmer 30B Meta, 2026년 9월 22일
    This architecture will work for a demo but will break badly under real concurrent use. Here are the critical failure modes, race conditions and bottle…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  29. Mercury 2.5 Inception, 2026년 9월 22일
    Critical Analysis of Collaborative Editor Architecture Below is a detailed breakdown of failure modes, race conditions, and scaling bottlenecks, along…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  30. Ling 3.0 Flash VL inclusionAI, 2026년 9월 퇴역, 2026년 9월 22일
    Architecture Audit: Real-Time Collaborative Document Editor Critical Issues Found: 25+ --- 1. REAL-TIME COLLABORATION FAILURES Issue 1 — Last-Write-Wi…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  31. Ling 3.0 Flash Sante inclusionAI, 2026년 9월 22일
    Architecture Analysis: Real-Time Collaborative Editor 🔴 Critical Issues 1. Cross-Server Communication Gap (Race Condition) Problem: Server A broadcast…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  32. Ling 3.0 Flash Fin inclusionAI, 2026년 9월 퇴역, 2026년 9월 22일
    Comprehensive Architecture Analysis: Real-Time Collaborative Document Editor --- 1. CRITICAL: WebSocket Broadcasting Is Siloed Per-Server Problem: Eac…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  33. Ling 3.0 Flash inclusionAI, 2026년 9월 22일
    Architecture Analysis: Real-Time Collaborative Document Editor --- 1. WebSocket & Cross-Server Communication Issue: Siloed WebSocket Servers (Critical…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  34. Hy3 Tencent, 2026년 9월 22일
    Below is a breakdown of the major failure modes, race conditions, and scaling bottlenecks in the described architecture, followed by targeted fixes an…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  35. Grok 4.7 xAI, 2026년 9월 22일
    This design will lose edits, split users into inconsistent views, and fall over under concurrent writing. The core problems are clock-based conflict r…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  36. GLM 5.3 FlashX Zhipu, 2026년 9월 22일
    Architecture Review: Real-Time Collaborative Editor This architecture has fundamental flaws that would break core functionality, plus scalability issu…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  37. GLM 5.3 Flash Zhipu, 2026년 9월 22일
    Architecture Review: Collaborative Document Editor This architecture has several critical flaws that would break the core product promise (real-time c…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  38. GLM 5.3 Zhipu, 2026년 9월 22일
    Architecture Review: Real-Time Collaborative Editor This design has serious issues across correctness, performance, and security. Let me break them do…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  39. GPT-6 Astra OpenAI, 2026년 9월 4일
    This design can support a collaborative-editor prototype, but its current consistency model will lose edits, its cross-server propagation will produce…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  40. Muse Spark 1.3 Contributor Meta, 2026년 9월 2일
    This architecture will not work as a Google Docs clone. It has correctness bugs that cause data loss and data leaks, plus several bottlenecks that get…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  41. Muse Spark 1.3 Meta, 2026년 9월 2일
    This architecture will work for 1 doc and 5 users on your laptop. It will fall over, lose data, and leak data in production. Here's why: P0 - Will Cau…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  42. Mercury 2.5 Preview Inception, 2026년 9월 퇴역, 2026년 9월 2일
    Here is an analysis of the potential failure modes, race conditions, and scaling bottlenecks in the proposed architecture, along with specific solutio…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  43. Hy4 Preview Tencent, 2026년 9월 2일
    This architecture contains several critical flaws that will lead to data loss, poor user experience, and security vulnerabilities. While it attempts t…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  44. Granite 4.2 8B IBM, 2026년 9월 2일
    Comprehensive Failure Mode, Race Condition, and Scaling Bottleneck Analysis (Based strictly on the provided architecture description; no assumptions b…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  45. Gemini 3.8 Flash Google, 2026년 9월 2일
    This architecture contains several catastrophic flaws that will cause silent data corruption, severe database exhaustion, high latency, and security v…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  46. Ox Alpha Code names, 2026년 8월 퇴역, 2026년 8월 24일
    Architecture Review: Real-Time Collaborative Editor This design has several critical flaws that would break core collaborative editing functionality. …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  47. Seed 2.1 Turbo ByteDance, 2026년 8월 18일
    Below is a structured analysis of all failure modes, race conditions, and scaling bottlenecks in the proposed architecture, paired with specific, acti…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  48. Seed 2.0 Code ByteDance, 2026년 8월 18일
    Analysis of Issues & Remediations We categorize flaws in the original architecture into Race Conditions, Failure Modes, and Scaling Bottlenecks, with …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  49. Qwen3.8 27B Qwen, 2026년 8월 18일
    Below is a comprehensive issue register based on the architecture you described. I’m assuming: - “change event” can be a small edit, possibly per keys…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  50. Qwen3.8 2.4T A95B Qwen, 2026년 8월 18일
    The architecture as written has several P0 correctness problems that can cause lost edits, divergent documents, and security/staleness issues, plus sc…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  51. Nemotron 3.5 Lightning NVIDIA, 2026년 8월 18일
    This architecture is a classic "Google Docs clone" design. While it works for a prototype or a small user base, it contains several critical failure m…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  52. LFM2.5-2.6B Liquid, 2026년 8월 18일
    Potential Failure Modes, Race Conditions, and Scaling Bottlenecks 1. WebSocket Connection Loss and Cleanup - Failure Mode: If a client disconnects une…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  53. Grok 4.6 xAI, 2026년 8월 18일
    Architecture Review: Real-Time Collaborative Editor This design will not behave like Google Docs. The dominant problems are incorrect consistency mode…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  54. Gemini 3.7 Flash Google, 2026년 8월 18일
    Here is an architectural analysis of the system, breaking down the critical failure modes, race conditions, and bottlenecks, along with concrete solut…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  55. Dots3-Note Preview dots, 2026년 8월 18일
    Of course. This is a classic and challenging architecture. While it's a good starting point, it has several critical issues that would lead to a poor …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  56. DeepSeek V4 Pro 0813 DeepSeek, 2026년 8월 18일
    This architecture as described would have serious consistency, availability, and security problems for a real-time collaborative editor. The biggest r…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  57. Qwen3.8 Max Qwen, 2026년 9월 퇴역, 2026년 8월 5일
    Below is a comprehensive failure-mode review of the proposed architecture. I will group related issues where the same root cause creates multiple symp…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  58. DeepSeek V4 Flash 0731 DeepSeek, 2026년 8월 5일
    Failure Modes, Race Conditions, and Scaling Bottlenecks The described architecture has several critical weaknesses that undermine reliability, consist…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  59. Claude Opus 5 Anthropic, 2026년 7월 24일
    Architecture Review: Real-Time Collaborative Editor Executive Summary Three flaws are fatal to correctness, not just performance, and no amount of sca…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  60. Gemini 3.6 Flash Google, 2026년 7월 23일
    This architecture contains several critical single points of failure, severe race conditions, data-loss mechanisms, and major performance bottlenecks.…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  61. Inkling Thinking Machines, 2026년 7월 21일
    This architecture contains fatal contradictions for real-time collaboration: server-local WebSocket state combined with round-robin load balancing, cl…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  62. Muse Spark 1.1 Meta, 2026년 7월 16일
    This architecture will fail in production under even moderate load and 2+ concurrent users. Here is a breakdown by severity. 1. REAL-TIME SYNC - CRITI…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  63. Kimi K3 Moonshot, 2026년 7월 16일
    Architecture Review: Collaborative Document Editor Overall assessment: The load-bearing problem is the sync layer — client-clock LWW + 2-second cross-…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  64. Grok 4.5 xAI, 2026년 7월 9일
    Architecture Review: Real-Time Collaborative Document Editor This design has fundamental real-time and consistency problems. Several choices (client c…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  65. GPT-5.6 Terra OpenAI, 2026년 7월 9일
    Below are the main failure modes, race conditions, correctness issues, and scaling bottlenecks in the proposed architecture, with concrete mitigations…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  66. GPT-5.6 Sol OpenAI, 2026년 7월 9일
    This architecture will work for a small prototype, but it does not currently guarantee convergence, ordering, durability, privacy, or predictable hori…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  67. GPT-5.6 Luna Pro OpenAI, 2026년 7월 9일
    Below are the major correctness, availability, security, and scaling problems in the design, together with concrete mitigations and their trade-offs. …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  68. GPT-5.6 Luna OpenAI, 2026년 7월 9일
    The architecture has several correctness, availability, security, and scaling risks. The most serious issues are the cross-server synchronization desi…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  69. Claude Sonnet 5 Anthropic, 2026년 6월 30일
    Failure Modes, Race Conditions & Bottlenecks Analysis A. Real-Time Sync & Consistency (Critical) A1. Cross-server broadcast gap Issue: A server only b…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  70. North Mini Code Cohere, 2026년 6월 24일
    Below is a structured audit of the proposed real‑time collaborative document editor. For each problem we list: What can go wrong? – failure mode, race…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  71. GLM 5.2 Zhipu, 2026년 6월 16일
    This architecture has several critical flaws that would prevent it from functioning as a reliable, real-time collaborative editor. Here is a breakdown…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  72. OpenRouter Fusion · Quality (Jun 2026) OpenRouter, 2026년 6월 13일
    I'll analyze this architecture thoroughly, drawing on multiple expert perspectives to ensure I catch the subtle failure modes. Architecture Review: Re…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  73. OpenRouter Fusion · Budget (Jun 2026) OpenRouter, 2026년 6월 13일
    This architecture contains several critical design flaws, race conditions, and bottlenecks that will lead to catastrophic data loss, high latency, and…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  74. Kimi K2.7 Code Moonshot, 2026년 6월 13일
    Below is a structured review of the proposed architecture. Issues are grouped by domain, and each includes a problem, impact, and a specific solution …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  75. Claude Fable 5 Anthropic, 2026년 6월 9일
    Architecture Review: Critical Issues & Solutions 🔴 Critical: Data Loss & Correctness Issues 1. Client clock timestamps for conflict resolution Problem…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  76. Nemotron 3.5 Content Safety NVIDIA, 2026년 6월 6일
    User Safety: safe
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  77. Nemotron 3 Ultra NVIDIA, 2026년 6월 6일
    Architecture Analysis: Real-Time Collaborative Editor --- Critical Failure Modes 1. Client-Side Timestamps for Conflict Resolution Problem: Last-write…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  78. Qwen3.7 Plus Qwen, 2026년 6월 4일
    This architecture contains several critical anti-patterns for a real-time collaborative system. While it might work for a handful of users, it will fa…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  79. MiniMax M3 MiniMax, 2026년 6월 2일
    Architectural Review: Real-Time Collaborative Document Editor This architecture has several critical flaws that would manifest as data loss, poor UX, …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  80. Claude Opus 4.8 Anthropic, 2026년 5월 28일
    Architecture Review: Collaborative Document Editor This design has several critical flaws that will manifest as data loss, poor UX, and scaling walls.…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  81. Qwen3.7 Max Qwen, 2026년 5월 22일
    This architecture contains several critical flaws that will cause data loss, severe latency, security vulnerabilities, and database collapse under mod…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  82. Gemini 3.5 Flash Google, 2026년 5월 19일
    Here is an architectural audit of your real-time collaborative document editor. The current design contains several critical flaws that will lead to d…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  83. ERNIE 4.5 300B A47B Baidu, 2026년 6월 퇴역, 2026년 5월 11일
    Here's a detailed analysis of potential issues in the architecture, along with solutions and trade-offs: 1. Real-time Sync Issues Problem: WebSocket b…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  84. Ring 2.6 1T inclusionAI, 2026년 5월 퇴역, 2026년 5월 8일
    Below is a catalog of the most dangerous failure modes, race conditions and scaling bottlenecks that are present in the described architecture, follow…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  85. Gemini 3.1 Flash Lite Google, 2026년 5월 7일
    This architecture, while functional for a prototype, contains several critical flaws that would lead to data loss, inconsistent state, and system inst…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  86. Grok 4.3 xAI, 2026년 5월 2일
    Here is a comprehensive analysis of the failure modes, race conditions, and scaling bottlenecks in this architecture: 1. Cross-Server Isolation (Criti…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  87. Owl Alpha Code names, 2026년 6월 퇴역, 2026년 4월 28일
    Critical Architecture Analysis: Real-Time Collaborative Document Editor 🔴 CRITICAL ISSUES 1. Stale Data Propagation Between Servers Problem: Server A'…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  88. Qwen3.6 Max Preview Qwen, 2026년 4월 27일
    Here’s a systematic breakdown of the architectural flaws, categorized by domain. Each item includes the failure mode/race condition/bottleneck, root c…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  89. Qwen3.6 Flash Qwen, 2026년 4월 27일
    This architecture contains several critical failure modes that would prevent it from functioning as a functional collaborative editor. The combination…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  90. Qwen3.6 35B A3B Qwen, 2026년 4월 27일
    Here’s a systematic breakdown of the critical failure modes, race conditions, and scaling bottlenecks in this architecture, along with specific soluti…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  91. Qwen3.6 27B Qwen, 2026년 4월 27일
    This architecture contains several critical flaws that will cause data loss, severe latency, security vulnerabilities, and scaling failures under real…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  92. Qwen3.5 Plus 2026-04-20 Qwen, 2026년 4월 27일
    Here is a systematic breakdown of the architectural flaws, race conditions, and scaling bottlenecks, followed by targeted solutions and trade-offs. --…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  93. GPT-5.5 OpenAI, 2026년 4월 24일
    Below is a comprehensive review of the architecture’s likely failure modes, race conditions, and scaling bottlenecks, with concrete mitigations and tr…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  94. DeepSeek V4 Pro DeepSeek, 2026년 4월 24일
    This architecture has several critical flaws that would cause data loss, stale states, high latency, and scalability limits. I’ve grouped them into fa…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  95. DeepSeek V4 Flash DeepSeek, 2026년 4월 24일
    Failure Modes, Race Conditions, and Scaling Bottlenecks The described architecture has several critical flaws that compromise reliability, consistency…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  96. Ling 2.6 1T inclusionAI, 2026년 5월 퇴역, 2026년 4월 23일
    Below is a failure-mode, race-condition and bottleneck audit of the proposed real-time collaborative editor, followed by specific fixes with explicit …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  97. MiMo-V2.5-Pro Xiaomi, 2026년 4월 22일
    Architecture Analysis: Failure Modes, Race Conditions & Bottlenecks This is a thorough red-pen review. There are some genuinely critical issues here —…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  98. MiMo-V2.5 Xiaomi, 2026년 4월 22일
    Architecture Analysis: Real-Time Collaborative Editor This is a substantial system. Let me walk through the failure modes, race conditions, and scalin…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  99. Ling 2.6 Flash inclusionAI, 2026년 4월 퇴역, 2026년 4월 21일
    Below is a structured analysis of failure modes, race conditions, and scaling bottlenecks in the described real-time collaborative document editor, al…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  100. Kimi K2.6 Moonshot, 2026년 4월 20일
    Here is a systematic breakdown of the critical flaws in this architecture, categorized by the type of risk they introduce. --- 1. The "2-Second Blindn…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  101. Claude Opus 4.7 Anthropic, 2026년 4월 16일
    Critical Issues in This Collaborative Editor Architecture This architecture has fundamental problems. Let me break them down by severity. 🔴 Critical I…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  102. Elephant Alpha Code names, 2026년 4월 퇴역, 2026년 4월 14일
    Excellent architecture. Let's break down the potential failure modes, race conditions, and scaling bottlenecks, and propose specific solutions with th…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  103. GLM 5.1 Zhipu, 2026년 4월 7일
    This architecture contains several critical flaws that will lead to data loss, race conditions, severe performance bottlenecks, and security vulnerabi…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  104. Gemma 4 26B A4B Google, 2026년 4월 3일
    This architecture contains several critical flaws that would lead to data loss, massive synchronization delays, and "split-brain" scenarios in a produ…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  105. Gemma 4 31B Google, 2026년 4월 2일
    This architecture contains several critical flaws that would make it unusable for a real-time collaborative experience. The most severe issues are the…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  106. Qwen3.6 Plus Preview Qwen, 2026년 4월 퇴역, 2026년 3월 30일
    This architecture contains several well-known anti-patterns for real-time collaborative editing. Below is a systematic breakdown of race conditions, s…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  107. Qwen3 30B A3B Thinking 2507 Qwen, 2026년 3월 29일
    Below is a comprehensive analysis of failure modes, race conditions, and scaling bottlenecks in the described architecture, with specific, actionable …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  108. Qwen3 30B A3B Qwen, 2026년 3월 29일
    Potential Failure Modes, Race Conditions, and Scaling Bottlenecks --- 1. Server Failure and Data Inconsistency - Failure Mode: If a server handling a …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  109. Qwen3 235B A22B Qwen, 2026년 3월 29일
    Potential Failure Modes, Race Conditions, and Scaling Bottlenecks --- 1. WebSocket State Isolation Across Servers - Failure Mode: Users connected to d…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  110. MiMo-V2-Pro Xiaomi, 2026년 5월 퇴역, 2026년 3월 29일
    This is a great exercise. Your architecture has a solid foundation but contains several critical flaws that would lead to data corruption, poor user e…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  111. MiMo-V2-Flash Xiaomi, 2026년 6월 퇴역, 2026년 3월 29일
    Of course. This is an excellent exercise. The proposed architecture is a common starting point for many applications, but it has several critical flaw…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  112. Llama 3 70B Meta, 2026년 6월 퇴역, 2026년 3월 29일
    What a fantastic question! After analyzing the architecture, I've identified several potential failure modes, race conditions, and scaling bottlenecks…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  113. GPT-3.5 Turbo OpenAI, 2026년 3월 29일
    Potential failure modes: 1. WebSocket connection drops for a client 2. API server crashes and loses unsaved changes 3. PostgreSQL database failure 4. …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  114. GLM 4.5 Zhipu, 2026년 3월 29일
    Failure Modes, Race Conditions, and Scaling Bottlenecks 1. WebSocket Server Failure - Issue: If a Node.js server fails, all connected clients lose rea…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  115. Claude 3.7 Sonnet Anthropic, 2026년 5월 퇴역, 2026년 3월 29일
    Architectural Analysis: Collaborative Document Editor Failure Modes & Race Conditions 1. WebSocket Connection Failures Issue: If a WebSocket connectio…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  116. MiMo-V2-Omni Xiaomi, 2026년 5월 퇴역, 2026년 3월 19일
    Architecture Analysis: Real-Time Collaborative Document Editor Critical Issues Identified 1. Conflict Resolution & Data Consistency Issue: Last-write-…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  117. MiniMax M2.7 MiniMax, 2026년 3월 18일
    Architecture Analysis: Real-Time Collaborative Document Editor Your architecture contains fundamental design issues that will cause data loss, inconsi…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  118. Mistral Small 4 Mistral, 2026년 3월 17일
    Here’s a comprehensive analysis of potential failure modes, race conditions, and scaling bottlenecks in your real-time collaborative document editor a…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  119. GPT-5.4 Nano OpenAI, 2026년 3월 17일
    Below is a structured list of likely failure modes, race conditions, and scaling bottlenecks in your design, with a concrete mitigation and trade-offs…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  120. GPT-5.4 Mini OpenAI, 2026년 3월 17일
    Below are the main failure modes, race conditions, and scaling bottlenecks in this design, with concrete fixes and trade-offs. --- 1) Last-write-wins …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  121. GLM 5 Turbo Zhipu, 2026년 3월 16일
    This architecture contains several fundamental flaws that would prevent it from functioning correctly at scale, and several others that would cause se…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  122. Grok 4.20 Multi-Agent Beta xAI, 2026년 3월 퇴역, 2026년 3월 12일
    Here is a comprehensive analysis of the failure modes, race conditions, and scaling bottlenecks in the described architecture. I have grouped related …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  123. Grok 4.20 Beta xAI, 2026년 3월 퇴역, 2026년 3월 12일
    Critical Failure Modes, Race Conditions, and Scaling Bottlenecks 1. Clock Skew & Broken Conflict Resolution (Severe) Problem: "Last-write-wins with ti…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  124. NVIDIA Nemotron 3 Super NVIDIA, 2026년 3월 11일
    Below is a thorough analysis of the proposed architecture for a real-time collaborative document editor. I've identified 12 critical failure modes, ra…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  125. Hunter Alpha Code names, 2026년 3월 퇴역, 2026년 3월 11일
    This is a fascinating architecture with several critical flaws that would manifest at scale. Let me break down the issues systematically. Critical Rac…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  126. Healer Alpha Code names, 2026년 3월 퇴역, 2026년 3월 11일
    Architecture Analysis: Real-Time Collaborative Editor Critical Failure Modes 1. WebSocket Connection Fragmentation Issue: When Server A broadcasts cha…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  127. Seed 2.0 Lite ByteDance, 2026년 3월 10일
    Below is a structured breakdown of all flaws in the architecture, categorized by type, with specific, actionable solutions and their inherent tradeoff…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  128. Qwen3.5 9B Qwen, 2026년 3월 10일
    This architecture contains several significant architectural flaws that would result in data loss, user frustration, and severe scaling limitations. W…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  129. Mercury 2 Inception, 2026년 3월 5일
    Below is a structured “failure‑mode → mitigation” map for the architecture you described. For each problem I note the symptom / risk, the root cause, …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  130. GPT-5.4 Pro OpenAI, 2026년 3월 5일
    This architecture will work for a demo, but it has major correctness, latency, and security problems for a Google-Docs-like editor. Highest-risk flaws…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  131. GPT-5.4 OpenAI, 2026년 3월 5일
    Here’s a structured teardown of the architecture: failure modes, race conditions, correctness issues, security problems, and scaling bottlenecks — plu…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  132. Gemini 3.1 Flash Lite Preview Google, 2026년 3월 3일
    This architecture contains several critical flaws that would lead to data loss, inconsistent state, and poor user experience in a real-time environmen…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  133. GPT-5.3 Chat OpenAI, 2026년 8월 퇴역, 2026년 3월 3일
    Key issues fall into consistency, real‑time propagation, storage, scaling, and security. 1. WebSocket broadcast only reaches clients on the same serve…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  134. Qwen3.5 Flash Qwen, 2026년 2월 26일
    This architecture is a common "MVP" design, but it contains several critical flaws that would prevent it from functioning as a true real-time collabor…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  135. Qwen3.5 35B A3B Qwen, 2026년 2월 26일
    This architecture contains several critical flaws that would lead to data loss, poor user experience, and security vulnerabilities in a production env…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  136. Qwen3.5 27B Qwen, 2026년 2월 26일
    This architecture contains several critical design flaws that would lead to data loss, high latency, security vulnerabilities, and poor scalability. B…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  137. Qwen3.5 122B A10B Qwen, 2026년 2월 26일
    This architecture contains several critical flaws that will lead to data loss, poor user experience, and system instability at scale. The "Last-write-…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  138. GPT-5.3-Codex OpenAI, 2026년 2월 25일
    Great prompt. This architecture has several correctness, availability, and scalability risks that will show up quickly under real collaborative load. …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  139. Gemini 3.1 Pro Preview Google, 2026년 2월 19일
    This architecture contains several critical flaws that would prevent it from functioning as a real-time collaborative editor (like Google Docs). It su…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  140. Claude Sonnet 4.6 Anthropic, 2026년 2월 17일
    Comprehensive Architecture Review: Real-Time Collaborative Document Editor Executive Summary This architecture has critical flaws that will cause data…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  141. Qwen3.5 Plus 2026-02-15 Qwen, 2026년 2월 16일
    This architecture contains several critical flaws that would prevent it from functioning as a reliable real-time collaborative editor. While the compo…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  142. Qwen3.5 397B A17B Qwen, 2026년 2월 16일
    This architecture contains several critical flaws that would prevent it from functioning as a usable real-time collaborative editor. While it resemble…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  143. MiniMax M2.5 MiniMax, 2026년 2월 12일
    Potential Failure Modes, Race Conditions, and Scaling Bottlenecks 1. WebSocket Broadcasting Limited to Same Server - Issue: Users connected to differe…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  144. GLM 5 Zhipu, 2026년 2월 11일
    This architecture contains several critical flaws that would lead to data loss, poor user experience, and system instability under load. Below is a de…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  145. Qwen3 Max Thinking Qwen, 2026년 2월 9일
    Critical Flaws in Proposed Architecture & Targeted Solutions 🔒 Security & Authentication Issue Impact Solution Trade-offs ----------------------------…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  146. Aurora Alpha Code names, 2026년 2월 퇴역, 2026년 2월 9일
    Below is a systematic “failure‑mode → root cause → mitigation” map for the architecture you described. I grouped the items into four logical layers (c…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  147. Pony Alpha Code names, 2026년 2월 퇴역, 2026년 2월 6일
    This architecture contains several critical flaws that would prevent the application from functioning as a true real-time collaborative editor ("Googl…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  148. Qwen3 Coder Next Qwen, 2026년 2월 4일
    This architecture has several critical flaws that would manifest under real-world usage, especially with multiple concurrent users. Below is a systema…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  149. Claude Opus 4.6 Anthropic, 2026년 2월 4일
    Comprehensive Architecture Review: Real-time Collaborative Document Editor This architecture has several critical flaws that would cause data loss, in…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  150. o3 Mini OpenAI, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  151. o1 OpenAI, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  152. TNG R1T Chimera TNG, 2026년 2월 퇴역, 2026년 2월 3일
    Here's a detailed analysis of potential issues and solutions for the proposed architecture: 1. WebSocket Broadcast Fragmentation Failure Mode: Changes…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  153. Solar Pro 3 Upstage, 2026년 2월 3일
    1. Authentication & Session Management Failure / Race Condition Why it happens in the current design Concrete mitigation (trade‑offs) ----------------…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  154. Qwen3 Next 80B A3B Thinking Qwen, 2026년 2월 3일
    Here'sa comprehensive analysis of the architecture, identifying critical failure modes, race conditions, and scaling bottlenecks. For each issue, I've…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  155. Qwen3 Next 80B A3B Instruct Qwen, 2026년 2월 3일
    This architecture resembles a simplified real-time collaborative editor but contains critical flaws in concurrency control, consistency, scalability, …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  156. Qwen3 Max Qwen, 2026년 2월 3일
    Critical Issues in the Collaborative Document Editor Architecture 1. Last-Write-Wins with Client Clocks (Critical) Problem: Client clocks are unreliab…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  157. Qwen3 Coder Plus Qwen, 2026년 2월 3일
    I'll analyze this collaborative document editor architecture systematically, identifying critical issues and proposing solutions. CRITICAL FAILURE MOD…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  158. Qwen3 Coder Flash Qwen, 2026년 2월 3일
    Critical Issues in the Architecture 1. WebSocket Connection Partitioning (Race Condition) Issue: Clients are connected to different API servers based …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  159. Qwen3 Coder Qwen, 2026년 2월 3일
    Here are the critical issues I've identified in this architecture: Critical Failure Modes 1. WebSocket Connection Loss & Server Failure Problem: When …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  160. Qwen3 30B A3B Instruct 2507 Qwen, 2026년 2월 3일
    Your real-time collaborative document editor architecture is well-structured for a scalable, low-latency system — but it contains several critical fai…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  161. Qwen3 235B A22B Thinking 2507 Qwen, 2026년 2월 3일
    Here's a comprehensive breakdown of critical flaws in this architecture, along with specific solutions and trade-offs. The most severe issues relate t…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  162. Qwen3 235B A22B 2507 Qwen, 2026년 2월 퇴역, 2026년 2월 3일
    The described architecture for a real-time collaborative document editor has several critical failure modes, race conditions, and scaling bottlenecks …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  163. Qwen Plus 0728 (thinking) Qwen, 2026년 2월 3일
    Here's a comprehensive analysis of the architecture's failure modes, race conditions, and bottlenecks, with specific solutions and trade-offs: --- 1. …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  164. Qwen Plus 0728 Qwen, 2026년 2월 3일
    This architecture for a real-time collaborative document editor has several promising elements (WebSockets, Redis, PostgreSQL, CDN), but it also conta…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  165. OpenAI o4-mini OpenAI, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  166. OpenAI o4 Mini High OpenAI, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  167. OpenAI o3 OpenAI, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  168. Nova Premier 1.0 Amazon, 2026년 2월 3일
    Failure Modes and Solutions: 1. WebSocket Server Failure: - Issue: If a single API server fails, all WebSocket connections to that server are lost, di…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  169. NVIDIA Nemotron Nano 9B V2 NVIDIA, 2026년 8월 퇴역, 2026년 2월 3일
    Okay, let's tackle this problem. The user provided an architecture for a real-time collaborative document editor and wants me to identify failure mode…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  170. Mistral Small Creative Mistral, 2026년 4월 퇴역, 2026년 2월 3일
    Here’s a comprehensive analysis of the failure modes, race conditions, and scaling bottlenecks in the proposed architecture, along with specific solut…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  171. Mistral Nemo Mistral, 2026년 2월 3일
    Based on the provided architecture, here are potential failure modes, race conditions, and scaling bottlenecks along with proposed solutions and their…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  172. Mistral Medium 3.1 Mistral, 2026년 2월 3일
    This architecture has several potential failure modes, race conditions, and scaling bottlenecks. Below is a detailed analysis with proposed solutions …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  173. Mistral Medium 3 Mistral, 2026년 2월 3일
    This architecture has several potential failure modes, race conditions, and scaling bottlenecks. Below is a detailed analysis with proposed solutions …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  174. Mistral Large 3 2512 Mistral, 2026년 2월 3일
    Here’s a comprehensive analysis of potential failure modes, race conditions, and scaling bottlenecks in this architecture, along with proposed solutio…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  175. Mistral Large 2 Mistral, 2026년 2월 3일
    Here’s a comprehensive analysis of potential failure modes, race conditions, and scaling bottlenecks in this architecture, along with proposed solutio…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  176. Mistral Large Mistral, 2026년 2월 3일
    Here’s a comprehensive analysis of potential failure modes, race conditions, and scaling bottlenecks in this architecture, along with proposed solutio…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  177. Mistral Devstral Small 1.1 Mistral, 2026년 5월 퇴역, 2026년 2월 3일
    Potential Failure Modes, Race Conditions, and Scaling Bottlenecks 1. WebSocket Connection Failures: - Failure Mode: WebSocket connections can drop due…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  178. Mistral Devstral Medium Mistral, 2026년 5월 퇴역, 2026년 2월 3일
    Certainly! Let's break down the potential failure modes, race conditions, and scaling bottlenecks in the architecture, and propose solutions for each.…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  179. MiniMax M2.1 MiniMax, 2026년 2월 3일
    Critical Analysis: Real-Time Collaborative Document Editor Architecture Executive Summary This architecture presents several significant design concer…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  180. MiniMax M2-her MiniMax, 2026년 2월 3일
    Let's analyze this architecture systematically: 1. Timestamp-based Conflict Resolution Issues Problem: Using client clocks creates potential race cond…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  181. MiniMax M1 MiniMax, 2026년 2월 3일
    Identified Issues and Solutions --- 1. Failure Modes: a. WebSocket Connection Drop During Server Failure - Issue: If an API server fails, all its conn…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  182. Mercury Inception, 2026년 4월 퇴역, 2026년 2월 3일
    Below is a systematic review of the described real‑time collaborative editor. For each problem I list: 1. What can go wrong (failure mode, race condit…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  183. Llama 4 Scout Meta, 2026년 2월 3일
    Potential Failure Modes, Race Conditions, and Scaling Bottlenecks: Failure Modes 1. WebSocket Connection Loss Failure mode: WebSocket connection is lo…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  184. Llama 4 Maverick Meta, 2026년 2월 3일
    Potential Failure Modes, Race Conditions, and Scaling Bottlenecks 1. WebSocket Connection Management - Issue: When a user reconnects to a different se…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  185. Llama 3.1 70B (Instruct) Meta, 2026년 2월 3일
    Potential Failure Modes: 1. Single Point of Failure (SPOF) in Load Balancer: If the load balancer fails, all incoming traffic will be lost. Solution: …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  186. Kimi K2.5 Moonshot, 2026년 2월 3일
    This architecture contains several critical flaws that would prevent real-time collaboration, create data consistency issues, and cause catastrophic f…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  187. Kimi K2 Thinking Moonshot, 2026년 2월 3일
    Here are the critical issues in this architecture, categorized by type: --- Race Conditions & Data Consistency Issues 1. Client Clock Synchronization …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  188. Kimi K2 0905 Moonshot, 2026년 2월 3일
    Comprehensive Analysis of Collaborative Document Editor Architecture 1. Real-time Synchronization Issues Race Condition: WebSocket Broadcast Gaps Prob…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  189. Kimi K2 Moonshot, 2026년 2월 3일
    Comprehensive Analysis of the Collaborative Editor Architecture 1. Critical Race Conditions 1.1 WebSocket Connection Imbalance Issue: With round-robin…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  190. INTELLECT-3 Prime Intellect, 2026년 6월 퇴역, 2026년 2월 3일
    Analysis of Potential Issues in Real-Time Collaborative Document Editor Architecture Failure Modes 1. WebSocket Connection Failures Issue: Users may e…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  191. Grok Code Fast 1 xAI, 2026년 5월 퇴역, 2026년 2월 3일
    Below is a comprehensive analysis of the provided architecture for a real-time collaborative document editor. I'll identify potential failure modes (e…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  192. Grok 4.1 Fast xAI, 2026년 5월 퇴역, 2026년 2월 3일
    1. No Sticky Sessions for WebSockets (Scaling Bottleneck & Failure Mode) - Issue: Round-robin load balancer doesn't guarantee client WebSocket connect…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  193. Grok 4 xAI, 2026년 5월 퇴역, 2026년 2월 3일
    Below, I'll systematically identify potential failure modes, race conditions, and scaling bottlenecks in the described architecture for a real-time co…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  194. Grok 3 Beta xAI, 2026년 5월 퇴역, 2026년 2월 3일
    Let’s analyze the architecture of this real-time collaborative document editor for potential failure modes, race conditions, and scaling bottlenecks. …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  195. Grok 3 xAI, 2026년 5월 퇴역, 2026년 2월 3일
    Below is a comprehensive analysis of the provided architecture for the real-time collaborative document editor. I'll break it down into three main cat…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  196. Gemma 3n 4B Google, 2026년 8월 퇴역, 2026년 2월 3일
    Analysis of the Real-Time Collaborative Document Editor Architecture: Failure Modes, Race Conditions, and Scaling Bottlenecks Here's a breakdown of po…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  197. Gemma 3n 2B Google, 2026년 5월 퇴역, 2026년 2월 3일
    Okay, let's break down the architecture for this real-time collaborative document editor. I'll identify potential failure modes, race conditions, and …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  198. Gemma 3 27B Google, 2026년 2월 3일
    Failure Modes, Race Conditions, and Scaling Bottlenecks in Real-Time Collaborative Editor Architecture Here’s a breakdown of potential issues with the…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  199. Gemma 3 12B Google, 2026년 2월 3일
    Okay, let's break down the potential failure modes, race conditions, and scaling bottlenecks in this real-time collaborative document editor architect…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  200. Gemini 3 Pro Preview Google, 2026년 3월 퇴역, 2026년 2월 3일
    This architecture contains several critical flaws that will prevent it from functioning as a collaborative editor, likely resulting in data loss, mass…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  201. Gemini 3 Flash Preview Google, 2026년 2월 3일
    This architecture contains several critical flaws that would prevent it from functioning as a "Google Docs" clone. The most significant issues involve…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  202. Gemini 2.5 Pro Preview 06-05 Google, 2026년 9월 퇴역, 2026년 2월 3일
    Of course. This is a great exercise. The proposed architecture has several critical flaws that would prevent it from functioning as a reliable, real-t…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  203. Gemini 2.5 Pro Experimental Google, 2026년 2월 퇴역, 2026년 2월 3일
    Of course. This is an excellent exercise. The provided architecture has several critical issues that would prevent it from functioning as a reliable, …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  204. Gemini 2.5 Pro (I/O Edition) Google, 2026년 9월 퇴역, 2026년 2월 3일
    Of course. This is an excellent exercise. The proposed architecture has several critical flaws that would prevent it from functioning as a reliable, r…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  205. Gemini 2.5 Flash Preview 09-2025 Google, 2026년 2월 퇴역, 2026년 2월 3일
    This architecture, while functional, has several critical flaws, particularly in its real-time synchronization, data consistency, and scaling strategy…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  206. Gemini 2.5 Flash Lite Preview 09-2025 Google, 2026년 7월 퇴역, 2026년 2월 3일
    This architecture has several significant flaws, particularly concerning real-time consistency, data integrity, and horizontal scaling complexity. Her…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  207. GPT-5.2 Pro OpenAI, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  208. GPT-5.2 Chat OpenAI, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  209. GPT-5.2 OpenAI, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  210. GPT-5.1-Codex-Mini OpenAI, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  211. GPT-5.1-Codex OpenAI, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  212. GPT-5.1 Codex Max OpenAI, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  213. GPT-5.1 Chat OpenAI, 2026년 7월 퇴역, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  214. GPT-5.1 OpenAI, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  215. GPT-5 Pro OpenAI, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  216. GPT-5 Nano OpenAI, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  217. GPT-5 Mini OpenAI, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  218. GPT-5 Codex OpenAI, 2026년 8월 퇴역, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  219. GPT-5 OpenAI, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  220. GPT-4o mini OpenAI, 2026년 2월 3일
    In the architecture you've described for a real-time collaborative document editor, various potential failure modes, race conditions, and scaling bott…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  221. GPT-4o (Omni) OpenAI, 2026년 2월 3일
    This architecture is designed to support a real-time collaborative document editor but has several potential failure modes, race conditions, and scali…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  222. GPT-4.1 Nano OpenAI, 2026년 2월 3일
    Let's analyze the architecture step-by-step to identify potential failure modes, race conditions, and scaling bottlenecks, along with proposed solutio…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  223. GPT-4.1 Mini OpenAI, 2026년 2월 3일
    This architecture broadly covers a real-time collaborative editor but has several potential failure modes, race conditions, and scaling bottlenecks du…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  224. GPT-4.1 OpenAI, 2026년 2월 3일
    Certainly! Here’s a thorough analysis of the potential failure modes, race conditions, and scaling bottlenecks in your collaborative document editor a…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  225. GPT-4 OpenAI, 2026년 2월 3일
    Failure Modes: 1. Single Point of Failure: If PostgreSQL database fails, the whole system will be unavailable. - Solution: Implement a multi-master or…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  226. GPT OSS 20B OpenAI, 2026년 2월 3일
    Below is a “code‑review” of the design you posted. I’ve broken it down into failure modes / race conditions / bottlenecks and for each one I provide a…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  227. GPT OSS 120B OpenAI, 2026년 2월 3일
    Below is a system‑level failure‑mode / race‑condition / scaling‑bottleneck map for the architecture you described, followed by a concrete mitigation (…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  228. GLM 4.7 Flash Zhipu, 2026년 2월 3일
    Here is an analysis of the failure modes, race conditions, and scaling bottlenecks in the proposed architecture, followed by specific solutions and th…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  229. GLM 4.7 Zhipu, 2026년 2월 3일
    This architecture contains several critical flaws that will lead to data corruption, poor user experience, and database failure under load. Specifical…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  230. GLM 4.6 Zhipu, 2026년 2월 3일
    This is an excellent and classic architecture for a real-time system that contains several critical flaws. Let's break down the issues, from the most …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  231. GLM 4.5 Air Zhipu, 2026년 2월 3일
    Failure Modes, Race Conditions, and Scaling Bottlenecks Analysis 1. WebSocket Connection Management Issue: WebSocket connections are server-specific, …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  232. GLM 4 32B Zhipu, 2026년 6월 퇴역, 2026년 2월 3일
    Potential Failure Modes, Race Conditions, and Scaling Bottlenecks 1. WebSocket Connection Failure - Issue: If a user's WebSocket connection drops (e.g…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  233. DeepSeek V3.2 Speciale DeepSeek, 2026년 5월 퇴역, 2026년 2월 3일
    We need to analyze the given architecture for a real-time collaborative document editor, similar to Google Docs. The description includes components, …
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  234. DeepSeek V3.2 Exp DeepSeek, 2026년 2월 3일
    Critical Issues in Proposed Architecture 1. WebSocket Broadcast Isolation Issue: Each server only broadcasts to its own WebSocket clients → users conn…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  235. DeepSeek V3.2 DeepSeek, 2026년 2월 3일
    Here’s a comprehensive analysis of failure modes, race conditions, and scaling bottlenecks in the proposed architecture, along with specific solutions…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  236. DeepSeek V3.1 DeepSeek, 2026년 2월 3일
    Of course. This is an excellent exercise. The proposed architecture has several critical flaws that would lead to data loss, poor performance, and a b…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  237. DeepSeek V3 0324 DeepSeek, 2026년 2월 3일
    Here’s a comprehensive analysis of potential failure modes, race conditions, and scaling bottlenecks in your architecture, along with proposed solutio…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  238. DeepSeek R1 0528 DeepSeek, 2026년 2월 3일
    아무 말도 하지 않았습니다.
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  239. DeepSeek R1 DeepSeek, 2026년 2월 3일
    Identified Issues and Proposed Solutions: 1. Cross-Server Sync Delay (Polling Bottleneck) - Issue: 2-second polling introduces latency for cross-serve…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  240. Claude Sonnet 4.5 Anthropic, 2026년 2월 3일
    Critical Issues Analysis 🔴 CRITICAL: Data Loss & Consistency Issues 1. Client Clock-Based Timestamps (SEVERE) Problem: - Client clocks can be wrong, m…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  241. Claude Sonnet 4 Anthropic, 2026년 2월 3일
    I'll analyze this architecture systematically and identify critical issues with their solutions. Critical Failure Modes & Race Conditions 1. Server Is…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  242. Claude 3.5 Sonnet Anthropic, 2026년 4월 퇴역, 2026년 2월 3일
    I'll analyze this architecture systematically and identify key issues along with potential solutions: 1. Clock Synchronization Issues Problem: Using c…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  243. Claude Opus 4.5 Anthropic, 2026년 2월 3일
    Comprehensive Architecture Review: Real-time Collaborative Document Editor This architecture has several critical issues that will cause data loss, po…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  244. Claude Opus 4.1 Anthropic, 2026년 2월 3일
    Critical Issues Analysis 1. Split-Brain Real-time Synchronization Problem: Users connected to different API servers experience 2+ second delays due to…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  245. Claude Opus 4 Anthropic, 2026년 9월 퇴역, 2026년 2월 3일
    I'll analyze this architecture systematically, identifying critical issues and proposing solutions. Critical Issues & Solutions 1. Split-Brain Problem…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  246. Claude Haiku 4.5 Anthropic, 2026년 2월 3일
    Critical Analysis: Real-Time Collaborative Editor Architecture 🔴 CRITICAL ISSUES 1. Split-Brain Synchronization Between Servers Problem: Clients conne…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  247. Claude 3.7 Thinking Sonnet Anthropic, 2026년 5월 퇴역, 2026년 2월 3일
    Collaborative Document Editor: Architecture Review Failure Modes Analysis 1. WebSocket Connection Failures Issue: Client connections can fail due to n…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  248. Claude 3 Haiku Anthropic, 2026년 9월 퇴역, 2026년 2월 3일
    The proposed architecture for the real-time collaborative document editor has several potential failure modes, race conditions, and scaling bottleneck…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

  249. ChatGPT-4o (March 2025) OpenAI, 2026년 2월 퇴역, 2026년 2월 3일
    This architecture for a real-time collaborative document editor is a solid starting point, but it contains several potential failure modes, race condi…
    기록된 프롬프트

    Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.

박물관의 모든 것