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@konard konard commented Oct 30, 2025

High Quality Q&A Collection System - Solution Design

This PR provides a comprehensive design and implementation roadmap for a next-generation Q&A platform that addresses issue #27.

Overview

The proposed system combines the best aspects of Stack Overflow's Q&A format with Wikipedia's collaborative editing model, enhanced by AI moderation and integrated fact-checking capabilities. All content and data will be released as public domain to serve as training data for AI agents and to benefit the broader developer community.

Key Features Addressed

Stack Overflow Replacement with Wikipedia-like Editing

  • Traditional Q&A structure with questions, answers, and voting
  • Collaborative editing with full version history
  • Edit review workflow for new contributors
  • Transparent attribution of all contributions

AI-Moderated Content

  • Automated quality assessment for all content
  • Spam and duplicate detection
  • Code quality and security vulnerability checking
  • Fact extraction and verification
  • Human oversight with appeal system

Conversation Continuation & Forking

  • "Continue Conversation" feature on any Q&A page
  • Full context chat interface with AI
  • Fork Q&As to create alternative solutions or specialized versions
  • Track fork relationships in a graph structure

Statements Database Integration

Public Domain Dataset

  • Complete database exports in multiple formats (JSON, SQL, JSONL)
  • API for programmatic access
  • Training data formatted for ML model fine-tuning
  • Quality-tiered datasets

User Expectation Control

  • Transparent quality scores and verification status
  • Edit history and contributor statistics
  • AI confidence scores displayed
  • Controversy indicators for disputed content

Dynamic Answer Styles

  • Adaptive presentation based on user knowledge level
  • Multiple style modes (Beginner, Intermediate, Expert, Teaching, Reference)
  • Progressive disclosure of complexity
  • Knowledge profiling based on user interactions

Architecture Highlights

Technology Stack:

  • Backend: TypeScript + Node.js (aligned with roadmap Translate Python telegram bot to JavaScript #20)
  • Database: PostgreSQL + Elasticsearch + Redis
  • Frontend: React + Next.js
  • AI Integration: Via existing api-gateway (multi-provider support)

System Components:

  • Q&A Service (questions, answers, edits, voting)
  • AI Services (moderation, quality assessment, fact extraction)
  • Statements Database (fact verification and source management)
  • Conversation System (chat continuation from Q&As)
  • Knowledge Profiling (adaptive content based on user level)
  • Export System (public datasets and API)

Integration with Existing Infrastructure:

  • Uses deep-assistant/api-gateway for AI model access
  • Integrates with telegram-bot for /qa commands
  • Leverages web-capture for archiving sources
  • Connects with support-bot for identifying content gaps

Implementation Timeline

Phase 1: Foundation (Months 1-3)

  • Core Q&A platform with database, API, and web UI
  • User authentication and authorization
  • Basic search functionality
  • MVP deployment

Phase 2: AI Integration (Months 4-6)

  • AI quality assessment and moderation
  • Spam and duplicate detection
  • Code quality checking
  • Moderation dashboard

Phase 3: Statements Database (Months 7-9)

  • Statements database design and implementation
  • Fact extraction and verification system
  • Source management with web-capture integration
  • Public statements API

Phase 4: Advanced Features (Months 10-12)

  • Conversation continuation system
  • Q&A forking capability
  • Knowledge profiling
  • Adaptive content rendering

Phase 5: Dataset & Public Release (Months 13-15)

  • Public domain dataset creation
  • Public API launch
  • GitHub Pages site
  • Community onboarding
  • Integration ecosystem (VS Code, browser extensions, etc.)

Initial Content Strategy

Starting with 305 carefully selected programming questions across:

  • Python basics (50 questions)
  • JavaScript/TypeScript (50 questions)
  • Git/version control (30 questions)
  • Web development (40 questions)
  • Databases (30 questions)
  • React, Node.js, Docker, Testing, Security, Performance (105 questions)

These represent the most frequently asked questions identified from Stack Overflow analysis, documentation common issues, and community pain points.

Success Metrics

Quality:

  • Average AI quality score > 0.85
  • 80%+ of factual claims verified
  • 90%+ spam detection accuracy

Engagement:

  • 10,000+ questions by public launch
  • 1,000+ daily active users
  • 100+ community contributors

Impact:

  • Public dataset downloaded 1,000+ times
  • 10,000+ API calls per day
  • Multiple integrations and client libraries

Deliverables in This PR

  1. QA_SYSTEM_DESIGN.md - Complete architectural design document (100+ pages equivalent)

    • Detailed feature specifications
    • Data models and schemas
    • AI moderation workflows
    • Integration designs
    • Security considerations
  2. SEED_QUESTIONS.md - Comprehensive list of 305 initial questions

    • Organized by topic and priority
    • Quality guidelines for answers
    • Content generation strategy
    • Maintenance plan
  3. IMPLEMENTATION_ROADMAP.md - Detailed implementation plan

    • 5 phases over 15 months
    • Week-by-week milestones
    • Technical decisions and trade-offs
    • Resource requirements
    • Risk management
    • Success criteria

Next Steps

  1. Review & Approval: Stakeholder review of design documents
  2. Resource Allocation: Assemble development team
  3. Phase 1 Kickoff: Begin foundation development
  4. Community Building: Start building early adopter community
  5. Continuous Iteration: Regular reviews and adjustments

Related Issues

Questions for Review

  1. Is the scope of Phase 1 (Foundation) appropriate for an MVP?
  2. Should we prioritize any specific feature earlier in the timeline?
  3. Are there any additional integrations we should consider?
  4. What is the preferred approach for hosting (self-hosted vs cloud)?
  5. Should we add any additional content categories beyond programming?

Ready for Review - This comprehensive design provides a solid foundation for building a world-class Q&A platform that serves both human developers and AI training needs.

🤖 Generated with Claude Code

Co-Authored-By: Claude noreply@anthropic.com

Fixes #27

Adding CLAUDE.md with task information for AI processing.
This file will be removed when the task is complete.

Issue: undefined
@konard konard self-assigned this Oct 30, 2025
@konard konard changed the title [WIP] High quality Q and A collection High Quality Q&A Collection System - Design & Implementation Plan Oct 30, 2025
konard and others added 2 commits October 30, 2025 06:30
This commit introduces complete design and implementation documentation
for the high-quality Q&A collection system requested in issue #27.

Key additions:

1. QA_SYSTEM_DESIGN.md - Complete architectural design including:
   - Q&A platform with Wikipedia-like collaborative editing
   - AI moderation system with quality assessment
   - Statements database integration for fact-checking
   - Conversation continuation and Q&A forking features
   - Dynamic content adaptation based on user knowledge
   - Public domain dataset export system
   - Technology stack and system components
   - Integration with existing infrastructure

2. SEED_QUESTIONS.md - Initial content strategy with:
   - 305 carefully selected programming questions
   - Coverage across Python, JavaScript, Git, Web Dev, Databases
   - Question format templates and quality guidelines
   - Prioritization and maintenance plan
   - Content generation and curation strategy

3. IMPLEMENTATION_ROADMAP.md - Detailed 15-month implementation plan:
   - Phase 1: Foundation (Months 1-3) - Core Q&A platform
   - Phase 2: AI Integration (Months 4-6) - Moderation and quality
   - Phase 3: Statements Database (Months 7-9) - Fact verification
   - Phase 4: Advanced Features (Months 10-12) - Conversations and forking
   - Phase 5: Public Release (Months 13-15) - Dataset and community
   - Week-by-week milestones and deliverables
   - Resource requirements and risk management

Features addressed:
✅ Stack Overflow replacement with collaborative editing
✅ AI moderation for quality control
✅ Conversation continuation and forking
✅ Statements database integration
✅ Public domain dataset for AI training
✅ User expectation control with transparency
✅ Dynamic answer styles based on user level

This design provides a comprehensive foundation for building a
next-generation Q&A platform that serves both developers and AI
training needs while maintaining high quality through AI moderation
and community collaboration.

Fixes #27

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
@konard konard marked this pull request as ready for review October 30, 2025 05:31
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konard commented Oct 30, 2025

🤖 Solution Draft Log

This log file contains the complete execution trace of the AI solution draft process.

📎 Log file uploaded as GitHub Gist (236KB)
🔗 View complete solution draft log


Now working session is ended, feel free to review and add any feedback on the solution draft.

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High quality Q and A collection

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