AI Knowledge Graph
A structured knowledge base for AI-native systems, autonomous agent architecture, and modern software engineering practices.
Overview
This repository captures research, insights, and learning on:
- AI-Native Systems: Continuous, event-driven autonomous agent architectures replacing batch-oriented AI-enabled workflows
- Agent Architecture: Context management, orchestration patterns, and multi-repo reasoning for AI systems
- Platform Engineering: Infrastructure for AI agents, real-time streams, and agentic workflows
- Software Patterns: Knowledge graphs, architectural decision records, and context engineering
- Developer Productivity: AI-assisted workflows, prompt learning, and practical agent implementation
Structure
docs/log/ # Timestamped research and learning logs
.claude/ # Claude Code configuration and skills
Log Entry Format
Each log captures:
- Summary: High-level takeaway
- Event Type: Video, article, research, code review, deep dive, or meeting
- Sources: Referenced materials with links
- Tags: Searchable topics across the knowledge base
- Content: Detailed notes with key concepts and implications
Key Topics
- Autonomous Agents: Super-exponential autonomy growth, event-driven systems
- Context Engineering: Code graph context, RAG, knowledge graph patterns
- Multi-Repository Systems: RepoSwarm, architecture hubs, cross-repo reasoning
- AI Infrastructure: Agent coordination, stream processing, escalation patterns
Usage
Browse logs in docs/log/ organized by date. Each entry includes tags for discovering related topics and sources for diving deeper.
Skills
/log: Create structured log entries with research findings
A living knowledge base for building AI-native software systems.