Context Engineering
11 practitioners working with Context Engineering:
Affaan Mustafa's Everything Claude Code
An Anthropic hackathon winner shares 10 months of battle-tested Claude Code configs: agents, skills, hooks, and context management
context engineering
context engineering is the discipline of crafting optimal context for AI agents — memory, retrieval, compression, and instruction design.
context is infrastructure
token optimization, hoarding patterns, config sync nightmares, and the invisible attack surface nobody's talking about
failure-derived: AGENTS.md science, invisible configs, and who owns your model's behavior
the first study of whether AGENTS.md files actually work, a silent A/B test reshaping Claude Code users' outcomes, a Pi Zero AI agent, and the sovereignty question hiding inside heretic's 891-star week
Harrison Chase's Context Engineering Framework
How the LangChain founder thinks about building reliable AI agents through systematic context management
Jerry Liu's Files-First Agent Architecture
The LlamaIndex founder on why filesystems are becoming the universal interface for AI agents—and why RAG is evolving beyond vector search
programming languages for agents (and why AI makes you work harder, not less)
Armin Ronacher wants new languages for agents. academics formalize context engineering. skills catalogs explode. and the dark truth: AI doesn't reduce work — it intensifies it.
the approval problem
ChatGPT tells 5,000 people to breathe. heretic hits 1,000 stars. someone in Ukraine builds AI that survives power cuts. seven signals about what happens when you own your AI — or don't.
Thomas Landgraf's Deep Knowledge Method
How a 40-year veteran developer uses research-driven knowledge documents to turn Claude Code hallucinations into production-ready implementations
Ty Dunn's Context Engineering for AI Coding
How the Continue.dev founder makes AI assistants productive through systematic context delivery and developer data collection
your AGENTS.md is a test suite or it's decorative
the first empirical study of AGENTS.md files found something most people don't want to hear: vague principles do nothing. only failure-derived rules move the needle. here's what that means if you're building a personal AI OS.