This isn’t pop AI. Every recommendation in nv:context links back to a real source: published papers, production data, experienced practitioners. Here’s the full library.
Pulled from primary sources. Each one upends a common assumption about how AI coding agents should work.
LLM-generated AGENTS.md files cut agent success by 3% and increase costs
20%+. Human-written files only marginally help (+4%). Most repos would do
better deleting their config than running /init.
16 senior developers in a controlled study were 19% slower with AI tools, despite feeling 24% faster. The 39-point perception gap is the cost of context failure.
Using 40% of the context window outperforms using 90%. A focused 300-token context can outperform an unfocused 113K-token context. More tokens is not the answer.
At 60% capacity context is safe. At 70% precision drops. At 85% hallucinations begin. Compact proactively. Never wait for the auto-compact at 95%.
Frontier models follow 150–200 instructions consistently. Claude Code’s system prompt already uses ~50. Every line in your CLAUDE.md competes with the actual task for attention.
Cached tokens cost 10× less than uncached. Keep system instructions and tool definitions static; append dynamic content at the end. A single changed token at the start invalidates the entire cache.
200+ external sources digested into 12 focused logs, organized by research dimension. Six parallel research agents. ~471 KB of distilled findings. Each log is open-source on GitHub.
Boris Cherny’s workflow, progressive disclosure, path-specific rules, skills, hooks, subagents, worktrees, and the compounding engineering pattern.
View on GitHubCLAUDE.md, AGENTS.md, .cursorrules, copilot-instructions, GEMINI.md, Windsurf. The complete file format matrix and the ETH Zürich findings.
View on GitHub32-source literature review: Anthropic, Manus, Martin Fowler, LangChain, LlamaIndex, Weaviate, Schmid, Karpathy, Inngest, Google ADK and academic papers.
View on GitHubdev.to, Hacker News, and developer communities. Anti-patterns, productivity data (incl. METR’s 19% finding), session management, and Claude Code source-leak insights.
View on GitHubThe theoretical foundation. Karpathy, Tobi Lutke, Schmid, Simon Willison, and the origin of the term “context engineering” (June 2025, AI Engineer conference).
View on GitHubReal CLAUDE.md files from production repos, shared skills, working hooks, the Ralph Wiggum loop, worktree patterns, and 38+ TypeScript exemplar repositories.
View on GitHubCLAUDE.md / AGENTS.md patterns from MCP Python SDK, OpenAI Agents Python, Google ADK, LangChain, LangGraph, Pydantic AI, and the elite uv-mandating consensus.
View on GitHubr/ClaudeAI, r/LocalLLaMA, r/MachineLearning. The golden rules of CLAUDE.md (under 60 lines), real before/after stories, and power-user task decomposition.
View on GitHubRepomix, Agnix, Context7, codebase-memory-mcp, LLMLingua-2, Mem0, Graphiti. The full tooling ecosystem for context packaging, memory, and validation.
View on GitHubThe origin story. Dex Horthy coining the term, swyx amplifying, Tobi Lutke’s viral tweet, Karpathy’s endorsement, and the timeline of how the discourse formed.
View on GitHubHow power users actually work: RPI workflow, Plan Mode, session management, subagent patterns, worktrees, TDD with agents, the 20% context-budget rule, and 12-Factor Agents.
View on GitHubVideo and audio sources. Dex Horthy’s Y Combinator talk, Jeff Huber on Chroma, conference keynotes, podcasts, and the "everything is context engineering" thesis on tape.
View on GitHubGrouped by origin. Each name below is a primary source cited inside the 12 research logs. The full source URLs live in the logs on GitHub.