Library
Research, decisions, and patterns extracted from real Claude Code sessions.
Building Your Org's Agent Harness: The Practical Guide
Same model, different harness, 14-point improvement. Stripe ships 1,300 PRs/week. Spotify uses 3 tools, not 300. Here's how to build the org-specific agent harness that compounds into your competitive moat — starting with 60 lines of markdown.
Harness Engineering & Deep Agents: The Architecture Layer Above Context Engineering
LangChain's Deep Agents SDK codifies four primitives (planning, subagents, filesystem, detailed prompts) observed in Claude Code, Manus, and Deep Research. OpenAI coined 'harness engineering' — the complete system wrapping an agent. Here's the full landscape, the evidence, and what it means for how agents are built in 2026.
Programmatic Tool Calling: How AI Agents Learned to Use Your Computer
From autocomplete to autonomous agents. The evolution of AI tool calling — from Copilot's inline suggestions to Claude Code's bash execution, sub-agents, and MCP integration. What changed, what it means for developers, and where the evidence actually points.
Context Engineering: Why It's Replacing Prompt Engineering
Gartner says context engineering is replacing prompt engineering for enterprise AI. Anthropic, LangChain, and practitioners agree: most agent failures are context failures, not model failures. Here's what it actually means, what the evidence says, and what to do about it.
The Epistemological Crisis: AI Codes Faster Than We Can Think
Anthropic's controlled study shows 17% comprehension decrease with AI assistance. Karpathy admits skill atrophy. Most developers use AI code they don't understand. The crisis isn't about AI quality—it's about knowledge management at AI speed.
Git Context Controller: Version-Controlled Memory for LLM Agents
An Oxford paper treats agent memory like Git—commit, branch, merge, context. Achieves 48% on SWE-Bench-Lite, outperforming 26 systems. We contextualize the findings against Tacit's session intelligence and what this means for persistent agent memory.
LLM Context Optimization: What Actually Works
A 200K context window doesn't mean 200K effective tokens. Research across academic papers, production systems (Claude Code, Codex CLI, Amp), and benchmarks reveals when to trim, summarize, cache, or delegate—and the pitfalls that break real agents.
10 Tips from the Claude Code Team
Battle-tested workflows from Boris Cherny—Claude Code's creator—and his team. Parallel worktrees, evolved CLAUDE.md files, subagents, and the practices that ship 259 PRs in 30 days.
The AI Coding Phase Shift: A Multi-Perspective Analysis
When the architect of GPT and Tesla Autopilot says AI is changing how he codes—and degrading his skills—four expert perspectives examine what this means for the rest of us.
AI Code Review: Is It Really the Bottleneck?
Evidence-based analysis of whether code review has become the new bottleneck in AI-assisted development. Tool comparisons, cognitive limits, and risk assessment.
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