I’m John Young.
I run engineering at Combine Capital and Infrared, and write research-backed essays on running AI coding agents in production — every claim traced to a primary source.
github.com/johnayoung linkedin.com/in/jyoung1985 john.anto.young@gmail.com rss
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Essays16
Pillars6
Evidence base verified2026-07-13
Recent Essays
- Stop Picking Your Coding-Agent Model Off a Leaderboard Evals & Verification Your team is about to swap coding-agent models over a two-point leaderboard gap. Anthropic measured six points of movement from infrastructure alone.
- Task Decomposition for AI Coding Agents: Draw the Graph First Task Design Across 20,574 agent sessions, the top failure was violating a stated constraint. Draw the task graph before the prompt: nodes, owned files, and contracts.
- Audit Your Agent Harness: The Deterministic Layer Nobody Reviews Agent Runtime You have reviewed your CLAUDE.md a dozen times and never opened the file that decides whether the agent’s next rm -rf runs. Start by inventorying what shipped.
- How to Structure CLAUDE.md: It’s a Loading Policy, Not a Document Agent Runtime Anthropic closed ‘Claude ignores my CLAUDE.md’ as not-planned, area:model. The fix is structural: route every line to the tier that loads it, not a longer file.
- Loop Engineering Breaks Your Single-Shot Context Playbook Agent Runtime A loop was always the agent primitive. The CLAUDE.md budget and JIT retrieval you tuned for one invocation don’t fail louder in a loop — they fail quieter.