Long-form writing on production AI agent engineering — validation loops, tool-call reliability, completion ownership, context engineering, cost forensics.
I ran eight building prompts through Higgsfield's Minecraft prompt-to-build. It nails single shapes in a minute but drops exact sizes, materials, doors, and whole scenes.
Independent CodeGraph benchmark on Hono (~280 TS files): -55% tool calls reproduces the published claim, but cost is a wash (+7%), not -35%. Raw CSV included.
Same model, same test cases, 20% better results. 7 out of 8 fixes were pure code, zero LLM cost. Here's exactly what I changed and why it worked.
Don't bind to a single AI. Run three in competition, make the final call yourself, and let results judge everyone. The operating model for staying valuable in the AI age.
Building an AI agent that works is easy. Building one that doesn't break is 90% of the work. Here's what that 90% actually looks like — from leaked source code and production A/B data.
How to use OpenAI's Codex plugin inside Claude Code — turning Claude Opus and GPT-5.4 into a dual-brain coding system. Setup, commands, rescue workflows, and when each brain wins.
Claude Code's memory system looks simple on purpose. This piece breaks down the tradeoffs behind Markdown memories, Sonnet side-queries, and the decision to avoid vector databases.
How Claude Code's context compression pipeline survives 1M-token sessions: 5 levels of progressive compression, dual-path algorithm, source walk-through.
Inside query.ts — the 1,729-line async generator that is Claude Code's beating heart. 10 steps per iteration, 9 continue points, 4-stage compression, and streaming tool execution. With line numbers.
AI API calls are unlike ordinary RPC: per-request cost varies 100×, tokens and models are first-class, streaming muddies timing, caching changes the pricing. A T-shaped instrumentation architecture — shared stem, specialized arms — that handles tracing, billing, and cost analytics without any of them contaminating the others.
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