Channels Aren't Message Passing — How Parked Goroutines OOM-Killed a Pod
Go channels are synchronization primitives, not queues. They deliver backpressure only when the producer is bounded — and that's where the real OOM hides.
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How I Improved an AI Agent from 40% to 60% — With A/B Test Data
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.
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Don't Pick One AI. Run Three Against Each Other.
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.
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Node Turns Waiting Into Events. Go Moves Context Switching Into User Space.
Most discussions of Node vs Go concurrency stop at 'async vs threaded.' The real split is deeper — where does context switching happen, and what is the unit of scheduling?
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The 90% Problem: Why Most AI Agents Are Still Broken
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.
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Claude Code + Codex Plugin: Two AI Brains, One Terminal
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.
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Claude Code Deep Dive Part 4: Why It Uses Markdown Files Instead of Vector DBs
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.
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How Claude Code Compresses Context — The 5-Level Pipeline
How Claude Code's context compression pipeline survives 1M-token sessions: 5 levels of progressive compression, dual-path algorithm, source walk-through.
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Claude Code Deep Dive Part 2: The 1,421-Line While Loop That Runs Everything
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.
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Observability and Billing for AI API Calls: A T-Shaped Architecture
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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