More rules should mean better output. That's the intuition. I spent weeks building a comprehensive CLAUDE.md — 200 lines covering naming conventions, security rules, error handling, architectural patterns, import ordering, type safety requirements, and more. I was proud of it. I'd thought through every scenario. Then I scored the output. 79.0 / 100. My carefully crafted documentation was actively
A week of intent-based trading for AI agents: five threads from the Hashlock Markets desk The Model Context Protocol surface for crypto trading filled out fast over the last few weeks. Bybit shipped MCP coverage. Gemini added an agentic platform. Alpaca, Kraken, Hummingbot, TraderEvolution, and a handful of community wrappers are all in the same SERP now. The category is real, and it is crowding
I've been spending too much time inside trading bot codebases lately. Most of them are one of two things: a 200-line Jupyter notebook that someone calls a "system," or a sprawling monorepo where the strategy logic and exchange integration are so tangled that you can't swap exchanges without rewriting half the code. A few weeks ago I went deep on AlphaStrike, a production-grade crypto perpetual fut
Have you ever looked at code you wrote six months ago and thought: "Who wrote this monster?"? Relax, it happens to all of us. In software engineering, writing code that a machine understands is the easy part. The real challenge is writing code that other humans (including your future self) can understand, maintain, and scale. This is exactly where Software Design Principles come into play. In this
Part 1 of 5 in The New Engineering Contract — what it means to lead engineers when AI is doing more of the coding. SWE-CI tested 18 AI models across 71 consecutive commits. Most broke something on commit 47 they'd already broken on commit 1. That's not an intelligence problem. That's a learning system that isn't learning. A paper made me uncomfortable this month. Not because of what it found about