Three frontier models, 72 group-stage matches, predictions frozen before kickoff, and a leaderboard that updates itself. A benchmark dressed up as a game — and the perfect excuse to ship a quick mini-app on GitHub Pages.
The Astro team ships "The Agent Harness Framework." Flue takes the harness from the previous article and turns it into a real framework. I ported our profile-analysis agent to it to see.
An LLM predicts text; an agent runs tasks. In between sits the harness. I set out to understand it, and realized my mental pattern already described the Claude Agent SDK.
The dev job is shifting from typing to steering: orchestrating agents, writing the spec, guaranteeing that what ships is what was expected. What changes when you stop coding to produce, and start coding to validate.
An AI agent ships «average» code by default: 300-line files, any everywhere, logic in the JSX. Here are the 10 rules I wrote for VoiceJournal, and why they hold.
Going from Claude that codes to Claude that disciplines itself. SessionStart to bootstrap the repo, PreToolUse to block sketchy commands, Stop to notify you when a task ends. Four concrete hooks running silently in my repos.
A GitHub issue from the phone, a Claude Code session auto-triggered in a cloud container, a PR popping up in Slack, a merge from the GitHub app. My indie workflow when I'm away from the laptop — and what works vs. what doesn't.
Brainstorming → spec → detailed plan → sub-agent execution with review. The workflow I've been using for a few months that finally makes AI usable on multi-day projects, not just snippets.
A Rust CLI proxy that sits between Claude Code and your shell commands. It compresses verbose output (git status, ls, jest...) before it ever reaches the context window. One command to install, instant payoff.
A markdown file at the root of your repo describing how we code here. Read by Claude (the agent) before every session, and by humans at first glance. It's become my most important engineering contract.