Category: lean-coder
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Proper use of workers for AI coding
Sub-agents are a scalpel, not a speed knob. Here’s what we learned making them work, and the ways they quietly fail. A “worker” (or sub-agent) is a scoped assistant your main agent, the driver , dispatches to do a self-contained errand: scout a module, run a test suite, make a bounded edit, summarize a directory.…
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Prompt caching cuts your bill, not your quota
On subscription plans the meter that throttles you counts tokens you thought were free. Here’s the trap, and the only lever that actually works. Prompt caching is one of the great wins of the modern LLM API: re-send the same prefix and the provider charges you a fraction, often a tenth, for the cached part.…
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Fan-out that isn’t: why two parallel workers were slower than one
“Just parallelize the agents” is a trap when they share a GPU. Measure before you believe it. lean-coder can dispatch multiple workers, sub-agents that each take a task and run headless. The obvious next thought is: run them at the same time, get the work done faster. We tested that assumption. It was wrong in…
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The CLI Contract: building command-line tools an AI agent can actually drive
I hand a lot of my infrastructure chores to LLM agents now. Not because it’s trendy, but because a well-scoped agent with shell access is genuinely good at the boring 80%: “upgrade these machines,” “what’s the status of X,” “back this up before you touch it.” But the first time I pointed an agent at…
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The Terminal Coding Agent I Use to Run My Own Servers
Most AI coding tools live in your editor. This one lives in the terminal and actually runs my servers, from backups to deploys.
