Tag: ai

The one idea underneath aipostgen, and the part I'd carry into any tool built on a language model. Do not let the model run your control flow. Put the state machine in code, where a loop is a loop and a gate cannot be skipped on the run where the model is feeling confident - and let the model do the writing. Reproducibility, debuggability, and human gates that actually hold.

A look inside aipostgen. The language model does the writing; plain code does the deciding - and almost every design choice falls out of keeping those two jobs apart. The state machine in TypeScript, a manager-and-two-contractors split on Claude Code, a cold reviewer that never saw the draft written, and voice rules in plain markdown files.

One idea, three platforms, three rewrites - so I built aipostgen to do the typing. It turns a link or a few words into paste-ready drafts for X, Bluesky, and LinkedIn, written in my voice and inside each platform's limits. The interesting decision isn't what I let it do; it's that it never publishes. Why a writing tool that won't press the button is the version I actually trust.

The scariest sentence in AI tooling is "and then it commits to main." When you build an agent that fixes bugs unattended overnight, the most important decision isn't a capability - it's a refusal. Why making mistakes cheap beats making the agent smart, and how trust in autonomous software actually gets built.

A one-shot "fix this bug" agent is a party trick. A system you can label twenty issues against and walk away from is engineering. Two pieces of craft close that gap - isolated, runnable sandboxes that make every fix prove itself, and conflict-free pull requests that merge in any order. The intelligence is rented; the trust is built.

Label your easy bugs at the end of the day, go home, and wake up to a pull request waiting for each one - written, tested, and ready to review over coffee. The pitch for an overnight bug-fixing agent, and the one design decision that makes it something you'd actually trust.