Autoresearch

An autonomous experiment loop. You make a change, measure it, keep it if it's better, discard it if it's not, and repeat. The idea is simple: given a clear metric and a way to measure it, you can run experiments indefinitely while the user sleeps, eats, or touches grass.

This works for anything with a measurable outcome: code performance, ML model quality, bundle size, test coverage, response latency -- if you can extract a number from a command, you can optimize it.

How it works

There are two phases: plan (interactive setup with the user) and loop (autonomous experimentation).

Phase 1: Plan

Before the loop starts, gather the configuration through a short conversation. Ask these questions one at a time, waiting for the user's answer before proceeding.

1. Goal

Ask: "What are you trying to optimize?"

This is a free-text description of the objective. Examples: "Reduce API p95 response time", "Lower validation loss on the language model", "Minimize Docker image size".