Weco AI

Weco AI is a code-optimization platform built around the AIDE algorithm, which pairs LLM-driven code proposals with tree search to iteratively improve a codebase against a user-supplied evaluation script. You hand it source code plus an eval that emits a number (latency, accuracy, memory, cost, throughput, quality score), and it loops: propose change, run eval, read metric, branch on what improved. The team describes the product as 'recursively self-improving AI' and ships a CLI (weco-cli) backed by docs at docs.weco.ai.

It's aimed at ML and systems engineers who have problems where the optimum isn't obvious and brute-force experimentation pays off: GPU kernel tuning (CUDA, Triton), model architecture tweaks, prompt engineering with measurable scoring, and general perf work. The same group is behind AIDE (the agent that posted human-level results on Kaggle-style data science competitions) and the Aiden agent that placed top in OpenAI's hiring challenge, so the research pedigree is real. Pricing isn't published on the marketing site; the CLI is open and the hosted autoresearch service appears to be the commercial layer.

It's language-agnostic and hardware-agnostic because the only contract is 'your eval prints a number.' That makes it powerful for the niche it serves and useless for tasks where success can't be expressed numerically or where a one-shot edit would do.