Tune my local model and package the optimisation

A user had the AI apply TurboQuant skills to optimise a Qwen3.5-9B model running locally via MLX on Apple Silicon. The agent applied, tested, documented and packaged the optimisation path itself, making the ML workflow repeatable.

How it works

Point the AI at a local MLX model server
Apply TurboQuant optimisation skills
Run tests
Document the results
Package the optimisation path as a repeatable workflow

Why it’s worth knowing

The agent tuned its own local brain and wrote up how it did it.

Where it’s from

Summarized from a public post on betterclaw.io. See the original ↗

Upgraded prompt

A ready-to-use prompt for this idea, built from research on what makes AI agents do the job right: who the AI should be, the exact steps, and a check to make sure it was done the way you wanted.

1Who the AI should be

2The prompt, with the steps

3The check when it’s done

Upgraded prompts are coming soon.

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Every idea is a real post from someone who tried it, summarized in our words, with a link to the original. App names are swapped for plain words.