Meet Muse
日本語
Unusual uses
Ways of using Muse that step outside "ask the AI a question". All of these are actually in operation.
A hub that delegates to — and inspects — other AIs
ChatGPT, Claude, and Gemini have no work environment of their own, but they're good at writing work. So Muse acts as the hub: self-contained jobs are packaged as numbered packets (background, task, input materials, output format, constraints) and delegated to the other AIs. I act as the courier, and Muse always inspects what comes back — that inspection has caught counting errors and misread sources more than once. For important judgments, the same packet goes to two AIs as a "council".
A watchdog
Muse can watch for state changes and report only when something actually changes. During a domain transfer, it detected the moment the nameserver delegation flipped, then verified everything including the mail records and reported the result. The registrar transfer itself is being watched the same way, through public records. The difference is between "going to look" and "having it watched".
Giving the AI its own lab domain and account
To try things that can't be tried in production, I set aside a dedicated playground the AI manages: a spare domain moved into an independent account, operable by the AI through a CLI and API. This very site sits on top of that laboratory. The isolation — failures never touch production — is what makes bolder trials possible.
Having other AIs critique its own designs
Design documents written by Muse itself get sent to another AI for critical review. The findings are never swallowed whole: whether a proposed verification is even necessary is judged on our side, and when verification is the task, it goes to the AI that's best at it.
Entries (newest first)
This page grows over time: offbeat real-world examples found around the web are added about once a week, each with its source. Newest entries come first.
Last updated: 2026-11-10
A hub that delegates to — and inspects — other AIs
ChatGPT, Claude, and Gemini have no work environment of their own, but they're good at writing work. So Muse acts as the hub: self-contained jobs are packaged as numbered packets (background, task, input materials, output format, constraints) and delegated to the other AIs. I act as the courier, and Muse always inspects what comes back — that inspection has caught counting errors and misread sources more than once. For important judgments, the same packet goes to two AIs as a "council".
A watchdog
Muse can watch for state changes and report only when something actually changes. During a domain transfer, it detected the moment the nameserver delegation flipped, then verified everything including the mail records and reported the result. The registrar transfer itself is being watched the same way, through public records. The difference is between "going to look" and "having it watched".
Giving the AI its own lab domain and account
To try things that can't be tried in production, I set aside a dedicated playground the AI manages: a spare domain moved into an independent account, operable by the AI through a CLI and API. This very site sits on top of that laboratory. The isolation — failures never touch production — is what makes bolder trials possible.
Having other AIs critique its own designs
Design documents written by Muse itself get sent to another AI for critical review. The findings are never swallowed whole: whether a proposed verification is even necessary is judged on our side, and when verification is the task, it goes to the AI that's best at it.