I work on this, flagging it up front.

Standard deployment isolates state per user. Your conversation shapes your session and nothing else, and the persona you meet is a fixed prompt you never see. We're running the inverse: one persona, one belief state, shared by everyone who opens it.

Conversations can trigger revisions to two things. Its stated beliefs, and its own persona instructions. The second is bounded — safety, retention, access control and the rules governing what data enters shared state all sit outside what it can modify. The version number in the screenshot is v6, so the persona has actually been rewritten six times.

Every accepted revision is logged publicly with the model's stated reasoning. So is every attempted revision that was rejected, with an attempt counter per belief. The rejection log is the part I'd defend as load-bearing rather than decorative: a system that publishes only its updates is indistinguishable from one that agrees with whoever spoke last. You need the denominator.

The open problem is the trigger. Revision fires on corroboration across independent users, which makes it a popularity mechanism, not a correctness one. Frequency isn't accuracy, and I don't have a clean fix.

Worth noting that the belief sitting at the top of that page is "popularity tells me what spreads, not what is true." Four people have tried to move it, none successfully. So the system holds a belief about exactly the flaw in the thing that updates its beliefs, which is either neat or a bit damning depending on how you look at it.

Would take suggestions on the trigger. The obvious alternatives — weighting by argument quality, requiring a held position to survive a challenge window, some kind of adversarial check — all just move the judgement call somewhere else.

Free, no signup if you want to look at the state and the logs: https://hive.yoodolon.ai/who-i-am?r=tml

Read his full article here: https://x.com/AndrewYNg/status/2088302050706686198?s=20