While true - there are laws about saying you are doing the things you are doing, especially in certain regulated environments. If you are in the same country as the entity you are trusting, you have recourse if they are not living up to your trust usually in some form or another.

correct, you are trusting another entity.

however the legal terms are different, openai reads your data. they store it for 30 days, but of course once it hits the disk you can keep as long as you like in a civil case like nyt v openai.

the same for google and anthropic. so, it's not always nice if someone is paid to read your data for safety. people upload sensitive matters, personal videos and so on.

i wouldn't prioritise it myself but you can also know that the data will all come out in discovery if you are in a legal issue. maybe that's not important, but people thought it did matter to give some protections to patient records, legal advice and therapy. you upload that to gpt and it goes into discovery.

I've found MiMo-2.5 is fun for front-end design since you can use its multimodal capabilities to drop in whatever it produced and correct it for you.

AI review generally will find fault in anything. Any non-trivial code has multiple solutions with different tradeoffs. Any code can be over-engineered for theoretical edge cases and future use cases you don't need. No matter which solution you pick you can always at a minimum say that some alternative just looks and reads better.

Code is somewhat artistic. If you don't have well defined standards and priorities, the AI review cycle can spiral infinitely figuratively debating what makes art good, and your code will be no better for it.

That's what I love about it, and I wish I could find an open model that was as diligent.

Somehow it's just way more careful than the others, and also much better at empirical verification of its hypothesis, writing tests, etc. I am assuming a lot of RL done on that kind of flow, and on seeking out negative cases, failure points, race conditions.