Juminuvi

I've only read this blog and not the paper so maybe they go into more detail there and someone can correct me, but they frequently bring up the model's ability to detect or at least the model activations hint it can predict when it's being tested. I can't help but wonder, as they build these larger and larger models, where they could be getting "clean" training data, untainted by all these types of blog posts and the massive numbers of conversations they spawn? If the models ingest data like that wouldn't it make sense they'd be inclined to have more activations attuned to questions they appear adversarial?

I think the UI is pretty slick but agree having a TUI option as well would be awesome. Everything in wsl management (import, export, register, compact, etc) can be done with shell commands I believe so a tiny performant TUI might be a straight forward thing for op to add in the future though.

Very much agree with the idea of red/green TDD and have seen really good results during agentic coding. I've found adding a linting step in between increases efficiency as well and fails a bit faster. So it becomes..

Test fail -> implement -> linter -> test pass

Another idea I've thought about using is docs driven development. So the instructions might look like..

Write doc for feat/bug > test fail > implement > lint > test pass

100% agree. Hopefully etiquette will catch up if enough folks talk about this.

Side note, the number of comments here from people who clearly didn’t read the article is impressive

I know you say you don't use the paid apis, but renting a gpu is something I've been thinking about and I'd be really interested in knowing how this compares with paying by the token. I think gpt-oss-120b is 0.10/input 0.60/output per million tokens in azure. In my head this could go a long way but I haven't used gpt oss agentically long enough to really understand usage. Just wondering if you know/be willing to share your typical usage/token spend on that dedicated hardware?

I noticed something similar. I asked it extract a guid from an image and it wrote a python script to run ocr against it...and got it wrong. Prompting a bit more seemed to finally trigger it to use it's native image analysis but I'm not sure what the trick was.

I always assumed the folks who intentionally do this either work for the company, are associated with the company, or are in some way part of q/a pilot user group.

Neat project! I use a tool called taskfile.dev for managing tasks like this in projects but I've always thought it would be nice to have a more global tool like this.

Right now what I do is write functions and add them to my profile but this leads to adding helper code to do stuff like handle errors, handle vars, printing out index of commends with documentation, etc. This makes my profile just messy so I think a tool like this could come in handy.

> Sure, they could try. And the Supreme Court would strike down the law they pass.

Isn't the balance to this that congress and the executive branch can restructure the courts? Eg. add justices, set term limits, etc.

I think the article just sited an anecdotal example from one user who had Firefox installed. Maybe others with Chrome see Chrome instead? Or maybe ms is trying to play nice with Google to make edge chromium development easier.

Don't they? Don't they push out new apps which get installed and pinned to the Mac dock after almost every other update without asking first? I could be mistaken, I don't use macs every day.

I think pretty much. If you have a PC that has ever have any form of activated windows on it in the past it should activate a windows 10 install. So this would prob only exclude people building on new hardware which are considered oem anyway.

Interesting! I knew that med school applicants were accepted on some sort of ranking and matching process but didn't realize there was a real algorithm behind it. This, or one similar, seems to be what's behind it.