As someone who uses LLMs in their coding but doesn’t pay attention to every development this is why I find it all so exhausting. I keep feeling like I need to keep up to date but there’s no point reading incremental updates because everything is so overhyped.
If you want to escape the hype, reading posts with no upvotes and no comments on https://news.ycombinator.com/newest is an option, though somewhat hit-or-miss.
Eg. This is not uncommon advice: https://maxwellforbes.com/posts/your-paper-is-an-ad/
What's confusing is they do have experiments that read raw weights, and the abstract is misleadingly written to make it sound like that's how the Qwen experiment works.
You can take it further with a reasoning model and let it generate + consume a bunch of additional tokens before getting your output. Still not writing or running any programs.
To my understanding, that's how the examples of determining which game a small neutral net was trained to play. The weights of the neural net for into the context window, and then Astra was able to analyze them purely by reasoning rather than running external tools.
They're pretty easy to type on Android keyboards and on macOS (option + shift + -). You can set up a similar compose key thing on Linux.
This is a difference without much distinction, at least to me. It's like being upset when someone says that Claude made a http call -- of course the LLM didn't make the call itself, it used a tool to do it.
The former will likely produce a much higher quality image, but claiming it as a demonstration of Astra's capabilities would be a lie.
This is like someone telling you they made a "native" brain-to-brain interface but it's just a tin-can phone.
I think there's your answer. A hell of a lot of people will often read the title without reading the contents of the article. I admit I'm often guilty of that
I'm fine with throwing the baby out with the bathwater in that case and just calling the whole article misleading.
Anybody who actually read the original research site was fully aware of the details of the experiments because it's a well-written research site that made everything very clear. This author is clearly just jumping on the hype as a sort of contrarian where they were frustrated with how much excitement the research was generating and wanted to "correct the record" for the set of people who don't matter in this space.
>They should be more upfront and clear with what they did from the first paragraph.
Please read the first paragraph and tell me what isn't upfront or clear about it: https://rikiyat.github.io/blog/weight-space-programming
I'd say it's spelled out so clearly that even people who don't understand this stuff could confidentally understand what they're doing here.
Unfortunately it’s hard to distinguish who to trust in threads as many commentators sound overly confident and knowledgeable. They think they understand the article and its nuances, but they actually don’t and end up perpetuating false info.
I would recommend going on Twitter (or a Nitter clone) and looking at e.g. Neel Nanda, Nick Camarata, roon, Jon Stokes, or other researchers that are directly involved in this stuff. HN when AI is involved is full of the most incompetent and embarrassing armchair speculators saying things so incoherent they aren't even wrong https://en.wikipedia.org/wiki/Not_even_wrong.