mugul

Has any of the targetted organizations (e.g the Australian government) engaged in legal actions against Anthropic/OpenAI? If not then I'm afraid the situation won't change. Even worse it could send the wrong signal that there is a "legal blur" around this topic.

Would have been great to have that sorted out and fixed before passing the EU AI Act and forcing all content generation providers to embed C2PA manifests in their outputs...

Very interesting article about crafting the building blocks of a browser music engine.

I think it is: look at the "in" which is likely one token, "strawberry" looks like it is 2x the length of "in". Same for the last word, "ing" seems less compressed than "bear" right before it.

Very interesting feedback. I'm a Go newbie and the goroutine/channel duality sounds delightful from where I stand, but once again I have no professional experience with Go yet, only sample programs to get used to the language.

One question though: your advice is to write things serially first before moving to concurrency, which for me is general programming common sense, but would you argue that once you start writing concurrent code then channels are not well suited compared to "good old" sync primitives (mutexes, etc.)?

Cool cool cool!

Looks like a pretty cool community building great tools with care. Using a functional language for data transforms sounds like a sane idea, haven't played around with it yet but it's definitely on my list now.

However they claim using Haskell for data science is "fast", which doesn't really mean anything until you have numbers to show. A little benchmark with pandas and polars wouldn't hurt I guess.

Very interesting insight on the training process, it's pretty cool to have some experimental justification for why they took these exact steps, what they tried and did not work, etc. Feels a bit less like dark magic.

However I agree the latency argument doesn't hold much value with Jev because it runs on a remote server. Seeing how many open Jev-like models came out recently it would be much more interesting to have a comparison with them.

Great tutorial, technical level and writing rhythm are just perfect (for me at least). Feels great to read something written with care that doesn't sound like yet another Claude-generated blog post.

Agree with you on that one, and if the agent somehow tries to re-invent the wheel I will definitely remove the code and do the install myself. I have way more trust for OSS projects maintained by motivated humans (who can be using coding agents if they want, I don't care, as long as project quality remains high).

Interesting, I'm guessing this is motivated by the increase of always-on AI features in browsers?

Thanks to these projects, what was an innovative-but-closed piece of technology one week ago is now much more accessible. Whether they're in it for fame or not, I couldn't care less!

Quite impressed by the energy people are putting into making OSS Jev-like models.

I understand the hype but I wonder: what are the use cases for this kind of model? Could it be used in the context of coding agents, or is it more relevant in totally different situations?

It would be very interesting to see how the whole scene is structured, not just the final rendered result. A lot of the work you put in when making Blender scenes is to make sure the scene stays nicely editable, in particular around the controls you are likely to tweak in the future. Kinda like software engineering when you think about it. Which makes me think that the Blender scene generated by a coding agent probably has the same issues as an LLM-generated codebase, e.g maintainability.

I think that beyond marketing this is also a way to collect "AI browsing" trajectories. Not an expert but I think this is the kind of great quality data that can be useful when training LLMs.

And then you have to click "update visualisation" otherwise you're still seeing the data from the previous bird

I hope the media industry will soon realize they are just being used as a very loud relay in a communication strategy feeding the hype of "AI" companies. This is quite sad (but I'll admit reading the article also made me laugh quite a lot).

Good read thanks, quite well documented!

I see your point but I'm not sure there is much more to say on the matter: apart from having fun, learning stuff and taking pride in your work, what other reasons do you see for "doing things the hard way"? You could argue that it makes you more competitive because you'll be better at babysitting a coding agent, but this feels less and less true with every new model. Unless you're seeing something else to take into account here?

Great job, very interesting! However I wonder: since you are targetting iOS, why only restrict yourself to generalist frameworks such as llama.cpp and LiteRT-LM? MLX Swift looks like a better fit, and the model catalog on HF's MLX community is pretty huge and diverse.