YHK - yet another hacker news clone

Also with this example the speed of new launches based on a launch is just incredible.

Learned also that Jev was trained on 100%(!) synthetic data.

What a great time to be alive.

As opposed to a fake choice?

It's trying to "emulate" Jev behavior using a regular small LLM model (Qwen3 0.6B or MiniCPM5 2B). And with the smallest model it takes like between half to two seconds to run in my M2 Max, so it's not super fast.

I mean, it's faster than asking to a regular LLM, but I think that's not proper to have Jev on the name (also legally...)

Edit: no shade, and I'll give it a try for some ideas. I'd also like to have an open weights Jev but I think the naming is misguiding. I also have to try Jev that, BTW, got access pretty quickly, less than a day I think...

Are there any huggingface mirrors out there?

"Customer wants to lear how to better talk in a company situation, and bring across their argument effectively"

Than had it choose what training would be fitting for this user: - Communication and Feedback - Leadership for Begninners - Soft Skills and Emotional Awareness

It picked always the third with an 80% confidence, while the answer should have been 1.

"Jev is TypeSafe's closed service for runtime-defined semantic decisions. This project reproduces that interface pattern with open models; it does not reproduce Jev's undisclosed model or training"

As someone else pointed out it isn't actually Jev... can someone enlighten me

The last step of an LLM is to take a softmax of the predictions and then generating a token from that. But there was tooling that would just generate all allowed next tokens from a grammar (e.g. restrict to valid JSON).

This seems to taking an approach from the pre-transformer days. Seq-to-seq is hard and we don't always need it. Let's to seq-to-1 because it's often way easier to get it training properly and so you can often get it optimized way better. And, more generally, make sure to pick the best option out of the possibilities: 1-to-1, 1-to-seq, seq-to-1 and seq-to-seq. Where seq-to-seq requires far more resources than any other option and so it's a case of "please don't".

Also note that "1" only means the input is fixed. It does not mean 1 number or ... it just means fixed. The best image description models remained 1-to-seq models 4 years or so after transformers were introduced. Even ASR models remained 1-to-seq + CTC models for quite a while ... I'm not sure if they lasted all the way to whisper release.

Even today training transformers remains expensive. So this should at least be a way to be a lot cheaper than any LLM can hope to be.

And I really like the doom demo. Obviously a pretty stupid model which is really cheap to run can still get a robot walking, if you run it quickly enough. That's how we get insects and mice and ...

And one might even add that biologically, humans aren't smart, or at least, most of the human nervous system isn't smart, compared to the whole, and does work independently if needed (and possible). The human mind is a LOOOOOOOONG chain of fast-but-stupid-and-totally-blind -> slightly-slower-but-smarter-and-not-entirely-blind -> slower-smarter-and-actually-senses-things -> all-information-you-could-want-but-at-most-1-signal-per-minute right up to the point that we have "neural circuits" (using Bishop's definition) that can run at >2khz (2000+ tok/s, say) and on the other end up to our frontal lobe that takes one decision per week if it feels like working hard. Per month if you're 40 or older.

I think one difference between OpenJev and Jev would be, then, is what it's trained on.

Jev is, on the surface, cheap enough for me not to seek self-hosted alternatives. On the other hand, I wish the free/open weight alternatives to Pangram were better.