I don't really understand the criteria for when something is 'proven' to the Pi team. Jev and the like took off less than a month ago, but MCP has been growing for nearly 2 years, and it only gets support now?
Pi felt nice when I used it, and I do value keeping things minimal, but I just find the criteria very uneven.
Classification models have been around for literally almost a century at this point. I think it's safe to say they are a proven technology.
The only thing that makes Jev and the likes particularly interesting is that it is a general purpose classifier. In the past, classification tasks meant training a new model to solve your problem. Now you can just use an off the shelf general purpose model and hit the ground running.
Well, according to claude and Jevbench, Qwen 3.6 35b with ninfer on a RTX 5090@480W is like 3-5 time slower but 10%-15% better performance on the public set, I could see prefill > 15k for 700-800decode.
Latency against what and which hardware? I don't really get jev...
Look I can convince my boss to pay for jev, but I won't convince him to run our prod stuff on a rented vast.ai 5090. And the pricing wouldn't be worth it. If you have ideas I would be glad to hear them
If you want something even lighter than Jev to compare against, there's also gutsy (<a href="https://github.com/kouhxp/gutsy" rel="nofollow">https://github.com/kouhxp/gutsy) runs on CPU
hhh · · focus · HN ↗
Pi felt nice when I used it, and I do value keeping things minimal, but I just find the criteria very uneven.
Zambyte · · focus · HN ↗
The only thing that makes Jev and the likes particularly interesting is that it is a general purpose classifier. In the past, classification tasks meant training a new model to solve your problem. Now you can just use an off the shelf general purpose model and hit the ground running.
alex7o · · focus · HN ↗
Foobar8568 · · focus · HN ↗
alex7o · · focus · HN ↗
luipugs · · focus · HN ↗
mrkn1 · · focus · HN ↗