alt Hacker NewsI built non-autoregressive decision models with RL a year ago
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Look at the bright side. You can ride the Jev marketing, because at the end of the day, post prototype, data privacy is always going to be top of mind and people are already looking for Open Source alternatives because Jev proved the usecase in a simple way most people could understand.
At this size (~400 million parameters), does it become viable running directly on CPU?
I did this a year before OP so how do you think I feel?
This is crying out to become an Excel or LibreOffice Calc add-in
tbh it is very sad though that ripped off the OSS version and played that classic “rewrite this.." with their agent
paper the reddit OP "published" (their words on reddit) to arxiv (before they put the vouching process in place). It's what you expect if you click through.
https://arxiv.org/pdf/2503.23303
Does not appear to be like what Jev is doing, they talk about RAG and embeddings and orchestrators (the stuff that was cool 1 year ago), no talk of system 1 vs 2 (before Jev), whereas Jev is apparently just a model.
There is a vLLM PR introducing Jev like capabilities for diffusion models (and more, have not delved deeply)
Distribution > Creation
I feel strong Schmidhuber vibes here.
in two weeks, a chinese lab will have a Pev-2.7-flash-qwen for 0.00004cents/million
Pangram believes this text was authored with an LLM:
Where can I subscribe to a hosted version of this? I don’t want to host my own GPU.
Jev is targeted to end users, and the tooling is really great. Unfortunately publishing papers without code or tooling or APIs will not attract the crowd as they want something usable quickly. That said, being second in this space is not the end of the world and the race is still on. If there is API access and proper tooling support like Jev, then it can win the game based on merit and not just marketing.
thank you for your work
I just built a server based on Jev API to run Laya here https://github.com/jlt-commons/laya-jolt
> Seeing the hype online feels both validating and deeply frustrating.
The post is conflating hype and money with technical innovation, they are not really correlated. Kurzweil is known for saying most innovations succeed based not on technology but on timing. Today, who talks about it might matter even more than timing.
Superior research often gets overlooked in favor of someone raising millions, sometimes people who have produced literally nothing manage to sell it. Not saying that's happening here, but I've seen this pattern a lot over my career.
Someone riding (or manufacturing) a hype wave is playing a completely different game from a researcher. If you're a researcher you can't really feel dejected when someone is making a business on the back of what seems like your research; legal protections are decades out of date, even ignoring vibe coding. If you want to make money/hype/whatever off of your work, do that. But realize that it's a path that's often orthogonal to research.
I'm just glad to see focus being shifted (albeit slowly) to conventional ML. Enough with LLM guys
but now you got the attention. It is alright. Few remember Atanasof too.
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I've been deeply impressed with Jev as it made a bunch of workloads we had on Luna or Gemini 10x cheaper and 2x faster (previously used non reasoning version for latency reasons).
Now Laya promises another speed up and it's open source. Tbh if it can't run on a CPU I anyway want to buy it from an inference provider. Managing gpus in production is a non trivial problem.
What I also wondered about Jev is how different it is from something like tabular foundation models. They seem to overlap in use cases. Which then leads to the question, what is actually learned? A lot of people in machine learning spend time to making things explainable and always struggled to move beyond data induced biases.
Having it open source is awesome as fine tuning might give additional performance on the task we care about.
Why do we ("society") need the "frontier" companies at all? Their business goal has settled on trying to CONFUSE the shit out of us so that we don't understand the big pictures about various aspects of AI.
THANK YOU, Nandakishor Mukkunnoth, for putting in the work to help to clarify this stuff!
You are like a firefighter compared to their fire-insurance racket.