https://x.com/dWeaths/status/2102415625301717065 - Here's my use case of jev, being able to accurately detect nouns (with adverbs, adjectives etc.) in realtime as the user is typing it, genuinely feels like it's running locally with how fast it comes back. I've sent over 1000 requests to Jev and its cost me $0.01 (probably rounded up!).
I had an idea for a 'write without space, basicallylikethis' and Jev (given how cheap it is) just being asked after every key press where inserting spaces (or making typo correction) would make sense.
Honestly just getting it integrated into mobile phone swipe keyboards would be a godsend. If I type "We need to get going " and then swipe the word "now", I really do not think "mower" should be the word it chooses. Present a set of swipe-based likely words and the preceding text message to Jev, or similar model, and pick its highest prob word.
Initial personal use case (on my side project https://mealplannr.io/lists) is lists have a "smart categorise" button to group items into 20 or so preset lists.
Previously this took up to ~30-60 seconds using deepseek v4 flash (even with a medium list size) - Jev is <1 second @ same cost with typesafe guarantee
As a bonus I can also instantly categorise new items - rather than sending them into an "unknown" category (and waiting for user to have to click "categorise" again)
i built wellposed as a plugin/skill which works with any agent (https://github.com/suraj-phanindra/wellposed) to ensure your agent understands how to choose the right kind of jev request and format it correctly not just for syntax but for completeness and correctness. it is well-documented in the "jagged-ness" docs (https://docs.typesafe.ai/model-jaggedness/jev-1.13) that typesafe include on their docs page that the absence of essential options can cause jev to choose the wrong option with high confidence (it cannot choose what it cannot see in the request) - so the responsibility to ensure whatever the intent behind your jev request is - it is captured correctly with the right states and options for jev to pick from falls on the user. wellposed should ideally make your agent better at converting NL intent into jev requests. please try and give me feedback. appreciate it!
"A compelling clip often leaves the useful questions unanswered: what did Jev decide, where is the implementation, and what can I reuse?"
Oh my god for the love of god and all that's precious please stop using Claude. Just reading this makes me want to set up a swarm of rogue agents to break into anthropic and fix this writing. I hope this changes!
At this point, I am finding it extremely hard to believe that Jev team is not on a massive astroturfing campaign. This is happening all over reddit too. All LLM subreddits are getting flooded by Jev posts, many of which are made by new accounts that only talk about Jev, many obviously advertising in guise of sharing knowledge (e.g. https://www.reddit.com/r/LocalLLaMA/comments/1wn4cni/removed...)
Multiple posts on HN, including this one, are from accounts that only ever talked about Jev. Each get unusually high number of upvotes early on, enough to put them on frontpage. A multitude of commenters on such posts also seem to talk about only 1 topic.
Can all this happen organically? Yes but with vanishingly low probability, from my vantage point.
Don't want to be a conspiracy theorist, but these past two weeks have seen what looks to be a coordinated campaign to boost Jev. Am I out of the loop or is this so revolutionary it warrants getting so much coverage on HN and other professional sites?
There's definitely a level of inauthenticity in the hype, but that's a function of the times we're in.
Revolutionary? With an appropriate harness could could do the same thing with the vast majority of modern large language models.
It's just so much faster and so much cheaper that it feels qualitatively different.
I also think it represents a bit of validation for folks looking for ways to bake models into hardware. Sometimes it's perfectly appropriate to sacrifice good for fast and cheap. I haven't asked Jev to do anything that GPT-3.5 would have likely done worse with.
It's definitely revolutionary, considering AI news is ~50% of the front page at any given time, and the frontier models are all pretty much doing the same thing and just getting slightly better. This is a whole different technology, it's cheap and fast, and it's usefulness at different tasks is still being established. Amazing for hackers!
JEV directly addresses many of the most common issues with LLMs for certain applications. It seems IMO to be overhyped right now but I think it may, like the broader llm ecosystem, be here to stay.
The demo is here: https://levmiseri.com/nospace
And so, I typed "Thisoneiscoolashell", and got "This one is cool a shell" hahaha
Great idea, and it's really quick
Previously this took up to ~30-60 seconds using deepseek v4 flash (even with a medium list size) - Jev is <1 second @ same cost with typesafe guarantee
As a bonus I can also instantly categorise new items - rather than sending them into an "unknown" category (and waiting for user to have to click "categorise" again)
Note: Not the technical side, but as an end user of LLM APIs.
https://github.com/midplane/clean-twitter
Oh my god for the love of god and all that's precious please stop using Claude. Just reading this makes me want to set up a swarm of rogue agents to break into anthropic and fix this writing. I hope this changes!
Demo here: https://x.com/RaahelSaidWhat/status/2102162969656475973
Multiple posts on HN, including this one, are from accounts that only ever talked about Jev. Each get unusually high number of upvotes early on, enough to put them on frontpage. A multitude of commenters on such posts also seem to talk about only 1 topic.
Can all this happen organically? Yes but with vanishingly low probability, from my vantage point.
If it's an astroturfing campaign it's very likely jev powered.
Which, comes to think of it, if true, is self-reinforcing once discovered.
Revolutionary? With an appropriate harness could could do the same thing with the vast majority of modern large language models.
It's just so much faster and so much cheaper that it feels qualitatively different.
I also think it represents a bit of validation for folks looking for ways to bake models into hardware. Sometimes it's perfectly appropriate to sacrifice good for fast and cheap. I haven't asked Jev to do anything that GPT-3.5 would have likely done worse with.
The fly brain thing on the other hand.