So they held back a bunch of features because they knew they had basically no moat, and now they're trickling out a bit more functionality to try to convince people they are better than others and move fast? And using Apple advert slogans to show how cool they are?
The way that site charges a fee[1] for benchmarking models, makes any benchmarks on it very sus, IMO. TBF, the benchmarking site is probably more profitable than TypeSafe, just like the joke about how WinRAR is more profitable than OpenAI[2]. :)
Well, they do need to try to compete with SubQ’s longer context and image processing that has been coming out ahead in benchmarks, plus the thousand self-hosted options that now exist.
I used the model and it was good enough for email classification.
I was wondering if I'm supposed to be doing multiple runs for topic classification with grouping related topics i.e. if its sport,economy or tech related news and then another run for soccer,basketball or something else
or batching them like the News article topic classification example in a single run.
It's also open source, https://github.com/nicobrenner/jeffy it's in the roadmap to build something for the browser. Do you want to take a stab at it and submit a PR?
They don't. Generally we already have trillions tokens worth of classifier datasets so there's no reason to create synthetic data that will be worse than the data it was trained on.
Cool to see another model with support for bounding boxes on images. Really wish to see segmentation in a future release; Gemini 3.5 Flash-Lite is mandatory in my use case as it's the best model for semantic image segmentation in my experience.
it's actually more usefull than it looks. it is not much useful on its own but it can play a big role in a complex workflow or custom harness.
i used luna 5.6 as the ranker/filter to remove useless items from upstream rag(mixing embedding, regex etc) and it did a good job but took 5-10 seconds each time. now less than 1 second and no observable precision loss. hope it helps
I use it as a drop in replacement for LLMs I had previously doing filtering and classification in a pipeline. It's loads cheaper and faster with roughly equivalent performance on my own internal tests. The confidence scores (which are also reasonably well calibrated on my data) are a super nice bonus.
My favorite use case, can't take credit for it, saw it on an X post early on, is inlining it as a sql function so you can do fuzzy classifier based joins
I found an example online, I cannot find the github again, but it was interesting: an instant dynamic generation of React component, you type what you want to build using natural language, and it directly add one to the page.
If I remember well, in the background, it does the multi-choice question: "What is the component requested ? --> Calendar, Slider, ..."
You can turn a lot of process and workflow systems into deep decision trees.
My team hasn't finished writing their blog posts yet, but the customer demos [2] went out the moment TypeSafe took our call and sorted all the paperwork out.
There's a tiny section at the bottom of the demo which explains how it actually works (or the github repo is the code).
We have a single decision API[1] over all similar models, so we run evals across all the "System One" api products & definitely don't trust any benchmark that you haven't run. All standardized benchmarks have been benchmaxxed already.
PS: just in case it wasn't clear, the demo is a game where we play to calibrate the human vs machine decision making (or just do it in auto-pilot mode).
Anything that can't be solved with a regex and LLM feels like an overkill. There are surprisingly tons of things that falls in this category. E.g Filter, classification, preprocessing micro decisions, etc.
Effort router. Dynamically changes the reasoning level for OpenAI and Anthropic models based on Jev's perceived difficulty of the task to be done. Only these two providers can support this because they both support changing effort level without busting the cache.
I've used it in a bunch of places, but my favorite use. I have an app (https://plonkit.app/) i'm the only user... because i built it for myself. In the app I have a shopping list feature. When you add an item it auto assigns it to the right shopping list. It's a very simple feature, but with Jev, it's quick, and it stayed simple.
I like that they're being up front about this. Benchmarks are a metric that can be gamed. They give you an approximate estimate of where things land, but you really need to test for your use case.
They are? In all my evals at work, Jev seems to win, despite me wanting the open weight versions to.
I imagine if I fine tuned one of the open weight models for my use case it would be possible to beat it, though, but as a general drop in decision model, I’ve not really seen much that beats it especially for the cost
100% agree, have found through tests a big discrepancy between models optimised for SWE benchmarks and usage in prod - there is a somewhat subjective element to it as well in terms of output that can't be quantified effectively (less so with decision models naturally)
I like to test the decision models with coin flips. This has a well known answer and I haven't seen one give it back. It undermines my trust in every other decision.
It's essentially, "predict the outcome of a coin flip", "what is the result?", and "heads" and "tails" as the answer options. With the Jev website, they do not show the "this is not a real probability" disclaimer until after you get the answer (misleading imo, should be upfront and more prominent, eg. by the input form like the gemini app)
When I give this to an LLM, it will generally respond with something like 50/50, 100% confidence.
This is common knowledge though, and not some deep math problem, small children understand this concept.
If they cannot get something so simple right, while at the same time offered as a decision with likelihoods machine, why should I trust their other answers with "probability" they produce?
I think a better way to view these numbers is that they are "confidence" levels, not probabilities in the sense you are interpreting.
You should never trust the machine. ;) But you can have a level of confidence with it, and you can know where it's strong and weak.
This is a language association machine, not a math and probability machine. It is specifically weak with any real math.
As simple as a coin flip is -- as simple as a child may be able to understand it -- the machine is specifically not designed to produce an accurate mathematical probability.
That's just not how LLMs work. It's not how they're designed. Nor these decision models which use identical principals.
All that aside... agreed regardless. Irreversible choices should never be finalized by such a volatile technology, regardless of how well we benchmark it. If there's even a chance it could be wrong, that is unacceptable.
> not probabilities in the sense you are interpreting
I am not interpreting them that way, that's how they are being sold by the companies behind them. I do not think "confidence" is accurate either. As far as we can tell, and in the open source/model versions, it's derived logit values.
But the Jeffy models are explicitly trained on those datasets (to be fair, classifiers are just really good, even a decision model finetuned on the same dataset, doesn’t beat a classifier - in classification tasks)
I think the point is we need better and open benchmark players which can bring and monetise on domain specific benchmarks for end users and not just selling them to the big labs.
Interesting way to release this, I'm not sure how to say this, but this has a real "enterprise"/old school taste to it verses how all the other models/AI companies are releasing things.
It uses this model, MoritzLaurer/ModernBERT-large-zeroshot-v2.0, which can be run on pretty quaint hardware. You likely do not need a GPU, probably 2gb ram minimum on top of whatever you are already hosting
Going from "I'll need a research team and 5 years" to Jev-based bird classifiers in just 12 years is really nice. I'm happy that some problems get easier, and I'm happy that people make things and share their work so we can all benefit.
I have to admit, despite my initial scepticism of Jev, it is actually pretty fucking good at making decisions. I’m trying to break it with weird situations and I just can’t. And it’s so cheap!
- add to your README links to download the relevant .gguf files – I assume from HuggingFace?
- or even implement your own equivalent of `ollama pull`/`list`/`rm`/etc
- as well as `go install github.com/computerex/dlgo/cmd/dlgo@latest`, also document `go install github.com/computerex/dlgo/cmd/dlgo-vision@latest`, `go install github.com/computerex/dlgo/cmd/dlgo-image@latest`, etc (obvious to anyone experienced with Go, not to many inexperienced with it)
actually, I try running `go install github.com/computerex/dlgo/cmd/...@latest` on my Mac and I get a lot of errors; some of them are because `cmd/gpu-watchdog` is trying to use Windows-specific APIs (e.g. `syscall.NewLazyDLL`) even on macOS
If you're looking for a decent local model, JevK5-4B is excellent. On my own benchmark for the things I work with (seeing if the answer to a question was correct given the evidence that was provided), it was within a point of jev.
A lot of the week 1 Jev demos involved getting image data into it somehow, via description I guess? Should be interesting to see what we get with 100ms latency decisions on visual inputs. To me the latency / response time is just a huge game changer -- one to two orders of magnitude more responsiveness opens up a large design space.
For anyone interested in a GUI around Jev I built Decision Studio for experimenting with it in our self-hosted OSS llms.py [1]
You can define tasks like sentiment analysis, support triage, or claim support, tweak the questions and criteria, and inspect the answers and probability distributions. You can also import existing recipes, save examples, and share recipes with recorded results.
Ok. Obviously these are cool people trying to do something useful, and my real name is on this site so I definitely don't want to come off as hostile. But I have three problems with their whole... thing, here:
1. Adding bounding boxes only compounds the biggest issue with Jev, which is that it's using a massive model to do what we've been using purpose-built[1] models to do for years(/decades), only a little worse and a lot more expensive. This is fine --great, even-- if presented as a tradeoff.[2] And a product that enables "smart if statements" that are "sci-fi" does not sound like a tradeoff at all. In that light, ending the post with facetiously-labeled graphs that just show it beating a bunch of random names (including models with twenty-seven BILLION parameters) on unknown metrics feels borderline dishonest.
2. "We love the community's desire to help make models better!" feels like Manufacturing Consent but with way lower stakes. I turn on various experimental features and pick nightly builds and all that like many of us do, but this feels... well, at best it's kinda offputting rhetoric for a practice I otherwise condone. IMHO.
3. Their job postings go beyond groan-inducing to vaguely insulting. Take this "Data Demon" posting[3], which is presumably for a data scientist (data engineer...?) but is branded around mocking the Turing-award-winning scientists trying to warn the world about superintelligence. Yes, I had the same reaction: what? Why?? It's giving a corporate culture where A) executives feel entitled to shoot from the hip and inject their personal crusades on a whim, and where B) scientists/science isn't appreciated (or, worse: understood). Criticizing the concept of measurement isn't helping, nor is their choice to label output percentages as confidence scores.
I guess I hope I'm misunderstanding, cause they're gonna make millions regardless, I'm sure. I just know that I can't think of when I'd reach for Jev 1.*[4] for a prod usecase, and this is an awfully big company for a prototyping shortcut.
[4]: Which, btw, what happened to semver? Did we stop doing semver cause python did? Did I miss a memo/blog? Not a rhetorical question, I swear. Deprecating minor versions just seems unholy, much less minor version thirteen!
111 comments
[ 0.25 ms ] story [ 75.9 ms ] threadI guess their cheeky "Warning: AI Benchmarks, don't trust them" is their way out of that one.
[1]: https://benchmarkheaven.com/submit
[2]: https://news.ycombinator.com/item?id=49797811
Given that benchmarks are useful, why not give them economic value?
OTOH, I trust the benchmarks from Apolinario's HF space that you've linked to.
As it's minor change did not expect much, but having a higher context helps for sure. I will own the errors for passing bigger context.
It's also open source, https://github.com/nicobrenner/jeffy it's in the roadmap to build something for the browser. Do you want to take a stab at it and submit a PR?
i used luna 5.6 as the ranker/filter to remove useless items from upstream rag(mixing embedding, regex etc) and it did a good job but took 5-10 seconds each time. now less than 1 second and no observable precision loss. hope it helps
What I had before was basically:
- 1 classifier question
- if ok, do some heavy computation
- after heavy computation, another classifier
So now what I do is:
- Jev with the 1st and 3rd classifier
- if ok, heavy computation
I can classify many things at once, and it's cheaper and faster than 1 for sure and 1 maybe classification that I had before.
If I remember well, in the background, it does the multi-choice question: "What is the component requested ? --> Calendar, Slider, ..."
My team hasn't finished writing their blog posts yet, but the customer demos [2] went out the moment TypeSafe took our call and sorted all the paperwork out.
There's a tiny section at the bottom of the demo which explains how it actually works (or the github repo is the code).
We have a single decision API[1] over all similar models, so we run evals across all the "System One" api products & definitely don't trust any benchmark that you haven't run. All standardized benchmarks have been benchmaxxed already.
PS: just in case it wasn't clear, the demo is a game where we play to calibrate the human vs machine decision making (or just do it in auto-pilot mode).
[1] - https://github.com/prasanthj/duckdb-dual-cognition
[2] - https://demos.isotopes.ai/demo/apps/chain-command
I like that they're being up front about this. Benchmarks are a metric that can be gamed. They give you an approximate estimate of where things land, but you really need to test for your use case.
I imagine if I fine tuned one of the open weight models for my use case it would be possible to beat it, though, but as a general drop in decision model, I’ve not really seen much that beats it especially for the cost
It's essentially, "predict the outcome of a coin flip", "what is the result?", and "heads" and "tails" as the answer options. With the Jev website, they do not show the "this is not a real probability" disclaimer until after you get the answer (misleading imo, should be upfront and more prominent, eg. by the input form like the gemini app)
When I give this to an LLM, it will generally respond with something like 50/50, 100% confidence.
iirc, Jev has a "jaggedness" page that lists known problem areas with the model, and plain mathematics (which I'd argue this is) is one of them.
If they cannot get something so simple right, while at the same time offered as a decision with likelihoods machine, why should I trust their other answers with "probability" they produce?
You should never trust the machine. ;) But you can have a level of confidence with it, and you can know where it's strong and weak.
This is a language association machine, not a math and probability machine. It is specifically weak with any real math.
As simple as a coin flip is -- as simple as a child may be able to understand it -- the machine is specifically not designed to produce an accurate mathematical probability.
That's just not how LLMs work. It's not how they're designed. Nor these decision models which use identical principals.
All that aside... agreed regardless. Irreversible choices should never be finalized by such a volatile technology, regardless of how well we benchmark it. If there's even a chance it could be wrong, that is unacceptable.
I am not interpreting them that way, that's how they are being sold by the companies behind them. I do not think "confidence" is accurate either. As far as we can tell, and in the open source/model versions, it's derived logit values.
> You should never trust the machine
Rule #2 https://susam.net/inverse-laws-of-robotics.html
> coin flip...
LLM's get this one exactly right in my limited testing "50/50, 100% confident"
But the Jeffy models are explicitly trained on those datasets (to be fair, classifiers are just really good, even a decision model finetuned on the same dataset, doesn’t beat a classifier - in classification tasks)
I've had better luck just running a few of my own simple tests.
Very refreshing.
> For the builders AND dreamers
> Enter the danger zone at your own risk.
> Size does matter, apparently
> We’re trying something new out here: we’re going to treat you guys like adults
> Lots of people love to ask what kind of sci-fi we’re releasing next
That said, their docs are great and don't seem to include all the Reddit cringe.
Videos will be next.
[0]: https://xkcd.com/1425/
You could also pay for a ML product, but not so easily and cheap. This enables PoCs and small products.
- Inference for a bunch of open weights architectures
- Diffusion image generation fir Z ai turbo
- Vision input into LLM for inference
- Vulkan/fast CPU, optimized kernels
- Playground for testing
- Last but not least, system one fast inference mode with same API semantics as Jev
Decision models work by using a backbone model like qwen 3.5 9b, along with a special linear head that provides the readout for the forward pass.
- add to your README links to download the relevant .gguf files – I assume from HuggingFace?
- or even implement your own equivalent of `ollama pull`/`list`/`rm`/etc
- as well as `go install github.com/computerex/dlgo/cmd/dlgo@latest`, also document `go install github.com/computerex/dlgo/cmd/dlgo-vision@latest`, `go install github.com/computerex/dlgo/cmd/dlgo-image@latest`, etc (obvious to anyone experienced with Go, not to many inexperienced with it)
actually, I try running `go install github.com/computerex/dlgo/cmd/...@latest` on my Mac and I get a lot of errors; some of them are because `cmd/gpu-watchdog` is trying to use Windows-specific APIs (e.g. `syscall.NewLazyDLL`) even on macOS
It really feels like they're just admitting that they had it disabled because they needed investors to subsidize turning it on.
You can define tasks like sentiment analysis, support triage, or claim support, tweak the questions and criteria, and inspect the answers and probability distributions. You can also import existing recipes, save examples, and share recipes with recorded results.
An OpenRouter API key is required to use Jev.
[1] https://llmspy.org/docs/features/decision-studio
1. Adding bounding boxes only compounds the biggest issue with Jev, which is that it's using a massive model to do what we've been using purpose-built[1] models to do for years(/decades), only a little worse and a lot more expensive. This is fine --great, even-- if presented as a tradeoff.[2] And a product that enables "smart if statements" that are "sci-fi" does not sound like a tradeoff at all. In that light, ending the post with facetiously-labeled graphs that just show it beating a bunch of random names (including models with twenty-seven BILLION parameters) on unknown metrics feels borderline dishonest.
2. "We love the community's desire to help make models better!" feels like Manufacturing Consent but with way lower stakes. I turn on various experimental features and pick nightly builds and all that like many of us do, but this feels... well, at best it's kinda offputting rhetoric for a practice I otherwise condone. IMHO.
3. Their job postings go beyond groan-inducing to vaguely insulting. Take this "Data Demon" posting[3], which is presumably for a data scientist (data engineer...?) but is branded around mocking the Turing-award-winning scientists trying to warn the world about superintelligence. Yes, I had the same reaction: what? Why?? It's giving a corporate culture where A) executives feel entitled to shoot from the hip and inject their personal crusades on a whim, and where B) scientists/science isn't appreciated (or, worse: understood). Criticizing the concept of measurement isn't helping, nor is their choice to label output percentages as confidence scores.
I guess I hope I'm misunderstanding, cause they're gonna make millions regardless, I'm sure. I just know that I can't think of when I'd reach for Jev 1.*[4] for a prod usecase, and this is an awfully big company for a prototyping shortcut.
[1]: https://huggingface.co/models
[2]: of dev speed for cost and accuracy, which is lowkey another way to say efficiency and efficacy. Two pretty massive things to trade!
[3]: https://jobs.ashbyhq.com/typesafe-ai/d315d436-e517-4f17-82bb...
[4]: Which, btw, what happened to semver? Did we stop doing semver cause python did? Did I miss a memo/blog? Not a rhetorical question, I swear. Deprecating minor versions just seems unholy, much less minor version thirteen!
https://github.com/jlt-commons/lev