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.
Using it as part of an agentic CAD workflow, where the model needs to be able to selectively segment from parts of a user's image as it works on a project. Using OpenSCAD so being a general purpose model is very useful to me.
If openscad is the thing Google models has consistently outpermormed the comparable competition in my experiences. Few other models has better physical spatial reasoning skills.
I've found OpenAI models the best (although I haven't tried Argon) for OpenSCAD.
I have found a couple of problems Opus could fix that Sol couldn't (smoothing a three way join between curved edges and a bezier curve face is one I can remember). But it's much slower than Sol and generally doesn't produce as nice results.
There's no reason that an agentic workflow has to use the primary agent model for every intuitive computing task; in fact, that would've sounded absurd before the singularity. Maybe an old flash model is the best in class for this, but A) I doubt it and B) it's still gonna be wildly more expensive.
Assuming I'm not just misunderstanding something critical (happens often!) and you haven't explored this already (very possible!), pointing your agent at this link and asking for some prototypes might be illuminating: https://huggingface.co/models?pipeline_tag=image-segmentatio...
I've had pretty good luck having Gemini or Claude use SAM as a tool. Gemini is really good at identifying things in images, but it doesn't do a great job doing segmentation per se, but SAM on its own can't really decide what you need to segment.
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 a twitter 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
Same here, and I have a project 100% hosted in Cloudflare ecosystem (AI Gateway, Workers, Queues, R2, D1, DO, etc) I was so happy when I saw clef. Tested, and it's bad. Slower and wronger than Jev.
Still waiting on Decisions API using Luna, as this what I was using before.
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?
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
- add some CI using Github Actions (or an alternative if you prefer)
- 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!
The font rendering on this site is something else. Is that a stroke being applied to every character? I thought this was a Linux glitch but it exists on iPhone too.
I love everything about this launch post. They feel like people i would get along with.
And i already sub to jev and i think this launch is going to make me a much more loyal user now lol
> We’ve heard you loud and clear on context lengths. Jev actually supports a much longer context length than we’ve enabled in production. We chose to limit it intentionally because Jev, like all models, is less reliable on longer contexts, and this constraint encourages more effective usage patterns in prod.
> We aren’t your parents though, so while we strongly recommend limiting context length, you no longer have to:
They’re upfront about their decisions and intentions. All of which make perfect sense. What gives it shady vibes for you?
Eg: Longer context has long been known to reduce response accuracy, so it makes perfect sense why they’d limit it especially at launch. Personal evals are in fact more useful than public benchmarks so it makes sense (and is refreshing) that they’re cautioning against them…
What about data residency and retention? They quickly mention ZDR for enterprise accounts only on their support but that’s it. At least to us, that’s a hard showstopper.
Too bad they seem too busy to answer these questions, IMO that feels like a missed early leverage opportunity for them. We benchmarked a bit self hosted alternative, and nothing reach quite the cost/performance of Jev, yet.
I second that. If they would offer zero data retention and optional even EU residency, we would immediately use it. In particular with the new image feature.
Not sure why you're being downvoted. This is very reasonable to expect people to run themselves if they have an expectation of privacy. There is basically no reason to trust someone at their word about data retention when you can run it yourself as easily as this.
I don't get this whole thing. This sounds like a small model, that's very possible to run on the edge, and it seems like most of the things it does actually are useful there.
The extra funny thing about this is that the target audience is people who use agentic AI systems (system one thinking as a compliment to system two thinking). Setting up local decision / classification models is as easy as telling the agent you already use "set up laya on llama cpp locally https://github.com/ggml-org/llama.cpphttps://huggingface.co/ggml-org/Laya-GGUF" (or your inference engine and model of choice, walk away for a minutes, and come back to it running on your computer.
The main benefit of Jev seems to be broader knowledge? But local models (especially the ones actually trained specifically for decisions, rather than ones that "support" it like embeddinggemma) are probably good enough for what most people use it for. They also seem to be quite easy to fine tune (I haven't tried, but seems to be what people say).
> Setting up local decision / classification models is as easy as telling the agent you already use "set up laya on llama cpp locally https://github.com/ggml-org/llama.cpphttps://huggingface.co/ggml-org/Laya-GGUF" (or your inference engine and model of choice, walk away for a minutes, and come back to it running on your computer.
I mean, yes, that works at dev time; but going from zero to nonzero local inference components makes an agent implementation suddenly much more complex to ship to production / drastically narrows the set of hosts such an implementation can be deployed onto.
It would be very useful for some use-case I have around deciding if an input has potentially sensitive data, to avoid sending it to AI providers. Having to call an external API makes this a catch 22.
I haven been requesting access to their trust center for weeks, even emailed them about it multiple times only for them to direct me to their trust center.
Main thing I need from Jev is high rate limits. I have about 500-600 I need to fire off concurrently. I use Clef and now OpenAI decisions to help make up the difference.
Let's test it in a real-world trading scenario. Jev 1.13 had quite good results, only beaten by Microsoft-Decision-1: https://pilabs.space/trading-arena
benaduggan | a day ago
aryabakh | a day ago
ranger_danger | a day ago
benaduggan | a day ago
I guess their cheeky "Warning: AI Benchmarks, don't trust them" is their way out of that one.
woadwarrior01 | 23 hours ago
[1]: https://benchmarkheaven.com/submit
[2]: https://news.ycombinator.com/item?id=49797811
Maxious | 20 hours ago
Given that benchmarks are useful, why not give them economic value?
woadwarrior01 | 13 hours ago
OTOH, I trust the benchmarks from Apolinario's HF space that you've linked to.
polski-g | 18 hours ago
yipinwong | a day ago
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.
therealpygon | a day ago
nico | a day ago
aryabakh | a day ago
totetsu | 18 hours ago
nico | 18 hours ago
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?
totetsu | 11 hours ago
nico | 9 hours ago
matt3210 | a day ago
nico | a day ago
himata4113 | a day ago
tyushk | a day ago
moonu | a day ago
tyushk | a day ago
gunalx | 23 hours ago
nl | 18 hours ago
I have found a couple of problems Opus could fix that Sol couldn't (smoothing a three way join between curved edges and a bezier curve face is one I can remember). But it's much slower than Sol and generally doesn't produce as nice results.
bbor | 20 hours ago
Assuming I'm not just misunderstanding something critical (happens often!) and you haven't explored this already (very possible!), pointing your agent at this link and asking for some prototypes might be illuminating: https://huggingface.co/models?pipeline_tag=image-segmentatio...
lifeisloving | 17 hours ago
lukeschlather | 19 hours ago
hexasquid | a day ago
ASalazarMX | a day ago
nazgul17 | a day ago
LewisOrton | a day ago
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
Rudybega | a day ago
vanuatu | a day ago
inerte | 23 hours ago
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.
SOLAR_FIELDS | 20 hours ago
einsteinx2 | 10 hours ago
lanyard-textile | 7 hours ago
ygouzerh | a day ago
If I remember well, in the background, it does the multi-choice question: "What is the component requested ? --> Calendar, Slider, ..."
daviddoran | a day ago
gopalv | a day ago
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
MisterMunchkin | 23 hours ago
samsmallmind | 23 hours ago
m4r71n | 22 hours ago
HamadMalikKhan | 19 hours ago
polski-g | 18 hours ago
swalsh | 18 hours ago
jjcm | a day ago
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.
pietz | a day ago
jwpapi | 23 hours ago
girvo | 23 hours ago
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
inerte | 23 hours ago
Still waiting on Decisions API using Luna, as this what I was using before.
Run your own evals, folks.
vilos1611 | 22 hours ago
CompleteSkeptic | 23 hours ago
samsmallmind | 23 hours ago
verdverm | 21 hours ago
willy_k | 19 hours ago
verdverm | 19 hours ago
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.
lanyard-textile | 13 hours ago
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.
verdverm | 6 hours ago
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?
Here's another post that shows how fragile or inconsistent, even dangerous, these decision models can be: https://zachlandes.com/trolley-problem/
Don't use decision models with important decisions, especially those that involve human lives or irreversible choices
nico | 21 hours ago
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)
nijave | 21 hours ago
I've had better luck just running a few of my own simple tests.
verelo | 19 hours ago
bptp24 | 17 hours ago
einsteinx2 | 10 hours ago
jofzar | a day ago
Very refreshing.
CompleteSkeptic | 23 hours ago
kypro | a day ago
> 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.
shrinks99 | a day ago
I've yet to see any company match the copy writing skills of Valve though.
ajs1998 | 19 hours ago
bothways | 7 hours ago
Toobad the fonts are ridiculous bad though. Relax.
throwaway98797 | 6 hours ago
120bits | a day ago
hnav | a day ago
lukeify | a day ago
dwa3592 | a day ago
Videos will be next.
kkarpkkarp | 14 hours ago
12345hn6789 | 9 hours ago
dwa3592 | 8 hours ago
Havoc | 23 hours ago
AftHurrahWinch | 23 hours ago
[0]: https://xkcd.com/1425/
adrianvi | 12 hours ago
You could also pay for a ML product, but not so easily and cheap. This enables PoCs and small products.
MisterMunchkin | 23 hours ago
computerex | 23 hours ago
- 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.
skissane | 20 hours ago
- 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
- add some CI using Github Actions (or an alternative if you prefer)
- 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
computerex | 18 hours ago
shepardrtc | 23 hours ago
vessenes | 23 hours ago
aliasxneo | 22 hours ago
It really feels like they're just admitting that they had it disabled because they needed investors to subsidize turning it on.
cat-whisperer | 22 hours ago
mythz | 21 hours ago
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
bbor | 20 hours ago
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!
karan_rajbhar | 19 hours ago
yogthos | 19 hours ago
https://github.com/jlt-commons/lev
whalesalad | 19 hours ago
HamadMalikKhan | 19 hours ago
lacoolj | 18 hours ago
And i already sub to jev and i think this launch is going to make me a much more loyal user now lol
> We’ve heard you loud and clear on context lengths. Jev actually supports a much longer context length than we’ve enabled in production. We chose to limit it intentionally because Jev, like all models, is less reliable on longer contexts, and this constraint encourages more effective usage patterns in prod.
> We aren’t your parents though, so while we strongly recommend limiting context length, you no longer have to:
> TypeSafe-Danger-Zone-Long-Context: true
TobyStillHuman | 17 hours ago
mt_ | 15 hours ago
Remember the relatioship incentive is skewed, you are giving in your precious data inexchange for random logprobs, that they offer to "cheap".
gk1 | 11 hours ago
Eg: Longer context has long been known to reduce response accuracy, so it makes perfect sense why they’d limit it especially at launch. Personal evals are in fact more useful than public benchmarks so it makes sense (and is refreshing) that they’re cautioning against them…
irthomasthomas | 11 hours ago
"...there's no way that our team has a full understanding of what is needed to spark delight in the world and build sick sci-fi."
"Size does matter, apparently "
calyhre | 17 hours ago
Too bad they seem too busy to answer these questions, IMO that feels like a missed early leverage opportunity for them. We benchmarked a bit self hosted alternative, and nothing reach quite the cost/performance of Jev, yet.
BrandiATMuhkuh | 15 hours ago
Maybe it's possible via their Digital Ocean offering https://ideas.digitalocean.com/changelog/now-available-jev-f...
scotty79 | 11 hours ago
If you are sending data over the internet to someone else's computer, who you can't or won't audit, you should keep you expectations realistic.
You can be sure that comapnies will only do ZDR if your data is more expensive to keep than its utility to them, even if they claim something else.
Zambyte | 9 hours ago
calyhre | 9 hours ago
torginus | 10 hours ago
Why does this thing have to be another Web API?
Zambyte | 9 hours ago
The main benefit of Jev seems to be broader knowledge? But local models (especially the ones actually trained specifically for decisions, rather than ones that "support" it like embeddinggemma) are probably good enough for what most people use it for. They also seem to be quite easy to fine tune (I haven't tried, but seems to be what people say).
derefr | 4 hours ago
I mean, yes, that works at dev time; but going from zero to nonzero local inference components makes an agent implementation suddenly much more complex to ship to production / drastically narrows the set of hosts such an implementation can be deployed onto.
ahoka | 9 hours ago
jimmydoe | 9 hours ago
calyhre | 9 hours ago
skinfaxi | 7 hours ago
ralusek | 16 hours ago
joelsstafford | 13 hours ago
aleksiy123 | 11 hours ago
I see box is now a thing but no span?
So render text to image then use box right?
phacker007 | 9 hours ago