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Introducing System One Models and Jev
- 10c8 17d agoWow, this is really cool. If this holds up to scrutiny, and has a decent context window (+16k), it suddenly changes our project's status from "cool concept, too slow and expensive to release" to "doable", just like that. Just joined the waitlist, excited to try it out!
- mercat 17d agoafaik Jev's context window is 32k
- kobe_bryant 17d agomy sons name is also Jev
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- hi_hi 17d agoIf I’m understanding correctly, this will work well for self driving cars?
- albelfio 17d agohttps://x.com/completeskeptic/status/2099925682726002904?s=46 https://x.com/completeskeptic/status/2099925682726002904?s=4... The doom demo is quite cool
- baist0 17d agolol! they reinvented aim bot for cheaters.
- ErneX 17d agoDirect link to the Doom video tweet: https://x.com/completeskeptic/status/2099925687465570372 https://x.com/completeskeptic/status/2099925687465570372
- thih9 17d agoThe doom video is also in the article itself (headline: "Doom"). I suppose this is the same video as the one from the parent comment, but I don't know for sure - I don't have a twitter account and the above link doesn't work for me.
- magicmicah85 17d agoThe doom demo is also in the article, for anyone that doesn't want to go to X.com. :)
- lelandbatey 17d agoLink to a raw MP4 of the video, from the parent article: https://framerusercontent.com/assets/rlL7ImEbISFoYt3IJEHHfvjpY.mp4 https://framerusercontent.com/assets/rlL7ImEbISFoYt3IJEHHfvj... It's in the parent article under a section named "Doom" in case that asset URL ever changes.
- einpoklum 17d agoBut when their system is given the instruction "do not fire, simply dodge" - it doesn't "simply dodge", it actually gets close to the fleshy pink demon rather than keeping its distance. Or am I misunderstanding?
- anthonypasq 17d agoi think its just telling the model that it cant output a shoot action
- caspar 17d agoI'm not sure the authors realize this is way more than "just a cool demo": if this holds up, it's going to be huge for game QA work. Instrument your game to output properties of entities near the player and the output is the various control inputs - moment to moment gameplay gets solved. Maybe augment with a tick-by-tick controlled stepping mode if particularly twitchy - an LLM can take care of the higher level reasoning then.
- Garlef 16d agoyes; and it could also be used to spot unexpected behavior such as inputs not registering due to rare bugs
- smusamashah 17d agoDoesn't this mean Jev can be used to drive a car as well?
- dgellow 17d agoSide note: it took me more time than I would like to admit to realize that Diogo Almeida isn’t a satirical version of the name Dario Amodei
- jdthedisciple 17d agothought the same lol
- jakintosh 17d agoIt wasn't until the demo videos that I realized the post wasn't satirical.
- bogzz 17d agoThat would have to default to Wario Amodei.
- Aboutplants 17d agoWell now I’m rooting for them!
- scottyah 17d agoWild that it doesn't generate text. I wonder how its technology compares to Tesla's FSD stack.
- jrickert 17d agoSigned up for the beta! :) would love to put this through some real-world shootouts against traditional LLMs to see where this type of model really excels. I’m guessing it might be able to replace maybe 40-70% of LLM calls for a given pipeline depending on the business task, cutting the API costs on those calls by an order of magnitude.
- pennomi 17d ago> Extraordinary claims require extraordinary evidence so see below for the receipts. Yes, that’s the kind of attitude I want to see in these model releases
- ramon156 17d agoBut the evidence is not there...
- pennomi 17d agoIndeed, they talk as skeptics but don’t offer a ton of evidence, other than a couple videos of demos. A live demo would be far more convincing.
- simianwords 17d agoThey gesture at not using benchmarks for some reason...
- meric_ 17d agohttps://typesafe.ai/blog/antibenchmaxxing https://typesafe.ai/blog/antibenchmaxxing But also effectively this is a classification model. It excels at specific certain types of workloads, and obviously will fail at others. Not really sure how one benchmarks this tbf. I can see their argument on why this requires a novel specific eval for whatever your usecase is. A consistent "global" benchmark might be hard to do
- esafak 17d agoLooks like a great model for NLP.
- hunterbrooks 17d agoum what is going on with the outfit changes in the launch video... https://x.com/CompleteSkeptic/status/2099925682726002904 https://x.com/CompleteSkeptic/status/2099925682726002904
- jbonatakis 17d agoThe whole video seemed generated to me
- Gecko4072 17d agoCan't tell if they're just having fun or if it is ai-generated. On the verge of not being able to tell. Voice sounds a little synthetic.
- CompleteSkeptic 17d agodefinitely not AI-generated - this is my real wardrobe we also thought the voice at the end was AI-ish, but apparently that's a real voice actor but slightly sped up
- deleted 17d ago[deleted]
- scrollaway 17d agoPretty sure that's an intended joke. Reminds me of this: https://www.reddit.com/r/ITcrowd/comments/tg05j1/i_cant_believe_ive_never_noticed_this_before_but/ https://www.reddit.com/r/ITcrowd/comments/tg05j1/i_cant_beli...
- vatsachak 17d agoIt could be used for coding if you gave it an AST. If you work at TypeSafe please try this. Side note: This is probably how LLMs would perform with better encoders and next-latent prediction, so eventually those will beat this architecture out. Still amazing though.
- ramon156 17d agoI've implemented tree-sitter in pi before, and while it works, I have no real proof it saves me tokens, or is more accurate. I think a better implementation is a model that's trained for AST's, not just "use tool, see what happens". I'd love to do research on this when I have the time.
- vatsachak 17d agoCool project! That's what I was insinuating through "better encoder"; the model creating more efficient representations of ASTs using something like JEPA
- Escapado 17d agoI saw the CEO reply elsewhere in the comments to some other question. Maybe he can shed some light on it. My gut feeling is that this is non-trivial and they did not get this to work (yet?), otherwise I can’t come up with a good reason as to why they would not demo that as I assume half of the crowd here (myself included) would line up as customers.
- vatsachak 17d agoYeah it would be quite trivial to try and implement an auto regressive AST generator for STLC with Jev provided that you had bounded variable names and integers. As you said, if it worked, they would have demoed it haha
- CompleteSkeptic 17d agothe hard part for coding is actually state engineering (e.g. getting your dependencies in context) - we haven't even tried it yet (because my philosophy is we should automate the easy tasks before the hard and we've been working on getting the model smart on the former) we do think there's a lot of potential though and do want coding themed releases soon
- sim04ful 17d agoThis sort of stuff almost sends shivers down my spine, it's like i'm looking 5 years into the future.
- darpa_hr 17d agoThere was no "AI Winter"
- zenlikethat 17d agojoin the discord! we love forward thinkers
- erichocean 17d agoI could put this to use today. I think we'll see a bunch of different architectures over the next five years.
- himata4113 17d agoThey never show exactly how they use it? Only a bunch of animations of it 'working'. Would like to see the actual code used for the demos!
- ricardobeat 17d agoThe doom demo shows the program state / query.
- zenlikethat 17d agoIt's a bit hastily put together, but I made a dspy fork where you can add a decorator to automatically use TypeSafe where possible on Signatures. It shows a fair bit of what actual, hands on usage looks like. https://github.com/typesafeainate/dspy-typesafeify https://github.com/typesafeainate/dspy-typesafeify
- snthpy 17d agoDSPy seems like the right comparison and this is the first comment I've seen mentioning it. Thanks for putting this together. I'm surprised the cost saving is so little though. I expected much more based on the post.
- bfeynman 17d agoSuper intrigued by this - large scale automation using LLMs is quite annoying due to deprecation cycles of models from frontier labs and cost of running your own being prohibitive when you have a blend of them.
- mushufasa 17d agoI would love for things like this to be accessible via hubs like open router or AWS bedrock. It's hard to justify adding new model vendors directly with all the heightened concerns about privacy and security, but if bold new capabilities are added to a centralized already-vendor like AWS, technical people can adopt them without going through a whole compliance/purchasing/vendor review process. And an extra middleman tax is well worth it when the cost savings of the model itself can be one-two orders of magnitude.
- oblio 17d agoThe thing is, in this climate it's hard to believe such tech will remain secret for long. So, assuming this is not vaporware, this would raise the tide for everyone because it shows what's possible.
- cheeze 17d agoIsn't openrouter the exact opposite of caring about security and privacy? I guess you can choose your provider still? But isn't the point that the lowest bidder is doing inference?
- ajmurmann 17d agoYou can set privacy requirements and define an allow list. To me the main value prop is that I get one bill for all models and can quickly try new models without signing up anywhere or changing my code. Oh! Also you can pass an array of models and if the first provider is down it automatically falls through to the next provider. More useful than it should be...
- hobofan 17d agoThat's still ultimately privacy by contract (where you have to trust the inference providers to uphold their end of the deal), rather than privacy by design.
- LeBit 17d agoI always setup guard rails so that only zdr providers are used.
- ramon156 17d agoThis sounds good but so far all claims just sound like marketing terms. I'd love to see real proof. e.g. "RLCD" and "parallel sampling" have nothing to back it up. also "70-500ms vs 3-329 seconds" are apples-to-oranges unless the LLM baseline is doing comparable work (e.g., long chain-of-thought). If Jev is skipping generation entirely for a narrow structured task, of course it's faster. Nonetheless i want this to be true, so I'm looking forward to Jev Edit: I really have to say that I like their manifesto https://typesafe.ai/manifesto https://typesafe.ai/manifesto
- why_only_15 17d agoThey have various benchmarks, e.g. how much time it takes them to do wikipedia page -> page games. Jev seems to take the same or fewer hops but in ~10x less time and for ~10x less money. It's totally reasonable to compare against LLMs doing chain of thought if it gets comparable performance.
- vatsachak 17d agoIt's not an LLM though it's a frontier model on structured data
- BoorishBears 17d agoDid you see the video where it plays Doom, it made it click for me
- simianwords 17d agoBTW it was not multi model playing doom, it was passing structured input and getting structured output. Its not what I thought: frames of video passed and real time game play.
- yieldcrv 17d agoso what? put an LLM on Cerebras and get its responses faster, and put Jev on Cerebras and gets its responses even faster
- 17d ago
- initsecret 17d ago> [others] Output tokens: ~5x more expensive than input tokens. > [them] Output tokens: FREE (too cheap to meter). I'm very confused by this.
- varenc 17d agoThe output tokens are just responses to your inputed questions and their probability. So relatively few output tokens. No unstructured text back in the response.
- quotemstr 17d agoThey're not doing autoregression, so all the outputs are computed in one big forward pass. Very cheap.
- ambicapter 17d agoI think OP is confused about "others" vs "them".
- initsecret 17d agothey’re talking about two totally different things, right?
- CompleteSkeptic 17d agoit's our output tokens that are free (under the system one / jev column)
- someguynamedq 17d agoThis is not confusing
- whalesalad 17d agoWhat is it about the rendering of this page that is so... off? It almost looks like the entire thing is a <canvas> element. edit: looks like a framer export where there is a text stroke being applied :|
- andai 17d agoWhy did they pick the name System One? It's not really explained what "System One tasks" and "System One shaped queries" are. Things that need a fast response? Does this imply it's a very small model? I couldn't find anything about the model itself.
- oblio 17d agoMaybe: https://thedecisionlab.com/reference-guide/philosophy/system-1-and-system-2-thinking https://thedecisionlab.com/reference-guide/philosophy/system...
- hunterbrooks 17d agoBingo. It's a Psychology term for the part of our brain that reacts instinctively rather than thoughtfully and logically
- totallygeeky 17d agoWoof, that page is hard to read. I don't understand what they've done to the way text is rendering but it's not great for my eyes.
- phenomen 17d agoIf you zoom in (especially on the large title), you'll see that the text is a semi-transparent gray with a black internal outline. It seems like all the typography is SVG-rendered. Actually insane. I've never seen this before. Not even the most vibeslopped websites have that.
- agos 17d agoI don't know if they changed it since your comment, but it's all just text to me
- jceg 17d ago> We deliberately chose not to publish performance against public benchmarks. In fact, we plan to only have one-off evals when we make product updates. lol, I bet they would publish them if their score on those benchmarks were good.
- larodi 17d ago"is this the real thing or is just fantasy"
- jawns 17d agoI could see this being fantastic for classification tasks. Last year I shifted from using LLMs for bulk data classification tasks (1M transcripts) to generating embeddings and categorizing based on cosine similarity. It saved a ton of costs and time, but wasn't as accurate as LLMs. This seems like it can give me Terra-level classification ability with the cost/speed I need.
- moffers 17d agoSo is it a structured data-based language model? Or is there a model and a harness? Hopefully they’ll open up and explain more.
- CompleteSkeptic 17d agoit is just a model, no harness yet ;) it is a structured data model, but technically not a language model (it doesn't generate language)
- skerit 17d agoSo in theory you could feed it incomplete text, and then ask it for the probabilities of what the next character could be?
- iforgotmypasswo 17d agoOr a partially completed song, asking for the next note. I’m not sure if you’re joking, but using it for space constrained next token generation within a grammar sounds like a really neat use case.
- copperx 17d agoFeed the generated note back into the input for the next query and you have ... autoregression?
- vatsachak 17d agoIf you provide it an AST of the english language, yes.
- CompleteSkeptic 17d agoyou could, but it the model is not optimized for text this is complex, but generating text is highly complicated and requires mode dropping to make long cohesive text
- mckngbrd 17d agoI think the joke here is getting missed
- zenlikethat 17d agoStrings trigger us
- aghilmort 17d agowas wondering same
- quotemstr 17d agoIt looks like a specialized encoder-only(-ish) transformer with scalar and ordinal output heads. Acausal in effect, maybe? Probably not even autoregressive? I'd use this as a tool an LLM can use for specialized tasks. It's not AI in itself.
- mkrishnan 17d agoIf this is true means, AI Stock bubble burst. (For good)
- deleted 17d ago[deleted]
- big_toast 17d agoIt seems like the docs[0] are a better explanation? The comparison to llm tokens is kinda confusing. It looks like the model takes as input a state (structured text? not sure if multi-modal) and a question (as a "Choice", "Score", or "Noul") with some additional augmentations possible. Then outputs the question's answers as appropriate (e.g. a choice, accompanying probabilities, confidence). Edit: On the AI primer page, it looks like they do the RLCD on a pre-trained base model? [0]:https://docs.typesafe.ai/concepts/system-one https://docs.typesafe.ai/concepts/system-one
- CompleteSkeptic 17d agoCEO here - that is right! I do agree that the comparison to LLM tokens is hard to understand (also because output tokens are not comparable). But yes, text or structured state (like a JSON with multiple pieces of text in) -> decisions out (e.g. choice maps to "match" statement, "score" maps to sorting, "noul" short for bernoulli maps to if-statements)
- ianbutler 17d agoI see this super interestingly as the "subconscious" to the llms "conscious" for lack of better terms. I'm super interested in this for broad and rapid decision making in the context of consumer agents so will be signing up for sure.
- CompleteSkeptic 17d ago1. I am extremely on the same page 2. I do think that subconscious is not only much smarter than we give it credit for, but also much more robust than the "jagged frontier" of current LLMs (shilling my blog post on that jaggedness: https://www.completeskeptic.com/p/lies-damned-lies-and-benchmarks https://www.completeskeptic.com/p/lies-damned-lies-and-bench...)
- cheesecakegood 16d agoI wonder if a good analogy would be, they are trying to tune the “gut feeling” of a model more directly
- mkrishnan 17d agoIf this is true, then AI Stock Bubble burst (for Good)
- bthornbury 17d agoIs the tradeoff of the parallel output that we don't get arbitrary string generation? like output # of tokens is fixed ahead of time? Either way, really cool and impressive.
- zenlikethat 17d agoYeah, it doesn't output strings, just decisions/answers.
- copperx 17d agoNon-hallucinated ones at that.
- kypro 17d ago> Outputs > LLMS > Strings / generated text. Strings are flexible and can be anything: chat responses, code, hallucinations, refusals, or even type-safe structured values. To be used by software, responses need to be parsed + validated. There is also always some risk that the AI goes off the rails. > Jev > Type-safe structured values. Possible outputs and structure are defined in advance. The model never makes type errors. All answers are accompanied with calibrated probabilities and confidence scores. I mean, this isn't even remotely comparable to LLMs so why compare? Also, why are they bringing up AGI given there approach is so restrictive that what they're building literally cannot have the creativity required for AGI? The video is 100% marketing slop... The bulk of the application of LLMs is that they generate reasonably reliable text which doesn't need to be defined in advanced. I'm sure there is a niche for this and congrats to the team, but please let's not hype this as if it's the next big thing in AI...
- bqsile 17d agoCreativity is not required for AGI, that's maybe the only thing that is not required for AGI actually. What a sad world would you live in if you don't keep creativity for the humans.
- charcircuit 17d agoParallel inference where you don't want a subagent seems niche. But there is a lot of random things where businesses ultimately want some kind of score instead of generating something. I think the interesting thing would be seeing if prompt injections still work with this kind of model.
- CompleteSkeptic 17d agowe have played with this! the fascinating thing we've found so far is that adversarial examples for our model are quite different from that of LLMs so that they work even better together
- deleted 17d ago[deleted]
- adroitboss 17d agoI am positive I know exactly how this works, I made something similar a few months back. But the problem is without generation you are extremely limited in the use cases. And while the model can't hallucinate, it can still be wrong. It just can't make up data.
- cooljoseph 17d agoLast year I also had a rather similar idea, but dropped it before I went very far in working on it. I wonder if you and I had similar ideas? 1. Start with an LLM, so that your model understands natural language. 2. Replace RoPE with a tree embedding scheme, and causal attention with a sparse attention on the graph structure. (You could use full attention... but it's cheaper to use graph attention.) 3. Chop off the final unembedding layer, replacing it with a projection down to two scalars, one for logits and one for confidence. 4. Each option of a choice is represented by a number of tokens in leaf position; average these tokens' logit outputs to get the option's logit. Average all of the confidences from all of the options to get the choice's confidence. 5. Train the logits by KL divergence from a true distribution (or NLL on samples from a true distribution). 6. Train the confidences on a subset of the data in which you know the entire true distribution. The hardest part is getting real world data for workflows, but I wildly speculate that you can get by with only ~50,000 documents if you first adapt domains using synthetic data.
- dennisy 17d agoAre you able to share how it works in that case?
- adroitboss 17d agoI'll tell you this. Output isn't too cheap to meter, there is no decoder.
- krackers 17d agoSo an encoder-only model with a classifier trained on the heads or something? DeepSeek recently switched to an encoder-decoder architecture in an attempt to get the best of both worlds (fast prefill while preserving generation capability), I wonder if that might be the future?
- yieldcrv 17d agooooooh it can play Doom! forget LLM benchmaxxing sidequests, I'm sold on the real benchmark
- entrep 17d agoThis puts the human even more out of the loop I'll guess?
- zenlikethat 17d agoThat's kinda the goal. Imagine all the automation in the world being able to embed intelligence directly inside it - factories could route based on more complicated questions, hardware could anticipate your needs. Customer support could be done without humans 90% of the time.
- bananaflag 17d agoFunny how it can do everything but not chat. Sort of how when I was a kid I thought of a medicine that could cure any disease except the common cold.
- yieldcrv 17d agothis is interesting, so not an LLM but can be used in these use cases that LLM's have been shoehorned into https://docs.typesafe.ai/concepts/use-case-map https://docs.typesafe.ai/concepts/use-case-map
- petesergeant 17d agoThis is basically a zero-shot classifier that can accept raw text (or structured text) as an input, and is able to classify that text as accurately (they claim) as a frontier-level LLM. I have workflows this would be useful for, looking forward to it showing up on OpenRouter.
- CompleteSkeptic 17d agoexactly right!
- ahackinghen 12d ago[flagged]
- jacobgold 17d agoFirst, congrats to the team on launching something genuinely interesting and new. Seems like a more accurate title would be "Jev: Trading general purpose generation for fast typed inference" or something like that. This is interesting, but the speed comparison seems misleading? A generative model that can output code in a Turing-complete language can do anything a computer can do. Jev can only generate structured output, right? This is probably super useful for classification/routing/scoring, but it's nothing like the code generating models we're all using today for code and automation. Also "can't hallucinate" seems wrong? Sure, it can't emit an invalid type, but it can still emit a completely wrong valid value. You can enforce structured output from an LLM too, with an appropriate harness, etc. Assuming there's no funny business, the Doom demo is cool.
- Flere-Imsaho 17d ago> Jev can only generate structured output, right? This is probably super useful for classification/routing/scoring, My first thought was that it would be ideal for robotics? As in control of limbs, general planning, route finding, etc.
- copperx 17d agoUm, Isn't SELF DRIVING the elephant in the room?
- aryamccarthy 17d agoOnly if you think that everyone cares about self-driving. Lots of niches require structured domains; self-driving is just one that has a lot of capital thrown at it.
- ygouzerh 17d agoThat's a great point! It quite looks like the System 1 model of Physical Intelligence
- dbbk 17d agoWhen they say "can't hallucinate" they mean they produce a confidence value for every result, so you could see for example it has 0.1 confidence, and you can disregard the result - that'd be different from hallucinating where it believes it's correct
- gok 17d agoSo... a classifier model?
- Havoc 17d agoWill need hands on to truly tell, but the doom demo seems very promising. If it can play that with text descriptions of where stuff is by distance and degrees in a 3D context then many GUI automation tasks should be easily doable
- silbercue 14d ago[dead]
- seinecle 17d agoCan this be used in practice to write code?
- zenlikethat 17d agoNot in the traditional sense of a coding agent, but we think there's a ton of opportunity in using it for context management ("do we _really_ need to pass all these tokens to the agent?"), semantic linting ("how does this score against this AGENTS.md: <...>"), etc.
- _davide_ 17d agoWhat's the difference compared to just taking an embedding and feed forward a simple net trained for the task?
- lubujackson 17d agoAfter much fumbling around with prompts and evals, this is exactly how I am using LLMs in production, to narrowly make choices and return structured data. Any deterministic work gets pulled out of the prompt and my goal is to narrow the model output to be as clearly defined and as minimal as possible. Jev's focus on structured I/O and confidence scores are game changing. If this does at all what it claims, I think this is going to quickly become the new standard approach for agentic systems.
- CompleteSkeptic 17d agowe hope so! the bigger hope is to not just eat LLM market share, but to allow for people to use AI much more in the inner loop of software
- copperx 17d agoI'm sure you've thought of self-driving. How does the model work in that space?
- bobtheborg 17d agoGreat question! Yes, this works much like the doom player. Sensor data (LIDAR, velocity, etc.) becomes the state. You use the score primitive to operate the controls ("What level of braking should be applied" 0: None, 1: just slightly slowing down, 2: there's a suspicious cat on the side of the road you don't trust, ... Full disclosure, I am not they :=)
- dozerly 17d agoThis smells like a tool a more broadly capable LLM would take advantage of extremely well.
- wg0 17d agoBut the real problem in self driving isn't the decision making but object description. That is, computer vision if with cameras. Decision making isn't that of a bottleneck I suppose.
- tidewave 17d agoCongrats on the release! Finetuning a language model for decision classification (with probabilities) is already well-understood. What specifically changes in the training objective with RLCD? Are its benefits isolated from Jev’s new architecture/parallelism?
- pixelmelt 17d agoInteresting concept, I can't see a reason to use a generalist classifier over an api rather then just training my own? If it was open weights I would probably mess around with it.
- hofo 17d ago[dead]
- wxw 17d ago> Input tokens: $0.042 / MTok ($42 per billion tokens). > Output tokens: FREE (too cheap to meter). Insane. The video demos are really compelling, in particular the speed. > Structured outputs slot into ordinary software as fuzzy decision rules: classify, route, score, extract, or branch where hand-written logic is too brittle. The surrounding code constrains their freedom, making them easier to compose into reliable systems. I buy this vision. A lot of LLM integration I see these days is ultimately exactly this. OpenAI-style structured outputs works decently but this would be a great improvement in cost, latency.
- CompleteSkeptic 17d agothanks a ton! constrained decoding (OpenAI-style structured outputs) make models dumber unfortunately - the short+dense version is that simply masking logits is insufficient because if ever a model was assigning probability to an invalid token, the model is by definition confused. you'd be better off erroring IMO
- theredsix 17d agoCongrats on the launch! What's different between Jev and Microsoft's Guidance package? https://github.com/guidance-ai/guidance https://github.com/guidance-ai/guidance Is it a diffusion generator under the hood?
- bilsbie 17d agoI’m not understanding what this is. It’s a faster cheaper LLM?
- pama 17d agoIs there a downloadable technical report somewhere?
- Gecko4072 17d agoFrom the person in the video regarding issues with benchmarks in general, and for LLMs. Also their approach. Good article. https://substack.com/home/post/p-215252866 https://substack.com/home/post/p-215252866
- tylermarques 17d agoWe had early access and found it to be pretty useful. Having a second form of verification, where you can ask multiple questions (in the form of Nouls) raised our confidence in the outputs of other models. [0] IMHO This type of model works incredibly well in concert with LLMs, not as a replacement. [0] https://goodstartlabs.com/research/verification-is-the-bottleneck https://goodstartlabs.com/research/verification-is-the-bottl...
- kevmo314 17d agoDon't your numbers suggest DeepSeek V4.1 Flash, for $100 more, gets you to slightly better agreement?
- tylermarques 17d agoYes they definitely do - not claiming it's a perfect solution, but as a V1 product it shows a lot of promise.
- torginus 17d agoI was thinking about something similar (maybe) - generally speaking, embeddings for LLMs tend to learn real world concepts - things like 'fruit' or 'France' or 'city' as directions in embeddings. But in things like programming, most concepts are abstract - 'if hungry eat an apple' in programming terms would look like 'if hunger > 50 {apples--; hunger-=30;}' and compilers work with 'concept erasure' - to them, tokens (which are like llm tokens) look like 'if var1 > 50 {var2--;var1-=30}'. They don't care about how these things map to real concepts. So all the embedding directions used to encode real-world concepts are just noise to LLMs when programming. This greatly reduces dimensionality and training costs. So does a token representation tuned for programming constructs, rather than natural language would probably have a more efficient encoding.
- ta988 17d agoCurrent models go beyond the simple embedding because you start to encode groups of concepts in the context-aware part of the model (attention heads or any other method). So it is never simply words/tokens in isolation anymore.
- darksaints 17d agoOkay, so it doesn't output text, that much is understood. What are the inputs like? I'm assuming maybe a text input? maybe an AST definition? Really hard to tell how this works at all from the demos, especially since we can't really try it out.
- CompleteSkeptic 17d agoinputs are structured program state. there is an example at around second 30 of the doom demo (though ideally everyone gets off the waitlist and can try it out for themselves )
- dinobones 17d agoThis is a good product but the naming/branding is pretty unfortunate. Typesafe.AI sounds like some typescript/structured output type of tool… What even is “system one” ? IMO the product/tech is really there, just needs better communication.
- toddmorey 17d agoI mean, it's a structured output model that (apparently) can't hallucinate. I don't mind the name.
- flyinglizard 17d agoIt can't hallucinate, but it doesn't mean it can't make wrong decisions. Just because it adheres to a specific output format at all time, while LLMs have the output format at their mercy, then the claim of not hallucinating is made technically true. I think that this specific part is not super interesting if your harness just recovers from invalid LLM outputs. The latency and cost - yes, those are super interesting.
- mhitza 17d agoYou can get rigid output format from "classic" LLMs https://docs.vllm.ai/en/latest/features/structured_outputs/ https://docs.vllm.ai/en/latest/features/structured_outputs/ though model support is limited. Would like to have something like in the original post but open weights.
- zenlikethat 17d agoThe model can't reason comprehensively (e.g., like Sol XHigh would to solve a complicated problem), but it's designed to be able to answer anything a human reasonably could quickly and intuitively, i.e., system one thinking: https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow
- vintermann 17d ago
- whazor 17d agoA question I have, with the type { output: string }, would the model not become a LLM? And if it does, shouldn’t it cost the same as a LLM for output?
- speedping 17d agoI don’t see this as an option in their website You could theoretically ask “what is the next appropriate character?” and add the entire ascii charset but i doubt it’d work well and you’d be implementing autoregressive churn across network latency…
- CompleteSkeptic 17d agostrings (and all sequential data structures) are not allowed at all - this is how we make sure all outputs can be computed in parallel (thus no output token cost)
- kylehotchkiss 17d ago"While Jev gives up string generation, it’s optimized for structured outputs and can’t hallucinate" Ouh! Any open weights models that can do this yet?? If not, how much longer? I have a Mac Studio coming soon.
- tensegrist 17d agowhat is the…epistemic status, for lack of a better way to put it, of the probabilities? what do they mean? what (probabilistic) guarantees do we have about, say, the responses to - is the capital of france paris? - it is august. is it raining in paris? (forgive the examples; they're probably not semantically the sort of thing jev is trained to work on. but i figure the point translates to various kinds of questions that come up in "inner loop of agentic pid controller" contexts) a normal text-generating model if asked to produce a number will also do that just fine. i assume in jev's case it was actually rled to essentially learn to express priors over things using its implicit world model, which definitely ought to help, but can we say more?
- elcomet 17d agoThe technology and the results are very handwavy. What is RLCD exactly ? What are scores on benchmarks compared to LLMs ? This website does not inspire confidence at all, it all sounds like a marketing piece. I wish it was true, some kind of text-prompted classifier with LLM performance would be cool, but I can't trust it with what we are given.
- warpspin 17d agoHaven't seen any docs or so. Is this actually a general model, or does it need training on the the data set it answers? Finding it suspicious you never see some kind of prompt. Edit: never mind, found https://docs.typesafe.ai/introduction/quickstart https://docs.typesafe.ai/introduction/quickstart by now
- CompleteSkeptic 17d ago1. yes a general model 2. no training at all 3. but it is focused on "System 1" tasks (more human judgment, less math reasoning)
- zenlikethat 17d agoIt's very generalized. Can't wait until everyone can see it.
- mixtureoftakes 17d agoDoom demo is beyond impressive, even scary
- bigglebear 17d agoIt's very misleading. If I'm actually playing a game I don't get the coordinates of enemies sent back to me so that I can feed into my mouse to snap my crosshair to. It's looking through walls too, because it's working off structured state in text form. You could re-create this whole demo without using AI. Have an LLM generate the state machine for you and no model is required to run it.
- someguynamedq 17d agoThe impressive part is that it is low latency enough to serve high quality answers at game speed through the model instead of a pre generated ad-hoc machine.
- bigglebear 16d agoA pre-generated machine can serve the answers in <1ms. It's a far better strategy.
- hspeiser 17d agothis might finally be smart enough and fast enough for jarvis. hard to feel like iron man when your assistant takes 8 seconds to decide to pause your music
- zmmmmm 17d agoThe eval is baffling me > we assume there is a correct compute graph (a “workflow” represented in code) and use the predictions of the largest, smartest, and most expensive external models as reference probabilities. ... Rephrased: every model gets the same workflow. We test how they compare to the average of the smartest models (in this case, Astra and Fable). They assume there is a correct graph, but they don't compare to that, they compare to the average of the smarts models? So the smartest models are getting it wrong but you compare that anyway as a benchmark? So the outcome is "how much of a Fable am I getting" etc. Why not compare the actually correct thing? But then even on this hand constructed eval, the first plot is showing Jev at less than Sonnet 5 accuracy. It is barely better than Luna. There are two Opus 5's and two Sonnet 5's without explanation. What is the plot showing? I gave up.
- Mentlo 17d agoHm, would be good to understand the architecture better. Is this answering just from a world model informed prior? How informed is it by the information in the prompt? I can't see this maintaining calibration across all domains and all types of structured output. Is there anything published on how it maintains calibration? Or when you say "outputs calibrated probabilities" you mean "as calibrated as frontier LLM models, just cheaper" - which is a different claim; as LLM's aren't particularly well calibrated
- alphazard 17d agoThere's a whole lot of information on this page that doesn't tell me anything about what this actually is. Can anyone spell out what the architecture is here? They claim it's not an LLM, which I read as "not an auto-regressive token generator". I assume they are still using a transformer, otherwise they would be talking about the thing that's not a transformer, instead of all the fluff on the linked page. But they emphasize parallel generation, so is it like a text diffusion model?
- tacoooooooo 17d agoSounds like its essentially a generalized zero-shot classifier that takes and option set at runtime and works on unstructured inputs. you pass in your "prompt" and options (described in natural language) that it can respond with, in addition to your input. it gives back that option set with a probability assigned to each one
- CompleteSkeptic 17d agoyes and can do many of those in parallel
- bigglebear 17d agoI would guess a tiny stripped down text diffusion model. It only has 32k context, and for choice mode it can only select from 10 choices.
- wesammikhail 17d ago> and for choice mode it can only select from 10 choices. Rip there goes my excitement. I have a task that something like this would be great for but the list of options is a zero or two larger than that xd
- copperx 17d agoThey said that it works with up to 255 options.
- 2001zhaozhao 17d agoFunny how the authors are asserting that "doing the right task > data > compute > algorithms" while simultaneously releasing AI model for calibrated decision making, which if they work, would mean that "compute > doing the right task"
- futurisold 17d agoThis, combined with contracts, could make a lot of things so much fun now! For those who don't know (which is probably everyone but me), I ported the design-by-contract pattern in Python and combined it with LLMs. This was early 2025. I originally wrote about it here: https://leoveanu.com/2025-03-01-dbc/ https://leoveanu.com/2025-03-01-dbc/ . Contracts are a core feature of SymbolicAI ever since. The community seems to have loved it too (https://news.ycombinator.com/item?id=44399234 https://news.ycombinator.com/item?id=44399234). I think I'm starting to glimpse the implications and it's gonna change agentic workloads if it holds up to scrutiny. It's too early for me to tell anything other than jot down some rough thoughts. In short, you get blazingly fast semantic branching you can use in control flows. For contracts, I can now directly take the data model that you have to design and convert it into Jev's expected format. Or I can use Jev for semantic branching in postconditions. If my understanding is correct, that should be doable, but I need to think more about it. It could be that with Jev I can finally “compile contracts” and better chain them into workflows, which is something I always wanted but didn't know how to do properly. Eager to test. On the waiting list.
- zenlikethat 17d agolove it. send me an email and i'll try to get you moved up on the list? nathan@typesafe.ai
- AdieuToLogic 17d ago[flagged]
- jbotz 17d agoJudgemental much? GP's first sentence isn't arrogant (at worst displaying a bit of false humility) because it's saying everyone but him doesn't know about a thing he did. Your second quote you apparently mis-parsed because of a minor English error (he should have said "to Python" rather than "in Python"), but to me it was pretty clear what he meant.
- AdieuToLogic 16d ago
- preommr 17d agoThis will be insane for tool usage, and probably where the major economics for day-to-day usage will be. The goal is going to be to use llms to distill operations down to some dsl, and pass it into something like Jev.
- johnecheck 17d agoThis makes me think of Expressions of Change [1], a project that aimed to make updates to a program a first-class primitive in a programming language. A model like this can't output code directly, but perhaps it would be well suited to select from the small set of discrete operations on code envisioned by the EoC author? [1]: www.expressionsofchange.org
- 2001zhaozhao 17d agoHasn't there been a lot talk about Astra's opaque reasoning capabilities (being able to think through complex questions without using a chain of thought)? Given that, can't you just replicate Jev by telling Astra "here is the question, you must make a multiple choice decision / output a score between 1-10, please answer directly in a single word, no reasoning allowed"? (Edit: Ok, Jev is much cheaper in input tokens so these two aren't directly comparable at all)
- CompleteSkeptic 17d agothe edit is right - jev would be cheaper, faster, and more self-consistent (in general) we actually use astra (and fable) in this way for our evals: evals.typesafe.ai someone on the team cooked hard on that and it shows example traces comparing our model to opus/sol
- postalcoder 17d agoThis has the potential to be huge for computer use. OpenAI has been teasing how fast computer use is with their models running on Cerebras chips but the difference here is a burning hole in your pocket.
- Gecko4072 17d agoLike which elements to select? Similar to the doom and wikipedia runs?
- postalcoder 17d agoYeah. Computer use is essentially a model navigating the OS-provided accessibility tree. I imagine a model trained on it would operate the computer exactly as we saw it control Doom. https://developer.apple.com/library/archive/documentation/Accessibility/Conceptual/AccessibilityMacOSX/OSXAXmodel.html https://developer.apple.com/library/archive/documentation/Ac...
- nowittyusername 15d agoI can think of many uses for this thing, robotics being one that could really benefit from something like this. A hybrid approach with this and action models and vllms could be really good mix, also agents inside simulated virtual environments, etc... basically anywhere where latency is important but you need some intelligence this will fill those gaps. Weave it with other systems and you have a nervous system as jev with other models like vllm or even text as the slower deeper thinker.
- mlcruz 17d ago[dead]
- hoppp 17d agoThis is amazing. I really could use this. I like the idea of System one models but all LLMs so far work as system 1 thinking because humans generate speech subconsciously with system 1. System 2 thinking requires consciousness which AI does not have, so even reasoning models are still system 1 thinking as system 1 in humans has reasoning with heuristics. Its limited but most people navigate the world with it completely, so it's enough for AI.
- bregmandiv 17d agoI'm trying to parse it down to what we had before vs what is new here. We already had encoder models that skipped text generation for giving us a numerical output that could be computed as a probability. we also got no hallucinations and faster inference for free there. So we already had 1. "unstructured state in, probabilistic decisions out" 2. "orders of magnitude faster and more efficient" What was hard there was to train the model head without ML expertise, and considerable amount of data. This seems like this is a democratization of those encoders? The addition over existing encoders seems to be coming from being able to specify the output shape (up to a cardinality of 255). It is unclear to me if this is possible using Jev without additional labels for fine-tuning. If so, that is still very impressive, but I think the faster inference and 0 hallucinations might come for free, from it not being generative.
- techn00 17d agoI can't see how this is different from a fine tuned LFM2.5 encoder
- abeppu 17d agoI think this is a great direction -- for some kinds of users. And this makes me wonder if the 'vs' framing is misleading. Yes, I think it's a mistake that many organizations are cramming LLMs inside of automated pipelines where the extreme generality/flexibility of the model is at odds with the fact that you're using it for a very specific task that gets repeated over and over, and needs a very specific structured output to be successful. But specifying your task carefully (as well as deciding what counts as your input state representation etc) seems like a form of programming. Something (a person or a model working in a relatively unrestricted way) will need to produce a configuration/specification for this system. So rather than Jev vs Claude I imagine that using Claude/ChatGPT/whatever interactively to define / refine your Jev config which then runs in prod might be the happy combination?
- findjashua 17d agoWould it be fair to say that this is tailored for tool-selection subagents?
- kroaton 17d agoSeems that way.
- silbercue 14d agoI used it today as browser agent, the "tool" is a menu of ~10–40 clickable refs from the accessibility tree. Jev picks one per step. 21–23 decisions for six benchmark cards, all correct (!), ~$0.001 total. Really good. The part it can't do is write the text for an input field so a nano model does that when Jev picks "type". Numbers and code in my top-level comment in this thread. Working very good.
- woggy 17d agoCan this be used in conjunction with a text-generating LLM for better quality code generation?
- poly2it 17d agoIs there a bottleneck which would hinder putting this architecture in charge of a humanoid? Would it be able to operate continuously, for example in conjunction with an LLM for long-term reasoning? Doom seemingly works extremely well.
- 8note 17d agodefining the workflow such that the operation is a set of relevant questions
- xynelius 17d agoThe Doom demo looks impressive but was it a fine-tuned model? It's the difference between a cool demo and revolutionary tech.
- copperx 17d agoShouldn't self-driving be a piece of cake if it works this well for Doom? Or what am I missing?
- pantelisk 17d agoI think the doom demo uses a text representation of the world and it's basically, "projectile coming your way" -> "Strafe". "Enemy ahead" -> "shoot. So it works well when spawned in a room of enemies (as we see in the video). If self driving is red means stop, green means go, and stay in your lane - then it would work great, but having to actually think and test which maneuver is optimal for a given situation while weighting safety, road rules, random unexpected actions and getting to your destination, I think it's a much bigger problem. A bigger model specifically trained on that maybe would do great, but then the output is not the constraint anymore. But I haven't tried the model, so I 'm just ballparking and could be very wrong.
- hamishwhc 17d agoThe model doesn't have image input capabilities (yet, it seems from the post), so for the Doom demo, a harness is extracting a bunch of structured information from the game (map layout, enemy locations, player ammo, health, etc) and providing it as a massive JSON blob to the model so it can make its decisions. This model _could_ be hooked up to make the decisions for a self-driving car, but it would need to be fed a structured blob of the situation around it, so all the computer vision problems of self-driving are still there. And that's before you get into the confidence and accuracy of this model.
- jamilton 17d agoDriving is more complicated than Doom, and it doesn't look that great at Doom to me.
- ernsheong 17d agoThis is potentially huge and can crash the Big Two's stock prices or block their IPOs completely.
- bqsile 17d agoIf it work as good as they say it does, confidence score + really fast response when you want very fast response, basically.. To me it is a crime against humanity to not open source it. Just get the money from cloud inference and cloud agentic sessions or whatever but open source it. This tech, a good harness, a good model provider, and you have basically a AGI building machine.
- kart23 17d agoThis makes me kind of nervous for the whole AI thing now. Are people gonna lose their jobs, etc.? so much of the economy is now built on top of LLMs.
- colordrops 17d agoWhat's different about this particular model that worries you?
- kart23 17d agoit doesn't require nearly as much compute as normal LLMs. anything depending on increased datacenter and compute spending would be threatened.
- invalidOrTaken 17d agoa world where people eat so they can feed Big Computer sucks. We need Little Computer, driving robots in the fields.
- kart23 17d agoI definitely agree with this, but the transition is gonna suck for some.
- filearts 17d agoIf we could come up with a system to classify the probabilities across a large number of candidate words (or components thereof) then this could actually be good at producing text, one element at a time. We could call these elements 'tokens' and picking the right one could be called something like 'decoding'. Crazy idea but hear me out... On a more serious note, it will be fascinating to see how this different spin on modelling inference will create new paradigms or slot into existing ones.
- cooljoseph 17d agoA few questions: 1. Do you provide any kind of largest common subtree caching for cheaper input? 2. Have you tried auto-generating Lisp programs structurally? 3. Have you tried augmenting a Lisp language with a `choice` function that makes choices given a prompt, the environment, and the continuation stack?
- zenlikethat 17d ago(1) Nope, it's always the same input token cost (2-3) No, but that's kind of a sick cook ... Want to get access and try it? nathan@typesafe.ai
- cooljoseph 17d agoThanks for the early access! I was testing the Lisp idea out in the playground, but I don't think the model is smart enough right now to generate actual code. I tried having Jev finish generating the code for a Fibonacci number function, but it kept wanting to create a literal number instead of refer to a variable which is a number. This happened both when I gave Jev the current program as a string and when I gave Jev the program as structured data. Maybe I'm just not doing a very good job at prompting Jev, but I think right now it's not quite capable enough to generate Lisp code. Link: https://console.typesafe.ai/playground?share=shr_148e12489842722475aae69a35758ccb0c5 https://console.typesafe.ai/playground?share=shr_148e1248984...
- bjconlan 17d agoYou know you're too old when you see the company name and think! Oh I wonder what Martin Odeskey , Jonas Bonér and co are up to. Wait, didn't they become lightbend... Altho this comment takes away from what these guys are doing which legitimately sounds interesting.
- _boffin_ 17d agoAny relation / inspiration to GLiClass?
- maltalex 17d agoThis is a very promising idea - a model that takes arbitrary text input (which can be a complex json), plus a set of questions (yes/no, multiple-choice, or score) and quickly (milliseconds) and cheaply ($0.042/MTok) answers those questions. Unfortunately, none of this is explained in the announcement, but the documentation [0] is pretty good. [0]: https://docs.typesafe.ai/concepts/how-to-build-with-system-one https://docs.typesafe.ai/concepts/how-to-build-with-system-o...
- 18al 17d agoAPI example[0] makes it clear how it'd be used: from typesafe_sdk import Choice, Noul, Score, TypeSafeClient with TypeSafeClient() as client: response = client.system_one( state={"document": "I was charged twice. Please fix this ASAP."}, questions={ "billing": Noul(instructions="Is this ticket about billing?"), "tone": Choice( instructions="What is the customer's tone?", criteria={"calm": None, "frustrated": None, "angry": None}, ), "urgency": Score( instructions="How urgent is this ticket?", criteria=["can wait", "this week", "today"], ), }, ) print(response.nouls["billing"].noul) print(response.choices["tone"].choice) print(response.scores["urgency"].score) [0]: https://docs.typesafe.ai/sdk/python https://docs.typesafe.ai/sdk/python
- lostmsu 13d ago[flagged]
- leobuskin 17d agoAn example with manual combinatorial exclusion in “not_for” field made me cry, this is a wild hybrid of code logic, textual definitions, and AI blackbox. It’s a cool idea, but the “glue” layer is too boilerplate-ish
- niutech 13d agoThe concept isn't new: zero-shot classification using e.g. Facebook BART has been available for years. And there is already an open source model Laya: https://laya.convaiinnovations.com/ https://laya.convaiinnovations.com/
- altcognito 17d agoIf it is so cheap, why such a limited release?
- nickstinemates 17d agoWe've already started using it for some pretty powerful decision tree stuff. We're just scratching the surface. We shipped an extension for Swamp[1] a few minutes ago and the combination is great! The one downside is that the context window is very small (32k.) So some initial ideas we had for initial evaluation of code reviews won't fit yet in the window. 1: https://swamp-club.com/extensions/@swamp/typesafe-ai https://swamp-club.com/extensions/@swamp/typesafe-ai
- nightshift1 17d agoThe whole page reads like it was vibe-written by an AI. If I'd built something as disruptive as this claims to be, I'd have spent at least fifteen minutes writing the announcement myself. Every time I see 'we' in an announcement like this, I picture one guy alone in his basement.
- CompleteSkeptic 17d agounfortunately all hand-written :( my chief-of-staff does unironically handwrite em dashes though
- nojvek 17d agoI really appreciated the hand-written release. Thank you.
- lwansbrough 17d agoFor what it’s worth, I didn’t get that impression, and even noticed a couple typos ;)
- edot 17d agoVery cool! Can you explain when I would use this vs. training a standard ML model on my data? Suppose I had a fraud dataset with features like customer ID, amount, merchant, online or in-person, etc. - I can't imagine that a general model like Jev would predict this more accurately or cheaply than even a basic XGBoost model trained on my dataset (one that I could build in a few minutes by asking Codex to build it). Where does Jev add value here?
- hangrymoon01 17d agoyou will need to collect data for every decision/usecase and then train a model. But this can be used for different use cases with just a prompt. Founders response to a similar question on X: https://x.com/CompleteSkeptic/status/2100067328620896408?s=20 https://x.com/CompleteSkeptic/status/2100067328620896408?s=2... pasting it here: zero-shot + general == programmable I would assume any extreme scale narrow task could then be fine-tuned for, but we'll see - I suspect putting it all in shared cognitive core has bit maintainability/generalization benefits
- edot 17d agoThanks. I do concede it’s very general but that is a double-edged sword. I don’t need a general fraud identification algorithm. I need an accurate one. If I have another classification task I’ll train another model for that task.
- passive 17d agoWhile I understand that accelerating development isn't necessarily the target for this, and it's not at all intended to generate code the way many of us are... I think this could be pretty decent in CI? There's a lot of "flakes" I've mediated that this could have handled much more efficiently. Maybe observability as well, triggering elevated logging and other initial measures?
- iamgopal 17d agoIf I understand correctly, it can play chess and rubic cube better than LLM ? ( may be go too ? )
- iamgopal 17d agook I've searched, it may not, but it can do "driving car" and "trade 0-DTE Option" much better.
- leo4242 15d agoI was wondering the same! So I asked Astra to build me https://jev-chess-master.vercel.app/ https://jev-chess-master.vercel.app/ where you play against Jev AI as chess player. You play White, and Jev plays Black. Rather than asking an LLM to generate move strings or JSON, the backend feeds all server-validated legal candidate moves into Vercel AI SDK's experimental_evaluate(). Jev picks Black's move and outputs its probability distribution across all legal candidates in a single forward pass (~300ms, ~$0.00004/move). Github link: https://github.com/qibinlou/jev-chess https://github.com/qibinlou/jev-chess Hope this helps!
- Wazzymandias 17d agoThis looks and feels a lot like productionized conformal prediction
- mmastrac 17d agoIs this a Markov/Diffusion model with some sort of external Engram memory? If so, this could be extremely interesting.
- dthedavid 17d agoLooks promising. I'm building an AI video editor and multi tool calls take >30s using Gemini. This would be a a game changer if Jev can take that down to single digits at p95.
- activehuman 17d agoI can see the value in this but looks like there's going to be trouble in communicating the difference between this and a regular LLM, and also proving the potential cost savings in using this to replace existing systems that are using LLMs with frameworks like langgraph, as this can't be a drop in replacement and would require a significant amount of re-architecting/reengineering of systems to get the type system to work
- strich 17d agoHuh this looks fantastic. The Doom demo really sold for me that this could be a great tool for accelerating QA at my gamedev studio. Signed up for early access.
- cfowles 17d agoWasn't really till seeing this home assistant demo they have (https://www.loom.com/share/18c4dbcf8db546dfb2d7f2ef018e78e4 https://www.loom.com/share/18c4dbcf8db546dfb2d7f2ef018e78e4) that the value really clicked for me. Seems really cool.
- ramoz 17d agoGuess I'm a bit less impressed seeing that for some of the more intelligent driven+action work -- splitting requests in the video -- they had to kick out to an anthropic model.
- kzsh 17d agoHaiku, to rewrite a sentence as two discreet commands. I agree that it was notable that they delegated to an existing LLM, but I don't think it detracts much from the value proposition (not yet proven) of their demo.
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- alpineman 17d agoAgreed but is it much easier to deal with if you need to have all of these sub processes integrated? How does one know when you need to reword a request? What if Anthropic then has a type error, then debugging that just got harder.
- cfowles 17d agoThat's fair, but it highlights how this would actually be used. It doesn't really seem like a competitor to other models but instead a way to make these real systems more enjoyable to deal with.
- iamjs 13d agoI wonder if they could have accomplished this with spaCy
- consumer451 17d agoSuper cool! Instantly joined the waitlist. It might be boring, but I can see exactly how I could use this right now to improve my agentic rag.[0] In two months I am supposed to deal with a giant corpus, while still maintaining responsive chat UX. I have been working my butt off to make our first big client happy. This could really help solve the chunk ranking problem. [0] assuming the policies are compatible with sensitive production workloads, some time in the near future.
- dozerly 17d agoVery cool. LLMs have been borderline unusable as functions for the longest time, very excited for this direction.
- iforgotmypasswo 17d agoCould you use this to build a proactive memory formation and retrieval system for LLMs that runs lightning fast? Last 32k of connect + Summary of current task: Did we learn something useful here (true/false)? What is the category to file it under? Then notify the LLM to file it away. What class of memory might be useful here? Model gives probability to each item in the list. Short description of all memories ordered by tagged class is used in the next round. Are any of these memories useful in the current context, such that they will inform the model and help in its task (yes/no)? I’m sure there’s some fine tuning to be had, but this sure seems like the basis for a substantially better proactive memory system that works around an existing LLM conversation. If I’m understanding what this does and how this works (generic input, intelligent classification with probabilities, rapid and cheap), this is absolutely nuts.
- vopi 17d agoThis is actually pretty cool. I think the undertalked about part of this for TypeSafe is that they can always "extract"/distill the frontier of this type of task from the newest LLMs for cheap. Jev seems seems to be GPT-6-Astra/Fable 5.1 but I imagine a bunch of training data is from earlier models? Then, you can serve it faster/cheaper than the frontier LLMs. It's basically distilling a small but extremely common use-case from LLMs and serving it. Then RLCD comes into play to update weights when a new model comes out, etc. Any thoughts on what the next potential "cheap" win to be distilled from frontier LLMs is? I'm going to need to play around with this.
- omeid2 17d agoCan HN have a tag for open-weight vs closed-source models please? The progress is nice, but if it is not released at least in papers or open-weight? These are just ads?
- faizshah 17d agoI think I missed why is this faster? What I’m reading here is it’s similar to constrained decoding but I’m not seeing the explanation of why it’s able to get those results.
- respectattentio 17d agoSeems like "some" of LLMs tasks are now Jev tasks.
- nelaggy 17d agoinsane doom demo i wonder what the limits of its intelligence are? i'm guessing it's not great at reasoning tasks, it seems breaking down the problem helps significantly, but how much does a problem need to be broken down for reliable performance? also this would be huge if it could run locally but it seems like there's no intention to do that at the moment
- wg0 17d agoCan I put it as Air Traffic Controller? With similar error rates as humans? That would be the litmus test. "Does not hallucinate" is not the same as "is never wrong". So the ATC test could be the benchmark.
- copperx 17d agoNot hallucinating is easy when you don't produce strings.
- vintermann 17d agoHallucinating as we use the word really only applies to generative AI. Non generative AIs can't hallucinate, they can just be wrong.
- hmartin 17d agoAm I the only one struggling to parse the distinction System One (the system/harness?) and Jev (the model?)?
- kroaton 17d agoSystem One seems to be more of a "class" of models, as it's a good classifier but can't do what traditional LLMs with chain of thought do.
- aryehof 17d agoAs a zero-shot classifier, I expect that effectiveness is dependent on the data trained upon. Jev input … > Unstructured data (e.g. text) with an emphasis on structured program state. What pre-training data/model is Jev based on? Surely result effectiveness is dependent (outside of one’s own input as “state”) on that?
- mortsnort 17d agoI am confused why they say it is not an LLM and then in the documentation it is shown as being an LLM derivative. The documentation makes it sound like they're taking a pretrained LLM and then giving it their unique post-training. How is that not an LLM? FAQ: Is Jev just a smaller LLM? Jev is neither small nor an LLM, hence being off the intelligence Pareto curve. Image in documentation: https://mintcdn.com/ts-docs/aFVnpmCIX68NpsV1/images/ai-primer/training-paths-dark.webp?fit=max&auto=format&n=aFVnpmCIX68NpsV1&q=85&s=2747633edb0e54fa3f14a8aba830f4fd https://mintcdn.com/ts-docs/aFVnpmCIX68NpsV1/images/ai-prime...
- riknos314 17d agoLLM seems to have become synonymous with Generative Transformer architecture. While this model may share much with GPT-style models on the encoder side, it clearly has a different decoder architecture. So is a high-parameter count language model an LLM even when it doesn't have a GPT-style decoder? The definitions are in flux.
- anentropic 17d agoYeah, it must be an LLM for some definitions of LLM It seems to take two forms of context input: 'state' and 'questions' https://docs.typesafe.ai/concepts/state https://docs.typesafe.ai/concepts/state > State can be as simple as a string > State can also be a JSON object or array containing related context, examples, and other information that helps the model answer the associated questions. > The state contains the content and supporting facts. The state seems to be schemaless, while the questions determine the output schema.
- Culonavirus 17d agoCool I guess. Definitely not worth the 1000+ points though.
- latteren 17d agoLooking at the example Jev use cases, it almost feels like Jev's incredible cost/task can make it competitive as a generalized "poor man's ranking" algorithm that can be useful for lean startups or any fast paced development org. I need to rank 1000 articles and pick the 5 most relevant for the user? Jev. I need to audit and strip out content because my user is affected by regional privacy laws (without hallucinating)? Jev. I need to surface the 3 funniest media comments that match the user's sense of humour? Jev.
- Gecko4072 17d agoWonder if this could lead to better recommendation algorithms.
- nullbio 17d agoMore like: I need to ...? -> Open-weight model. I'm sure someones working on this as we speak using an open-weight LLM base (Qwen or something would be a perfect fit). This sort of task is a perfect fit for a very small model capable of semantic parsing. You can get away with a LOT less parameters without all the autoregressive generation and long-context reasoning.
- rana3g 17d agoyou don't say - https://huggingface.co/harshatheg/Qwen-2.5-1B-RLCD https://huggingface.co/harshatheg/Qwen-2.5-1B-RLCD
- latteren 17d agoCrazy, looks like this was just published a few hours after the TypeSafe post!
- smusamashah 17d agohttps://x.com/harshagundal/status/2100044305536889015 https://x.com/harshagundal/status/2100044305536889015 tweet by the author
- freshnode 17d agoI like it. What is it?
- _davide_ 17d agoThis is too much for me. ML playing doom was a thing since before LLMs, decisions tree were always insanely and no one ever used then anyway, i can't see anything new in this yet everyone is treating this as a revolution. This technology was always there and quite easily accessible all along.
- paraschopra 17d agoI'm trying to understand what difference does this make over LLMs. LLMs are universal simulators, their latents model the world. So I bet if you compare their logprobs with probabilities output by this model, it will be highly correlated. Someone should do this quick experiment. I bet there won't be enough of a meaningful difference.
- virajk_31 17d agoGreat to see something new.. However I don't understand how are they claiming zero hallucination, how does giving confidence score fix hallucination? or am I missing something here?
- Imanari 17d agoSeems like LLM can do everything Jev can do (just structured outputs?) but Jev is highly optimized and purpose built for it and thus way faster and cheaper. Is that a fair description?
- padolsey 17d agoI'd love to know if Jev is still fundamentally LLM-shaped in architecture. Like is it using a single forward pass with a learned readout over the predefined options (i.e. a discriminative head on a transformer, no decoding), or something else? I did similar things for zero-shot criterion-based classification using a 4B Qwen model but could not reach the level of intelligence they've got here. Tho speed/cheapness was similar.
- Ozzie_osman 17d agoThis is quite the paradigm shift. Can't wait to get my hands on it.
- flowerboy-t 17d agodo you all see the use cases being similar to what you might use Fastino's Gliner models for? i see similar differentiation from general purpose LLMs in the sense that they can take natural-language input and return outputs adherent to a user-defined schema. https://fastino.ai/blog/gliner2-5-span-free-information-extraction https://fastino.ai/blog/gliner2-5-span-free-information-extr... im thinking about how well Jev could be used to replace a current LLM-as-Judge evaluation workflows, specifically on chat transcript data (think ~1,500 tokens) i wonder if the reasoning usually required pushes it a bit out of scope. didnt see anything published about constraints on the state size, so would be curious to hear about that.
- mary776 17d agodefinitely seems like a modified version of GLiNER2 or 2.5: - encoder-based (no text generation) - multiple tasks in a single forward pass - deterministic outputs - constraint-based classification
- Imanari 17d ago> AI Map Reduce over Big Data > Search for relevant information over giant corpuses Do you mean as an alternative to embeddings?
- mentalgear 17d agoOverall this seems like a classifier that gives weighted scores per custom labels. It's certainly useful, but whether it brings higher quality than an LLM in structured output mode has to be seen in objective benchmarks.
- StevenWaterman 17d agoZero shot classifier indeed. Reminiscent of asking an llm a yes/no question, constraining the output to either yes or no, and looking at the logits directly And each question is a separate single token model completion done in parallel
- padolsey 17d agoI think what this shows is how important branding and comms are. They've captured imaginations with their demos and nomenclature, despite the arguably non-novel architecture. One forward pass, read the embedding space, train some regressors on predicate structure, [??]
- sreekanth850 17d agoThis is best thing to use for decision making, evaluation, classification. If I'm not wrong.
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- anshumankmr 17d agoa) Is this available on Bedrock? b) Does it support structured outputs? c) What about trying it out?
- brainless 17d agoI am not an expert in this domain but as an engineer-turned-researcher, this looks a lot like GliNER with a fitting harness. This is something I focus on in a bunch of my experiments - how to get immense value out of tiny models (<1b params). There are lots of different architectures out there and there is so much to optimize if you know what you are asking and have a grammar to constrain with. Great to see this and I hope this is a lot on top of what is already openly available.
- Otterly99 17d agoAlways exciting to see people working on novel models, rather than the Nth version of the same slightly tweaked LLM. I'm very curious how much ressources are needed to run such a model. This could be a complete game changer for local applications.
- taysdafu 17d ago[dead]
- vintermann 17d ago> Structured outputs slot into ordinary software as fuzzy decision rules: classify, route, score, extract, or branch where hand-written logic is too brittle. Oh, I have one of those use cases, matching people in genealogy trees. You can ask all sorts of questions: do the names match? Do they match within some edit distance? Do they match according to soundex/ metaphone rules (which are themselves a ginormous set of rules for letters and letter combinations which may or may not result in the same sounds, hand-coded as a huge if tree by a linguist not a programmer)? What about their relatives, do they match by the same rules? Should we incorporate domain knowledge about local naming customs? Etc etc. I pointed a coding agent to this problem, and it aggressively started coming up with complex scoring rules and testing them against real datasets. Which led to sort-of acceptable results, but it still missed lots of cases which were obvious to a human, and had false positives which were obvious to a human. Which I could trade off, and slightly improve, with more back and forth with the coding agent. Pointing a good LLM to all the information about two people, would of course give great results. Maybe even better than human judgment. But I can't do that for 100000^2 people, it would be too expensive in all sorts of ways. I need a fast, reliable scorer. I could maybe train an embedding, but that would be a huge job and where would I get the quality data?
- camdenclark 17d agoYou need blocking! Fundamentally this is an entity resolution problem. An LLM can score pairwise really well but scoring all the pairs would be insanely computationally difficult. If you can constrain the set of potential matches up front by querying the dataset for things that could be matches it gets a lot more tractable to use an LLM for this. Are there any heuristics you can use to reduce the search space? You mentioned soundex transformation and maybe prefixes of last names could work? Even if you get the number of potential matches down by a few orders of magnitude this gets more reasonable! Check out https://moj-analytical-services.github.io/splink/index.html https://moj-analytical-services.github.io/splink/index.html
- vintermann 17d agoThe coding agent was pretty good at coming up with heuristics for matching - even more than the dozen I suggested from domain experience. And it used some of them sensibly for blocking, too. I'm sure I could get it to perform a little better and a lot faster with more agent wrangling. I did consider using the heuristics just for blocking, and letting a local LLM do the actual evaluation, but if Jev or Jev-like models work as advertised, maybe we can have the best of both worlds. Thanks for the link, it is an interesting topic.
- paraschopra 17d agoCool approach, i think less latency and cost is the way to go. Here's how this would have likely been made. - Tiny transformer or equivalent model (maybe a few bn or so?), explaining latency and cost - Questions are sent in parallel to multiple copies of it (I'm sure they're edge located) - The model is post-trained for calibration in a wide variety of data (the recipe is relatively simple, and likely targeted on distillation of logprobs / confidence of a bigger model) Notice how cost is ONLY for input tokens as output is merely numbers (few tokens) because input could be huge (questions and options). At 0.042-per-million price they have, Astra estimates the model to be 3bn parameters. One could replicate this by post training Qwen 3.5 2Bn. I expect people to do so soon!
- niutech 17d agoHow does Jev compare with encoder language models like BERT/RoBERTa, which could also be used for classification?
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- niutech 17d agoHow does Jev compare with encoder language models like BERT/RoBERTa/DistilBERT, which could also be used for text classification?
- outlore 17d agoFinally a fast solution to isOdd / isEven :) /s
- lkm0 17d agoOne application that sounds pretty interesting would be the creation of wikidata pages for anything. Plug a topic/word/concept/historical event in, take a bunch of wikidata properties, rephrase them as questions with the choices being the existing property values. Then feed it to LLMs or something. Does that make them more reliable? Probably not.
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- singularity2001 17d agoHow is that different from machine learning 101 "regression"? And why don't they just put a regression or softmax head on top of a trained transformer? (or do they?)
- arbayi 17d agoI don't know if it's just me but comments under Twitter post felt like paid partnerships to me. The idea sounds cool though
- claud_ia 17d ago[flagged]
- samayashar 17d agoAmazing work by the team! Looks like they've traded accuracy for speed and this is most likely going to be the case with the next class of models. This is a valid tradeoff for one-off responses but if we're dealing with a distributed system (eg: Kafka), then only the high-confidence responses (>0.8) should move forward as input to the next service. If a low confidence output is propagated, then it can break the entire chain.
- agnishom 17d agoTLDR: Like an LLM, the input is a string, but the output is not a completion. The output is a ranking of elements from a certain enum (e.g, [Yes/No], [A/B/C/D]). They use a technique called Reinforcement Learning for Calibrated Decisions (RLCD) instead of RLHF. Also, inference is a lot faster. https://docs.typesafe.ai/primitives https://docs.typesafe.ai/primitives has a much better explanation
- someguynamedq 17d ago"can't hallucinate" feels like some word game Olympics
- ymir_e 17d agoI was previously working on LLMs to extract key info from data rooms for energy assets, and this looks great for that use case. "Does this contract contain ____?" is a pretty typical query for many industries, and then you can have follow up questions that nest down into further info about X, Y or Z thing. Looks really good for that use case, especially with certainty as part of the output, as you could flag things that didn't have high enough of a certainty to human review. I'm sure legora and the other legal AI tech softwares are all over this.
- sourcecodeplz 17d agothis reminds me of laravel boost, which does something similar. it can generate classes/models/routers etc via tool calls, doesnt write the actual code.
- anentropic 17d agoSo it's kind of like BERT but you don't have to train it for each request/response shape ?
- rattray 17d agoSuper cool. Does it, or will it, work with image, audio, or video input?
- mackross 17d agoCan’t wait to use this. Amazing work.
- freddex 17d agoVery cool, I immediately jumped on the waitlist and shared this with my co-founders. Any plans for offering this through a European provider at some point after launching in the US? We work in EdTech, so non-EU-sovereign solutions are a harder sell to our customers.
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- YPCrumble 17d agoWhat are peoples' thoughts on whether a local version of Jev is possible? Having to call an API for something that's main benefit is speed is orthagonal to their ethos.
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- prometheus1992 17d agoJust trying to validate my understanding - so this is a Large natural language classifier, a general purpose or zero shot classifier ?? it can recognize entities, can classify text into some pre-defined classes ? right? or did i miss something amid all the marketing terms such as system one or RLCD or whatever??
- fallingbananna 17d agoLooks like it. A general AI classifier that can be set up easily and used to classify anything… but with probably lower quality than a purpose built one.
- prometheus1992 17d agoIf you are wait listed and eager to try this, I will save you some time. Try this model - https://huggingface.co/MoritzLaurer/deberta-v3-large-zeroshot-v2.0 https://huggingface.co/MoritzLaurer/deberta-v3-large-zerosho... . They are using something similar under the hood. The comparison to LLMs on their blog post is definitely shady.
- prometheus1992 17d agoExample to try on this model: customer complaint - My credit card was charged twice for the same subscription labels - billing, technical, sales The model will always return something from the above classes - "so it can't hallucinate".
- Kurtz79 17d agoI was going to ask if the inspiration for System One name came from Kahneman's and Tversky's research, then I read the FAQ. I listened to "Thinking fast and slow" recently and I was surprised how closely in behavior a LLM approaches the "System One" as defined there. I approve of the clever branding!
- spacedoutman 17d agoThe fact this isn't open-source is troublesome. Such large advances shouldn't be locked up away from local hardware.
- aslkalska 17d agoHonestly all AI research should be open source but that's just a dream
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- ursuscamp 17d agoIs this fundamentally different from other text-based LLMs, or is it the same except with special reinforcement learning a safe guards around generating valid types? Surely it’s still generating some kind unstructured data internally? For example, what if I told it to generate a short story, but the short story is output as a JSON string?
- jw1224 17d agoAn LLM takes (text in) -> (text out). Jev takes (…questions in) -> (…probabilities out) So Jev won’t write a story or emit arbitrary structured data. But if you ask it the right questions, it can make near-instant “decisions” against those questions, with accuracy and world knowledge on par with LLMs. The economic advantage is that it’s parallelizable and can give back up to 255 answers at once, in milliseconds.
- sonink 17d agoSpent a lot of time - but this makes zero sense to me. It can, maybe, return type safe outputs faster than larger llms - but there is little reason to believe that it will be more accurate. It does absolutely hallucinate - and seems to me that the claim is largely misleading. You architect your systems with typesafe - because it is marginally faster, but inaccurate - to do what ? You can just wait for the next version of LLM's to get more accuracy at the same cost - or just use a faster model right now from a different provider.
- brooksy 17d agoFor certain tasks, a model like Jev may be intrinsically more efficient than an LLM because it doesn't have to predict a token distribution and can instead focus solely on the probability of a single question/action
- bigmadshoe 17d agoIt isn't marginally faster, it seems to be approx 100x faster and 10-100x cheaper.
- lostmsu 13d agoAccording to https://goodstartlabs.com/research/verification-is-the-bottleneck https://goodstartlabs.com/research/verification-is-the-bottl... it is only 2.6x cheaper than DeepSeek V4.1 Flash, and they did not test v4.0 Flash, which would have been same price.
- gogoout 17d agoIs this the reverse of LLM? Ie, "what's the capital of France?" LLM picks from "Paris" 99.9% / "London" 0.001%, LLM then with some randomness output you "Paris". For Jev, you ask it to give probability of a set of answers "what's the capital of France?" choose from answers (Paris/London), it then gives you (99.9%/0.001%)
- sva_ 17d agoSomeone else noticed the base64 encoded block on the launch site[0]? Its the fast inverse square root algorithm q_rsqrt[1]. I guess its meant as a joke to put this algorithm that makes use of type punning on a site called typesafe.ai? Or maybe because of its efficiency? 0. https://typesafe.ai/ https://typesafe.ai/ 1. https://en.wikipedia.org/wiki/Fast_inverse_square_root https://en.wikipedia.org/wiki/Fast_inverse_square_root
- saldubai 17d agoWhen we speak about good judgement in models and agents we are talking about humans skills such as critical thinking , judgement and decision making , emotional iq, mindfulness etc. We have been building since 2018 a structured good judgement data lake tied to ten core humans skills and sub skills levelled queaisn and answers against blooms taxonomy from a tagged community of experts , outliers , contrarians . Open ended situational questions and answers like how the real world operates in various sectors to capture tacit knowledge . We have a playground with a tiny slice of just 100 of our over 500k base good judgement scenarios that can then be synthesized across sectors and workflows . With just a tiny slice it outperforms Fable and Open Ai models. Exceptional human judgement outperforms consistently models…. Good judgement is just also rare in us humans like common sense ( no pun intended) . Check us out at lovelyhumans.ai. Holler if curious. Sallyann Dellacasa on LinkedIn .
- yymir 17d agoThis could be killer for ingame AI for grand strategy games like Victoria 3 or EU5
- brooksy 17d agoIt doesn't produce text -> it does not hallucinate statements This is kind of trivially satisfied and they make this sound more extraordinary than it is. Also I suppose it can still hallucinate in the sense that for out-of-distribution data it will give miscalibrated probabilities. Anyway a great step in the direction of calibrated AGI
- rock_artist 17d agoA little off-topic, I have to admit, I did play Doom back in the 90s and I know it is just a game. But is it just me or other folks feel uncanny seeing "a machine" playing Doom with low-latency as the first demo. While I'm optimistic that humanity is good (but sometimes makes bad decisions), My first thought was seeing such models used by armies.
- ggcr 17d agoInteresting. Perhaps I can see this being quickly adopted in LLMs-as-a-judge, where you normally need (a) a structured answer, say, with lots of different fields (metrics) and (b) you want the judge to be fast, not being a bottleneck.
- boutell 17d agoI wonder how many choices you can give this thing in multiple choice response mode. I'm guessing you could give it enough choices to produce Turing complete programs one symbol at a time when running in a loop, or to hold a conversation when given a vocabulary as the choices. It would be particularly hilarious to just let it choose the next ASCII character of output in a loop. My guess is that due to its design there is no support for prompt caching, as there would normally be no reason for it. So the performance of my idea would probably be appalling as every step in the loop would reevaluate every input and output token. But it would be interesting to see the outcome.
- bigmadshoe 17d agoIt likely isn't trained on that task so performance would be worse than frontier models specifically trained for coding.
- stillpointlab 17d agoIIRC, Carmack was working on getting AIs to play Amiga games. The Doom demo suggests a very interesting direction to take this research. I'm curious to hear his take on this approach.
- johnsmith1840 16d agoSo how do we evaluate how good this is? Couldn't you hook it up to a multiple choice exam? It's still LLM like, how smart is it? I'm wary of something that the company states they don't want to benchmark it across public benchmarks.
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- Instantnoodl 16d agoWouldn't that be fun for text adventures? As you can get probabilities for finite actions relating to objects that are in the world/room
- raoulbiagioni 16d agohi
- txhwind 16d agoIt looks like a Transformer encoder post-trained on classification and regression tasks. The encoder-only model is less noticed in recent years, but this product finds a nice application for it.
- leo4242 15d ago[dead]
- jbdamask 15d agoReally fun model! Thanks for opening up access as quickly as you are.
- blackqueeriroh 15d agoOh man, I’m building a system to classify very large streams of data, this should replace several pieces of my workflow!
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- devin-2030 15d agoDoes lightbend still own the typesafe trademark in software? Or did they rebrand because they couldn’t get it?
- kraayen_jon 15d agothis is so cool. i've been obsessing with jev since i discovered it. I've been collecting what people built with it in the first days: https://madewithjev.com https://madewithjev.com feel free to add your demo or project.
- conview 15d agoexplanation with example: https://mchromiak.github.io/articles/2026/Sep/17/Jev-Typed-Decisions-for-Enterprise-AI/ https://mchromiak.github.io/articles/2026/Sep/17/Jev-Typed-D...
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- xnx 13d agoJev is the latest hype everyone has to talk about for a few days to show how cutting edge they are.
- Pranav_Ghoghari 13d agoI am not sure for how long the output will stay absolutely free. But apart from the pricing advantage of Jev itself, I just love the simplicity of having only an input price. Input is pretty easy to estimate and calculate upfront, which makes the cost of running something at scale much more predictable. With LLMs, even with JSON schema constraints and structured output, the actual cost can still be hard to predict because of varying output lengths and, especially, unpredictable reasoning costs. There is something really nice about being able to tokenize and predicting the budget beforehand.
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- redpublic 12d agoIf we are getting general purpose classifier, decision engine, probabilistic inference machine and likely a couple of other use cases for the fraction of latency/time/price of using SOTA LLMs for such tasks or training specialized classifiers this is very interesting project. Considering in future we would be able to supply it with our own facts knowledge bases and fine-tune for specific domains, this enables a lot of interesting use cases in various software domains.
- leoswing 12d ago[dead]
- peterbecich 11d agoLooks cool! Is there any relation to this area: "Type-constrained code generation with language models"? https://news.ycombinator.com/item?id=43978357 https://news.ycombinator.com/item?id=43978357