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- Hacker News
- I 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.
by brainless - Looking 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.
by latteren - Wonder if this could lead to better recommendation algorithms.by Gecko4072
- More 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.
by nullbio - Side 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 Amodeiby dgellow
- thought the same lol
- Well now I’m rooting for them!by Aboutplants
- It wasn't until the demo videos that I realized the post wasn't satirical.by jakintosh
- That would have to default to Wario Amodei.by bogzz
- Just 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??
- After 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.
by lubujackson - Curious what your use case is if not confidential.by wg0
- we 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
- So in theory you could feed it incomplete text, and then ask it for the probabilities of what the next character could be?by skerit
- was wondering sameby aghilmort
- Or 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.
- If you provide it an AST of the english language, yes.by vatsachak
- you 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
- I think the joke here is getting missedby mckngbrd
- It 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?
by big_toast - > the model takes as input a state (structured text? not sure if multi-modal)
Input, and criteria/instructions can both be defined as structured input (JSON). This ends up being pretty powerful because the model is trained to understand structure.
e.g.: https://docs.typesafe.ai/primitives/advanced#structured-inst...
> not sure if multi-modal
just JSON... for now :)
> outputs the question's answers as appropriate
correct!
by zenlikethat - CEO 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)
- This 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-o...
by maltalex - An 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-ishby leobuskin
- API example[0] makes it clear how it'd be used:
[0]: https://docs.typesafe.ai/sdk/pythonfrom 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)by 18al - This, 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/ . Contracts are a core feature of SymbolicAI ever since. The community seems to have loved it too (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.
by futurisold - love it. send me an email and i'll try to get you moved up on the list? nathan@typesafe.aiby zenlikethat
- > 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?
by vintermann - You 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
by camdenclark - There's also two other important limitations to using an LLM and just providing it with pairs of records.
It does not know enough about the records in the context of the overall dataset:
- what is the data quality and to what extent do we expect a errors in some fields - how unusual are certain values such as names in the context of the dataset as a whole, e.g. some names would be very common in some countries but rare in others.
I've written in more detail about this here: https://www.robinlinacre.com/fellegi_sunter_accuracy/
by RobinL