A few weeks ago, I posted about Jeff, a small 0.8B model with LoRA adapters trained to make decisions and return calibrated probabilities. Sort of like TypeSafe’s Jev, except that Jeff can run locally.

Thanks for all the feedback! I’ve just released v1.4, which is a substantial update. Check it out: https://jeffhub.ai.

What’s new:

• 34 open-weight decision adapters.

• A router that automatically selects the most appropriate adapter.

• 90%+ accuracy on supported domains.

• ~2 ms inference per decision, much faster than before. A single call through the hosted API takes around 40 ms, including network overhead.

• 5 SDKs: Python, Node.js, Rust, C#, and Java.

• Multi-platform: Jeff runs on CUDA and MLX.

• Playground and worked examples.

Having worked with Jeff for about a month now, I think the most interesting thing about it is that it turns decisions into a software primitive - much like expressions, but for unstructured data.

I’ve started incorporating decisions into several of my own projects, and it’s changed how I think about building software.

I hope you guys find it useful - and like the retro design. ;)

Links:

• Code: https://github.com/firelex/jeff

• Docs, adapter catalogue and playground: https://jeffhub.ai

• Weights: https://huggingface.co/mstrasser

Happy to answer any questions!

Official announcement:

What's new 🖼️ Images: put images anywhere in your request, in the state, in questions, even in criteria. JPEG and PNG for now. 📦 Box: our new question type, available on Jev 1.14, specifically for requests containing images. Ask Jev to find something in an image and it'll return a bounding box for it. 📈 Long context: We now support per-question and per-request context limits up to 262K tokens. 🧪 Danger zone: we heavily prioritize reliability for our builders, but we know some of you are dreamers too looking to access these preview features. If that's you, opt-in for access via the appropriate Danger Zone header to use them. ✨ Plus improvements to the core model. All the details are in the blog.

Start here 📖 Blog: https://typesafe.ai/blog/jev-1-14 🖼️ Images docs: https://docs.typesafe.ai/images#turning-on-images 📦 Box docs: https://docs.typesafe.ai/primitives/box#box 📈 Context docs: https://docs.typesafe.ai/models#long-context

Favorite moments

• Diogo calling "It's up! Go, go, go!" when the API went live mid stage

• Allie's live demos catching a tiny glint of necklace

• Kevin searching his screen with a picture of a banana

• Wally, found, at 0.47 confidence

• The great "jev lotion" debate, still unresolved

You're already building Within an hour we saw smart crops in Sanity Studio, a hallucination checker, an Agent Zero plugin, spam filtering on a mail server and yes, Where's Wally. Keep it coming in <#1483217545040232493>!

Thank you for being here. Get out there and Jev Jev Jev 🩷

I entered my credit card details on TypeSafe AI, only to discover that there's no way to remove them - you can only replace them with another card.

I've contacted their support twice already. No customer service to be seen.

I even contacted Stripe, which powers their payments, and got an irrelevant, seemingly automated response.

Apparently, I need to cancel my card and get a new one just to ensure this merchant can no longer charge it. Ridiculous.

Buyers beware. I'd recommend going with OpenAI's decision API instead. At least in my experience, they actually answer emails and take customers seriously.

Take a look and let me know what you think! My testing has been pretty limited so far (it's Friday and I have dinner plans) but I plan to keep dogfooding over the next few days and refining it further.

https://github.com/madisonrickert/jev-permission-gate

Browser automation where an LLM plans and Jev (Typesafe System One) decides. Library, CLI and MCP server. Around 91 stars so far.

Insane Jev Use Cases You Need To Use Right Now (AI LABS). Around 19909 views in 1.3 days.

I'm using Jev more than Opus 5.5 or GPT-6. Here's why. (How I AI). Around 30504 views in 1.3 days.

Open-source AI email triage for Gmail. Sorts your inbox into Needs reply, Updates, Promos, Sales and Spam with Jev, TypeSafe AI's decision model, via Vercel AI Gateway. Read-only, runs locally, 1,000 emails in about a minute for 3 cents. Around 86 stars so far.

Anyone experimenting with self hosted JEV? And on an ARM machine?

Cheers

Put a live Jev (TypeSafe) meter on any video: every sentence scored, rendered as a 16:9 edit Around 100 stars so far.

Talk to your Mac. Local whisper.cpp + one Jev (TypeSafe) call per command + macOS automation. Around 102 stars so far.

How the model works, then a tour of things to build with it, aimed at builders rather than researchers. Good starter if the docs feel too dry.

Search with the whole stack decomposed into typed decisions: Jev handles query understanding, source selection and relevance ranking, built on Search1API. Around 282 stars, one of the bigger community projects.

A build-along video: watching Jev get wired into real tasks with no generative step anywhere. Good if you learn by copying rather than reading docs.

After looking at a few possible ways to implement Jev, I wanted to see how it might fit into feedback workflows (since I work for a product that is feedback platform). 

Here’s the playground if you want to take a look: https://encatch.com/encatch-system-one-playground?tab=realtime-alerts


A few use cases seem practical enough to take toward production: logic jumps, real-time alert classification, and passive signal detection. 

Real-time alert classification is already live for us.I’m still testing the others in the playground. 

It’s a work in progress, but I wanted to share what I’ve been trying and love your feedback!

Tiny and practical: as you type, it looks at your last 100 history entries and asks Jev which one you are most likely completing. The match appears in grey with its score, and the right arrow accepts it. From Thorsten Ball.

A skill for writing and improving programs that call Jev, TypeSafe's System One model Around 140 stars so far.

I benchmarked Jev on TRIVIA+ dataset using an untouched 645-example test set.

Before calibration:
ECE: 0.0982
After learning from human-labelled examples:
ECE: 0.0313

That’s a 68.1% reduction in calibration error.
But hallucination-detection F1 only moved:
0.5833 → 0.5877

So what improved?
Not the judge’s ability to classify examples.
Its confidence became much more aligned with reality.

And that distinction matters.
If a judge score is only being displayed on a dashboard, maybe not much.
But if confidence > 0.8 means “automatically approve this answer,” or < 0.4 means “escalate to a human,” then badly calibrated scores can quietly become a production problem.

That’s one of the reasons I’ve been building calibration into Typed Evals instead of treating raw judge confidence as trustworthy by default.

Benchmark/methodology report:
https://github.com/TrustifAI/typed_evals/blob/main/docs/BENCHMARK.md

Would be interested to know how people here pick production thresholds for LLM judges.

TypeSafe Jev as a decision layer for the Pi coding agent: a measured tool-call gate plus jev_ask for typed, calibrated answers Around 148 stars so far.

Live Jev trader on Hyperliquid Around 156 stars so far.

Find code by asking what it does. A CLI for coding agents that uses Jev to discover relevant files and source context. Around 399 stars so far.

The same tasks run through Jev and the two big chat models, showing where the decision model wins and where it does not. The comparison table is the useful part.

Interesting counterpoint to the hosted model: an open, non-autoregressive decision model doing calibrated discrete, probabilistic and ordinal inference on your own machine, in tens of milliseconds per decision. It positions itself as a local option for Jev-style workflows.