Full Jev Tutorial What it is, how you can build with it and what new applications it can unlock → 0:00 Intro → 0:34 Jev explained → 4:06 API setup → 5:59 Demo 1: Voice-controlled browser → 11:33 Demo 2: AI memory → 17:27 Demo 3: YouTube predictor
decider
An independent open reproduction of the System One model class: Qwen3.5-based 2B and 35B mixture-of-experts models that return typed Choice, Score, and Noul probabilities in one forward pass, with nothing distilled from Jev.
- Category
- Practices & Patterns
- Published by
- Community
- Author
- Mapika
- Added
- 2026-09-22
Tagscommunitypythonopen-modelscalibrationbenchmarks
Highlights
- decider-2b is built on Qwen3.5-2B-Base and decider-35b-a3b on Qwen3.5-35B-A3B-Base, trained on public data plus labels from a local Qwen3.5-27B teacher.
- The project says nothing was distilled from Jev and it is not affiliated with or endorsed by TypeSafe AI.
- On JevBench it ranked #10 of 36 (decider-35b-a3b, 68.9) and #21 (decider-2b, 64.6); on the Decision Index the NVFP4 build sits 4th of 32.
- decider-2b v10 adds 384 steps of calibration-aware RL, lifting sampled browser play from 83% to 93% and belief from 0.47 to 0.22 nats above the exact laws.
- Published on PyPI as decider-ai with MPS acceleration for Apple Silicon; Pong decisions run in 43 ms median on one B300 in bf16.
Quickstart
bash
pip install decider-ai
git clone https://github.com/Mapika/decider && pip install -e ".[serve]"Watch out
Apache-2.0. Unaffiliated with TypeSafe; serving needs Python plus a GPU or Apple Silicon MPS, the 2B trails on calibration and the 35B on cost, and a Blackwell cached shared-state bug was fixed in 1.0.2.
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From the community
Posts from builders shipping with Jev right now.
Full Jev video tutorial
The case against Jev-scored compaction
This is a terrible compaction strategy that fundamentally doesn't understand how compaction and context management work. Seems like a lot of people are confused so let's break this down. 1. Compaction isn't a filter The role of compaction is to clean up history to keep the Show more
found the perfect use case for @typesafeai Jev: instant compaction in 2026, why is compaction still a summarization prompt? Jev can make it instant by scoring every tool call and dropping what’s irrelevant
Classifying rows in DuckDB
I made a DuckDB extension where you can use @typesafeai 's Jev to do quick classification of rows in any csv/parquet file or duckdb table about 10sec for 1k rows ~ better than using an LLM, way more ergonomic than a classifier game-changing for data analysis!
A playable 16-judgment demo
typesafe's jev is fun! live demo you can play with: typesafe-demo.val.run
AI multiple choice, not essay writing
WTF is Jev by @typesafeai? Here’s the tl;dr ELI5: Think AI multiple choice, not AI essay writing. It doesn’t chat. It makes decisions your software can act on: “Spam or not?” “Which tool should this agent use?” “Does this need a human?” The exciting part: roughly 200x faster Show more
Screening agent actions with Jev
Tested TypeSafe’s Jev (no-text, probability-only model) as an AI agent safety monitor. Checking each action first worked well caught most attacks with almost no false blocks, and much faster than Gemini.
@CompleteSkeptic
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x