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 Show more
Von
An open, non-autoregressive System One model with published weights and a TypeSafe-compatible /v1/systemone API; the 395M-parameter OptionMarker checkpoint scores discrete and ordinal questions locally.
- Category
- Repos & SDKs
- Published by
- Community
- Author
- wfzyx
- Added
- 2026-09-22
Tagscommunitypythonopen-modelsclassificationconfidence
Highlights
- The 395M-parameter OptionMarker checkpoint runs locally on CUDA, ROCm, Apple MPS, or CPU and answers in sub-25 ms.
- The README headlines 91.23% accuracy on adversarial multi-hop benchmarks, while its own 49-task table reports 72.0% macro and 72.4% micro.
- Author-reported ViZDoom results give 9.00 average kills versus 5.62 for hosted Jev 1.13, at sub-18 ms local latency.
- Calibrated with joint cross-entropy and Brier-score loss at temperature 1.0367; training used 250,000 class-balanced examples.
- Installs as pip install von-sdk or npm install von-sdk and ships a Doom benchmark script for reproduction.
Quickstart
bash
pip install von-sdk
pip install git+https://github.com/wfzyx/von.gitWatch out
Apache-2.0-licensed. Benchmarks are author-reported with no matching result artifact in the repo; the model runs locally with about 1.5 GB of weights and is not affiliated with TypeSafe.
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From the community
Posts from builders shipping with Jev right now.
The launch post
Trading bot, one decision per block
I built a trading bot with Jev! Jev decides if it should "buy" or "sell", given the price feed of an asset pair, and executes real trades. It uses Monad to place the orders on Kuru's on-chain order book in every 300ms block. Demo link → jev-trader.vercel.app
Classifying 1,500 real emails
this model is actually insane at email classification i tested it on 1500 of my own emails to see how well it works and I am blown away
@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
Fast browser use with Stagehand
we built blazing fast computer/browser use with Jev + @Stagehanddev. this task cost $0.001 and executed at near instant speed (in a remote browser btw) the loop: observe the page, send a11y tree as state + actions as questions, Jev decides the next action, then Stagehand Show more
@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
LLM-as-a-judge, sped up
Jev has spoken. It picked which model is AGI. 20–200x faster. 40–400x cheaper. This could make things like LLM-as-a-judge insanely fast and nearly free. (I tried a bunch of prompts and still didn’t burn through $0.10.)
@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
Instant compaction with Jev
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