18 curated entries. Documentation, write-ups, and community hubs for learning Jev, from the canonical docs to independent tutorials, reviews, and videos.
A practical tour with JavaScript examples: typed choices, scores, probabilities, real latencies and costs, the jagged edges, and how to split jobs correctly.
The raw HTTP contract behind every SDK: POST a state plus typed noul, choice, and score questions to /v1/systemone, and get one answer per question, with error codes and retry guidance.
The model card for jev-1.13.0: $42 per billion input tokens with free output, a 64k context, 250k tokens per second, and how to list the models your account can call.
The idea behind Jev: models that return typed decisions and probabilities instead of generated text, named after Kahneman's fast-thinking System 1, with confidence to route around uncertainty.
Why TypeSafe trains decision models with RLCD instead of RLHF: calibrated probabilities where 0.2 outcomes happen about 20% of the time, and the case for machine-to-machine automation.
TypeSafe AI's official launch post: the company is out of stealth and developers can get access to Jev from the website, alongside the founder's launch thread.
A Python walkthrough of typesafe-sdk: sync and async clients, grouping answers into choices, scores, and nouls, and routing uncertain cases to review, with arithmetic kept in Python.
A practical guide through SDK setup, the three primitives, and five patterns including speculative fan-out, confidence-gated routing, composite scoring, and a retrieval-then-judge cascade.
A plain-English guide to Choice, Score, and Noul with Python and curl examples, confidence-gated branching for support, moderation, and fintech, plus a skeptical read of the benchmarks.
Diogo Almeida's launch thread: after co-inventing ChatGPT, he spent two years on RLCD and Jev, a System One model he pitches as 20-200x faster and 40-400x cheaper for typed decisions.
A hype-free framing of Jev: not a replacement for GPT or Claude, but a new tool class for fast, cheap structured decisions, like 2016-era classifiers with 2026-level intelligence.
A curated thread of the strongest Jev community projects found on X within the first days of launch, from the creator behind the Jev voice browser and a full video tutorial.
A 22-minute walkthrough covering what Jev is, API setup, and three demos: a voice-controlled browser, AI memory, and a YouTube predictor, with chapter timestamps in the post.
A practical tour with JavaScript examples: typed choices, scores, probabilities, real latencies and costs, the jagged edges, and how to split jobs correctly.
The canonical introduction: Jev is TypeSafe's flagship model and the first System One model. Send state and typed questions, get structured answers your code can use directly.
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 StagehandShow more
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
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.)
Diogo Almeida
@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
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
This is actually insane. This uses @typesafeai Jev model, as a plugin in Claude to review all the un-nesseasary tool calls, and it takes 1s to run!
Like, literally, 1 second to take my Claude session from nearly 1M to ... 86K tokens! 😮
Ask your claude to install it and be Show more
tamara
@tamarajtran
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
We're seeing extraordinary results from @typesafeai. Default mode in 𝚏𝚡 is auto, with a safety reviewer analyzing every command.
That reviewer runs on GPT Luna today. Jev is up to 18x faster (p95) *and* more accurate. It's coming to @vercel AI Gateway and likely new default.
Pranit
@fazxes
We benchmarked fx auto mode (safety) classifier with @typesafeai's Jev.
tl;dr: ~5-18x faster and more accurate than 𝚐𝚙𝚝-𝟻.𝟼-𝚕𝚞𝚗𝚊, our current top choice
Jev by @typesafeai is now on OpenRouter, in beta.
Jev is a System One model. Instead of generating text, it takes your app's state plus a typed question and returns a typed decision with a probability attached. There is no JSON prompting, parsing layer, and nothing to validateShow more
