855 projects, posts and guides about Jev, the System One model from TypeSafe AI. Each entry links to its source and shows the cost and speed its author reported.
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 cheaper (w/ output tokens free)
• Frontier composable intelligence optimized for decisions
AFAICT the shortest path to AI-based economic revolution
we announced Jev 1.14 on 10.14 at 1:14pm PT 🔥
a new and improved model, along with experimental features for our dreamers:
• images
• a new box type
• longer context
surprise, Jev was already able to see you! 👁️
BUT our bar for production-grade intelligence is quite high, so builders beware, these are preview features.
enter the ~*~ danger zone ~*~ at your own risk. 🚨
Jev can serve as better natural language search on websites.
It can scan thousands of Zillow listings and classify properties by things you can't normally filter for - e.g. architecture, renovation status, proximity to freeways.
This was done in <20 sec and costs $0.18 👇
Our explainer: what a System One model is, the three question types, what it costs, what it cannot do, and the builds that show it working.
Gregor Zunic
@gregpr07
Breaking: Browser Use + Jev = Ultrafast ⚡
Findings flights took 7s and cost only $0.0039 🤯
> new action space every step
> DOM state space
> small LLM fallback to type
(this video is at 1x speed btw)
Built a tiny open source browser agent. try it below ↓
how to add a decision model to your AI workflow
1,000 yes/no decisions can cost around:
~$10 on GPT-6 Astra
~$4 on Claude Opus 5.5
~$0.04 on JEV
use language models for planning, writing and coding, and decision models for decisions
a decision model can:
> check your agent’s output before it ships
> approve a draft or request revisions
> route a task to the right agent or tool
> classify leads, tickets or comments
> score fit, quality or priority
> check whether content follows your brand rules
> pick an option from a shortlist
> decide whether to continue, stop or request a review
at a much faster speed as well than any regular LLM
here’s how to add one
1. find the decisions in your workflow
look for repeated steps that need a choice, classification or score
start with one decision you can compare against examples you’ve already reviewed
2. define the question and allowed answers
give the model the information and references it needs
for a content workflow, that could include your draft, voice DNA file, anti slop rules and approved examples
ask it to return a choice such as approve, revise or human review
3. connect the answer to an action
your software uses that answer to decide what happens next
approved drafts move to your review queue, revision requests return to the writing agent, and uncertain cases go to you
4. test before letting it act
compare its answers with your reviewed examples
if it returns confidence scores, check whether those scores match how often it gets the decision right
start with actions you can easily reverse
5. put simple checks first
use code for fixed rules, such as required fields and character limits
then use the decision model for checks that need context, before starting more expensive work
6. track quality and cost
measure cost per completed task, incorrect decisions and how often another model or a person needs to review
use the failures to improve your questions, references and allowed answers
I built a browser extension with Jev @typesafeai that can hide/collapse posts on X based on natural language.
It's so fast that it's not noticeable and insanely cheap...this must be the future of "ad blockers" and content firewalls.
Every guide on this site in one page, grouped by the question it answers: what Jev is, what a decision costs, how to put it inside an agent, and what it is already being used for across the 855 builds catalogued here.
Riley Brown
@rileybrown
Yeah Jev by @typesafeai is very cool. It classified 500 emails in seconds. And it costed 3.5 cents.
jev is insane 🫣
it makes realtime virtual try-on hauls possible.
built this experiment for Drape with @typesafeai
> i talk
> jev reads transcript + what i'm wearing
> picks from my closet
> changes my outfit in realtime
cost: $0.0011 per decision
time: ~620ms per decision
imagine getting ready like this:
Jev + Astra beats the Ender Dragon in Minecraft in 8 minutes 43 seconds! ⏱️
Cost less than $1 ($0.01 Jev, $0.96 Astra)
I open sourced the code and explain the harness setup below. This type of movement is only possible with Jev's near instant decisionmaking, and some continually learning skills from Astra.
Our guide to the term: split an agent into an LLM that writes, Jev that decides and code that acts, with the rules the builds on this site have in common.
Jerry Liu
@jerryjliu0
DocJev is the fastest way to classify and split complex document packets ⚡️. (and the default is fully free and OSS!)
I made a sick teaser video below. TY Opus 5.5 🙏
check it out: github.com/jerryjliu/docj…
I built a free internal linking tool using @typesafeai Jev for classifying and selecting the links.
BYOK or pay $1 to use mine. It works for up to 500 pages and gives you a CSV or JSON to pass to an LLM to implement.
ian.is/tools/internal…
Jev + on-device models = results in seconds with no LLM in the loop.
Quick demo app to show the possibilities. Drop in an audio file: Ear detects the language, Voz transcribes it and Redact removes PII.
Then @typesafeai's Jev makes about 20 decisions in one call in milliseconds, and picks which of our on-device models to run. Voice memo to to-do list. Meeting to redacted transcript. Podcast to clips.
What people actually use Jev for, from 855 catalogued builds: 100,000 posts scored in 20.4 seconds, 500 emails triaged for 3.5 cents, a flight search in about 7 seconds.
Meet Shukla
@meetshukla_
i’m probably going to regret this.
10,000+ videos were imported by our users into Ghostfeed in last 6 months.
this weekend, we picked 1,066 reaction videos and analysed all of them with JEV + GEMINI.
now you can search by structure, reaction, and who’s on camera.
opening the library today which has been our months of data gathering, clone all videos to mass publish them on instagram, tiktok and youtube.
comment "reactions" if you want the access to the library.
I built a Sales Copilot using Jev
→ Listens to sales call live
→ Tells you what to say next
→ Helps you follow the script and handle objections
→ Shows you what stage of the call you're in
→ Gives you live signals and probability of closing
Demo below on a recorded sales call:
Our own: a Mac app that sorts, labels and marks your Gmail with a System One model running on your machine, so nothing is uploaded and nothing is installed in your browser. In build now, and the first 50 on the list pay $5.
Justin Schroeder
@jpschroeder
I rebuilt Tesla Full Self Driving with Jev in less than an hour.
This model is a total unlock.
jev is INSANE.
in 40 seconds it broke down 724 live ads from 37 brands.
every hook. every format. offer. cta. awareness stage. landing page mismatch. used 9 cents of tokens.
(will be avail in @stealads + mcp)
No: TypeSafe serves Jev from its API and has not published the weights. These are the open models that answer typed questions locally — Julia 1 at 144.3M, Laya at 421M, von under 15 ms.
Faadil Shaik
@faadilhshaik
got @typesafeai’s new model Jev to play Super Mario Bros.
fast inference + structured outputs makes it surprisingly good for real time use cases.
I'm excited to see what can be done with these new models!
Jev + SuperX = virality solved ✅
Every post gets 61 questions in ~1s for $0.0004 🤯
> fitted on 9,481 real posts from 207 creators
> picks the viral post 2 in 3 times
> never rewards reply bait
So: write, score, rewrite, stop when it peaks.
Free, no signup. try it below ↓
Our own count: every public Jev build in week one, with the median published cost per decision, the median decision time, and the stars and languages of every repository created since launch. Free to cite, with the rows as JSON.
OpenRouter
@OpenRouter
1/ Jev, a decision model by @typesafeai, sparked a burst of projects and discussion. We tested it using Ori Eval against popular LLMs on OpenRouter at judging.
Jev was >5x faster than the next fastest model, and even its slowest requests beat every other model's median.
Jev + LinkedIn bookmarks = your saved posts are finally searchable ✅
479 posts classified in 225 seconds for $0.0195 🤯
LinkedIn has no native way to search what you saved Jev reads every post and tags the topic, hook, and format you can finally find that post you saved 6 months ago
So: save, classify, search. Never lose a good post again.
Free and open source. try it below ↓
We built a plugin that gives Jev a browser in Cline, and have been blown away by the results.
1. Install it in our new desktop app: Customize > Marketplace > Plugins > search 'jev-browser'
2. Create a Vercel AI Gateway API key, then save it to ~/.cline/plugins/cline-jev-browser.config.json as {"gateway": {"apiKey": "..."}} and restart Cline.
3. Ask any browser task and it will launch Chrome in the background to complete it.
Your first Jev call, line by line: the state, the questions, the typed answer and what to do with the confidence score. Then the SDKs, the gateways and 58 video walkthroughs.
855 Jev projects, with the figures their authors published
Made with Jev is a directory of what people have built with Jev, the System One model from TypeSafe AI: 855 projects in 21 use cases, 358 of them open-source repositories. Every entry links to its source. New to the model? Read what Jev is, then what people use Jev for. The counts behind this page are in the Jev statistics.
