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
Ai is evolving.
Jev can be armed at all times.
I can speak freely and it knows ( from probabilities ) if im asking my computer to do something or blaberring away at something else.
no wake word.
speed + affordability + intelligence is getting to the point where an always on ambient jarvis style assistant is possible.
Im loving where we are going.
CNVS is still the future of vibecoding.
Jev is really good at intent-based search!
How it looks in Gmail:
(for a huge inbox you'd prob let semantic search / embeddings pull first but still much better experience)
e2e + jev from @typesafeai ⚡
I'm building an open-source framework for running e2e tests with agents. supports web, mobile (and more!)
available soon: tester.army/e2e
building a computer assistant with Jev
local whisper listens to everything I say which then gets classified by Jev to determine what actions to take
it uses a small Swift app to provide the full accessibility tree to Jev (i.e. tell its what's on my screen, what can be clicked, etc)
still super early, but promising and all real-time
which Jev was also local though. then it would be completely private
First @typesafeai use case, live in our Mac app: setup and troubleshooting help when no model is loaded.
Model downloading, load failed, API returning 503, phone won't pair: the user asks, Jev reads the question with the whole built-in manual as state and decides, with probabilities, what it is and which article answers it, or that nothing does. The app then shows the real documentation and live status. Jev decides, the app answers from its own docs. No model loaded, nothing invented.
42/42 on a held-out set: paraphrases, typos, French, German, Spanish, features that don't exist, follow-ups. Median 0.93 s.
Great breakthrough by the TypeSafe team. Thank you.
everything you need to start building with jev, in one article.
code, architecture, diagrams... everything you need to follow the build and make it your own.
Avid’s builder’s guide, and the fullest public write-up of the pattern: where the decision layer sits in a coding harness, the five rules that keep it enforceable, and keel, the Rust app it was built in. Honest about the self-improvement part it has not proved.
😎Nick 常胜
@isNickMa
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.
cyril on where the 200× and 400× headline figures come from and why they are a ceiling rather than a typical result: 31 bounded checks counted in one real session, and the decision-point audit that finds yours.
unicode’s 12-step roadmap for a self-managing company: the Grok Bot marketplace supplies the workers, and Jev answers the five forks between them — who goes next, is the research good enough, continue or stop, is it done, does this need a person.
Together AI fine-tunes a Jev-like classifier on Qwen3.5 4B: about 38,000 questions sampled from eight public datasets, roughly 25 minutes of training, and the tev1 repository that runs it. The $17 is the training job, not the system around it.
GREG ISENBERG
@gregisenberg
Jev is HERE and this is the CLEAREST explanation of what it is and what NEW businesses it unlocks.
(and at the end I'll tell you how to get Jev even if you're on the waitlist)
WHAT IT IS
You know how you open your inbox and have to decide what's junk, what needs a reply, and what can wait? Jev does that part. It looks at each thing and says "this is junk, I'm 94% sure."
It doesn't write anything back to you. It just sorts.
1,700 emails for 18 cents, instantly.
That sounds kinda trivial but the important part
WHAT IT UNLOCKS
My explanation of Jev sounds small until you realize HOW MANY jobs are exactly this. Someone reading a stack of applications. Someone deciding which support ticket goes to which team. Someone looking at inbound and deciding who's worth calling back.
A few ideas on what it unlocks:
1/ Instant quotes that are actually instant. Every quote form on the internet says "we'll email you by end of day." Build the version that answers in under a second, for roofers, movers, insurance, legal intake.
2/ Lead scoring as a product. Every agency and service business has a contact form full of junk. Score every submission and send the real ones straight to the owner's phone.
3/ Support triage for companies with no support team. The ticket gets classified and routed before anyone opens it.
4/ Clipping tools. Pass in a transcript, get the best moments scored in three seconds. Every clipping product just got a cheaper engine.
5/ Application piles. Grants, permits, insurance claims, job apps, loan docs. Someone reads that stack one item at a time today.
6/ Marketplace matching. Someone types what they need and gets matched to the right local business instantly instead of waiting for callbacks.
7/ Browser agents that actually move FAST. That makes bulk browser work practical: pulling quotes from five carriers, filing the same form for 200 clients, checking supplier inventory in real time etc.
TLDR; find an expensive queue and put Jev at the front of it.
HOW TO GET IT
I didn't realize you can skip the waitlist because Jev is live on the Vercel AI Gateway right now, so you can start calling it today. In this episode, we share how.
Episode now live on @startupideaspod (thanks to @ryanvogel for coming on and spilling the sauce today)
Watch: https://www.youtube.com/watch?v=4mTLpuQpB80
Jev is a big deal because this is a whole new way to do AI
Really cool
Happy Jev day.
Greg Isenberg: find an expensive queue and put Jev at the front of it. Seven ideas, from instant quotes to lead scoring, plus a Startup Ideas Pod episode with Ryan Vogel.
Fayaz Ahmed
@fayazara
Made myself a little image classifier with OCR + Jev
It was able to categorise ~900 images in 40 seconds
Pretty cool
Introducing Jev for 'Website to App'
Turn any website into a native mobile app.
Just paste a URL.
jev-1.13.0 decides how to build the original website as a *native* mobile app, then shipper submits to the app stores for you.
We’ve been using this internally a ton for iOS/Android apps.
thanks to @typesafeai jev I no longer have fill out all of those fields on prompt boxes. It picks the agent / model / computer / folder for me.
- For a major rewrite it uses Fable + Claude Code.
- Changes to an ios app run on one of my macs
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!
Yoshimasa Iwase’s notes on Archer Hume’s article: shared state, isolated questions, calibration and a likely mixture of experts.
Kai
@hqmank
I rebuilt my job crawler with Jev.
The task: start at a company's official homepage, find Careers, and identify jobs that match my profile.
Before, with an LLM: ~5 minutes.
After, with Jev: just over 20 seconds in my test.
Every company organizes its website differently. Jev identifies the Careers entry point, chooses which links to follow, recognizes job pages, and scores each role against my profile.
This is where Jev makes sense to me: automation that needs lots of small decisions, with faster responses and lower costs than calling an LLM at each step.
Packaged it as a skill: jev-job-hunter. Demo below.
everyone's making demos with Jev
but nobody is making real products
introducing lurk.so
find and monitor reddit threads to get cited by AI
> FREE
> 4000 reddit threads scanned
> email, discord, slack
only possible to give for free bc of Jev & @getanyapi
I made a chrome extension to label all the X posts on my timeline. It tells me if each post is clean, engagement bait, promo, secondhand or filler.
$0.03 for 1000 posts.
Open sourced if you want to try it out.
Jev dropped the price of SEO/GEO fixes by 90%
Agents that audit and fix a client's SEO/GEO used to cost us ~$250
Here's where the savings come from:
1/ 30x faster reads of Search Console and PostHog/Mixpanel data
2/ 30x faster checks of what ChatGPT searches on Bing
3/ 30x faster modeling of what users ask Gemini and Claude
4/ 30x faster scans of who ChatGPT and Claude cite
5/ 30x faster analysis of the sources behind those citations
6/ 30x faster gap analysis: why they get cited and we don't
7/ 30x faster fixes across 1,000s of pages on large client sites
8/ 30x faster sorting of which page types ChatGPT cites
9/ 20x faster creation of the pages that make ChatGPT pick you
Available in the Ryze AI app and MCP/Claude Connector, link in the 1st comment 👇
JEV is insanely fast.
We gave it a massive dataset based on thousands of outreach messages and asked:
Which intent signals generated the most booked demos?
40 seconds later, we had the answer.
Cost: less than $0.20.
JEV can also rank leads, measure prospect-message fit, and uncover what actually drives campaign performance.
Coming soon to @GojiberryAI + MCP.
jev is an insanely cool product
Saw a post about jev and decided to check it out
This thing is straight up gold whoever built it is a genius
jev is the future
Scanned over 700 live ads in 40seconds flat, and it cost me just 4 cents.
If you're in marketing, this kind of crazy fast data crunching is hands down the best thing out there.
Doing this through Opus 5 would run around 2 to 4.5 million tokens, set you back $15 to $50, and take anywhere from 10 minutes to an hour. That’s 200 to 500 times more expensive than Jev, and way slower.
Jev killed 7 more SEO/GEO workflows 👇
1/ Assess which competitor pages to copy
-> It scores every competitor page on answer, depth, proof and freshness, then checks its rank in Google and ChatGPT to show which ones are worth copying
2/ Identify which page elements to change to get cited
-> It reads the title, meta, H1, FAQ and schema on every page and returns keep or change for each, with a confidence score
3/ Check if your pages answer what people ask AI
-> It matches real buyer questions to your best page, which shows the questions you have no page for and who AI cites instead
4/ Rate how likely each page is to get cited
-> Every URL gets a citation chance and the first fix to make, like adding a compare table
5/ Sort search terms
-> It asks "is this query from a buyer?" across the full Search Console export, so you write only for terms that convert
6/ Build the internal link map
-> For every page it checks the 15 closest candidates and links only the ones with an honest reason
7/ Verify AI-written pages
-> Each draft goes through 20 yes/no checks, and only the ones that pass reach a human
Available in the Ryze AI app and MCP/Claude Connector, link in the 1st comment 👇
Using Jev to filter through my @wip todos
It allows me to super quickly find all the instances where I increased revenue, got stuck, switched to a different SaaS provider, etc
Things a regular keyword search would never catch
today i'm releasing Jev for content marketing.
still doomscrolling to figure out what to post on social media?
that's over now...
Jev watches EVERY video in your niche and judges it before it ever reaches you:
1. research: pulls every video in your niche from a database of 12.8M viral videos
2. analyze: Jev watches, studies, and judges each one, the hooks, the formats, the angles, and why they worked
3. create: turns it into a data-backed script, based on proven winners (via Claude)
no more guessing on TikTok & Instagram
currently available for free in @virlomain + MCP.
link below ↓
I just built a Jev X Viral Post Analyser.
100,000 viral X posts. 20.4 seconds. $0.67.
Claude Opus 5, same corpus, same clock, got through 214 posts and spent $0.98.
per post that is ~680x cheaper the full Opus pass would have run $458.
viral analysis is the perfect Jev job.
• it is not writing, it is 14 yes/no calls per post:
> does the hook open a loop,
> is there a number in the first line,
> is the proof real or claimed.
classification, not prose.
• what it found:
1,220 posts broke into the top 1%. baseline 1.22%.
> superlative claim - 2.34% viral. 1.92x baseline
> contrarian take - 1.59%. 1.31x
> launch / tool drop - 1.46%. 1.19x
and numbered lists, the thing everyone writes: 0.55%. below baseline.
the most used hook is the least viral one. full stop.
• what you are watching:
left is the post under analysis, right is Jev answering 14 typed questions about it, each with a confidence score.
the run stops at 20.4s because that is when Jev finished all 100k.
pulled the corpus through a few X APIs, one parallel pass into Jev.
should I drop it to public?
Read my latest article on Jev Engineering below and turn your ideas into reality.
We turned Jev into a general browser skill for agents: jev-browser.
Give it a website and a task. The browser opens automatically, and Jev decides every click based on what's on the screen.
Here's a demo:
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
Moritz Kremb’s video: what Jev is, API setup, and three demos: a voice-controlled browser, AI memory and a YouTube predictor.
Jev demos and threads on X
What people posted on X while they built with Jev. Every card plays the original video or shows its images here, so you see the demo before you open the thread.