# What people built with Jev since it launched

> Source: <https://madewithjev.com>
> Published: 2026-09-18 18:00:40+00:00

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.

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 ↓

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)

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 ↓

TypeSafe’s launch post: the model, the evals, and their caveats.

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!

I gave Jev 3,282 of my X posts across 100M views and asked it to find what actually works for growth.
4,252,330 tokens $0.1282 for the full 8m 34s run!
Each post got 8 questions about the topic, hook, tone, whether it teaches something, etc.
How-to posts got 150 median likes vs the average median of 44.
AI and coding was a 1.9x multiplier topic compared and SEO, despite recent posts, was right at base median 1.0x - surprisingly.
The recommended topic + angle + voice formula was:
AI coding + teach something + provocative

I used Jev to classify 1,018 AI research papers.
The result: $0.08 total cost and 256ms median end-to-end latency per paper.
The pipeline was:
1. Summarize each paper with DeepSeek V4 Flash
2. Send the title + summary + 24 possible topics to Jev
3. Use Jev to classify each paper
4. Visualize everything on http://1kpapers.com
The summaries cost $3.99 on @togethercompute. The classifications cost $0.08 on @typesafeai.
So for just over $4 of inference, I ended up with a pretty useful way to explore the top AI research papers from the past year.
I think this is where things are heading: different models for different parts of the workflow, instead of using one model for everything.
I’m running evals on the Jev classifications before replacing the current ones, but the site is already live: http://1kpapers.com

Accuracy, cost and time per case for four workflows, by model.

Tony Dinh

@tdinh_me

Just trying out Jev, I made a Chrome extension that:
- Listens to your YouTube audio (optional)
- Detects if it gets to a sponsor segment
- Skips it ➡️➡️➡️
- All in real-time while costing ~$0.005 per video
Prototype project, BYOK, open-source:
github.com/trungdq88/yout…

Just created this with Jev by @typesafeai. A live viral post analyzer. As soon as you stop typing for .5 seconds it analyzes the viral potential.
Going to try and actually make this good, will need to scrape a lot of twitter data...
Notice how it also categorizes the tweet live... I could have it surface similar tweets on the right side for inspiration... idk just experimenting.

Routing between models, and blocking risky tool calls with a Jev decision.

Kyle Jeong

@kylejeong

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 executes it.

Computer use on macOS, with one Jev decision per step.

ALAndrew Levin

~$0.0002

Jarrod Watts

@jarrodwatts

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

we almost never test new foundation models but we've been testing this for ~a week @every and it's pretty wild.
the kind of things that will be obviously indispensible in 6-12 months
it doesn't produce words as output, it produces probabilities. so it can efficiently act as a judge in cases where you'd need a Fable-level model—but in our testing was 25x faster and 600x lower priced
excellent vibe check by @hammer_mt on @every:
https://every.to/also-true-for-humans/mini-vibe-check-typesafe-s-jev-judged-everything-i-ve-written-in-0-7-seconds?utm_cta_source=home_main_a_3

Jev picks from the legal moves, compared with reasoning models.

MSMaxim Saplin

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

Jev + Kimi K3 for fraud detection!
TLDR: Jev classified 100 emails in 1.42 seconds, then I routed the uncertain cases to Kimi K3. The full pipeline got 96/100 correct for only ~$0.07.
Video is not sped up, check out the live run!
Here was my process:
I gave Jev 100 emails to classify (a mix of 50 legit & 50 fraudelent emails). It classified all of them in 1.42 seconds.
An underrated feature about Jev is it will give you the confidence score for a classification, so I routed any prediction under 95% confidence to Kimi K3 to be fully sure.
31 emails fell below that threshold. After routing those to Kimi K3, the combined pipeline reached 96% accuracy.
The full run took 16 seconds & ~$0.07 in inference costs:
- $0.068 from Kimi K3 on @togethercompute
- $0.003 (1/3 of a cent) from Jev on @typesafeai.
I think this is a really interesting pattern: use a fast specialized model like Jev for the narrow task, then route the uncertain cases to a larger LLM.
I feel like this kind of approach could be a game changer for use cases like fraud or anything realtime. You can use the speed & low cost of Jev while having a larger LLM as a fallback to ensure high accuracy.

The launch discussion, including the doubts about the benchmarks.

Zachi

@iam_zachi

I think I just cooked something 🔥
jev(): a PostgreSQL extension that searches your whole database in natural language. No index, no embeddings, just one function.
WHERE jev(people, 'could work from home')
or
WHERE jev(people, 'name sounds european')
129 rows judged in ~1s for $0.0009. Second run: 6ms from cache.

Okay so Jev can actually do computer use really well
Without any screenshots, or LLMs and no Pixels leave my mac
I dont even read the Dom elements
A local CoreML model segments every button and UI element on screen.
On-device OCR reads the labels. That text is all Jev gets.
It returns a probability across those elements and tells me the best one to click.
Then it clicks, re-runs detection, and decides again. In a loop until the goal is done.
~90ms per decision. Faster than any LLM computer use I've tried.
Blazing fast computer use, without any latency
@typesafeai is building something really interesting

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.

Ian Nuttall

@iannuttall

Cloudflare Workers has Jev now so I'm putting it to the test on keep.md
- 7x faster search rerank compared to the current hybrid
- 50x faster tagging of content vs GLM 4.7 Flash with no failures

built `jev-review` @typesafeai
it's an experimental, local-first MCP plugin that gives coding agents a score quality feedback loop across different metrics.
agents call jev while they work, get scored, make improvements, and repeat the loop
try below 👇

got @typesafeai's new model Jev as a chief of staff for bots
Jev reads the task, wakes the right teammates off the bench
and gives each one the right model
It is possible on OpenMausBot as it supports all the LLMs from your existing subscriptions
Jev as a decision engine is great

hype-free explanation of jev:
jev does not replace gpt / claude
jev is just a *really* smart switch statement
like if 2016 ml classifiers got 2026 levels of intelligence
it's a new* type of tool that will make a lot of workloads insanely fast, cheap, and accurate
* = and by new, i mean rebranded
~~~
it needs a predefined set of options and it will tell you which one to take
it cannot:
- write code
- generate natural language
- reason step by step / show its work
- produce any output you didn't define in advance
- pick from more than ~255 options in one shot
but it can:
- classify, route, score, rank
- give confidence
- pick the right branch, tool, model, or sub-agent
- judge / verify / guardrail an llm's output
- label tons and tons of rows
~~~
i'd imagine a lot of workflows that look like:
llm proposes options → jev decides → code executes
and i see this fitting *really* well with code mode and mcp
~~~
implying this will lead to agi seems incredibly far fetched to me, but i don't want to discount the types of applications that this will make possible

Acabo de terminar la implementación de @typesafeai + Chromium Headless para que mis agentes puedan navegar por internet a una buena velocidad!
En este ejemplo le pido al agente que entre a la página del término "Café" en Wikipedia y navegue por los hipervínculos hasta terminar en la página "Inteligencia artificial"
Logra completar la prueba en 20 segundos. Creo que pocos humanos podrían hacerlo en ese tiempo.

A browser you control by voice, from Moritz Kremb’s Jev tutorial.

MKMoritz Kremb

Paarangat

@paarangatrai

this is the easiest way to understand Jev:
LLMs generate answers.
Jev makes decisions.
that sounds like a small difference, but it actually changes the entire use case.
say you give a normal LLM this:
“here’s a user, their account history, payment behavior, support chats, device data, etc.
tell me if this looks risky.”
the LLM might reason through it and return:
“yes, this looks high risk.”
maybe in JSON if you ask nicely.
with Jev, you define the possible decisions upfront:
risk:
* low
* medium
* high
manual review:
* yes
* no
and Jev returns something closer to:
risk = high (96%)
manual review = yes (91%)
that’s basically the product.
it’s not trying to be another ChatGPT.
it’s more like an AI-native if statement.
instead of:
if transaction > $10,000:
review()
you can start thinking more like:
if “does this behavior look suspicious?” > 95%:
review()
and that opens up a pretty interesting category of software.
a few assumptions I had at first that turned out to be wrong:
1. “so it’s just a classifier?”
kind of, but that undersells it.
the input can be messy real-world context, and you can ask multiple typed questions about that state at once.
fraud?
churn?
escalate?
eligible?
priority?
all from the same input.
2. “so it replaces GPT / Claude?”
not really.
I actually think the interesting architecture is:
Jev decides WHAT needs to happen
Claude / GPT reason or generate WHEN deeper intelligence is needed
normal code executes the deterministic stuff.
Jev becomes the routing layer.
3. “it can’t hallucinate?”
this one needs nuance.
if your allowed answers are:
LOW
MEDIUM
HIGH
Jev won’t suddenly invent:
“EXTREMELY HIGH 🚨”
the output structure is constrained.
but it can still be wrong.
HIGH at 92% can still be the wrong decision.
so “no hallucinations” doesn’t mean “always correct.”
4. “why not just force an LLM to return JSON?”
you can.
we already do this everywhere.
but you still deal with generation latency, schema validation, retries, weird outputs, confidence estimation and a lot of glue code.
Jev is designed around the decision itself rather than text generation.
5. “why should I care?”
because most software is ultimately a giant tree of:
if this → do that
if this → route here
if this → escalate
if this → reject
if this → ask a human
Jev is basically asking:
what if those if statements could understand messy human context?
that’s a much more interesting framing than “another AI model.”
I can see this being very useful for:
fraud / risk
support routing
moderation
PR / QA automation
lead scoring
compliance
workflow orchestration
agent routing
especially as the cheap + fast decision layer sitting in front of larger reasoning models.
early tech, obviously.
but the category itself makes a lot of sense.

Paarangat explains the difference with a user-account example.

nader dabit

@dabit3

Also have been playing with @typesafeai Jev, insane!
So many immediate use cases and new apps are possible. What a time to be a builder!
Sharing some experiments here starting with:
Keystroke oracle / predictive launcher:
Your launcher ranks by aliases, fuzzy match, and habit.
Jev reads intent: type "the pdf I just downloaded" and the newest PDF is already the top hit with a full confidence on every keystroke, in ~100 ms

jev will make agents 10x faster and cheaper, here's how:
1/ model routing: pick the right model for each task, without training a custom router
https://x.com/mdlahfir/status/2100314182201802811?s=20
2/ computer use: faster, cheaper and more reliable for action-heavy tasks
https://x.com/gregpr07/status/2100411066966749359
3/ auto review: ask jev whether an action is safe, instead of using a slow and expensive LLM
https://x.com/fazxes/status/2100300097695232164?s=20
4/ less obvious: subagent orchestration
long-running agents (cursor projects, grokbot, energy) parallelize work with subagents.
but every user message, email, or subagent reply can wake the expensive orchestrator.
example: it costs $1 to wake up gpt 6 astra w 100k input tokens
jev can decide what each event needs:
- route directly to a subagent
- queue for later
- wake the orchestrator

David Fant on model routing, computer use and more, with links to examples.

ILIAS ISM

@illyism

Now using @typesafeai Jev in http://aiseotracker.com, http://linkdr.com, http://genppt.com, etc
AI ends up vibe coding so much AI regex slop if you don't read the code, so I can finally move all this hard-coding to Jev and it's insanely fast!
Also for regular LLM calls, it is around 10x faster, 50% cheaper

Claude me integró Jev con Playwright para buscar usados. Lee unos 26 artículos por minuto y decide qué hacer con cada uno en 406 milisegundos
Descartó los que no encajaban con lo que busco, ofertó por los que sí y hasta les mandó un mensaje a los vendedores cuando faltaba algún dato en la publicación.
Toda la búsqueda salió USD 0,00085. O sea: con un dólar revisás unas 26.000 publicaciones.
Una IA que por fin puede navegar por internet más rápido que nosotros es un game changer

TL;DR of my new article: WTF is Jev by @typesafeai, and the 9 things people are already building with it. The thesis: 𝗮 𝗰𝗼-𝗰𝗿𝗲𝗮𝘁𝗼𝗿 𝗼𝗳 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝘀𝗽𝗲𝗻𝘁 𝘁𝘄𝗼 𝘆𝗲𝗮𝗿𝘀 𝗶𝗻 𝘀𝘁𝗲𝗮𝗹𝘁𝗵 𝗼𝗻 𝗮 𝗺𝗼𝗱𝗲𝗹 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻𝗻𝗼𝘁 𝘄𝗿𝗶𝘁𝗲 𝗮 𝘀𝗲𝗻𝘁𝗲𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗶𝗻𝘀𝗶𝗱𝗲 𝟳𝟮 𝗵𝗼𝘂𝗿𝘀 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝘄𝗶𝗿𝗲𝗱 𝗶𝘁 𝗶𝗻𝘁𝗼 𝗲𝘃𝗲𝗿𝘆 𝗰𝗵𝗲𝗮𝗽 𝗷𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝗰𝗮𝗹𝗹 𝗮𝗻 𝗮𝗴𝗲𝗻𝘁 𝗺𝗮𝗸𝗲𝘀.
Think AI multiple choice, not AI essay writing. It doesn't chat. You hand it app state plus a typed question, it hands back a decision with a probability attached. 𝟯𝟭.𝟰𝗠 𝘃𝗶𝗲𝘄𝘀 on the launch post in two days (@CompleteSkeptic, who co-invented RLHF). I ran @slashlast30days on it 11 times, then checked every big post by hand.
🌐 𝗔 𝘁𝗶𝗻𝘆 𝗼𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝗯𝗿𝗼𝘄𝘀𝗲𝗿 𝗮𝗴𝗲𝗻𝘁 𝗳𝗼𝘂𝗻𝗱 𝗳𝗹𝗶𝗴𝗵𝘁𝘀 𝗶𝗻 𝟳 𝘀𝗲𝗰𝗼𝗻𝗱𝘀 𝗳𝗼𝗿 $𝟬.𝟬𝟬𝟯𝟵. New action space every step, DOM as state, Jev picks the click, a small LLM only wakes up to type. The Browser Use founder built it (@gregpr07, 7.2K likes, 1.8M views) and had to note the video is 1x speed
🧹 The sleeper: instant compaction. Score every tool call, drop the junk, skip the summarization prompt entirely. "𝘪𝘯 2026, 𝘸𝘩𝘺 𝘪𝘴 𝘤𝘰𝘮𝘱𝘢𝘤𝘵𝘪𝘰𝘯 𝘴𝘵𝘪𝘭𝘭 𝘢 𝘴𝘶𝘮𝘮𝘢𝘳𝘪𝘻𝘢𝘵𝘪𝘰𝘯 𝘱𝘳𝘰𝘮𝘱𝘵?" asked @tamarajtran, 5K likes, then shipped the answer that afternoon. Run as a Claude plugin it took a session 𝗳𝗿𝗼𝗺 𝟭𝗠 𝘁𝗼𝗸𝗲𝗻𝘀 𝘁𝗼 𝟴𝟲𝗞 𝗶𝗻 𝗼𝗻𝗲 𝘀𝗲𝗰𝗼𝗻𝗱 (@altryne). Diogo's reply: "𝘧𝘳𝘦𝘦 𝘤𝘰𝘥𝘪𝘯𝘨 𝘢𝘨𝘦𝘯𝘵𝘴 𝘧𝘳𝘰𝘮 𝘥𝘦𝘴𝘪𝘨𝘯𝘪𝘯𝘨 𝘢𝘳𝘰𝘶𝘯𝘥 𝘵𝘩𝘦 𝘒𝘝 𝘤𝘢𝘤𝘩𝘦"
🛡️ Vercel put it in production as the safety reviewer in fx auto mode. 𝗨𝗽 𝘁𝗼 𝟭𝟴𝘅 𝗳𝗮𝘀𝘁𝗲𝗿 𝗮𝘁 𝗽𝟵𝟱 𝗮𝗻𝗱 𝗺𝗼𝗿𝗲 𝗮𝗰𝗰𝘂𝗿𝗮𝘁𝗲 than the model it replaced, per @rauchg, 3.7K likes. LangChain open-sourced the same idea the next day as AutoModeMiddleware. The closed danger classifier inside every coding harness is now a 100ms primitive
🚦 Model routing as middleware instead of a paragraph in a system prompt. About a dozen lines, probabilities left in agent state so you can audit the choice. The LangChain writeup by @sydneyrunkle is the cleanest how-to-wire-it piece anyone has published
🔎 RAG precision, solved the dumb way: retrieve as usual, run Jev on every chunk, delete the irrelevant ones. "𝘢𝘭𝘴𝘰 𝘥𝘪𝘥 𝘢𝘯𝘺𝘰𝘯𝘦 𝘳𝘦𝘢𝘭𝘪𝘻𝘦 𝘫𝘦𝘷 𝘴𝘰𝘭𝘷𝘦𝘥 𝘱𝘳𝘦𝘤𝘪𝘴𝘪𝘰𝘯 𝘪𝘯 𝘙𝘈𝘎?" (@kushbhuwalka, 416 likes)
🎮 Minecraft in real time: 𝗝𝗲𝘃 𝗿𝗲𝗮𝗰𝘁𝘀, 𝗚𝗣𝗧-𝟲 𝗔𝘀𝘁𝗿𝗮 𝗽𝗹𝗮𝗻𝘀, and they fight multiple zombies at once (@wuyang_zhou). A launcher that reads intent on every keystroke in about 100ms (@dabit3). TypeSafe's own demo is Doom at 10 decisions a second, roughly $7 an hour
📬 Email triage at scale: 1,500 emails in batches of 100 with 8 workers, 60,996 views on the demo. "𝘞𝘦 𝘰𝘯𝘭𝘺 𝘩𝘢𝘷𝘦 𝘢 𝘣𝘢𝘭𝘢𝘯𝘤𝘦 𝘰𝘧 $5 𝘥𝘰𝘸𝘯 𝘩𝘦𝘳𝘦, 𝘸𝘩𝘪𝘤𝘩 𝘫𝘶𝘴𝘵 𝘴𝘩𝘰𝘸𝘴 𝘩𝘰𝘸 𝘤𝘩𝘦𝘢𝘱 𝘵𝘩𝘪𝘴 𝘮𝘰𝘥𝘦𝘭 𝘪𝘴"
🗂️ 𝟳𝟳𝟳 𝗷𝘂𝗱𝗴𝗺𝗲𝗻𝘁𝘀 𝗶𝗻 𝘂𝗻𝗱𝗲𝗿 𝟬.𝟳 𝘀𝗲𝗰𝗼𝗻𝗱𝘀 𝗳𝗼𝗿 𝗮 𝗾𝘂𝗮𝗿𝘁𝗲𝗿 𝗼𝗳 𝗮 𝗰𝗲𝗻𝘁. Every's head of evals asked 21 questions of 37 documents in one request, and that is what came back
🧪 Jev in your browser: Reflex, a Qwen model doing structured decisions on WebGPU, built at Shopify by @kshetrajna and passed around by @tobi. Three independent clones inside 72 hours. 𝗧𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 𝗶𝘀 𝘁𝗵𝗲 𝗶𝗻𝘃𝗲𝗻𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝘁𝗵𝗲 𝘄𝗲𝗶𝗴𝗵𝘁𝘀
🔌 Already behind the gateways you use: @vercel AI Gateway inside 48 hours (2,341 likes, the company's second-biggest post), Cloudflare, and @OpenRouter in beta
💸 𝟱,𝟬𝟬𝟬 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 𝗳𝗼𝗿 𝗮𝗯𝗼𝘂𝘁 $𝟮. That was one developer counting his bill on day one (@MichaelLee04, 3,060 likes). Input is $0.042 per million tokens. Output is free
🧨 The honest part: Every's second test came out 𝟮𝟱𝘅 𝗳𝗮𝘀𝘁𝗲𝗿, 𝗻𝗼𝘁 𝟮𝟬𝟬𝘅, and Jev caught 6 of 7 planted defects to Fable 5.1's 7. The HN launch thread (1,863 points) spent most of its length on "can't hallucinate." Top critical comment: "𝘪𝘵 𝘤𝘢𝘯'𝘵 𝘦𝘮𝘪𝘵 𝘢𝘯 𝘪𝘯𝘷𝘢𝘭𝘪𝘥 𝘵𝘺𝘱𝘦, 𝘣𝘶𝘵 𝘪𝘵 𝘤𝘢𝘯 𝘴𝘵𝘪𝘭𝘭 𝘦𝘮𝘪𝘵 𝘢 𝘤𝘰𝘮𝘱𝘭𝘦𝘵𝘦𝘭𝘺 𝘸𝘳𝘰𝘯𝘨 𝘷𝘢𝘭𝘪𝘥 𝘷𝘢𝘭𝘶𝘦." Diogo called the "it's a zero-shot classifier" read "𝘷𝘦𝘳𝘺 𝘢𝘤𝘤𝘶𝘳𝘢𝘵𝘦!" And the biggest Reddit thread is someone who open-sourced the same architecture a year ago, 1,568 upvotes. Top reply: "𝘉𝘶𝘵 𝘥𝘪𝘥 𝘺𝘰𝘶 𝘱𝘰𝘴𝘵 𝘪𝘵 𝘴𝘢𝘺𝘪𝘯𝘨 𝘪𝘵'𝘴 𝘵𝘩𝘦 𝘯𝘦𝘹𝘵 𝘣𝘪𝘨 𝘵𝘩𝘪𝘯𝘨? 𝘙𝘰𝘰𝘬𝘪𝘦 𝘮𝘪𝘴𝘵𝘢𝘬𝘦"
Bonus: the name is not Kahneman. It's William Stanley Jevons, of Jevons paradox. Make a resource cheaper and people consume far more of it. Naming your decision model after that is a thesis statement.
𝗞𝗲𝗲𝗽 𝘁𝗵𝗲 𝗯𝗶𝗴 𝗺𝗼𝗱𝗲𝗹 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗮𝗻𝗱 𝘄𝗿𝗶𝘁𝗶𝗻𝗴. 𝗨𝘀𝗲 𝗝𝗲𝘃 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗿𝗮𝗽𝗶𝗱-𝗳𝗶𝗿𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗶𝗻 𝗯𝗲𝘁𝘄𝗲𝗲𝗻. That's the whole article.

I built a chat bot with jev, no LLM at all! Responses are instant, no hallucinations.
I hooked it up to web search, wikipedia, weather, todoist and home assistant.
Jev decides what tool to call and what args to use based on the prompt. Instant answers cite sources as well!

jev is insane 🤯
Here is Jev playing subway surfers at super human speed, and also playing 50 games at once.
cost less than a cent to do this run.
Jev does not replace llms like astra or fable, but opens up an entirely new world of capabilities.

jev is insane 🤯
here is jev playing smash bros against itself
he is controlling all 4 different characters.
and literally deciding whats the best
move to play against itself
all within a fraction of a second
i used over 22 million tokens to play this match
and it only cost me a couple of cents...
jev does not replace gpt6 astra
but the possibilities with its instant response time
are endless

Prediction: millionaires will be made using custom Jev style models (parallel constrained decoding) to make the agent systems companies already run more token efficient.
Let me explain with a scenario:
Imagine a company already has an agent workflow running where an llm reviews every item before it moves on: a support ticket gets triaged, an invoice gets approved or held, a claim gets flagged.
Every one of those goes through a frontier model today, a few seconds and a few cents each, on the way to a decision that in most cases is obvious. Behind that flow sits years of humans (or agents) making the exact same call, with the outcome attached.
Now imagine you first run each item through a custom PCD or similar model that costs a fraction of the llm and returns a classification of what to do at that step, with a mathematically accurate probability attached.
When it's confident, the item skips the llm entirely.
When it isn't, the llm handles it as normal.
The model has seen years of your team making this exact decision, usually a constrained set of decisions, so it should be right most of the time. Say it comes back confident on 6 out of 10 items. That's more than half your llm spend potentially gone from that step, likely with comparable accuracy.
This pre processing idea works in a bunch of other use cases too, such as:
- model/request routing: cheap model, frontier model, or a human
- picking which skill or subagent to load for a turn instead of stuffing the whole catalog into context
- reranking retrieved context so only the relevant chunks reach the window
- guardrails on every agent turn: contradictions, policy issues, prompt injection
- extracting typed fields from unstructured data emails, PDFs and transcripts before anything expensive touches them
Every one of those is a decision an llm makes today, that could potentially be done by another, cheaper model class. Very excited to see Jev/PCD-based pre processing use cases get deployed to agents at scale.

Scott Williams on using parallel constrained decoding to make existing agent systems use fewer tokens.

nader dabit

@dabit3

Another crazy @typesafeai Jev example:
Predictive spreadsheets
Spreadsheets recalculate numbers, not meaning. Jev reads intent.
Type "Urgency" at the top of a column and, as you type, it figures out you want each row rated from "no follow-up needed" to "urgent" in ~100 ms.

I build an undetectable realtime adblocker extension with typesafe
It checks every dom element and classifies as ad/non-ad and removes it if true
Extremely fun to work with, expecting an incredible shift in how AI is being used in the future

Jev is fun! One-click invoice finder for any website 🧾
- Automatically finds billing pages using @typesafeai's Jev
- List/download all invoices with 1 click
- Works with Stripe billing portals too
- Remembers where invoices live for next time
Should I open-source it?

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 amazed
Use this prompt
```
Install, and configure :
https://github.com/tamaratran/fast-jev-compaction
```

Alex Volkov: a Claude session went from nearly 1M tokens to 86K in about one second.

RaZaan

@razaanstha

I built a Chrome extension for agentic browsing using Jev by @typesafeai, fx.sh including AI Gateway by @vercel.
Now agents can browse, click, and interact with websites directly in your browser. Cost effective and fassst.
Decision-making by Jev.

Announcing Jev Detector
The world's fastest AI slop detector, built on jev from @typesafeai
~10,000 words scanned for slop in ~2 seconds
Best part, its free, no sign up required, enjoy!
jevdetector.com

A podcast segment on TypeSafe AI, its $40 million raise, and why the name is a nod to Jevons paradox.

Tonino Catapano (tonnoz)

@tonnoz

You still don't understand the use cases Jev unlocks.
I've been waiting for something like this since early ChatGPT models.
prediction: we will see the fastest-growing SaaS by MRR in history within the next month or two