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Another crazy TypeSafe AI 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.

nader dabit

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276,707 views • 8 days ago •via X (Twitter)

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Also have been playing with TypeSafe AI 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

Also have been playing with TypeSafe AI 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

nader dabit

430,743 views • 8 days ago

Analyzed a few thousand hours of agentic runs with TypeSafe AI Jev - turns out it's: • The best option I've tested at measuring progress and estimating completion • Dangerous if you use it for detecting harmful commands (more on that below) and • Not very good at catching models being lazy Actual prompts and results in the article:

Analyzed a few thousand hours of agentic runs with TypeSafe AI Jev - turns out it's: • The best option I've tested at measuring progress and estimating completion • Dangerous if you use it for detecting harmful commands (more on that below) and • Not very good at catching models being lazy Actual prompts and results in the article:

Hrishi

28,176 views • 5 days ago

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 Together AI - $0.003 (1/3 of a cent) from Jev on TypeSafe AI. 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.

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 Together AI - $0.003 (1/3 of a cent) from Jev on TypeSafe AI. 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.

Hassan

60,099 views • 9 days ago

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 The summaries cost $3.99 on Together AI. The classifications cost $0.08 on TypeSafe AI. 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:

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 The summaries cost $3.99 on Together AI. The classifications cost $0.08 on TypeSafe AI. 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:

Hassan

333,468 views • 9 days ago

Jev + GrokBot is the best AI agent system I’ve built in my life It just made my setup CHEAPER and FASTER than what 95% of people are running... setup takes literally 7 minutes: prompt → GrokBot → Jev decision → GrokBot execution → result step 1 → open TypeSafe AI , create API key (keep it off chat paste) step 2 → tell Grok Bot: store TYPESAFE_API_KEY in the secure field step 3 → prompt Grok Bot: install typesafe-sdk on Agent Computer + smoke system_one (Choice) step 4 → tell Grok Bot: build the usage lab (router, dry-run, config, logs) - or clone Github below step 5 → add skill jev-usage-router: before browser / research / retry / extra bot → call the router, honor action step 6 → stay shadow first, read logs, then active when you trust it - kill switch: bypass jev or enabled: false step 7 → flip active: GrokBot obeys route - Jev decides - GrokBot executes - humans control irreversible actions the result: Jev + GrokBot the best and fastest agent running directly on your computer rn, I’ve already tested it on routine tasks - and the results are genuinely incredible You can come up with endless ways to use Jev + GrokBot - but the most important thing is to install it as soon as possible Copy this 2028 setup, explore my repo below - then read the full Jev deep dive ↓

Jev + GrokBot is the best AI agent system I’ve built in my life It just made my setup CHEAPER and FASTER than what 95% of people are running... setup takes literally 7 minutes: prompt → GrokBot → Jev decision → GrokBot execution → result step 1 → open TypeSafe AI , create API key (keep it off chat paste) step 2 → tell Grok Bot: store TYPESAFE_API_KEY in the secure field step 3 → prompt Grok Bot: install typesafe-sdk on Agent Computer + smoke system_one (Choice) step 4 → tell Grok Bot: build the usage lab (router, dry-run, config, logs) - or clone Github below step 5 → add skill jev-usage-router: before browser / research / retry / extra bot → call the router, honor action step 6 → stay shadow first, read logs, then active when you trust it - kill switch: bypass jev or enabled: false step 7 → flip active: GrokBot obeys route - Jev decides - GrokBot executes - humans control irreversible actions the result: Jev + GrokBot the best and fastest agent running directly on your computer rn, I’ve already tested it on routine tasks - and the results are genuinely incredible You can come up with endless ways to use Jev + GrokBot - but the most important thing is to install it as soon as possible Copy this 2028 setup, explore my repo below - then read the full Jev deep dive ↓

codila

378,387 views • 7 days ago

Jev + GrokBot is the best GTM system I've ever built It just made my GTM x200 CHEAPER and x400 FASTER than what 95% of teams are running... setup takes literally 9 minutes: prompt → GrokBot → Jev routes every candidate → GrokBot opens only survivors → campaign step 1 → open TypeSafe AI, create API key (keep it off chat paste) step 2 → tell GrokBot: store TYPESAFE_API_KEY in the secure field step 3 → prompt GrokBot: install typesafe-sdk on Agent Computer + smoke system_one (Choice) step 4 → point it at a target: "read end to end, pull positioning, ICP, every proof point. no copy yet" step 5 → add skill gtm-router: every candidate from X / LinkedIn / YouTube gets 6 typed questions → CHEAP SKIP / OPEN FULL / FLAG KOL step 6 → let Jev rank hook archetypes by viral rate, not by how often people post them - this is where the whole playbook comes from step 7 → GrokBot assembles the KOL shortlist and the 7-day plan - you stay in control of anything irreversible the result: → 3,412 candidates, X, LinkedIn and YouTube. → 15.7 seconds. $0.41. → me doing the same reading: 6h 12m. 8 grok bots brief it, Jev decides, GrokBot executes. no fine-tuning, no embeddings, no vector db - 6 typed questions and a schema. Setup your GrokBot JEV GTM today, then read the article below to learn how to build an agent team with JEV.

Jev + GrokBot is the best GTM system I've ever built It just made my GTM x200 CHEAPER and x400 FASTER than what 95% of teams are running... setup takes literally 9 minutes: prompt → GrokBot → Jev routes every candidate → GrokBot opens only survivors → campaign step 1 → open TypeSafe AI, create API key (keep it off chat paste) step 2 → tell GrokBot: store TYPESAFE_API_KEY in the secure field step 3 → prompt GrokBot: install typesafe-sdk on Agent Computer + smoke system_one (Choice) step 4 → point it at a target: "read end to end, pull positioning, ICP, every proof point. no copy yet" step 5 → add skill gtm-router: every candidate from X / LinkedIn / YouTube gets 6 typed questions → CHEAP SKIP / OPEN FULL / FLAG KOL step 6 → let Jev rank hook archetypes by viral rate, not by how often people post them - this is where the whole playbook comes from step 7 → GrokBot assembles the KOL shortlist and the 7-day plan - you stay in control of anything irreversible the result: → 3,412 candidates, X, LinkedIn and YouTube. → 15.7 seconds. $0.41. → me doing the same reading: 6h 12m. 8 grok bots brief it, Jev decides, GrokBot executes. no fine-tuning, no embeddings, no vector db - 6 typed questions and a schema. Setup your GrokBot JEV GTM today, then read the article below to learn how to build an agent team with JEV.

Movez

84,874 views • 6 days ago

$Jev + Muse is the first AI agent system that actually automate 100% of my life 99% of people pay 200x more for slower AI agents - while 1% run this 2030 setup just 5 min and setup is ready: prompt → Muse → Jev decision → Muse execution → result step 1 → create your Jev API key (typesafe website) step 2 → clone and install the complete router from Github below python3 -m venv .venv && .venv/bin/pip install -r requirements.txt && cp config.example.yaml config.yaml step 3 → export the key before running anything: export TYPESAFE_API_KEY='YOUR_KEY' add the same export to ~/.zshrc or ~/.bashrc if you want it to survive a new terminal session step 4 → give your agent skill/jev-decision-layer.SKILL.md and connect it to src/router.py + recipes/ , raw Jev returns probabilities - the router converts them into executable actions step 5 → test the entire chain, not the raw Jev API: .venv/bin/python -m src.cli '{"goal":"what is 2+2?","kind":"chat"}' the final JSON should contain action, reason, mode, jev_used and confidence details step 6 → keep mode: shadow for 20–50 real decisions: the agent works normally while Jev’s routes are logged and checked; promote only reliable question packs step 7 → switch to mode: active with hard confidence gates: ≥0.80 act automatically, 0.50–0.79 advisory only, <0.50 escalate to the human the result: Jev + Muse is a system that decides what to do, what to skip and when to bring in - I’ve tested it across my daily workflows, and it’s the best setup I’ve found for automating routine Take the exact stack I built, run it yourself from the repo - then read the full Jev architecture behind it ↓$

Jev + Muse is the first AI agent system that actually automate 100% of my life 99% of people pay 200x more for slower AI agents - while 1% run this 2030 setup just 5 min and setup is ready: prompt → Muse → Jev decision → Muse execution → result step 1 → create your Jev API key (typesafe website) step 2 → clone and install the complete router from Github below python3 -m venv .venv && .venv/bin/pip install -r requirements.txt && cp config.example.yaml config.yaml step 3 → export the key before running anything: export TYPESAFE_API_KEY='YOUR_KEY' add the same export to ~/.zshrc or ~/.bashrc if you want it to survive a new terminal session step 4 → give your agent skill/jev-decision-layer.SKILL.md and connect it to src/router.py + recipes/ , raw Jev returns probabilities - the router converts them into executable actions step 5 → test the entire chain, not the raw Jev API: .venv/bin/python -m src.cli '{"goal":"what is 2+2?","kind":"chat"}' the final JSON should contain action, reason, mode, jev_used and confidence details step 6 → keep mode: shadow for 20–50 real decisions: the agent works normally while Jev’s routes are logged and checked; promote only reliable question packs step 7 → switch to mode: active with hard confidence gates: ≥0.80 act automatically, 0.50–0.79 advisory only, <0.50 escalate to the human the result: Jev + Muse is a system that decides what to do, what to skip and when to bring in - I’ve tested it across my daily workflows, and it’s the best setup I’ve found for automating routine Take the exact stack I built, run it yourself from the repo - then read the full Jev architecture behind it ↓

codila

88,901 views • 4 days ago

I shared 10 Jev use cases for marketers. Here are 10 more: 11. Ad creative scoring - Feed it 100 ad variations. Jev can score which hooks, headlines, or angles are most worth testing first. 12. Social post filtering - Monitor thousands of posts. Jev can flag the ones worth replying to, reposting, or using as sales signals. 13. ICP detection - Give it a company, profile, or website. Jev can score how closely it matches your ideal customer. 14. Buying signal detection - Someone posts that they're switching tools, hiring, raising money, or struggling with a problem. 15. Comment prioritization - Get hundreds of comments across LinkedIn, X, YouTube, or Product Hunt. Jev can score which ones deserve a reply first. 16. Review analysis - Feed it thousands of customer reviews. Jev can classify sentiment, complaints, feature requests, and purchase intent. 17. Influencer matching - Give it 5,000 creators. Jev can score which ones best match your product, audience, and campaign. 18. Sponsorship qualification - Feed it newsletters, podcasts, or creator media kits. Jev can score audience fit, relevance, and whether they're worth reviewing. 19. UGC selection - Give it dozens of videos, screenshots, and testimonials. Jev can score which ones are strongest for ads or landing pages. 20. Product Hunt monitoring - Scan launches, comments, and makers to find competitors, customers, partners, or interesting products. The more repetitive marketing decisions you have to make at scale, the more interesting Jev becomes.

Yum⋆₊˚

385,317 views • 7 days ago

You've probably scrolled past a dozen posts about Jev this week without anyone telling you what it actually is. It's the first model from TypeSafe, a lab started by one of the researchers behind ChatGPT. It's a decision engine: you give it options, it picks one and tells you how sure it is. It cannot write a single word, and that is the interesting part. Every other AI you use writes. That is the whole interface. So when software needs a plain yes or no, we make a model write a paragraph and then dig the answer back out of it. Fine in a chat window where a human reads it. Bad inside software, where code has to act on it. The bet is that the valuable half was never the writing. It was the deciding. That problem showed up in WordPress years ago, and it is the reason WPVibe works the way it does. The AI does the work. Anything permanent stops and waits, because a delete that skips the trash is not something software should decide on its own.

You've probably scrolled past a dozen posts about Jev this week without anyone telling you what it actually is. It's the first model from TypeSafe, a lab started by one of the researchers behind ChatGPT. It's a decision engine: you give it options, it picks one and tells you how sure it is. It cannot write a single word, and that is the interesting part. Every other AI you use writes. That is the whole interface. So when software needs a plain yes or no, we make a model write a paragraph and then dig the answer back out of it. Fine in a chat window where a human reads it. Bad inside software, where code has to act on it. The bet is that the valuable half was never the writing. It was the deciding. That problem showed up in WordPress years ago, and it is the reason WPVibe works the way it does. The AI does the work. Anything permanent stops and waits, because a delete that skips the trash is not something software should decide on its own.

John Turner

21,029 views • 9 days ago

🌎Build the AI Worlds You Want to Play In World Engine makes creating 3D worlds as easy as typing a prompt. Say “gimme animals” or “build a jungle village” and it fills out your world instantly. You can auto-generate environments, characters, and even ideas for quests or scenes. No coding or game dev skills needed. Just type, tweak, and play. Whether you're building games, hangout spots, or streamable experiences, World Engine helps you move fast. Want a closer look at World Engine? Check it out 👉

🌎Build the AI Worlds You Want to Play In World Engine makes creating 3D worlds as easy as typing a prompt. Say “gimme animals” or “build a jungle village” and it fills out your world instantly. You can auto-generate environments, characters, and even ideas for quests or scenes. No coding or game dev skills needed. Just type, tweak, and play. Whether you're building games, hangout spots, or streamable experiences, World Engine helps you move fast. Want a closer look at World Engine? Check it out 👉

NeuralAI

19,361 views • 1 year ago

Smartest AI update for Chrome You can now use Gemini from anywhere: - Type "@" in the top bar - Select "Chat with Gemini" - Write your prompt next Press Enter and Chrome will answer you directly using Gemini! No longer need to open a new tab, access Gemini and then finally type your prompt. Everything is done directly from the top bar in one go: a huge productivity boost!

Smartest AI update for Chrome You can now use Gemini from anywhere: - Type "@" in the top bar - Select "Chat with Gemini" - Write your prompt next Press Enter and Chrome will answer you directly using Gemini! No longer need to open a new tab, access Gemini and then finally type your prompt. Everything is done directly from the top bar in one go: a huge productivity boost!

Paul Couvert

62,511 views • 2 years ago

funny thing, my original loader doesn’t use keyframes. it just loops opacity with a per‑pixel delay array + a duration param. less flexible than keyframes, but gives a different effect here is one example in case you want to try to understand it hahah: { name: 'wave-lr', // Delay in ms before each of the 9 grid cells starts animating, indexed as [0,1,2], [3,4,5], [6,7,8] delays: [0, 100, 200, 0, 100, 200, 0, 100, 200], // How long (ms) each cell takes to fade in/out duration: 600, ]

funny thing, my original loader doesn’t use keyframes. it just loops opacity with a per‑pixel delay array + a duration param. less flexible than keyframes, but gives a different effect here is one example in case you want to try to understand it hahah: { name: 'wave-lr', // Delay in ms before each of the 9 grid cells starts animating, indexed as [0,1,2], [3,4,5], [6,7,8] delays: [0, 100, 200, 0, 100, 200, 0, 100, 200], // How long (ms) each cell takes to fade in/out duration: 600, ]

Kevin Grajeda

134,435 views • 7 months ago

Sep 30 is **HYPERSHIP** day on Product Hunt. If you think you can build and ship fast, and you haven't launched recently, submit your launch by midnight Sep 30. Be prepared to build and ship multiple features in real-time. Appropriately, given the speed theme, we’ve partnered with TypeSafe AI, makers of Jev, a frontier model at real-time speed, and Supabase, the backend platform you know and love! Prizes will include Jev and Supabase credits, swag boxes, and secret prizes we’ll announce later. And if you're not launching: Mark your calendars to try new products, make feature requests, and watch the products evolve over the course of the day. This is the day where we redefine what shipping velocity means.

Sep 30 is HYPERSHIP day on Product Hunt. If you think you can build and ship fast, and you haven't launched recently, submit your launch by midnight Sep 30. Be prepared to build and ship multiple features in real-time. Appropriately, given the speed theme, we’ve partnered with TypeSafe AI, makers of Jev, a frontier model at real-time speed, and Supabase, the backend platform you know and love! Prizes will include Jev and Supabase credits, swag boxes, and secret prizes we’ll announce later. And if you're not launching: Mark your calendars to try new products, make feature requests, and watch the products evolve over the course of the day. This is the day where we redefine what shipping velocity means.

Product Hunt 😸

49,543 views • 2 days ago

This is a shot from Inferno. The typical generative AI you'd be referring to is where you type in a prompt and it creates it for you. This shot involved hours of manual rotoscoping and tracking, and the only 'AI' present is upscaling-based, to improve picture quality.

This is a shot from Inferno. The typical generative AI you'd be referring to is where you type in a prompt and it creates it for you. This shot involved hours of manual rotoscoping and tracking, and the only 'AI' present is upscaling-based, to improve picture quality.

Jacob Booth

117,465 views • 1 year ago

New AI workflow in my design process: Lottie animations straight out of Claude. Uploaded a bunch of SVGs and explained the type of animation, the timing I wanted, and how each element should move. It generated Lotties from that, ready to drop into Framer.

New AI workflow in my design process: Lottie animations straight out of Claude. Uploaded a bunch of SVGs and explained the type of animation, the timing I wanted, and how each element should move. It generated Lotties from that, ready to drop into Framer.

Adrian

11,546 views • 4 months ago

David Dobrik’s advice if you’re feeling stuck right now “Time’s gonna move on regardless and you could be in a crazy different spot in a year from right now” “All you have to do is start and you’re gonna be at the end of the year no matter what” “It’s up to you whether you want it to be a good year or just the same as it was before”

David Dobrik’s advice if you’re feeling stuck right now “Time’s gonna move on regardless and you could be in a crazy different spot in a year from right now” “All you have to do is start and you’re gonna be at the end of the year no matter what” “It’s up to you whether you want it to be a good year or just the same as it was before”

Jack

90,018 views • 5 months ago

I really don’t understand when people complain about the sight lines in Hollywood lol. Does this not look cool as shit? A bunch of different IPs stacked on top of each other going up the hillside! It’s some straight up Ready Player One type stuff. I wish you could see Hogwarts Castle from here too! Universal Studios Hollywood

I really don’t understand when people complain about the sight lines in Hollywood lol. Does this not look cool as shit? A bunch of different IPs stacked on top of each other going up the hillside! It’s some straight up Ready Player One type stuff. I wish you could see Hogwarts Castle from here too! Universal Studios Hollywood

Universal HIGH 🌎

57,075 views • 1 year ago

⚠️ NSFW ⚠️ Looks like Hume made a virtually uncensored voice-native LLM called Octave—the first language model built specifically for TTS. You can generate any type of voice you want with a prompt, and their WebUI tool can auto-generate corresponding example dialogue! The model grasps user intent from text quite well, but as you’ll hear in the second example there can be (somewhat unsettling) glitches inb4 glitch orgasms become a new fetish category 🙃

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⚠️ NSFW ⚠️ Looks like Hume made a virtually uncensored voice-native LLM called Octave—the first language model built specifically for TTS. You can generate any type of voice you want with a prompt, and their WebUI tool can auto-generate corresponding example dialogue! The model grasps user intent from text quite well, but as you’ll hear in the second example there can be (somewhat unsettling) glitches inb4 glitch orgasms become a new fetish category 🙃

Pliny the Liberator 🐉󠅫󠄼󠄿󠅆󠄵󠄐󠅀󠄼󠄹󠄾󠅉󠅭

36,379 views • 1 year ago