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I built JevSearch, search the web & validate your results with Jev. Give a query and selection criteria, use Browserbase search to get the t25 results, then Jev scores and returns the t5 results. Jev often chooses urls outside of the initial top 5 as more relevant.

Kyle Jeong

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56,907 просмотров • 3 дней назад •via X (Twitter)

Новости и политика Образование Наука и технологии

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Built a model router with Jev by TypeSafe AI. Jev decides what model fits your request best and the request is sent to that model.

Built a model router with Jev by TypeSafe AI. Jev decides what model fits your request best and the request is sent to that model.

Duncan

116,081 просмотров • 9 дней назад

The World Cup is about to start and Brave Search has you covered. Scores and standings are shown right at the top of the results page for World Cup-related queries so you won't miss the latest results! ⚽️

The World Cup is about to start and Brave Search has you covered. Scores and standings are shown right at the top of the results page for World Cup-related queries so you won't miss the latest results! ⚽️

Brave

53,881 просмотров • 3 месяцев назад

we built blazing fast computer/browser use with Jev + Stagehand. 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.

we built blazing fast computer/browser use with Jev + Stagehand. 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.

Kyle Jeong

69,508 просмотров • 9 дней назад

Search across your chats just got faster and more powerful 🔎 From the sidebar, you can search chats, projects, images, and documents in one place across web, iOS, and Android. Use filters to narrow results, then select anything to open it directly in ChatGPT.

Search across your chats just got faster and more powerful 🔎 From the sidebar, you can search chats, projects, images, and documents in one place across web, iOS, and Android. Use filters to narrow results, then select anything to open it directly in ChatGPT.

ChatGPT

152,453 просмотров • 2 месяцев назад

Jev vs GLM 5.3 at chess! Results: ◾ GLM 5.3 won by checkmate in 29 moves ◾ Jev: ~0.3s and <$0.0001 per move ◾ GLM 5.3: ~5.8s and ~$0.008 per move ◾ The whole game cost 24 cents My main takeaway is that it's often useful to use each one to their strengths: ◾ Fast, well-defined classification → specialized models like Jev ◾ Classifications that need reasoning or lookahead → LLMs like GLM 5.3 ◾ Real classification pipelines → hybrid. Jev handles the easy calls, an open model handles the hard ones. The future is multi-model!

Jev vs GLM 5.3 at chess! Results: ◾ GLM 5.3 won by checkmate in 29 moves ◾ Jev: ~0.3s and <$0.0001 per move ◾ GLM 5.3: ~5.8s and ~$0.008 per move ◾ The whole game cost 24 cents My main takeaway is that it's often useful to use each one to their strengths: ◾ Fast, well-defined classification → specialized models like Jev ◾ Classifications that need reasoning or lookahead → LLMs like GLM 5.3 ◾ Real classification pipelines → hybrid. Jev handles the easy calls, an open model handles the hard ones. The future is multi-model!

Hassan

38,792 просмотров • 8 дней назад

experimenting with the first Jev powered websites 1. Ploy looks through your site data, conversion rates, enriched visitors, etc. 2. Finds what's working and where specific types of users are dropping off 3. Comes up with hypotheses for how to align messaging to those user groups 4. when a page is loaded, JEV selects the right copy AND design for each individual part of the page to align to that audience segment 5. tracks the results in realtime and improves the hypotheses ... in 25ms with no real impact to LCP 🤯

experimenting with the first Jev powered websites 1. Ploy looks through your site data, conversion rates, enriched visitors, etc. 2. Finds what's working and where specific types of users are dropping off 3. Comes up with hypotheses for how to align messaging to those user groups 4. when a page is loaded, JEV selects the right copy AND design for each individual part of the page to align to that audience segment 5. tracks the results in realtime and improves the hypotheses ... in 25ms with no real impact to LCP 🤯

brryant

21,503 просмотров • 6 дней назад

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 просмотров • 7 дней назад

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 просмотров • 7 дней назад

Found a great production use case for Jev. I used Jev to organize ~2.3K AI research papers. The total cost was $0.14, and it took about 83 seconds. The process: The papers already had old tags, which I ran through a previous open model (DeepSeek V4 Flash). However, I wasn't confident in the classifications, and I didn't want to spend more on tokens unless I spent time tuning it into a good LLM classifier via few-shot (more expensive). Too tedious, too costly, and unsustainable. Luckily for us, we now have Jev to help us with organizing papers better. So Jev first went through all the papers and retagged them. It agreed with 75% of the previous tags. Jev found about 579 high-confidence topic changes. I evaluated reliability by manually labeling 30 disagreements and accepted all of them. I was astonished by Jev's classification capabilities. We applied and verified all changes in production. I'm much more satisfied with the classifications, but I think there is still room for improvement. Check out the papers here: I will experiment with Jev more. The takeaway is that pipelines can be significantly improved by carefully combining System One and System Two models. Jev clearly unlocks more interesting ways to organize papers and offer a more useful discovery layer for research papers. More updates on that soon.

Found a great production use case for Jev. I used Jev to organize ~2.3K AI research papers. The total cost was $0.14, and it took about 83 seconds. The process: The papers already had old tags, which I ran through a previous open model (DeepSeek V4 Flash). However, I wasn't confident in the classifications, and I didn't want to spend more on tokens unless I spent time tuning it into a good LLM classifier via few-shot (more expensive). Too tedious, too costly, and unsustainable. Luckily for us, we now have Jev to help us with organizing papers better. So Jev first went through all the papers and retagged them. It agreed with 75% of the previous tags. Jev found about 579 high-confidence topic changes. I evaluated reliability by manually labeling 30 disagreements and accepted all of them. I was astonished by Jev's classification capabilities. We applied and verified all changes in production. I'm much more satisfied with the classifications, but I think there is still room for improvement. Check out the papers here: I will experiment with Jev more. The takeaway is that pipelines can be significantly improved by carefully combining System One and System Two models. Jev clearly unlocks more interesting ways to organize papers and offer a more useful discovery layer for research papers. More updates on that soon.

elvis

15,427 просмотров • 5 дней назад

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 →

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 →

Jarrod Watts

1,276,499 просмотров • 9 дней назад

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 просмотров • 5 дней назад

instead of generating text, jev from TypeSafe AI generates structured output this makes it great for classification tasks like model routing, tool selection/search, and guardrails of many forms! it's also ridiculously fast and cheap compared to LLMs doing the same tasks

instead of generating text, jev from TypeSafe AI generates structured output this makes it great for classification tasks like model routing, tool selection/search, and guardrails of many forms! it's also ridiculously fast and cheap compared to LLMs doing the same tasks

Sydney Runkle

52,469 просмотров • 8 дней назад

Introducing the official Firecrawl connector for Claude 🔥 Add state-of-the-art web search to your AI agents (94.7% on SimpleQA). Powered by our live indexes for fresher results across research, the open web, and more. Live in the Anthropic connector directory now!

Introducing the official Firecrawl connector for Claude 🔥 Add state-of-the-art web search to your AI agents (94.7% on SimpleQA). Powered by our live indexes for fresher results across research, the open web, and more. Live in the Anthropic connector directory now!

Firecrawl

90,406 просмотров • 1 месяц назад

Built a tax document classifier with Jev. We ingest thousands of tax documents using an LLM pipeline I built last tax season. I read multiple articles as late as April this year claiming AI fails at tax document classification. Wasn't the case back then, and it's proved wrong again now. Jev classifies 100% of our tax document corpus at $0.001 per page. 34x cheaper and 6x faster than the LLM setup. Open sourcing it:

Built a tax document classifier with Jev. We ingest thousands of tax documents using an LLM pipeline I built last tax season. I read multiple articles as late as April this year claiming AI fails at tax document classification. Wasn't the case back then, and it's proved wrong again now. Jev classifies 100% of our tax document corpus at $0.001 per page. 34x cheaper and 6x faster than the LLM setup. Open sourcing it:

Nakshatra Saxena

243,799 просмотров • 8 дней назад

The new Product Page Header image in the App Store will not matter. First you need users to actually want to enter your App Store listing. And the reality is that a lot of users NEVER enter the listing. They search for something, see the app in the search results and download it directly from there. So the important update here is the new Search Results image. It's an app banner inside the App Store search results. And you should think about it as a persuasive ad. COPYWRITING will be what matters. Not the fancy "branding". Users are searching because they want an answer to something. Your creative needs to give them that answer immediately. And this becomes even more important with Apple Ads. You can pay to appear for the keyword, but your creative still needs to convince the user that your app is the answer they were searching for.

The new Product Page Header image in the App Store will not matter. First you need users to actually want to enter your App Store listing. And the reality is that a lot of users NEVER enter the listing. They search for something, see the app in the search results and download it directly from there. So the important update here is the new Search Results image. It's an app banner inside the App Store search results. And you should think about it as a persuasive ad. COPYWRITING will be what matters. Not the fancy "branding". Users are searching because they want an answer to something. Your creative needs to give them that answer immediately. And this becomes even more important with Apple Ads. You can pay to appear for the keyword, but your creative still needs to convince the user that your app is the answer they were searching for.

Teodora @DesignerAnts

69,953 просмотров • 1 месяц назад

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 просмотров • 8 дней назад

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

59,800 просмотров • 9 дней назад

Your competitors don't need to rank higher than you anymore... They just need a fake DMCA, a faked lawyer site, and Google's own systems will remove your website from search results ⚠️ The page comes down IMMEDIATELY!!! The reinstatement takes weeks, and by then your rankings are gone. Oh, and they can repeat the entire thing as many times as they want, perpetually keeping your pages suspended from search results. I just published the full exposé on how this works, why Google won't fix it, and what's coming next 👇

Your competitors don't need to rank higher than you anymore... They just need a fake DMCA, a faked lawyer site, and Google's own systems will remove your website from search results ⚠️ The page comes down IMMEDIATELY!!! The reinstatement takes weeks, and by then your rankings are gone. Oh, and they can repeat the entire thing as many times as they want, perpetually keeping your pages suspended from search results. I just published the full exposé on how this works, why Google won't fix it, and what's coming next 👇

Charles Floate 📈

30,933 просмотров • 4 месяцев назад

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.

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

276,707 просмотров • 8 дней назад

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 просмотров • 9 дней назад