An AI lab building intelligence beyond chat.
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Ladies and gentlemen, agents and assistants,
we are psyched to announce
Jev is back.
Capacity has increased and signups are open!
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TypeSafe AI retweeted
Anthropic released native mods for customizing Claude Code yesterday, so I did the obvious thing and built a Jev-based classifier for auto mode! 2x faster permission checks!
github.com/madisonrickert/je…
#ClaudeCode #Jev
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Jev can search 🔍 through a long document just like you would! 🕵️
Inspired by @EGafni’s Twitter thread on combining Jev with PageIndex.
We show how to build long-document search with @typesafeai Jev + PageIndex.
No vector database. No embeddings.
Open source: github.com/VectifyAI/jev-doc…
🧵👇
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TypeSafe AI retweeted
me to our GTM guy: do enterprises want benchmarks
him: no, the fast ones have already benchmarked their internal use cases. the slow ones are copying the fast ones.
he's onboarded a quarter of the fortune 500 already 🥵
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🗣️ Ever felt like AI bots don't really listen? 🗣️
They just don't have a li'l Jev helping them yet!
One really cool use case I’ve been exploring with Jev: a semantic VAD for real-time voice AI.
I plugged it into @AgoraIO ConvoAI and used the live transcript + recent conversation as context to decide whether the user has actually finished speaking.
That makes Jev surprisingly good at handling things like hesitation, unfinished thoughts, and those moments where you stop talking for a second because you’re still figuring out what you want to say.
It’s a nice example of where Jev fits really well in a real-time AI loop.
Here’s what it looks like in action 👇
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Jev is a phenomenal reranker, you heard that right.
Jev is a phenomenal reranker. At 75% of the previous reranking cost, it improved our main-app search by 30.64% in matches per 200 candidates on searches recruiters actually run.
Most recruiting benchmarks measure precision and accuracy across just 10 candidates. That isn’t representative of real recruiting and sourcing workflows, where you may need to source hundreds of candidates to make a hire.
We’ll release the full benchmark soon.
We're excited to work with @hmartenjoyer, @mathfax , and the rest of the @typesafeai team to continue to bring Jev to more Wrangle features. Stay tuned 👀
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@typesafeai's Jev model just landed in n8n.
Jev’s job isn’t to write, it decides: returning a choice + confidence you can use for branching, sorting and routing in your workflows.
Think of it as a smarter If/Switch for the fuzzy stuff.
bit.ly/4hFjS8A
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TypeSafe AI retweeted
Your agent doesn't know Jev.
Most code calls LLMs the long way:
state → prompt → text → parse → if statement
Decision models like Jev skip the text and plug into your logic.
Here's 30 minutes with @typesafeai's @allietheicon on building with it.
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This office hours recording from @DSPyOSS is a gold mine of excellent Jev use-cases and explainers!
Today at 2pm PT, @isaacbmiller1 and I will be walking through @DSPyOSS's Jev/System One implementation & the ReAnchor optimizer. Will also sharing some design patterns for for selective compaction, tool approvals, and subagent delegation using Jev. streamyard.com/watch/sbMTpJA…
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Jev is a huge power-up for research agents!
@edwardirby built a @youdotcom agent that monitors business risks, and compared Jev to an ordinary LLM judge.
• Same report quality
• But the LLM flip-flopped: 0.35 -> 0.68 -> 0.50 on the same threat
• the LLM missed 5 of 11 investigations. Jev missed none.
• Jev was 250x cheaper, 3-6x faster
Write-up 👇
How to build a reliable risk agent without a frontier model ($0.02/sweep):
• @youdotcom search
• @typesafeai Jev for typed judgments
• @QwenDevs for proposals & synthesis
• MCP for integration
Full architecture & cost breakdown below 👇
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TypeSafe AI retweeted
this demo of codemode + jev in pi beautifully demonstrate both the power of codemode and general classification models
well done w/ this example @badlogicgames, @mitsuhiko, and the rest of your team
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TypeSafe AI retweeted
the @DSPyOSS town hall about dspy+jev was really good. here are some of my favorite highlights
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TypeSafe AI retweeted
8 hours left to ship feature requests for Hypership Day!
So far we've had:
149 Feature Requests
102 Shipped Features
Get those feature requests in so that Makers can keep on shipping 🚀
And this is what the top launches will win today 🎉
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For the back of the room:
• production-grade reranking for sales data with Jev
• 20x faster
• 10x cheaper
• 12% more accurate
Are you getting it yet?
We used Jev to retrieve sales data 20x faster, 10x cheaper, and 12% more accurate than GPT-5 Mini.
Every time a Rox agent answers a query, it pulls from relevant transcripts, emails, CRM notes, news, and documents.
We benchmarked two ways of retrieving them:
LLM-based reranking
Jev classification
The results speak for themselves. Jev was faster, cheaper, and more accurate.
Revenue agents require thousands of points of context to understand accounts, chart relationships, and execute on sales. This research will help us serve that context with frontier-level performance for our customers.
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Get ready to HYPERSHIP!!
Founders are building the features you dream up for them right now on @ProductHunt , take your shot!
Hypership Day is live on Product Hunt!
Today is all about shipping velocity. Users request features directly on launch pages, and makers build and deploy them in real time.
🗣️ Users: Drop your feature requests and bug reports on the launches.
🛠️ Makers: Ship features as fast as you can today to climb the leaderboard.
Whoever ships the fastest takes home the glory... and prizes from @typesafeai and @supabase!
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