Meet the @typesafeai connector in UiPath Integration Service. 🔗 Bring Jev models into your workflows to make fast, confident decisions at lower cost & latency: uipath.com/blog/product-and-…

We put Jev inside the Lightpanda binary and built judge() on top of it. @typesafeai No more sending a whole page to an LLM just to find out it's a cookie banner. > Blocked pages, CAPTCHAs, cookie walls, logins, paywalls, error pages, empty searches and pages still loading can all return an HTTP 200 > judge() asks a set of predefined questions and tells you if the page has real content, and what kind: article, listing, product or docs > One call, ~0.2s In the video: > 30 sites, ~14s total > Lightpanda backs off when rate-limited, accepts cookie walls, and keeps or skips each page

Congrats to TypesafeAI on the Series A! At the first ever Jevathon, we watched developers build some seriously impressive apps on their new System One model. Can't wait to see what's next!

Is this a hot dog? find out what your favorite LLMs said over at hotdogbench.com

Typesafe's CEO @CompleteSkeptic does not trust public benchmarks. Neither do I, Diogo. So @p0 and I wrote a blog evaluating 8 decision models on real company training datasets we have personally audited and iterated on. For what it's worth though, Jev is still on top 👑 parallel.ai/blog/are-all-sys…

Crazy launch! Copycats! Massive round! On the one-month anniversary of the launch of Jev, we unpack it all with @CompleteSkeptic, CEO of @typesafeai: what Jev is, how it fixes what's broken in AI, and why the fast-followers are missing the hard part. 00:00 “95% Einstein, 5% Mr. Bean” 1:09 Why intelligence per dollar matters 3:25 Why TypeSafe built in stealth for two years 5:50 “We could have had AGI last year” 6:48 Why the product carries the marketing 9:33 What is Jev? Machine-native AI, built for software 10:53 System One vs. System Two 12:26 From simple decisions to composable AI 15:21 Automating email: giant prompts vs. smart if-statements 20:03 Probabilities, thresholds, and developer control 23:43 Why Jev is faster and cheaper than chat models 25:39 “Classification is AI-complete” 27:16 RLHF: how OpenAI trained models to please humans 31:28 Sycophancy, refusals, and the hidden costs of RLHF 34:22 RLVR: why benchmark gains don’t mean reliable automation 37:51 RLCD: optimizing for machine-native intelligence 39:54 Why TypeSafe builds its training data in-house 41:49 The Bitterest Lesson: define the task before scaling 44:39 Building Jev from open-weight models 45:56 Is Jev just a classifier? Why that’s a compliment 48:35 “Build prod, not God” 50:11 Embedding AI in software, beyond coding assistants 53:05 Jev’s copycats: matching the interface vs. the intelligence 55:33 Why benchmarks mislead the users they should help 58:37 Open weights vs. APIs: how TypeSafe weighs the tradeoff 1:00:04 Jevons paradox: a new market for machine intelligence 1:02:50 Why Diogo is skeptical of recursive self-improvement 1:04:47 Can practical automation avert an AI winter?

Jev can cross America navigating by street signs alone Here's it completing the Cannonball Run from NYC to LA, picking a direction at every turn, given just a destination address and the name of the street it's on

If your app doesn’t have AI features you’re ngmi We made them simple to build: just ask Lovable. Now powered by even more models from @claudeai, @firecrawl, @GeminiApp, @OpenAI, @perplexity_ai, @p0, and @typesafeai.