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?

Meet the Lago CLI, for developers and AI agents. MCP is useful for discovery and investigation. The CLI is better for multi-step workflows. The right primitive is still the API. Both use the same Lago API.

"If you get paid hourly, you probably hate AI. If you care about the outputs of an industry or a company, you love AI." Recursive co-founder (and author) Richard Socher on why AI opinions are split so hard. It's about who owns the upside. @RichardSocher, @Recursive_SI, @mattturck, @FirstMarkCap, @jason

OpenAI runs the agent. Daytona gives it a computer. Run OpenAI Agents API sessions in secure, isolated Daytona sandboxes: outbound-only, least-privilege keys, bring-your-own-image. Guide:

The first goal of a benchmark is to foster model improvement. We joined the coval benchmark with leading latency and then got much worse rankings. This was because latency was now measured based on first hearable audio rather than first audio packet. That made us realize our first audio packets were completely silent, now fixed! Thanks @covaldev for making our models better.

Introducing Express-3, our most advanced Avatar model yet 💥 - Sentiment-aware performances - Better lip sync and more natural body motion - 2x faster video generation Express-3 also powers Style Avatars - stylized characters you can create from a prompt or preset via Avatar Builder. Available on all plans.

Bionic 1.1.2 just shipped! You should feel noticeable performance improvements across the board, and especially for session loading and tab switching. Also contains a number of fixes for screen reader users, with more on the way.

AI Could Take Over in 2029. Is It Already Too Late? My conversation with @RyanGreenblatt of @redwood_ai about AI 2040, RSI, AI alignment, and how he believes the transition to superintelligence actually unfolds. 01:24 The AI CEOs are aware of the risks, but "proceeding anyway" 03:27 Astra paused, and the "Pacing the Frontier" letter 05:45 "Not bad. Dangerous." What superintelligence actually threatens 09:55 Recursive self-improvement, and the intuition objection 14:16 SSI rumors: does continual learning change the picture? 17:27 His timeline: "plan as though it happens in 2029" 19:11 Is it already too late? 21:23 Ryan's path: COVID, podcasts, Redwood 26:30 The alignment faking story, told by the person who ran it 31:30 What AI 2040: Plan A actually is 33:35 Plans D, C, and B: the doors nobody should pick 36:51 The deal with China: "mutually assured compute destruction" 39:55 What if compute stops mattering? 43:00 What happens to OpenAI and Anthropic under Plan A 45:31 How the pause ends, and who decides 48:54 "Plan A isn't likely to happen": then why write it 50:40 200x GDP growth in the 2030s, explained 53:45 Grading the summer: the letter, Astra, the secret review 59:01 The internal deployment gap 1:01:38 Zuckerberg's manifesto 1:04:56 The Hugging Face investigation 1:05:44 What AI control looks like in practice today 1:12:23 Ryan's sobering timeline: from today to takeover, year by year

The evolution of a workday: Before AI: [decision] [process] [process] [decision] [process] [process] [process] 10pm: still going With AI: [decision] [decision] [decision] [decision] [decision] 3pm: [brain empty] [need coffee] [staring at wall]

Right now, you're either: * an AI-native rocketship, which sounds fun until you realize it means being in a permanent fundraising situation at ever more scary valuations to hoover up capital and talent, and in a constant death match with other rocketships to capture customers, sacrificing gross margins; or * basically left for dead, regardless of how good a company you actually are This has been the case for a while, but feels like it just escalated a notch.