> built the app with Claude > shipped to App Store > told X it was live > got downloads > opened the dashboard > nothing > checked the code > StoreKit looked fine > checked App Store Connect > Paid Apps Agreement not active > banking missing > tax forms unfinished > Small Business Program never applied for > Apple still taking 30% > first payout still 45 days after fiscal month close > external payment copy sitting in one forgotten settings screen > reviewer would have found it instantly The app was not broken, the business setup was before you launch an iOS app, do this: > set up banking > complete tax forms > sign Paid Apps Agreement > apply for Small Business Program if under $1M > create real product IDs > test sandbox purchase > test restore > test cancellation > test subscription grace period > remove every sloppy external payment reference > then submit vibe coding gets you the app App Store Connect gets you paid

Exa CEO, Will Bryk: "AI systems really want perfect search. They don't want SEO. They don't want ads" He expects AI searches to pass human searches this year and hit 1,000x within a few years 17 minutes on why search built for people fails the agents that will soon do most of the searching Every weak page an agent reads still eats its token budget So I wrote a guide on screening 50 sources with Jev before your agent writes a word It also shows how to catch the good papers the filter throws out Watch it, then grab the starter and prompts below

Mastra CTO, Abhi Aiyer: "If I had 200 tools and I asked about refunds, the classifier would say this dude's asking about refunds, that's the refund tool domain" 16 minutes of live code wiring Jev into Mastra: workflow branches, guardrails, tool pre-selection, classifier judges His own tests put Jev 3rd out of the tool search options he tried So before a Jev filter decides what your agent reads, count the useful sources it threw away Watch it, then read the guide below to put a Jev screen in front of 50 research sources, with a review queue and a count of the useful papers it archived

Teams still pay a heavy LLM to make calls a panel of humans would answer in 5 seconds LangChain sat down with the team behind Jev for 46 minutes on where those calls belong In the live demo an agent tries to delete a customer, Jev marks the call risky, and the tool never runs A second middleware sends the easy prompt to a fast model and keeps the big one for the hard task Their rule for scoring: describe every level well enough that a 1.5 means the same thing on every document you score And the cutoff stays yours to prove, because the confidence you get back is a statistic over the probability map, not a silver bullet My guide turns that into a research filter: 50 papers, a 0 to 3 relevance rubric, a review queue, and a check on the useful papers it archived Watch the session, then grab the starter and the prompts below

LangChain put Jev inside their own agent loop and showed the three jobs they hand it In the demo an LLM took about 5 seconds to say whether a text contains PII, and Jev answered immediately with 98% So their coding agents can ask Jev how hard a task is and save the powerful model for the hard ones Their auto mode middleware asks Jev whether a tool call is risky and blocks it at runtime, a check she had switched off because it used to be too slow And the same model grades an agent's answer against a rubric in online evals: correct, matches the reference, grounded, cites a source My guide points that setup at research sources, with a 0 to 3 relevance rubric over 50 papers and a review queue for the low-confidence calls It also checks which useful papers the filter quietly archived Watch the session, then grab the starter and the prompts below

Your research agent can miss the best paper before it writes a word Joe Maddalone explains where he'd put Jev in his workflow: score incoming ideas before passing them to an LLM for development Before trusting that approach for research, check how many useful papers the filter rejects Watch this 62-second clip, then save the guide below for the Python workflow to screen 50 papers and send uncertain cases to manual review

Steve Sewell, builder of Agent-Native: "Jev only makes decisions." In this 10-minute breakdown, he explains how selecting tools and skills for each prompt keeps irrelevant instructions out of your agent's context. Watch it, then use the guide below to build a Jev router that sends uncertain decisions for review.

Shopify CEO Tobi Lütke: “We have always created superintelligence around us” He means cities and societies: people combining abilities no individual has alone For AI agents, compare what each agent can do alone with what the group can do together Pascio's article argues we should watch agent traffic and capabilities that only appear across a whole pipeline Watch the 56-second clip, then read the article below for its checklist of what to track between agents

OpenClaw creator Peter Steinberger: "You want to extend the loop. So any input can actually be verified" 19 minutes on how he runs his open source repos with agents that check the work before he does A new issue gets checked against his vision.md, then a second agent reviews and repairs the PR, so it's ready to merge when he looks His agents "need much less babysitting because you just give them more tools to do the work" Watch it, then copy the gate and stop rules from the guide below

OpenClaw creator and OpenAI engineer, Peter Steinberger: "The agent runs the inner execution loop. I set the direction and I make decisions in the outer loop" When someone files an issue on his open source project, a manager agent checks it against the project's goals, a worker writes the change and runs the tests, and a second agent reviews it He skips the intermediate messages, reviews the PR once, approves, and the change lands after the checks pass 6 minutes on how he went from juggling 10 terminals to managing a manager agent Watch it, then copy the loop prompt from the guide below: success gate, blocked actions, stop rules, run log

vibe coding is fast enough that you can finish the app and still be stuck making the video explaining it. that’s where i’d use @videoclaw, which is publicly launching its desktop video creation agent. i’d start with posts i’ve already researched and written. there’s plenty there to turn into videos. it puts an AI editor, video generator and avatar maker in one chat-driven app. AI automates complex production tasks, and you make precise edits through simple requests. i’d much rather ask it to shorten an intro than spend my afternoon figuring out how. that back-and-forth is what makes creating video feel like vibe coding. we’ve seen this with acrylic paints, Photoshop, Canva and AI image generation. lower the barrier and more people try making something themselves. Videoclaw brings that to video for founders, developers, marketers and the rest of us who have something to share. you can speak video even without the time, skills or team to handle production yourself. content engineer might become a perfectly normal job title for the next generation of creators. download the Mac app at videoclaw.com, connect your ChatGPT or Claude, and start prompting. more platforms are coming soon. the public beta is free to use, with $10 in AI generation credits included. the first 300 people get $50 in credits.

Replit CEO, Amjad Masad: "We closed the loop and we have an agent internally at Replit that is constantly evolving Replit Agent" Every night that agent reads the traces from Replit users and opens a pull request with prompt changes for the errors and sentiment issues it finds Each PR ships as an A/B test, and the result decides whether Replit releases the change or edits it 46 minutes with SaaStr's Jason Lemkin, whose agent 10K emailed 331 investors on its own Watch it, then copy the general loop prompt from the guide below

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x cheaper (w/ output tokens free) • Frontier composable intelligence optimized for decisions AFAICT the shortest path to AI-based economic revolution

A teenager learns to drive in a few dozen hours That works because they've spent their whole life watching things fall and crash into each other Many robots start with none of that and learn each task from thousands of repeated demos, which the Odyssey team calls brute force Odyssey-3 from @odysseyml learns more like the teenager It's a world model trained on a massive amount of visual data from the real world, so it learns cause and effect and how people behave first Then a few hours of data from a specific machine is enough to teach it the controls In the preview, one model powers a humanoid, controls robot arms, drives a car, flies a drone, plays video games and trains other AIs They call it a physics agent, and it goes public in the coming weeks If the approach holds up, the payoff goes way past demos Machines could take over jobs that put people at risk and help with the everyday tasks that get hard with age or disability I'll be watching how it does once people outside the team get their hands on it

Introducing Bolt Forge. Free until Oct 14th: - Up to 50x more usage - The new frontier: GLM, DeepSeek, Kimi - Zero usage charges Live now in your model picker on bolt.new And one more thing... 👇