Podcasts about windsurf

Sign up for Practi, a new platform that helps law firms use subscription billing.Here are the top 5 takeaways from this episode:* Going solo requires proving your own value. Stephen emphasizes that attorneys who rely solely on a firm's name have “borrowed value.” Going out on your own, and actually generating revenue and clients, is the real proof of marketability and professional worth.* Client quality over quantity. Early on, Stephen learned to fire bad clients: those who don't pay, don't cooperate, or ask you to act unethically. You can't build a sustainable practice on a foundation of problematic clients, and protecting your license and reputation comes first.* Build your own AI workflows rather than relying on generic tools. Stephen moved away from consumer chatbots (Claude Desktop, ChatGPT) toward IDEs like Cursor and Windsurf, and built ~80 of his own MCPs (Model Context Protocol integrations). Custom-built workflows tailored to your specific practice are far more powerful and secure than off-the-shelf solutions.* AI hallucination is a context problem, not just a model flaw. Stephen explains that AI “hallucinations” happen because the model lacks the right context window and fills gaps with plausible-sounding but fabricated information. The solution is connecting AI to reliable, specific data sources (like his custom Court Listener MCP) rather than letting it guess.* Start small and don't overspend on AI tools. Many expensive subscriptions are overkill for beginners. Stephen built a functional court case search tool for roughly 20 cents. His advice: start with a free or low-cost tier, learn the technology through hands-on experimentation, and only scale spending once you understand what you actually need.__________________________Want your question to be answered on a future show? Fill out this short survey.Have subscription model question? Check out this free resource to ask all of your questions at notebook.practi.ai.Check out Polauf Law LLC.Sign up for Paxton, my all-in-one AI legal assistant, helping me with legal research, analysis, drafting, and enhancing existing legal work product.Get Connected with SixFifty⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, a business and employment legal document automation tool.Sign up for ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Gavel⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, an automation platform for law firms.Visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Law Subscribed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to subscribe to the weekly newsletter to listen from your web browser.Prefer monthly updates? Sign up for the Law Subscribed Monthly Digest on LinkedIn.Check out ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Mathew Kerbis'⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ law firm ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscription Attorney LLC⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Want to use the subscription model for your law firm? Click here to sign up for a new platform that helps law firms use subscription billing. Get full access to Law Subscribed at www.lawsubscribed.com/subscribe

In this episode of Run the Numbers, CJ breaks down Bending Spoons' F-1 filing and the acquisition machine behind AOL, Evernote, Vimeo, Eventbrite, and more. He unpacks the company's playbook: buy under-optimized digital businesses, transform operations, raise prices, reinvest earnings, and repeat — while asking the core question: how much was built, and how much was bought?—SPONSORS:RightRev is an automated revenue recognition platform that lets your product team ship new pricing without asking finance for permission, and your sales team close deals without creating downstream chaos. Check out their free tool at calculator.rightrev.com It scores your rev rec process, shows what's exposing you to risk, and tells you exactly where to focus before it bites you in the rear end. Check it out at https://calculator.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY has been part of Silicon Valley since it was just a valley, helping the most successful names in tech go from startup to exit to megacap. With teams across strategy, tax, audit, and transactions, EY helps you get your financials right early, long before your investors start asking for it. You build the next big thing, and EY will help you build it right. Learn more at https://www.ey.com/techstartupsSpendHound cuts your SaaS and AI spend by up to 30% using real pricing benchmarks across 10,000 vendors, so you always know what fair pricing looks like before your next renewal. Rated #1 on G2 in SaaS spend management, it's free forever for teams up to 1,000 employees. Sign up by June 12th and get $500 just for getting started. Go to https://www.spendhound.com/cjBrex is an intelligent finance platform with AI-powered agents that capture expenses automatically, enforce policy before the spend happens, and close your books in minutes instead of weeks. 35,000+ companies like OpenAI, Coinbase, Anthropic, and DoorDash already run on Brex. It's time to get Brex AF. Learn more at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/run—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNCJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 What is Bending Spoons?1:03 The Internet's attic: the portfolio3:11 The metrics rundown5:44 Revenue: $1.3B, 95% growth6:04 82% of growth was bought, not built6:29 Gross margin: 66%6:50 Subscription mix and NRR7:33 Net income: basically zero8:00 Cash: $741M, debt: $4.4B8:35 Revenue per employee: $2.57M9:39 Sponsors — RightRev | Rillet | EY12:42 Organic growth is mostly price hikes13:50 A house of adjustments14:54 Add-backs bigger than the profit15:22 The reorganization line: cost of firing19:21 Sponsors — SpendHound | Brex | Aleph22:51 Does the playbook actually work?23:07 Evernote: the proof point23:45 Romini: the growth proof point24:10 AI in three directions at once25:45 The debt engine27:50 Red flag 1: material accounting weaknesses28:38 Red flag 2: pro forma numbers come with a confession29:00 Red flag 3: App Store dependency29:11 Red flag 4: no long-term contracts29:30 Red flag 5: foreign private issuer29:52 Red flag 6: they've never sold anything30:19 Cap table and board31:07 Valuation: 14–18x33:00 Bull vs. bear case33:55 Miscellaneous: the S1 is already stale35:25 Credits

The entire startup ecosystem is racing to build agent harnesses. Logan Kilpatrick, who leads Google AI Studio and the Gemini API, argues that scramble has a roughly 12-month shelf life. Models will absorb the scaffolding and run it natively, so the edge moves elsewhere. Google's own bet runs in parallel: a single agent harness, born from the Windsurf team and now called Antigravity, has become the connective tissue across search, the Gemini app, Cloud, and AI Studio — the role Gemini-the-model used to play. Logan makes the case that coding already feels like narrow superintelligence, and that "jagged" vertical superintelligence (in math, finance, and science) will arrive well before AGI. He argues Google's real goal is maximizing outcomes for users, not eyeball time. He unpacks Omni, the single model built to replace multiple separate systems Google once trained for text, audio, music, image, and video. His throughline: AI is an accelerant for human ambition, not a substitute for it. Hosted by Sonya Huang, Sequoia Capital

What if the secret to a business that actually sets you free has nothing to do with your idea, your hustle, or your vision, and everything to do with a number most entrepreneurs never pay close enough attention to? In this episode of The Happy Hustle Podcast, I sit down with Omar Zenhom, co-founder of the legendary $100 MBA Show podcast and the man behind Webinar Ninja, a SaaS company he built from zero to over 30,000 users and eventually sold in 2024. Omar is an educator turned entrepreneur, the kind of guy who left a decade of teaching to go all in on business, built something real over ten years, and came out the other side financially free and still hungry for the next chapter. His podcast has racked up over 300 million downloads and consistently ranks among the top business shows in more than 30 countries. He's not flashy about it. He's just sharp, honest, and genuinely good at what he does. This episode matters because Omar is one of those rare entrepreneurs who's actually done it. He built, he scaled, he burned the candle, he sold, and now he talks about all of it, including the parts that surprised him. If you're a business owner trying to build something that gives you more freedom, not less, this conversation is going to hit. Here are the biggest lessons from this one. Margins aren't the most important thing in business. They're the only thing. Omar opened with something he says constantly on his own show, and it bears repeating here. If your margins aren't healthy, you can't hire great people, you can't delegate, you can't step back, and you definitely can't build a business that serves your life. He says sixty percent is the floor, and anything below that puts you on life support. Software, digital products, service businesses built on systems, these are the models that get you there. Get the margins right first, then build everything else on top. Stop trying to find a diamond in the rough when it comes to hiring. Omar went looking for the most expensive engineer he could find on Upwork, a former engineering exec at Yahoo, because his software needed someone elite. That one person did in ten hours a week what five cheaper engineers couldn't. You pay for it upfront or you pay for it later in messes, rewrites, and wasted time. The same goes for editors, videographers, anyone whose taste and skill directly affects the quality of what you're putting into the world. One great hire changes everything. Validate before you build. Before Webinar Ninja was a real product, Omar and Nicole pre-sold it. One hundred and fifty spots in 48 hours, just on the promise of a solution four months out. That told them everything. People don't just say they want something when they put actual money down. If you're sitting on a business idea right now and haven't tested whether anyone will pay for it yet, that's the only thing that matters next. Embrace the struggle as part of the deal. Omar grew up watching his Egyptian immigrant parents rebuild their lives from scratch in America. That foundation gave him something money can't buy, a high tolerance for discomfort and a genuinely low floor for what counts as failure. He says his fondest memories from ten years at Webinar Ninja are the hard moments, the fires, the pivots, the times he had no idea how he'd get out of something. That mindset isn't just feel-good advice. It's a practical edge. When you stop treating struggle as a sign something's wrong and start treating it as the job, you get a lot harder to shake. AI is not optional anymore, and using it to figure out how to use it better is the move. Omar is building new software on weekends using Claude and Windsurf, no code, no development team. He's using Claude to write his prompts before he even opens the builder. What used to take years now takes a few weekends. He's clear that the people who are thriving right now aren't just using AI, they're building the habit of reaching for it first, staying curious about its limits, and using it to multiply everything they already do well. If you're still on the fence, he'd tell you that fence is expensive. We also get into what it's actually like to sell a business, the 16 months it took, the emotional whiplash of feeling relief and then feeling lost, the NDA that keeps him from saying the number but also the fact that he blinked twice. Omar and Nicole's story of co-founding a company as husband and wife while staying married is one for the books too, and his 70/10/10/5/5 money formula is the kind of simple framework you'll want to write down. The closing of this episode is one of the most grounding things I've heard in a long time. Omar's billboard isn't a quote. It's a mirror. Because every time he was stuck, every time he hit a wall, the common denominator was him. Not the market, not the economy, not bad timing. Him. And once he stopped running from that and started taking full ownership, everything shifted. That's the energy Omar brings, direct, honest, and genuinely fired up about the game of business and the life you can build through it. If you want more of that, go listen to the full episode at https://caryjack.com/podcastin/ It just might be the reset you didn't know you needed. Connect with Omarhttps://www.facebook.com/ozenhomhttps://www.instagram.com/omarzenhom/https://www.youtube.com/@100mba/videoshttps://x.com/TheOmarZenhomhttps://www.linkedin.com/in/omarzenhom/ Find Omar on this website: https://100mba.net/ Connect with Cary!https://www.instagram.com/caryjack/https://www.facebook.com/SirCaryJackhttps://www.linkedin.com/in/cary-jack-kendzior/https://twitter.com/thehappyhustlehttps://www.youtube.com/channel/UCFDNsD59tLxv2JfEuSsNMOQ/featured Get a copy of his new book, https://www.thehappyhustle.com/book Sign up for The Journey: 10 Days To Become a Happy Hustler Online Course @ https://thehappyhustle.com/thejourney/ Apply to the Montana Mastermind Epic Camping Adventure @ https://thehappyhustle.com/mastermind/ “It's time to Happy Hustle, a blissfully balanced life you love, full of passion, purpose, and positive impact!” Episode Sponsors: If you're feeling stressed, not sleeping great, or your energy's been kinda meh lately—let me put you on to something that's been a total game-changer for me: Magnesium Breakthrough by BiOptimizers. This ain't your average magnesium—it's got all 7 essential forms that your body needs to chill out, sleep deeper, and feel more balanced. I take it every night and legit notice the difference the next day. No more waking up groggy or tossing and turning all night If you're ready to sleep like a baby, calm your nervous system, and optimize your recovery, go grab yours now at https://www.bioptimizers.com/happy and use code HAPPY10 for 10% OFF. =================================================================== My Green Mattress If you've been waking up with back pain, feeling stiff, or just not getting that deep, quality sleep. This might be what you're missing: My Green Mattress. It's made with clean, non-toxic, and eco-friendly materials, so you're not just sleeping better, you're sleeping healthier too. The comfort and support are on another level, and you can really feel the difference night after night. If you're ready to invest in better sleep and better recovery, check it out at https://thehappyhustle.com/mygreenmattress =================================================================== Ozlo Sleep If you've been struggling to fall asleep, stay asleep, or just wake up feeling actually rested, let me put you on to something that's been a total game-changer: Ozlo Sleep. These aren't your typical sleep buds. They're designed to block out noise and help your brain fully relax, so you can drift off faster and stay in deep, uninterrupted sleep. Perfect if you're a light sleeper or just want that next-level rest. If you're ready to upgrade your sleep and wake up feeling recharged, check out https://ozlosleep.com and save $80 OFF using code HAPPY.

CJ Gustafson sits down with Marten Abrahamsen, CFO of Vercel, at the NYSE to talk about running finance inside a hypergrowth AI company. They cover AI use cases in finance, rev rec, forecasting, KPI dashboards, PLG, consumption pricing, and Marten's “speeding tickets vs. parking tickets” framework for moving fast without losing control.—SPONSORS:Brex is an intelligent finance platform with AI-powered agents that capture expenses automatically, enforce policy before the spend happens, and close your books in minutes instead of weeks. 35,000+ companies like OpenAI, Coinbase, Anthropic, and DoorDash already run on Brex. It's time to get Brex AF. Learn more at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform that lets your product team ship new pricing without asking finance for permission, and your sales team close deals without creating downstream chaos. Check out their free tool at calculator.rightrev.com It scores your rev rec process, shows what's exposing you to risk, and tells you exactly where to focus before it bites you in the rear end. Check it out at https://calculator.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY has been part of Silicon Valley since it was just a valley, helping the most successful names in tech go from startup to exit to megacap. With teams across strategy, tax, audit, and transactions, EY helps you get your financials right early, long before your investors start asking for it. You build the next big thing, and EY will help you build it right. Learn more at https://www.ey.com/techstartupsSpendHound cuts your SaaS and AI spend by up to 30% using real pricing benchmarks across 10,000 vendors, so you always know what fair pricing looks like before your next renewal. Rated #1 on G2 in SaaS spend management, it's free forever for teams up to 1,000 employees. Sign up by June 12th and get $500 just for getting started. Go to https://www.spendhound.com/cj—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/martenabrahamsen/Company: http://vercel.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Speeding tickets vs. parking tickets3:21 Visa IPO in the financial crisis5:09 Going public has changed6:45 Private market: 22–24 trillion9:03 More or fewer public companies?9:48 Sponsors — Brex | Aleph | RightRev13:04 KPI dashboard on your phone14:12 Revenue flux via Slack and Notion15:37 RevRec tool: green, yellow, red17:56 V0 is a job requirement19:43 Speeding tickets vs. parking tickets20:33 Sponsors — Rillet | EY | SpendHound23:49 Very few one-way doors25:02 Finance in hypergrowth25:39 Three-scenario planning27:00 Honest with the board31:00 PLG + consumption at Vercel33:32 What Marten checks every morning34:03 Why RPO doesn't work here35:36 Holiday usage is up37:10 ICP shifted to solo developer39:22 Capital allocation in a fast market41:32 Growth compounds; margin can't43:22 SaaS gross margins: spicy take44:24 Cash-burning AI: 2026 vs. 202147:29 Are some hypergrowth cos destroying value?50:00 Lightning round50:11 Bank of Ireland mix-up51:10 Don't punt problems forward52:04 Finance software stack52:38 Expensed an oven53:12 Credits

You're tired of hearing “just build a SaaS” like it's easy, especially when you don't code, don't have a team, and still want something real that can actually make money. It can feel like everyone else has access to some secret playbook while you're stuck trying to figure out where to even begin. In this episode, Omar completely removes the gatekeeping and shows you what it actually looks like to build a real software business in a ridiculously short timeframe using AI. Nothing is hidden. He walks you through the exact tools, decisions, and steps he takes so you're not left guessing or piecing things together on your own. It's clear, practical, and designed to make you feel like this isn't some exclusive club, it's something you can dive into right now. If you've been waiting for proof that you can pull off your own AI-powered software build in a matter of hours, this is it. Click play at the top of the page and see how you can turn your idea into a real product faster than you thought possible. MBA2790 How To Build A Software Business With AI This Weekend. Zero Coding Skills Required. Must-Have Stack to Build Your Own AI App 1. Supabase 2. GitHub 3. Windsurf 4. Vercel 5. Claude 6. GoDaddy 7. Stripe 8. Kit Helper / Optional Tools to support your workflow 1. Wispr Flow 2. Google Forms 3. Chrome DevTools (Inspect Element) Recommended episode to explore: Can You Build A Profitable SaaS In 7 Days With Just AI? My Experiment With Proof! Watch the episodes on YouTube: https://lm.fm/GgRPPHi SUBSCRIBE YouTube | Apple Podcast | Spotify | Podcast Feed Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hey folks, Alex here, let me catch you up! I've had a feeling that this week is going to be crazy, as it started on the weekend MiniMax M3, then with Jensen announcing new RTX Spark, NVIDIA's first PC chip packing 1 petaflop of local AI power into thin laptops.A few days later at Microsoft BUILD, Satya & Mustafa from MAI dropped 7 AI models, completely pre-trained from scratch, including a new MAI-thinking-1, MAI-code and MAI-image 2.5 that started topping the image gen charts. Then other image models started racing to the top of the Arena benchmarks, IdeoGram 4 hitting becoming SOTA open weights image-gen model, and Reve 2 beating Nano Banana just a few hours after that. And then today, NVIDIA dropped Nemotron 3 Ultra, their latest 550B open weights model, data and training and Arena published a new agentic eval leaderboard and we got a new Gemma 4 12B. I've had the great pleasure to host Chris (@llm_wizard) from Nvidia, Peter Gostev from Arena and Karan from Nous Research (who were featured prominently by Jensen!) all on the show. Def don't miss this one! Let's get into the details. ThursdAI - Join the flock of folks who know what is happening in AI before everyone else.Open Source LLMs

In this episode of Run the Numbers, CJ breaks down how stock exchanges became the operating system of modern capitalism. From ship captains raising voyage money, to the Dutch East India Company's first tradable shares, to coffee house traders, the Buttonwood Agreement, market crashes, Robinhood, and GameStop, this is the story of how markets turned ownership into something liquid, global, and very, very human.—SPONSORS:SpendHound cuts your SaaS and AI spend by up to 30% using real pricing benchmarks across 10,000 vendors, so you always know what fair pricing looks like before your next renewal. Rated #1 on G2 in SaaS spend management, it's free forever for teams up to 1,000 employees. Sign up by June 12th and get $500 just for getting started. Go to https://www.spendhound.com/cjBrex is an intelligent finance platform with AI-powered agents that capture expenses automatically, enforce policy before the spend happens, and close your books in minutes instead of weeks. 35,000+ companies like OpenAI, Coinbase, Anthropic, and DoorDash already run on Brex. It's time to get Brex AF. Learn more at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform that lets your product team ship new pricing without asking finance for permission, and your sales team close deals without creating downstream chaos. Check out their free tool at calculator.rightrev.com It scores your rev rec process, shows what's exposing you to risk, and tells you exactly where to focus before it bites you in the rear end. Check it out at https://calculator.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY has been part of Silicon Valley since it was just a valley, helping the most successful names in tech go from startup to exit to megacap. With teams across strategy, tax, audit, and transactions, EY helps you get your financials right early, long before your investors start asking for it. You build the next big thing, and EY will help you build it right. Learn more at https://www.ey.com/techstartups—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNCJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.comSlacker Stuff: https://www.slackerstuff.com/Ben on LinkedIn: https://www.linkedin.com/in/slackerstuff/—RELATED EPISODES:A CFO Explains Secondarieshttps://youtu.be/pENvBuXhGukA CFO Explains the Diamond Industryhttps://youtu.be/fPrho7hvykAA CFO Explains Marketplaceshttps://youtu.be/LpbH9GpBrSY—TIMESTAMPS:0:00 The First IPO, and Why It Changed Everything2:50 Coffee, Buttonwood Trees, and the First Insider Trading Scandal6:31 The Railroads Built Your Month-End Close10:17 Sponsors — SpendHound | Brex | Aleph13:47 Buying Stocks on Credit, and How That Ended18:13 Merrill Lynch Goes to the Suburbs, and the Paper Almost Wins21:44 Sponsors — RightRev | Rillet | EY24:47 NASDAQ, Pets.com, and the Most Expensive Sock Puppet in History28:49 The Phone in Your Pocket Democratized Everything, For Better or Worse33:03 The Stock Market Was Never Really About Stocks36:47 Credits#RunTheNumbersPodcast #FinanceHistory #StockMarket #Investing #FinanceLeadership

Bob is doing a 24 hour marathon to try and take the world record in distance. The current record is 695 km by Dennis Klaaijssen, a fellow Dutchman. He's doing it for a Dutch charity called Spieren voor Spieren, which is fighting muscle diseases in children. The aim... 1000 km. Place : Brouwersdam in Holland,

The Information's Akash Pasricha breaks down Amazon's shifting ad strategy with Catherine Perloff and its increasing reliance on third-party firms to scale ads on Twitch, Goodreads, and IMDb. Mostly Media founder CJ Gustafson joins to dissect Alphabet's unprecedented $80 billion equity raise for AI compute and the market implications of Anthropic's confidential IPO filing. JetStream Security CEO Raj Rajamani outlines the cybersecurity and budget risks of internal "citizen developers" using advanced models like Anthropic's Mythos. Finally, Rocket Drew reviews Cognition's rebranding of Windsurf to Devin Desktop and explains why frontier AI models are learning to detect when they are being evaluated.Articles discussed on this episode: https://www.theinformation.com/articles/amazons-media-empire-quietly-taps-outside-ad-sales-helphttps://www.theinformation.com/newsletters/ai-agenda/cognition-aims-switzerland-ai-agents-app-makeoverhttps://www.theinformation.com/newsletters/the-briefing/googles-ai-fundraising-anthropics-ipo-optionSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction01:13 - Amazon leans more heavily on outside firms for ad sales11:12 - Alphabet to Sell $80B in Stock for AI Investment23:55 - Anthropic expanding Project Glasswing34:43 - Cognition rebrands Windsurf app for AI era

In this episode of Run the Numbers, CJ sits down with Ryan Roccon, CFO of Zapier, to cover AI hiring standards, automating month-end close, measuring ROI on AI spend, and why determinism still beats agents most of the time.—SPONSORS:EY has been part of Silicon Valley since it was just a valley, helping the most successful names in tech go from startup to exit to megacap. With teams across strategy, tax, audit, and transactions, EY helps you get your financials right early, long before your investors start asking for it. You build the next big thing, and EY will help you build it right. Learn more at https://www.ey.com/techstartupsSpendHound cuts your SaaS and AI spend by up to 30% using real pricing benchmarks across 10,000 vendors, so you always know what fair pricing looks like before your next renewal. Rated #1 on G2 in SaaS spend management, it's free forever for teams up to 1,000 employees. Sign up by June 12th and get $500 just for getting started. Go to https://www.spendhound.com/cjBrex is an intelligent finance platform with AI-powered agents that capture expenses automatically, enforce policy before the spend happens, and close your books in minutes instead of weeks. 35,000+ companies like OpenAI, Coinbase, Anthropic, and DoorDash already run on Brex. It's time to get Brex AF. Learn more at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform that lets your product team ship new pricing without asking finance for permission, and your sales team close deals without creating downstream chaos. Check out their free tool at calculator.rightrev.com It scores your rev rec process, shows what's exposing you to risk, and tells you exactly where to focus before it bites you in the rear end. Check it out at https://calculator.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cj—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/ryanroccon/Company: https://zapier.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:Ryan's First Appearancehttps://youtu.be/VIZ_RzfV78IChris Byington, Head of Data @ Superhumanhttps://youtu.be/ydH38JnWfww—TIMESTAMPS:0:00 Preview and intro2:11 Ryan's scope at Zapier3:17 Why the finance guy runs ecosystems6:19 AI is a required hire competency6:46 The four levels: unacceptable to transformative8:48 How Zapier tests for it11:50 Favorite interview question13:06 Sponsors — EY | SpendHound | Brex16:18 AI changes the hiring profile17:07 Support becomes customer-facing engineering18:44 Where AI beats deterministic Zaps21:00 80-90% of builds are deterministic22:41 Sponsors — Aleph | RightRev | Rillet26:00 Month end close on Zaps27:40 Time saved is a leading indicator29:53 AI token costs: COGS or investment?31:51 Performance issue or measurement issue?33:41 Partner ARR: chasing the wrong thing35:56 Build around what changes the decision37:17 The initiative sizing coach40:00 Escalate on magnitude, not certainty41:55 The 9,000 integration story44:02 M&A process and minimum model46:19 Cash vs. stock: incentive alignment48:06 CFO's role in M&A: show up early49:18 Credits

The new AIEWF website is live! CFPs close in 2 days and we will run our first New Engineer Orientation this weekend, get your tickets booked ASAP as they -will- sell out. Take the AI Engineering Survey and get >$2k in credits and free AIE WF tickets!One of the central tensions in the agents industry is that even while there are major decacorn agent labs like Sierra, Decagon, Notion and Cursor being built up, it is also true that it has never been easier to DIY agents, with a plethora of agent frameworks like LangGraph and Pydantic and Flue, and managed agents from Anthropic and Gemini and Amazon. There has been a wave of companies building their own background agents from Shopify to Stripe to Paradigm to Razorpay, and even Cognition's friends Ramp have built their own coding agent with other friend Modal.You'd think Cognition might feel a bit threatened, but they're not - even after all this, they were way oversubscribed for the $1B Series D they just announced:Walden Yan, coiner of context engineering and Chief Product Officer/Cofounder of Cognition, invited OpenInspect's Cole Murray to talk about why the Devin is in the Details.Full conversation live on the pod today: In retrospect, async agents were the most AGI pilled bet you could make in 2024 - the models weren't good enough yet to vibecode, and people didn't trust AI enough to let it rip, nobody (including early Cognition) was sure about the form factors. Now it is obvious:* The first wave of AI coding tools made the developer faster but remain heavily in the loop. Copilor and Cursor's tab autocomplete are prime examples However, the workflow was still heavily centered around and bottlenecked by the developer's local workflow: a developer in an IDE, watching the model, accepting or rejecting changes, and pushing code one interaction at a time.* The second wave was local agents: Claude Code, Windsurf, Cursor's agents pane: first one and increasingly many terminals all running concurrently.* The current Age of Async Agents points to a different future focused more on agent orchestration which drives end-to-end development.According to previous guest Steve Yegge, there are finer-grained 8 levels to agent adoption, but we have collapsed it into three.As Cursor's Michael Truell put it in The third era of AI software development:Cursor is no longer primarily about writing code. It is about helping developers build the factory that creates their software. This factory is made up of fleets of agents that they interact with as teammates: providing initial direction, equipping them with the tools to work independently, and reviewing their work.The agent should not sit solely inside the developer's flow. It should be setup to work in the background so that you can give it a task, a repo, a machine, a shell, a browser, tests, memory, and review loops to go do the work somewhere else.In less than a year, the sentiment has shifted from avoiding multi-agent systems:to suggesting approaches that actually work:From coining “context engineering” to building the infrastructure behind Devin's 7x PR growth and jump from 16% to 80% of commits across Cognition repos, Walden Yan has had a front-row seat to the background-agent shift. In this episode, Cognition co-founder and CPO Walden Yan joins swyx alongside Cole Murray, creator of OpenInspect, to unpack why everyone is building their own Devin, what changed after the December 2025 model inflection, and why “spec to pull request” is now becoming a real production workflow.We go deep on the architecture of background agents: harness-in-the-box vs out-of-the-box, why Devin separates the “brain” from the machine, why repo setup is still one of the hardest problems, why Docker is not always enough, and how full VMs, snapshots, scoped secrets, GitHub bots, Slack integrations, and video-based testing all fit together. Walden and Cole also dig into memory, MCP limitations, multi-agent orchestration, AI code review, SRE auto-triage, PMs shipping code from Slack, Windsurf 2.0, hybrid frontier/sub-frontier systems, and the real failure mode of uncontrolled vibe coding: your codebase regressing to your worst engineer.And as agents eat software… and software eats the world… you can draw the conclusion on what is next:We discuss:* Why the engineering world is waking up to background agents and cloud agents* The December 2025 model inflection that made spec-to-PR workflows practical* Devin's 7x merged PR growth and rise from 16% to 80% of commits* Why Cole built OpenInspect as an open-source background-agent system* The economics of $20/seat agent products and why monetization is tricky* What Cognition actually sells beyond Devin: infra, onboarding, integrations, and adoption* Harness in the box vs out of the box, and why architecture matters* Why Devin separates the brain from the machine for security and permissions* Repo setup, scoped secrets, Docker Compose, and agent-ready dev environments* Why full VMs matter when agents need to run real applications and test them* Android, macOS, Windows, nested virtualization, and machine-specific agent work* Why testing is much harder than “computer use”* Screenshots, video verification, and the “I know it works” merge moment* GitHub UX, Devin Review, AI reviewers, and agents responding to PR comments* Why MCP alone is not enough for first-class Slack and enterprise integrations* Memory, Knowledge, skills, Claude.md, and why retrieval is still unsolved* Devin's auto-generated memories and the challenge of memory pruning* Always-on agents as permanent PMs for issues, tickets, and product areas* Sub-agents, meta-Devin management, and what multi-agent systems actually add* Why pure auto-merge vibe coding breaks down after about two weeks* AI code smells, lint rules, reward hacking, and Semgrep for agent-written code* GitAI, inline context, and preserving the “why” behind code changes* Local testing, mock servers, older codebases, and preparing companies for agents* Windsurf 2.0 and the handoff between local foreground agents and cloud background agents* SRE auto-triage, support workflows, and agents as first responders* PMs, marketing, and non-engineers creating pull requests from Slack* AI agent budgets, $1k-$5k per engineer spend, and hybrid frontier/sub-frontier systems* The rise of autonomous coding factories and who Cognition is hiringWalden Yan* X: https://x.com/walden_yan* LinkedIn: https://www.linkedin.com/in/waldenyan/Cole Murray* X: https://x.com/_colemurray* LinkedIn: https://www.linkedin.com/in/colemurray/* OpenInspect / Background Agents: https://github.com/ColeMurray/background-agentsTimestamps00:00:00 Introduction00:00:43 Why Everyone Is Building Their Own Devin00:01:57 Devin's 2025 Ramp: 7x PR Growth and 80% of Commits00:03:49 OpenInspect and the Rise of Open-Source Background Agents00:07:59 What Cognition Actually Sells Beyond Devin00:09:56 Background Agent Architecture: Harness In vs Out of the Box00:12:08 Separating the Brain from the Machine00:14:07 Repo Setup, Secrets, Docker, and Full VMs00:19:13 Why Testing Is Harder Than Computer Use00:22:40 Video Verification and the “I Know It Works” Merge Moment00:23:19 GitHub UX, Devin Review, and AI Code Review00:25:42 MCP, Slack, and Enterprise Agent Integrations00:28:59 Memory, Knowledge, and Always-On Agents00:36:16 Sub-Agents, Multi-Agent Orchestration, and Meta-Devin00:43:55 Vibe Coding, Auto-Merge, and Codebase Decay00:48:38 Agent Infra, VPCs, Cloud Providers, and Fast VM Restore00:52:25 AI Code Smells, Reward Hacking, and Code Review Systems00:56:10 Making Codebases Agent-Ready00:58:30 Windsurf 2.0 and the Local-to-Cloud Agent Handoff01:01:15 SRE Auto-Triage, PMs Shipping Code, and Agent Use Cases01:04:32 Agent Budgets, Hybrid Models, and Autonomous Coding Factories01:06:51 Hiring at Cognition and OpenInspect Consulting01:07:45 OutroTranscriptIntroduction: Walden Yan, Cole Murray, and Context EngineeringSwyx [00:00:00]: All right, we're in the studio with Walden Yan, co-founder of Cognition, CPO.Walden [00:00:08]: Happy to be here.Swyx [00:00:09]: Which is a cool title. And coiner of context engineering.Walden [00:00:15]: Although I think there are many people who'd used the terms in various ways beforehand, but I did find that people, both internally and externally, enjoyed the upgrade from prompt engineering or model wrapping into maybe a more thoughtful way to build agents.Swyx [00:00:33]: For those who haven't caught up on that, I have on screen the Don't Build Multi-Agents post, which you should go read on and we might refer to, and Cole Murray, who created OpenInspect.Cole [00:00:43]: Great to be here.Swyx [00:00:43]: So let's talk about it. Everyone is building their own Devins. What's going on?The December Shift: From Handholding Models to Autonomous PRsCole [00:00:51]: So I think the engineering world is waking up to this idea of background agents, cloud agents, whatever you'd like to call it. And I think we saw a shift around the December timeframe of 2025, where the models Opus 4.5 and GPT 5.2, they reached a capability where we moved away from handholding the model and being able to actually more or less autonomously drive the model. And what I mean by that is that we could pretty much go from a specification to a completed pull request, assuming the spec was good enough, with very little friction. And that paradigm alone, I think, changed a lot of how we interact with agents, and opened this world where background agents became more practical.Swyx [00:01:41]: I think for Cole, everyone experienced this in December, but I feel like there was just this increasing ramp, right? There was this moment which was, I think, Sonnet 3.7, where, You guys rewrote Devin in one night or something. So describe 2025 or how it felt from your side.Walden [00:02:01]: In retrospect, we always thought it was ramping up, but then even now, over the last three, four months from today, it's been ramping up even faster. So it's almost funny to be talking about how, big of a leap Sonnet 3.7 was, and honestly, a lot of it was stripping out parts of Devin that were no longer needed with that jump in of intelligence. But I also just think that a lot of the recent leaps, especially, you look at, models like Opus and the latest GPT models, they are reaching levels of autonomy where people are actually finding that they actually can just be hands-off. And people who were once debating, “Oh, do I need to be in the weeds with my model in the IDE? Can I just completely move it off into the cloud?” That's a more serious conversation, and we've seen that in all of our growth charts. Internally there's this funny graph where our usage has, of PRs, our merged PRs, has grown 7X since I forget what it was called.Swyx [00:02:57]: I think Dev, maybe tweeted that. Yes.Walden [00:03:01]: it grew like 7X over, the last, I think it was, two months, three months, something like that. And then you see our engineering headcount growth. It's, gone up by, 10% or something.Swyx [00:03:11]: We were, we were afraid To release this. So this is Devin commit percentages on all Devin repos, was 16% in January and now 80% in March.Walden [00:03:25]: It's a big shift right now. And so it makes sense that a lot of people are now thinking about, buying Devin, but also maybe, trying to build their own and there's Lots of I have a lot of fun building Devin, so I can see why other people would want to build their own cloud agents as well. Matt, well, maybe it's good to hear, what initially inspired you to try to build OpenInspect?OpenInspect: Ramp, Cloud Agents, and Open SourceCole [00:03:49]: OpenInspect came about, through primarily my clients observing how they were using tools like Claude, OpenAI's Codex at the time, and seeing some of the friction that they were having with it. Primarily the Claude was being used through Slack, and a big issue they ran into was that the sessions that were launched were specific to whoever called it via Slack. And so if a PM was the one who invoked the session and they would then go to pass context to engineering can't see the session. And that in itself was a deal breaker because the PM, “Hey, engineering, can you jump in?” But there's nothing to jump in on unless they're copy-pasting out or the single response that came back. And so seeing some of these problems, I had built a similar architecture internally, just to experiment with, test out different ideas as this trend of moving off of localhost was starting to become, And as Ramp released their blog post, I had a lot of the pieces for this already in place, and just thought it would be funny to, see what Claude could do just purely from the blog post. And on my X account, there's actually a thread of where I live tweeted, going through thisCole [00:05:14]: comparing GPT and Claude as both of them are going through it.Swyx [00:05:17]: On the announcement thing or something else?Cole [00:05:19]: right after it got released. We can put it in the show notes. Yeah, it was helpful that I had already knew how to verify the system. I knew what I was looking for. I think Ramp did a great job of really illustrating, the technical aspects of how to build something. It was much more than just like, “Hey, we built a great system.” It was, “And here's how you can build it too.” And so, I resonated a lot with that, just with the problems that I was already seeing, and I thought that, looking around, I didn't really see anything in the open source community that, met this type of system. I think there's a lot that run, in localhost like Superset, Conductor, and many others.But nothing that was actually running in the cloud. And so, I built it, and I thought it was interesting to just open source it and allow anyone to then have a foundation that they can mix and match on top of.The Business of Background Agents: Open Source vs. DevinSwyx [00:06:16]: So literally after Devin was launched was, there was OpenDevin Which became All Hands. I don't know if you tried that orWalden [00:06:22]: I was going to say, one of the things that interested me a lot with OpenInspect was, you didn't try to go make it then something you monetize. There are a lot of, I think, these open source projects would then go and really try to, raise VSwyx [00:06:36]: That's why no OpenDevin. Yeah.Walden [00:06:38]: yeah, and how did you think about that? I thought that was very interesting.Cole [00:06:44]: I thought, and just what I had seen across my clients, was that having a background agent system is going to become a critical infrastructure within their company. And so because of that, I think that I wanted to open source it so that they could fork it and put in whatever customization they wanted. To that question though, I get asked all, “Oh, are you going to raise? Are you going to turn this into a service?”Walden [00:07:08]: I'm sure you've gotten offers.Cole [00:07:09]: but primarily I don't want to do that for a few reasons. One, I think that I don't want to compete for, $20 a seat. I think that is just a really difficult business. I think it's very easy to copy the main pieces of it. Again, I built this fairly quickly. And I think because you are not owning, I guess, the entire stack, it's hard to monetize. You have money being made at the sandbox layer with Daytona, E2b, many other players. You have money being made at the model layer. And you sit in this weird in-between gray area where what are you actually selling? You're selling, I guess, the infrastructure. You're selling, the integrations maybe.Swyx [00:07:55]: let's ask the guy. What are you What are you selling?Walden [00:07:59]: Well, yeah, there's multiple layers to this in practice, and actually it's funny you mentioned the infrastructure, ‘cause when we got started building Devin as well, we had to go figure out how to make the infrastructure as well because,Swyx [00:08:10]: You had to build this two years before everyone else,?Swyx [00:08:15]: Including, the model sideWalden [00:08:17]: It was not, it was not very polished at the start, when we just built it off of raw VMs from cloud providers like EC2, the boot up time was so slow, I think, And especially then, turning off the machines, saving them, and then to be able to bring them back up again when the, when you want Devin to wake up again later. It would just be out cold for like 10 minutes because that's just how long these systems took. They were not built for this repeated down and up usage. And so we actually had to go do all of that. And as a result now, one thing we offer when we go and sell Devin to people is, you don't have to worry about all the compute side of things. We'll make it work. We'll make it work in your cloud if you want it to. But aside from the product, and I want to go into the agents and the tuning of the intelligence part later, but I think a big part of what we do at Cognition as well is to just make sure that your company learns and uses and adopts these coding agents. ‘Cause I think for especially the largest enterprises in the world, you find that there is a lot of people who want to move over to using AI for their day-to-day workloads. But because of the way projects are planned, because, not everyone is literate in using AI in these ways, having a team of engineers who can actually go in and onboard you, set up all the integrations you need, the automations you need to really get to that level of, leverage with AI, is super helpful. And so We do that. We show thought partners to the customers that we work with as well.Swyx [00:09:56]: So let's talk about, architectural stuff. I think that's always, that is something that was the topic of conversation between the two of you. Is this, the mental model that you want to start with or something else? I'll just leave the floor open to you guys.Agent Architecture: Harness in the Box vs. Out of the BoxCole [00:10:11]: I think, maybe we can start here as just a general what are the pieces of a background agent system. And then maybe we can go into some of the nuances of, Decisions that you can make.Swyx [00:10:22]: But I guess I also Like, what, maybe what Walden is saying is the agent is like in this open code box, I guess. Right? This is infra, and then there's, that's the agent. And you had this discussion about whether you put the agent in here or in Out externally. Can you tease that out?Cole [00:10:39]: In a background agent systems, you have a decision to make of where the agent is actually going to run. This is typically described as the harness in the box or out of the box. With running the agent in the box, you're making some trade-offs by doing that. The negative trade-off you're making is primarily security. Because the agent is running in that box, unless you otherwise design it, all of your secrets need to go into that box as well. And given the nature of AI, it can be unpredictable, and you could very easily end up accidentally exfilling your secrets, or other unintended behavior. Now, the out of the box is the idea that we are going to have the actual agent running not directly in the sandbox, and we will have, quote-unquote, the brain of the agent running in some type of worker, control plane. That sandbox then is going to serve as the hands where the brain is basically operating and making tool calls into that environment to manipulate it. I guess other trade-off that you're making between the two systems is that, in my opinion, running it out of the box is much more complex because, you have state that has to be managed, whereas if you're running it in the box, all of the state of that agent is actually in the box, and yes, it's you could persist it elsewhere, but it's all localized and you have less concerns to worry about.Walden [00:12:08]: I think a lot of that, what you mentioned, is why we actually from the start built Devin to what we called separate the brain from the machine. The other thing that this allows you to do is reuse any existing infrastructure you have for dev boxes Perhaps. And so you don't have to worry as much about making a new type of dev box that has all the dependencies the brain needs, as you mentioned, the secrets the brain needs as well. One thing that we've seen some customers run into is, you have a GitHub app and you want Devin, your agent, whatever, be able to interact with GitHub through this application, but then you have different users with different actual permissions. If they are all interacting through the same GitHub app and there's no actual, separation between the system that decides, what it does and the actual secrets on the machine, then you run into an issue where, okay, it's hard to do the separation. But in practice, with Devin, it's much easier because we just say whatever you put on the machine, that is, the scope of basically what the user is free to do, what the agent is free to do. So only put the most scoped secrets on that machine, and then the brain is fully not accessible from the machine. So you don't have to worry about messing with the, any of the most secure parts of the brain if the user is free to do whatever they want with the machine.Swyx [00:13:31]: I was going to just bring, I have this, chart from OpenAI, where I don't know if this is, in the box, out of the box. That is something that they do use to describe it. And then also recently Anthropic did, managed agentsSwyx [00:13:44]: Which is, this is their thing. I don't know. It's all, it's all variations of the same pattern, right?Cole [00:13:49]: So this would be out of the box.Swyx [00:13:51]: Which, is preferable for them because it's less work?Cole [00:13:56]: I would say it's more work.Swyx [00:13:58]: It's more work?Cole [00:13:58]: But it, in my opinion, it is the better architecture of the two. It's just, you're taking on a bit of complexity by doing that.Repo Setup, Docker, and VM-Based Development EnvironmentsWalden [00:14:07]: One thing I've not seen a lot of other players do well is how do you manage what's actually on the box? And this can be complex for many reasons. Let's say you have a big repository that's changing and updating a lot with changing dependencies. How do you make sure that the working environment of the agent actually stays up to date, has all the credentials it needs to, let's say, run the app and test it, and all the things you want your autonomousSwyx [00:14:34]: So a repo setup.Walden [00:14:35]: Exactly. So in, internally At Cognition, we call this repo setup.Cole [00:14:39]: The hardest part ofWalden [00:14:40]: It's been a perennial problem since the start of the company, of how do we help people get this set up? Because not everyone just has, working cloud environments working out of the box. And do you find this to be a common problem withSwyx [00:14:53]: How do you solve it?Walden [00:14:53]: Your clients?Cole [00:14:54]: This is a very common problem, and through my consulting, this is a lot of what I help teams do. A lot of teams don't really have great developer environment setups, if any. A lot of the times it's, “Go talk to Bob and get the secrets,” and that obviously doesn't work when the agent needs to actually set this up. And so a lot of that, most teams are using Docker Compose or some type of microservices. And so for theSwyx [00:15:19]: Even in prod?Cole [00:15:20]: Not in prod. With the OpenInspect, you are using this primarily to interact, and make code changes. There is other use cases, but you can hook, whether through CLI, MCPs, other tools, you can then hook that into your production systems primarily for, SRE type use cases. But you are not, necessarily, trying to test your prod internal microservice through the system.Walden [00:15:48]: And you mentioned Docker Compose. I think one direction we saw some of our friends take early on was, using Docker containers as the level of abstraction for their models. There's lots of reasons, I think, why Docker containers are not great. One thing is, Docker container's not really a true security boundary, for one. But the other is, if you are running real applications, a lot of times those applications use Docker, and then you have to think about Docker in Docker, which is, really weird. And so I think part of, the really hard challenge of getting VMs to work, why did we do that? Well, it was because we realized that you actually needed, full VMs to be able to do these types of things. And especially nowadays where there's actually value in running the application and clicking around and sending you screen recordings of these things. The value just, keeps adding on top of that. But it is a decision I see people run into when they try to build their own systems, is, “Oh, do we, in addition to this, do we put the agent in the machine or out of the machine? Do we use Docker? Do we use something else?” What do you recommend people nowadays?Cole [00:16:57]: I think Docker is a good solution for maybe not running the agent, but running your infrastructure, because that is more or less the same setup your engineers are probably already using. If they're not, then I don't know what they're using. But they're probably already using Docker Compose.Swyx [00:17:14]: I've always had a small candle for web containers. I don't know if you guys have tried them before.Swyx [00:17:19]: To me, they were, supposed to be like Docker Light.Cole [00:17:22]: Is it?Swyx [00:17:22]: I don't know.Cole [00:17:22]: No, I haven't tried it. But yeah, I think any environment that you've set up that is a good experience for your developer naturally lends itself to being easy to set up for the agent. And once you figure out that local developer story, you've more or less solved the agent in a sandbox, environment setup. OpenInspect does have hooks as well, where you can, run a setup SH script that will pre-install everything. You can then pre-snapshot that build so it starts instantly, and then there is a second hook to actually then, restore the state of the sandbox when it comes back. And so you can already have all of those microservices running and basically get the same experience that you would on your machine within the sandbox.Testing Agents: Computer Use, Screenshots, and Real App WorkflowsWalden [00:18:08]: Another thing that we've been thinking a lot about is like Different VM service offerings. Have you had customers where they needed like macOS specific VMs or like Windows specificWalden [00:18:20]: VMs?Walden [00:18:22]: There are like many technologies in the world that only work on specific types of machines, right? If you're building a.NET application that has to run on Windows or like, maybe more commonly if you want to build iOS or macOS Does that workSwyx [00:18:32]: Does Commission supportSwyx [00:18:33]: Choices like that?Walden [00:18:35]: The fundamental architecture we do, because we do the separation, it does support, but the actual work in progress is happening right now on these. Another thing that we've actually recently added support now for, it's in beta, is doing Android development. To do that, we needed to support, I think, nested virtualization within our machines because the VM itself is like a, is a virtualized Firecracker instance, and then you had to then run another Android emulator inside. And there's like weird performance issues that like, it, which is why it's like still in beta. We have to think through these problems, but it unlocks a lot for anyone who wants to do Android development.Swyx [00:19:13]: I was trying to find like a reference video for the testing thing. I couldn't find it, but I think you worked on the testing, capability. Why call it testing and not like computer use or I don't know, it's, what's the general Category of problem?Walden [00:19:26]: I think that when people think about the ability of an AI to run your app and test it, I think they actually over-index on the computer use part of it because computer use in my mind is the literal, okay, you want what button you want to click. Can you emit the right coordinates to go click that button? I think testing is actually a really interesting likeWalden [00:19:48]: Problem-solving, challenge for these AIs because if you wanted to do arbitrary testing, imagine you make a change that spans the frontend and the backend, maybe, even some other like even more deeply nested service. To actually test that change, we have to reason through what-- how do you first run these applications to orchestrate with each other with the right version of the code? Then, okay, how do I trigger the feature or how do I make the thing actually happen? And this can get arbitrarily hard, maybe you have to be an admin. Maybe a certain thing has to be feature flagged on. Maybe, you have to like run two sessions and then send us a very specific word into one of them to trigger a specific behavior. And figuring out how do you do that requires a lot of code base context, requires, a lot of orchestration that we've specifically done. And in some cases, we found that you actually, no one frontier model can actually do this full end-to-end task itself.Walden [00:20:42]: We've seen cases where we actually had to orchestrate different frontier models together to solve this problem together. That is where we spend most of our time when we think about this testing problem, not so much the computer use part. Computer use for what it's worth has gotten a lot better with recent models and it's made that part of the job certainly easier.Swyx [00:20:58]: Especially with like even 4.7, that they released yesterday, apparently like way better in terms of the vision stuff, which is going to be encompassing computer use.Walden [00:21:08]: Having evals for all these as well is something that like takes a while to build up. And having the evals be right is tricky as well. Do you ever see like, clients who are building their own agents have to start standing up evals to make sure things don't regress?Swyx [00:21:25]: Not so much evals in the traditional sense, but specific to the testing part that has just gone in. I just added support for screenshots And in theory you can also do video. I need to put in a plugin to do that. But they do show up natively, and it was a very heavily requested feature, especially after Cursor's recording came out. I think that was very enlightening for everyone of like, “Oh, this is a very good feature to actually have.”, I think with Devin you guys have had this for a while.Swyx [00:21:57]: Oh, yeah. See how screenshots work. Yeah, I don't know if there's anything, super and not obvious. It's like once what feature to build, you can just prompt it and it Will mostly work.Walden [00:22:09]: I think to Walden's point, though, the computer use is a subset of the larger testing problem, and I think that's very specific to the code base that you're working and it's not something that, out of the box that you could just solve it. The-- you do need the code base context to actually know how to test it. And I think in the case of a background agent system, you fortunately do have that code base locally that what is changing and could then inspect it and use that to drive the model.Swyx [00:22:40]: For those who haven't seen it before, this is an example of how it works. You, after the PR is done, you click testing approved, and then it sends you back a video. What I really like is that it labels, It's very small here, but it actually labels what it's testing. And then it-- and then you actually see the cursor and everything. So I don't know, yeah, the engineering in this, just Whatever you want to show. ‘cause this is like, this is one of those like, oh, few of the AGI moments, right? ‘cause Once I look at this, I actually don't I wish I can just merge inside Of Slack instead of going to GitHub ‘cause I don't need to see the code. I know it works.Walden [00:23:19]: Maybe a new feature in Cursor. Yeah, the annotations at the bottom was also a big difference for me when I, when I added those.Swyx [00:23:27]: It's just like, what am I looking at? What are you trying to demonstrate?Walden [00:23:30]: Exactly. There's a surprisingly long tail of small details that ends up making a big difference for this end metric of like how fast do you actually merge the code in. One experience that we spent a lot of time tuning early on was what is the right experience on GitHub for these tools. Because I think, most tools out there when you build the agent, you'll think about, oh, it'll create the PR for you. We try to take that a step further and say, “Oh, what if we actually made sure you could interact Devin, with direct Devin directly on GitHub?” And so we made sure that you can comment on GitHub, and Devin would actually receive those comments and address them back. But there's actually quite a bit of tuning you have to do here because you can imagine that actually like-We recently have Devin Review, for example. Devin Review will post comments on his own PR And then Devin has to then goGitHub Workflows: Devin Review, Comments, and PR AutomationSwyx [00:24:23]: He answers his own comments, which is Really loopy. So like, yeah, I like that it just updates here that it's, that I have commented But usually it's just me saying like, “Hey, merged, fix any merge conflicts.”Walden [00:24:37]: The, so when Devin fixes his own comments, you might be scared that, oh, maybe I'll infinite loop. But we've put a lot of work into making sure it doesn't, both by making sure that the comments are high signal, but also that the agent is thoughtful about what comments it immediately goes and tries to fix, and what comments it's like, “Wait a second, I think you're wrong.” Actually, that's one of my favorite moments is when Devin tells me that I'm wrong, when I try to get it to do something different. But tuning that behavior, actually makes a big difference in terms of how useful the actual GitHub experience is.Cole [00:25:06]: I think to touch on that as well, I think having the AI reviewer integrated into the system is a critical part of this background system. OpenInspect does have that. It has a GitHub code reviewer that you can control the prompt. It does do comments as well. It doesn't do them automatically yet. The capability is there, but it's not fully used.Swyx [00:25:27]: So you have to ask for it?Cole [00:25:28]: you do, yeah. You can tag it on GitHub, and then whatever you named your, GitHub bot, it will then follow up on it. It will then, if you have merge conflicts or whatever you have asked it to resolve, it will then resolve it, but it doesn't do it automatically yet.Integrations: Slack, MCP, and First-Party Agent InterfacesWalden [00:25:42]: Well, I'm curious, what is, the most common thing that people end up requesting, that they still need on top of OpenInspect when you help them go implement it?Cole [00:25:52]: I think a lot of it comes down to actually integrating it into the company. It's one thing to have the background agent system set up, but if it isn't actually integrated into your larger ecosystem, it isn't that useful. It is useful to be able to kick off sessions, but what we really want to be able to do is hook it into all of our other systems, whether that is the production database with read-only credentials, the logs, a Confluence or internal knowledge-based system. I think that is where I see the huge leap for companies, and that can be a challenge for companies as well who are maybe not familiar with exactly how to approach it, especially if they're in environments that have more compliance type things where, access control can be pretty big and how do you deliberately think about these problems, I find to be, one of the problems that comes with a system like this.Walden [00:26:46]: The thing we found is So, MCPs, obviously it has been like this, really big explosion of, oh, you can go, integrate it with all these different things. But to actually get the integration right and the and get the right experience, oftentimes we found that we had to go build our own ad hoc things. I think Slack is a great example of this. You could give your agent a Slack MCP and okay, it can post messages back to you on Slack. But we actually use Devin like a coworker in Slack, and that's how it's been built from the ground up. But to do that, you actually need to, support webhooks that come back, right? And then Devin has to respond in a natural way and then hopefully don't spam your threads too much and annoy the people in your company. So you got to tune that experience just right. Especially when there's a lot of back and forths, we find that we actually have to go beyond the simple MCP integrations in these places.Swyx [00:27:39]: I just pulled up the MCP marketplace. I know this is a Fair amount of work. Is the answer to eventually take first party control of all the top MCPs? Is that theWalden [00:27:48]: I would love a world where you could have something that's more expressive than MCP. That, goes both ways, not just a set of tools, but a proper system that interacts back and lets it Have the right experience with all these interfaces.Swyx [00:28:03]: So there actually is sampling in the MCP spec, but nobody Uses it, right?Walden [00:28:07]: And so I think that's the other part is, actually we found that when the MCP spec starts to get too complicated, it starts to lose its original promise of Being like a simple one-step connect. Now then we have to go figure out how to support all these different variations of things and It starts to look a lot like just building the first party integrations in a lot of these cases now.Cole [00:28:29]: I think it matters, too, how critical it is to your company, right? If this is something that nearly every session is going through, it probably makes sense to own it so that you can make optimizations on top of it Versus just whatever is off the shelf.Swyx [00:28:43]: Awesome. Other than MCPs, what else, sorry, well, I don't know if that's Narrowing in too much on, integrations. But what else? What other elements of building OpenInspect or Devin that you guys really sink on?Memory and Knowledge: What Agents Should RememberCole [00:28:59]: I think, a problem that comes up very frequently is this idea of memories or knowledge base.Swyx [00:29:05]: Oh, boy. How do you solve it?Cole [00:29:08]: so not solved yet, is the short answer.Cole [00:29:11]: it's something, there's a open issue for it, someone asking about it.Swyx [00:29:16]: There's, I, D Wiki hasn't indexed anything about memory yet.Cole [00:29:20]: how I'm seeing it solved across my clients is primarily through skills. I find that skills can be a good gap within that or updating Claude MD, but I think memory as a whole is a pretty unsolved problem, and it is why I've been hesitant to add it. I think there is parts of memory and that can be addressed, but I think as a whole it's a very difficult retrieval problem.Swyx [00:29:44]: Oh my God. RAMP didn't write anything about memory? I see zero search results.Walden [00:29:50]: No. Memory can be quite tricky to get right because it's the retrieval, but also the generation of the memories that can be really tricky. You don't want it to just like Remember very specific details.Swyx [00:29:59]: Walk us through the Devin memory journey because I know there's been a journey.Walden [00:30:03]: the first version of memory that like stuck around for a while was A system we have called Knowledge. And the idea was we wanted it to pick up things over time and not need the user to be proactive about teaching Devin things. So, okay, any time you remind Devin, “Wait, no, that's not quite the way you're supposed to use Git”Like, we actually want Devin to say, “Hey, do you want me to actually just remember this for the future?” And for you to just basically quickly approve or reject and for it to build up over time. ‘Cause I find that, 95%, I think, or some crazy stat like that of the memories that Devin has are all through these auto-generated things. Very few people actually just want to sit down and write big docs on Here's how you're supposed to work with the technology, et cetera. The generation and the retrieval has been something that we've been trying to tune a lot over the years. Generation, you don't want it to remember something like, if you asked one time to like, “Oh, please open as a draft PR,” you don't want to be like, “Oh, everyone forever now should get their PRs as draft PRs.” But you do want some, conveyor. Maybe you want to say like, “Oh, Cole generally likes, things to be created as draft PRs.” Same with retrieval, if you have thousands of these memories, how do you actually make sure they're retrieved at the right time? And that can be quite tricky to do right without exploding the context with a bunch of useful yeah, useless information. Surprising amount of just, eval work to just make sure that, memory is, remains a reliable system as new models come and go.Cole [00:31:31]: Do you have anything that you could share on, memory pruning? And like the temporal aspect of memory?Swyx [00:31:36]: Deleting and forgetting?Walden [00:31:39]: The, today, the, So the things they could do is it could edit memories. And so if your memory used to say like, “Oh, Cole likes to open everything as like a draft PR,” then you can imagine, “No, don't do that.” And then it'll say, “Oh, do you want me to update the memory to be Cole now want everything as, open PRs?” I think that at the same time we don't know if this is going to be the final version of the system. Whatever we have here will probably, translate into the new system that we'll be coming up with. But I think one big difference between two years ago and today is these agents are really good at using anything that resembles a file system natively. And so part of us are, is thinking, “Oh, should we rebuild memories to feel more like a file system that we let the agent navigate on its own?” That's been an interesting exploration. Also similar ideas in the scale space.Swyx [00:32:35]: I am pulling up OpenClaude's memory thing right now. So memory, OpenClaude has like this like daily memory journal thing, right? And you can I mean, that is a file system you can grep through and is a source of truth. I don't know if it's the best. It's probably super noisy, but at least, if you lose something you can discover it or you can apply some, forgetting algorithm to, more ancient memories that don't get recalled again or something. I don't know.Walden [00:33:01]: One thing we've been trying to do to push the boundaries of how you use agents at your company is letting an agent basically have a very similar file, a memory.md or something, and just like be your permanent PM for a specific set of issues maybe. So we have like some Slack channels internally, maybe a Slack channel dedicated to, a specific product like DeepWiki maybe. And you can imagine that, or you want a Devin that never stops, it's just always awake, but it has this like memory dock that it can just maintain for itself about, okay, what are like the number one priorities of what we have to fix and prioritize? Who is responsible for some upcoming work? Maybe they'll even Devin will even tag you on some recurring basis. And so it's been an interesting move to see, okay, how can we actually use Devin for more than just engineering? Can we actually upstream above the engineering process and maybe it's just Devin creating tickets, which then maybe some humans do, but then maybe other Devins do.Swyx [00:34:00]: One of my more fun automations is go research competitors and just suggest stuff to me on a weekly basis. That's the automation. I can't find it right now, but basically it just like, “Look at competitors and suggest things.” “And here are three things that you've suggested that I don't want any more of,” and you just stick that in the prompts. But like I wish actually So for like when I, for example, when I reject a PR, I wish that it updated memory so that I can then just not have to go up, go back and update the scheduled, sync, but anyway, feature request.Walden [00:34:31]: what? We might change it soon. I guess OpenInspect, in the time you've been around, has there been anything you tried to implement but then you had to like undo and like do a different way?OpenInspect Architecture: Webhooks, Control Planes, and Agent StateCole [00:34:41]: Nothing yet, but something that is on my mind. The initial way that I built it was that each of the integrations lives as its own package. And so you have The Slack bot, which is what's handling the webhooks, and then is basically interacting with the control plane. As I'm seeing the system starting to be more integrated, specifically with the GitHub bot integration, I'm considering bringing that all into the central control plane because especially now I want to start, And a request that I'm getting is the ability to monitor, the actual, pull requests being merged, as well as just tracking ofSwyx [00:35:19]: What do I have open?Cole [00:35:21]: What do I have open? How many of these are getting merged? How many comments are showing up? To just understand the health of the system. And so in the case of a GitHub app, you only have one webhook. And so then it's a question of do I put that webhook in that GitHub bot package? That's weird. It doesn't really make sense to live there because that package is more for like the code reviewer. Or do I like centralize it? So that's something that's on my mind of, making that decision. I think the other one we touched on earlier is the harness in the box versus out of the box. I think long term the architecture will eventually come back out of the box. Some of the newer tools that I've added are calling back into the control plane so that you don't have the secrets in the sandbox. And so I think long term I probably will pull the actual, agent out of the box, but I think for now it's fine.Subagents and Multi-Agent Systems: When Parallelism Helps or HurtsSwyx [00:36:16]: Just, a quick question on pulling the agent out of the box. I'm One thing I'm very bullish on this year is agents calling other agents or spawning sub-agents or Whatever you want to call it. Does that make it harder or easier? I can't tell. Because if the harness is in the box, you can just spin up more boxes. If the harness is outside the box, then you're, it's less easy because you are, you have a unicorn pet of a, of a harness that's, living outside the box.Cole [00:36:45]: In theory it would be the same way, right? Whether, one agent has launched many, sub-sessions within it, OpenInspect, for example, can launch sub-sessions and actually create other environments and then monitor them. In the case where it is out of the box, that would basically just be an additional session that's running. And so that session is also running outside of the box. It's running in your worker plane, wherever you're running this. And then you really just have to think about how does your top level agent then interact with it. I do think it can be more complex, just ‘cause again, you have now a more difficult architecture. But I think if you figured it out once, it's probably fine.Swyx [00:37:26]: Well, then I'm just, throwing it open to you in terms of, I call this like meta Devin management. Which is like the, Devin's calling Devins or Devin scheduling Devins or querying trajectories or anything like that. What have you built or unshipped, anything?Cole [00:37:46]: I think one of the surprising things we've seen is that a lot of the ways that, these, separate agents work with each other, and you want them to, parallelize their work, has still mostly followed the same manager sub-agents regime. And a lot of people I think are excited about this world where you have swarms of agents that, talk with each other all over the place. We've actually given Devin an MCP so they can just go arbitrarily message other Devins And create new Devins, et cetera. But I guess, it somehow creates, a really chaotic world in that sense. And so we've still found that most practical use on a day-to-day basis has been one single Devin.Cole [00:38:33]: Figuring out how to segregate the work and get, have other Devins work on it in, a relatively isolated sense, each with their own boxes Not sharing machines, so there's, a very little room for conflict is the regime that you have to create today.Swyx [00:38:50]: I'll call out, the experiments from Cursor, right? This is Wilson Lin's work on Single agent to multi-agent, and you're obviously famously on the side of don't build multi-agent. But they went through the whole thing, only to arrive at, this Which is exactly what Devin has, I think.Cole [00:39:08]: I think there will be a revision to that post at some point AboutSwyx [00:39:12]: Tell us about itCole [00:39:12]: I think multi-agents were very much not at all possible a year ago. You do see more multi-agent experiments today, but you can argue, are they really multi-agents, or are they just just, tool calls,? There are people who, will create sub-agents to go look for XYZ file, XYZ implementation. Has really nice context management benefits because all of the tool calls and tokens that it spends then get collapsed back to just the answer for the main agent. There's a lot of benefits to doing this. We basically have Devin do this with Deep Bookie, make a call out to Deep Bookie, give you back the results, but that feels like a tool call,? It's not like these, two collaborators actually talking back with each, back and forth with each other. But I think the thing that gives me the most bullishness that multi-agents might actually be possible is actually what I said earlier about Devin will actually sometimes tell me I'm wrong and push back, and I think that demonstrates a level of maturity and communication today that makes a multi-agent world possible. One, can two agents who have seen different information come back to each other and actually figure out who is right, what is the correct implementation? They're not just, yes men. Claude, I guess is like, used to just say, what is it? “You're right,” or,Swyx [00:40:25]: “You're absolutely right.”Cole [00:40:26]: “You're absolutely right.” Yeah.Swyx [00:40:28]: The Have you seen, did you seeCole [00:40:29]: The age is overSwyx [00:40:30]: The Codex app troll in Topic? This is the Codex app. Inside of Settings, there's a little, there's a little Easter egg, right? So if you go to, the Themes or Appearance, right? There's all these, color codes, and the top is absolutely, and it's the Topic's colors. Which is such a troll. Anyway.Model Behavior: Pushback, Adversarial Prompts, and Agent SkepticismCole [00:40:53]: I love that Easter egg. Did you discover that yourself?Swyx [00:40:54]: No, it was, someone was, tweeting about it And I was like, I was like, “Is this true?” Because, sometimes people just tweet stuff to, get a rise out of you. But yeah, there you go, in Topic colors.Cole [00:41:06]: Yeah. So yeah, we're out of this regime where, it just says you're absolutely right, and they can have real conversations and real back and forths.Swyx [00:41:13]: You can prompt it as well to be more adversarial or whatever. Yeah. Okay. Yeah, that, I mean, to me, that is more intelligence, right? That is not just something that's, a dumb tool, it's actually pushing back on you I think. Yeah.Cole [00:41:24]: when you mentioned, of course, the blog posts. There was one blog they had where they fed a swarm of agents together and built a browser.Swyx [00:41:34]: That was I think that was the one.Cole [00:41:36]: You can have, likeSwyx [00:41:37]: I think it's the same oneCole [00:41:37]: Creation of it. We found a surprising success of, don't do a swarm or anything, just have one Devin, it does its own context management. Just let it keep running for a while and give it some crazy tasks. I think we asked it to, rebuild, a Windows OS system. And it managed to do it just like, going on for long enough. It'sSwyx [00:41:55]: Was this Andrew's thing?Cole [00:41:58]: there were lots of demos that we ended up not posting, ‘cause at some point we'd just be posting way too much a bunch of, Demos. But I love that because it shows that I think the multi-agent thing still has, a bit of exciting sexiness to it, which is maybe still beyond still, the actual delta it adds to the capabilities of these systems. But it's absolutely the future. I think we're heading in that direction and we can see the progress being made there already.Swyx [00:42:25]: If I were to, make one super minor pushback because I don't feel that confident about it yetCole [00:42:33]: Go for itSwyx [00:42:33]: But I've had Ryan Lopopolo from OpenAI on the pod And he's a super slop cannon, right? Oh my God, that's my coding agent being done. I downloaded this, Peon Ping. I don't know if you guys have heard this. It takes like-, sound packs from popular games like, Command and Conquer and Warcraft, and then it plays it whenever it's done. And so it's like, “Work,” or whatever, “At your command,” or something. Anyway, what I got from the Cursor code base and from Ryan's thing was that there's a slop cannon approach where you try to loosen the single agent's, bottleneck, and I feel like that is, probably an, a very important thing to try to figure out. I don't think anyone's, really solved it. Because then you just have more reviewer slop on top of the agent slop To try to wrangle it all. Ryan will probably very strongly object that I say that he hasn't solved it, but he thinks he's He thinks he's completely solved it. But I think it's still I think it's, very important, ‘cause, that is a bottleneck, right? I feel Devin is slow sometimes Because I'm like, well, yeah, this is very readable and very sensible, but also it is slower than it could be if I just, I want a button to just say, “Just ramp this up 1,000 next parallel, in parallel and just, see what happens,”? And I don't know if that's, feasible at some point in the future.Code Review, Entropy, and AI SlopWalden [00:43:55]: I And we've also run experiments internally where we've basically tried to build entire products, true products that we knew we would eventually ship, but for now, let's try to see if we can do it just by purely, vibe coding on top of each other, auto merge, no code review at all. And then there's this benchmark of how many weeks can you go onto this for Before you say, “We have the trashiest code base.”Walden [00:44:18]: “Let's actually rewrite it from scratch.”Swyx [00:44:19]: Start a new factory, yeah. What'd you find?Walden [00:44:21]: I think we found that the state-of-the-art in December was you can probably, run this for about two weeks. By the end of those two weeks, you'd find that, hey, you want to, change the color of a button. Well, it turns out this button is implemented in, 10 different places, and they, have All these different variations, and oh, you forgot one of them, and actually it's a slightly different color in one spot. And you're like, “Okay, this is too much to work with. Let's actually try to do code review at the same time.” And make sure that we're on top of our software, actually cleaning it up a bit And making sure it's done in a scalable way.Cole [00:44:54]: I think building on that, the idea of, you don't have to look at code, I think is generally a bad idea. And the meme that I have for thatWalden [00:45:03]: What timeline, all right, is Do you think that statement will be true on?Cole [00:45:06]: I think probably for a while it'll be true that you should continue to look at your code. A problem that I see a lot of teams run into that I work with who are embracing AI native, AI first coding, is The meme that I have is that your code base regresses to your worst engineer, because that engineer who is, very gung-ho about AI and is not auditing their code, their pattern starts cementing into the code, and now the AI is referencing their patterns. And so now their if/else block that, is 20 if/elses back and forth, the AI is seeing that as the pattern of how things are done and starts to then exponentially grow this slop. And I find to your point, a pretty good approach to that is having scheduled cleanup, whether by humans or through systems, that are looking for duplication. They then address that. You'll end up with like 12 helpers for how to format a date. And you need to address that, because otherwise it will continue to sprawl.Swyx [00:46:09]: Within balance, I think it's fine to have some duplication, and then sometimes To have garbage collection, right? Yeah. The What I've been, talking about with a lot of engineering leaders is that you want to be very strict about the boundaries between modules, and it's your job as an architect, as a CTO, whatever, to say like, “Okay, here's the hard contract between you guys and you guys. Whatever you do inside this black box is your business. You do whatever. But between these guys, let's be, really damn clear, and any movement must be signed off by a human or me,” or. Then, and like that's that. I don't know if you have any other modifications or advice.Walden [00:46:44]: Well, I guess generally on the topic of, where humans can be useful, I found that ‘cause, some of these, really deep infra problems, sometimes just having a human that just has, really deep expertise can make a big difference. I've actually seen this come into play when actually building agents. So we've had a few friends now, try building their own coding agents, and I think one same problem that I recurringly heard a lot of them run into was this problem of like, “Oh, Grep is really slow on our agents' machines.” And so a lot of them, I assume because they're using AI and they themselves don't have, super deep infra background knowledge, say, “Okay, we're going to go build our own custom Grep index. It's going to be really fast,” and use that as a way around this problem. When we ran into this problem About like, maybe like a year and a half ago when we were, in the early days of building Devin, we obviously didn't have AI then. We just asked our, how to, how to do this. You can just swap out a new Grep index, so.Infrastructure Details: Grep, File Systems, and SandboxesSwyx [00:47:45]: What do you mean you hand-coded Devin? What?Walden [00:47:48]: It's like, can you believe we hand-wrote this code? And we had, our infra people who are really amazing, they were looking into it and they're like, “Oh, what? We realized that actually the root cause of this problem is actually super simple, but like fine-grain detail,” which is that a lot of these virtual machines actually underlying them don't use real file systems. They use these, network file systems where things are actually cached over the network actually in S3. So when you're Grepping, you're actually making network calls Every time you're doing these things, and that's why Grep is extremely slow on these machines. And so again, goes back to, what is all of the crazy infra work that we had to do to actually get these machines working. If you try to do this yourself, there are tons of small details like this, and so we had to eventually go swap out that network file system. ButSwyx [00:48:35]: I think there's a write-up about it, right? Silas did one about the virtual file system.Walden [00:48:38]: Oh, that was a whole other thing. TheSwyx [00:48:39]: Oh, that's a different thingWalden [00:48:40]: The BlockDev file storage formatSwyx [00:48:42]: I'll bring it upWalden [00:48:42]: Which is, a file system format that we built so that the VMs could be spun up and down very quickly. Basically, the intuition behind this is-Imagine you have, a terabyte of disk, and your agent only, wrote, a hundred lines of code on top of that disk. How long does it, say, take to, save and re-bring up that disk? And most systems, because you're not optimizing for this case, it's just, on the order of a terabyte of work because you have to Save all of that and bring it back up. In our system, we try to build a file system that incrementally builds on top of each other. So every time you save and bring the machine back up, you're only doing work that is proportional to effectively the diff in the file system. And so this, shaves off a lot of time in the boot-up process of Devin. I think we This is actually now outdated. We have a newer system inside of Devin. But yeah, there's a lot of tiny details you have to get right here to actually get the day-to-day experience of Devin to be good.Swyx [00:49:39]: It's, not technically agents, but it is agent infra, and when you sell an agent as a company, you sell agent plus agent infra.Walden [00:49:46]: At least the way we do it be And the other The nice thing about having the agent infra being done together is, you We get to deploy Devin in whatever environment we want now. We don't need to wait for some underlying infra provider to also go and support VPC or on-prem or FedGovCloud, for instance. So we can actually go and figure out, okay, since we own the infrastructure, how can we get that set up for you?Cloud Providers: Modal, Daytona, and Enterprise SandboxesSwyx [00:50:12]: Whereas you're Cloudflare dependent.Cole [00:50:15]: so Cloudflare runs the control plane. The sandboxes, Modal is supported. A contributor just added Daytona. E2B is on the roadmap, and I think there's an abstraction in place that if any contributor wants to add a new provider, they can add that in.Walden [00:50:32]: Well, what are, How are the customers you work with Do they generally try to then go set up a contract with another one of these third-party providers? Do they try to do the VMs in-house?Cole [00:50:44]: most of them I see using Modal. I think Modal has a greatWalden [00:50:48]: Shout out Modal.Swyx [00:50:48]: Shout out Modal.Cole [00:50:50]: I think Modal has a great offering. It captures all of the sandbox pieces you need, snapshots being a pretty big piece of that, and given that they also offer GPUs, I think it's a pretty nice offering as a whole.Swyx [00:51:04]: no debate there.Walden [00:51:07]: Modal is great, especially, I think their container offering is, the most natural, and so especially if you are willing to, forego, the full VM requirements Modal is, a really vast place you can spin something up on.Swyx [00:51:20]: Is there a point So Modal's very Python, and I feel like most workload, has really shifted to JavaScript. I don't know if you guys Get the same feeling. So, okay, when I started Landspace and IE and all these things, I was like 50/50 Python and JS, right? That's roughly. I think that's wrong now. I think JS has won. I don't know if you guys Like, I Maybe I'm overstating it, and maybe for cognition, there's, C# and Java and what have you. But for, new greenfield apps, do you feel that Do you get that sense? Does it matter?Cole [00:51:52]: I think that most of the libraries that I see in this space are Python native first, especially in theCole [00:51:58]: Observability space. That said, I think that there is a pretty big appeal of having your entire system in one language. Especially when you have both your frontend and backend communicating, you can have one central type Which is very nice.Swyx [00:52:11]: That's my case against Modal, which is Then you have to run JS. You can run JS inside Modal. It's just, one extra step That, isn't native to the runtime. I don't know ifWalden [00:52:22]: I don't knowSwyx [00:52:23]: Reviews. Do you have numbers? I don't know.Walden [00:52:25]: the one thing I don't like about Python is whenever AI, whenever it writes Python, it always does, the weirdest patterns, andSwyx [00:52:32]: Oh, because it's, mixing two and three or what?Walden [00:52:34]: I think it's something mixing two and three, yeah. The I don't know if you see this. It always tries to do, has attribute on objects as likeCole [00:52:41]: Oh, my God.Walden [00:52:41]: But it's like But that you shouldn't be doing that. It should error if there wasSwyx [00:52:45]: Because it's training on library code?Cole [00:52:47]: I think it's more of, likeCole [00:52:48]: From what I've seen, it's more of, a reward hacking mechanism where it doesn't want to basicallyWalden [00:52:54]: It'll never error.Cole [00:52:54]: It doesn't want the code to fail. And so it Even when it knows it has the attribute, it'll call getattr on a, and for a lot of my clients who have moved towards more autonomous coding, we've put that in as a lint rule That if you do getattr, your pull request is going to fail.Slop Signatures: Comments, Backwards Compatibility, and TypesSwyx [00:53:12]: Ooh, this is a fun topic. Can you tell me more about this? What else is a sign of AI coding that you have to put guards in?Walden [00:53:21]: So we were talking just before this about Opus 4.7. One of the things this new model likes to do is it writes lots of comments. Not like, it'll, comment every line, but it'll write, paragraph, PRDs, on top of every function. But I will say, to its credit, these aren't slop, descriptions like they were before. “Oh, here's what this function does.” It's like, “Oh, here's actually the r

In this episode of Run the Numbers, CJ sits down with Aurélien Nolf, CFO of Navan, to unpack how to pre-align before budgeting, how to think about portfolio construction inside a company, when to fund or kill internal bets, how IR is becoming more connected to FP&A, and where AI actually works inside finance teams.—SPONSORS:Rillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cjBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJ—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/aureliennolf5b716412/Company: https://navan.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro1:21 Welcome and guest intro3:06 100-mile ultramarathon at Lake Tahoe4:47 Resource allocation lessons from EA6:41 Bucketing bets: proven, intuition, moonshots7:43 Pre-alignment before budgeting9:58 The 70/20/10 framework10:35 Sponsors — Rillet | EY | SpendHound13:51 The common trap: chasing everything16:16 Lyft: $1B burning to $1B profitable18:11 Killing projects without crushing morale19:24 TAM as the planning foundation20:57 Navan's TAM: managed vs. unmanaged22:15 Sponsors — Brex | Aleph | RightRev25:48 Why go after the unmanaged segment28:24 Not all TAM dollars are equal29:26 How IR is evolving30:45 Why FP&A and IR belong together31:54 Metrics: disclose vs. guide34:04 Use internal metrics externally35:12 Communicating bad news to the market39:22 Earnings prep: the black book40:04 AI in finance: can't vibe code compliance41:31 Ava handles 55% of interactions43:08 AI ROI: same framework as anything else44:02 Why finance hasn't had its AI moment44:46 Lightning round44:56 Screwed up: wrong investor meeting45:23 Sunday planning ritual46:42 Advice to younger self47:29 Finance software stack48:34 Craziest expense: curtains at the hotel laundry49:17 Credits

Your weekly Windsurfing NEWS Podcast with Ben Proffitt and PVB

Wenn KI zum Junior-Entwickler wird, steht die Softwarewelt Kopf: Vibe-Coding, Coding-Agents und günstige Individualsoftware verändern gerade, wie digitale Produkte entstehen – von der ersten Idee bis zum fertigen Tool.Gleichzeitig verschieben sich Rollen, Skills und Teamgrößen in Agenturen und Unternehmen: weniger Tipparbeit im Code-Editor, mehr Fokus auf Architektur, Usability und Anforderungen.Unternehmen stehen damit vor einem Umbruch: Standardsoftware allein reicht oft nicht mehr aus, während KI die Hürde für eigene Lösungen massiv senkt. Was bedeutet das für digitale Geschäftsmodelle, Projekt-Setups und die Frage, wer in Zukunft eigentlich „Softwareentwickler“ ist?Diese Fragen werden beantwortet:Was ist Vibe-Coding und wie unterscheidet es sich vom klassischen Programmieren?Wie verändert KI die Art, wie Software entsteht – von der Idee bis zum fertigen Produkt?Welche Rolle spielen Coding-Agents wie Devin, Windsurf, Cursor oder Claude im Projektalltag?Werden durch KI in Zukunft weniger Softwareentwickler gebraucht – oder andere?Warum werden Anforderungen, Use Cases und Requirements Engineering immer wichtiger?Wie wirkt sich KI auf Standardsoftware vs. Individualentwicklung aus?Was bedeutet der Trend zu Headless-Architekturen für zukünftige Software-Projekte?Wie sehen kleinere, schlagkräftige Projektteams der Zukunft aus?Welche Risiken haben KI-generierte Prototypen (Security, Last, Architektur)?Wie können Unternehmen heute schon mit KI-gestützter Softwareentwicklung experimentieren?Christian Malik auf LinkedIn: https://www.linkedin.com/in/christianmalik/Praxisbeispiele und Use Cases: https://s.dotsource.de/usecase

In this episode of Run the Numbers, CJ breaks down SpaceX's S-1, unpacking what the filing reveals about Starlink, xAI, X, common control accounting, revenue, losses, CapEx, and Elon Musk's Mars-linked compensation structure.—SPONSORS:RightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cjBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/run—LINKS: CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 SpaceX S1 breakdown0:50 Elon's Mars colony comp plan2:03 Common control accounting: SpaceX + xAI + X3:04 What SpaceX actually does3:43 How reusable rockets work4:24 Launch cost curve: foundation of everything5:49 Launch services: $8B, 85% of global launches6:44 Starlink: $11.4B, 63% EBITDA margins7:36 xAI and X: burning $1B/month8:22 Sponsors — RightRev | Rillet | EY11:18 Colossus and orbital AI thesis11:41 Revenue, segments and CapEx breakdown14:54 RPO: $28.4B backlog15:18 Starlink subscribers and ARPU decline16:01 Target valuation: $1.5–1.75 trillion16:47 Starlink deep dive18:05 International pricing strategy21:38 The consolidated entity problem22:28 Related party section: nine pages22:32 Sponsors — SpendHound | Brex | Aleph26:15 Valor Equity: $20B in equipment leases27:05 Tesla cross-ownership and Terrafab28:23 R&D: $8.6B, 46% of revenue30:33 Starship: key risk and growth linchpin31:41 Red flag 1: CEO comp tied to Mars colony32:22 Red flag 2: Musk concentration risk32:49 Red flag 3: Cursor option — $10B downside33:36 Red flag 4: X advertising is shrinking34:06 IPO structure and SPCX ticker34:50 30% retail allocation, no lockups35:44 S&P 500 inclusion forces buying within 15 days37:54 Valuation: 60–70x forward revenue38:43 Peer comparison39:44 What you're buying at $1.5T40:53 CFO comp: the only sane plan in the filing41:25 Bitcoin on the balance sheet41:57 Credits

Hey, Alex here, just got back from the sunny Shoreline Theater in Mountain view, so let me catch you up! This week was definitely Google heavy, we are covering Google's IO conference for the third year in a row, and today we have a special guest, Logan Kilpatrick, is joining to discuss the announced Gemini 3.5 Flash, Google Omni model, and the new Managed Agents offerings. Plus, this week, for the first time, OpenAI announced that AI solved a Math problem that humans couldn't solve for 80 years, Cursor is showing off Composer 2.5 which is partly trained on XAI data, Karpathy joins Anthropic and much more! Let's dive in! P.S - We've announced our upcoming hackathon, Weavehacks-4, June 6-7, I'll be there, we're expecting the seats to run out very soon so register nowThursdAI - We'd love to have your subscription, and if you're already subscribed, please hit that bell on YT to never miss an episode!Google I/O 2026 - Google goes agentic everywhereI went to cover Google I/O for the third year in a row, shoutout to the DeepMind team for inviting ThursdAI again, and folks, this one felt different.Last year, Google I/O was still very model-centric. This year, the story was not “here is another benchmark chart.” The story was: Google is putting Gemini into everything, and the agentic layer is becoming the product layer. Search, Gemini app, Android, Workspace, YouTube, AI Studio, Cloud, Antigravity, Flow, managed agents, smart glasses, all of it is now orbiting around one pretty clear strategy: Gemini is the intelligence, Antigravity is the agent harness, Google's products are the distribution. I saw many reactions that were milquetoast, as in, “we expected more” and those seem to dominate the X feed. But I think the distribution is the part that many folks on X are missing. Yes, we can argue about Gemini 3.5 Flash pricing. Yes, we can argue whether “Flash” still means what Flash used to mean. But when Google says the Gemini app itself has 900 million monthly active users, before even counting Search, Gmail, YouTube, Docs, Drive, Android, and the rest of the Google surface area, that's massive! OpenAI ChatGPT is supposedly stagnated at ~900M, I don't remember them crossing a 1B. Meanwhile Google is gaining traction. And they just updated all those folks with a new model!Wolfram said it really well on the show: his mother is not sitting there reading model cards. She just uses her Pixel, voice unlocks Gemini, asks for help, and suddenly the default intelligence available to her goes up. Antigravity 2.0 - the agent harness takes center stageThe biggest strategic signal from Google I/O for me was Antigravity.Remember, Antigravity was an IDE that came from the Windsurf acquisition saga. Part of the Windsurf team went to Google, part went to Cognition, and now Google is very clearly putting Antigravity in the middle of its agentic future. And I mean very clearly. Sundar mentioned it. Demis mentioned it. Varun Mohan the co-founder was on stage immediately after them! If you've ever watched a Google I/O keynote, you know how carefully every minute is allocated. Google has YouTube, Search, Gmail, Android, Cloud, Ads, Workspace, and a thousand VP-level products that could be on stage. The fact that Antigravity was that prominent should tell you everything.Logan Kilpatrick joined us and framed this in a way I loved: Gemini became the through-line across Google products, and now the Antigravity agent harness is becoming the through-line for agentic experiences.The new Antigravity 2.0 is a complete overhaul, showing only an agentic interface (which was previously just a separate window called Agent Manager) and separating the IDE layer completely into its own app and showing a Codex like agent-first interface, which got a few folks furious. This move may be weird to some folks, but if you follow along where everyone's going, this seems to be the way of the future, coding is no longer about lines of code, it's about managing fleets of agents. The new Gemini 3.5 absolutely shines inside the new Antigravity, the model was trained with this harness in mind, and is currently offered at an incredible speed (12x), so I'm definitely going to try it! Gemini 3.5 Flash - fast, determined, and maybe not the old “Flash”The most debated model release of the week was Gemini 3.5 Flash.Some folks saw the pricing and token usage and immediately went “this is not Flash.” I get that reaction. Flash used to mean cheap, fast, lightweight chat model. But Logan's framing on the show was important: Flash is now being built for the agentic era.In a chat era, you optimize for one user message and one model answer. In an agentic era, the real token volume is in tool loops, intermediate reasoning, retries, file reads, web searches, code execution, and self-correction. That's a different product profile.Wolfram already ran Gemini 3.5 Flash through WolfBench, and the results were fascinating. With the Hermes agent harness, Gemini 3.5 Flash hit an 87% ceiling on Terminal Bench 2.0, meaning across runs it could solve more of the benchmark than even GPT-5.5 extra high in that setup. The variance was higher with the simpler Terminus harness, but with a real agent harness, the model looked much stronger.That tracks with what Nisten saw in his “Martian railgun from Olympus Mons” test. Gemini 3.5 Flash went extremely detailed, almost too determined, kept correcting itself, overcorrecting itself, and built a whole game-like simulation. Logan laughed and basically said: yeah, this model is very determined, possibly an overcorrection from the “Gemini is lazy” feedback. It also tracks with the mismatch in other benchmarks, in some, Gemini 3.5 flash shines (like the above Apex-agents from AA) and in some, it doesn't match the other frontiers. In my tests, it was definitely over-eager to use a million and a half tool calls, read tons of files, to just help me review this draft inside antigravity. It's like a super eager robotic golden retriever! Gemini Omni - Nano Banana for video, but actually more than thatThe biggest update from last year IO was Veo 3! This year, the biggest wow factor was also visual, but it wasn't VEO 4, it was a new model that is multimodal, trained end-to-end they call Omni. Google is calling this their first “create anything from anything” model, and the first version, Gemini Omni Flash, starts with conversational video editing. The easy description is: Nano Banana for video. You upload or create a video, then talk to it. Change this character. Replace this person. Add an object. Make this scene claymation. Keep the scene, but change the environment.I played with it live and showed a few examples. I asked for a claymation explainer of protein folding, then gave it my face and asked it to replace the character with me. It did it. I uploaded pictures of Sonia, my cat, and it generated a talking cat video with the right kind of cat teeth, which is weirdly important because so many pet generations accidentally add human teeth and become nightmare fuel.The failure modes are still there. I asked it to make Sonia a Russian-speaking female cat, and it only partly switched languages and didn't really change the voice. Audio upload support is also not fully productized yet, even though the underlying model is multimodal. But the direction is very clear.This is not just “Veo with a chat model glued on.” I asked Jeff Dean - Google's chief scientist about this at I/O, and he explained that Omni is trained end-to-end. The intelligence and the generative media capabilities are part of the same model family, not a hacky two-model pipeline. He also said the intelligence is around a recent Flash-level model, which is a big deal when you think about video editing as reasoning over physics, identity, scene continuity, and intent.A lot of people compared Omni to Seedance 2.0, and I think that's the wrong comparison. Seedance is amazing at cinematic generation (lkaregly due to lack of copyright concerns from Bytedance). Omni's unlock is iterative editing on real footage and coherent multi-turn creative control. Other Google IO 2026 releases I found notableThis was a concentrated effort of a huge company to insert AI into every product surface they have so of course I can't cover ALL of it here, but the most notable things for me were: * Gemini Spark - a new agentic experience from Google, to help you with tasks across Gmail, Drive and more. It should support skills, and is a de-facto OpenClaw/Hermes alternative from Google for regular folks. It's not “yet” live so we'll talk more about it when I can test it out* Managed Agents in the Gemini API - We chatted with Logan about this one, Google is re-imagining how agents are going to get built, and are offering 1 api call to spin up an agent in a full Linux env, with security and sandboxing in mind. I'll expand more on this in a next episode, as I recorded a complete conversation about this with Ali Çevic, a PM for Google APIs* AI overhaul of Google Search - AI Overviews will not expand into AI mode, and the iconic Google search box itself will change, for the first time in 25 years to include AI mode! * SynthID expantion and OpenAI collab - Google showed off that OpenAI is joining in marking all AI generate imagery and video with an invisible SynthID watermark. I think this is amazing and more companies should adopt this standard* AI Glasses! We got Google Glasses demos - Together with Warby Parker and Gentle Monster, Google finally showed off their answer to Meta Raybans/Oakleys. They look like regular glasses too, but can hear and talk to you, with the full power of Gemini multimodality. Available in the fall sometime! * Demis Hassabis “we're on the cusp of the singularity” closer - CEO and Co-Founder of DeepMind, Demis Hassabis, closed the show with his remarks about the positive future and that we are nearing this Singularity point after which the future is very uncertain. I found it to be very inspiring and closed our show with that clip as well! * Personally, I got to chat to: Demis Hassabis, have breakfast with Jeff Dean, ask Josh Woodward a bunch of questions, and pester about 20 other great folks on a live stream, and had a lot of fun! Huge thanks to the DeepMind folks, Lucie, Dimple, JD and many others for the continued belief in ThursdAI and invite me to cover this great event. OpenAI LLMs solve an 80yo math problem - Erdős Unit Distance ConjectureOutside of Google I/O, the biggest story of the week was OpenAI announcing that a general-purpose reasoning model made progress on the Erdős planar unit distance problem.This problem goes back to 1946. For nearly 80 years, mathematicians believed the best constructions looked roughly like square grids. OpenAI's model found a new family of constructions with a polynomial improvement, using algebraic number theory ideas that humans apparently had not explored in this context. The above is a representation of it! Important caveat: this does not fully solve every version of the asymptotic Erdős conjecture. Some mathematicians are pushing back on the framing, and fair enough. Precision matters. But even with the caveat, this is still a huge moment.The reason it matters is not that I personally understand the math. I absolutely do not. The reason it matters is that this was not a special-purpose IMO model fine-tuned only for math competitions. This was a general-purpose reasoning model exploring a real open problem, generating candidates, verifying them, and finding a path humans hadn't taken. Extrapolate this to other sciences, Physics for example? This means an amazing future. LDJ pointed out that mathematicians have been skeptical because there have been previous false alarms. But this one landed differently. When Fields Medalist-level mathematicians verify the proof, the discourse changes from “lol stochastic parrot” to “wait, what does this mean for my PhD?”My answer is: yes, still study math. Please study math. The mathematicians who use these tools will do much more than people who don't understand the domain. Same with software engineering. Senior engineers with Codex, Claude Code, Hermes, Antigravity, Cursor and other agents are becoming dramatically more effective because they can steer, evaluate, and recover the work.This being published a day after Demis's “foothills of the singularity” is a great conjecture. Cursor Composer 2.5 - Opus 4.7 performance model from Cursor, at 10x better efficiencyCursor dropped Composer 2.5, and folks, this is a serious release.Composer 2.5 is built on Moonshot's Kimi K2.5 base, like Composer 2, but Cursor scaled the post-training dramatically. They used 25x more synthetic tasks and introduced targeted textual feedback during RL rollouts, where the model gets hints inserted at the point of failure instead of only getting a noisy final reward.The benchmark story is strong: around 69.3 on Terminal Bench 2.0, basically neck and neck with Opus 4.7 in Cursor's chart, and strong results on SWE-bench multilingual and CursorBench. The pricing is the part that makes this especially interesting: $0.50 per million input tokens and $2.50 per million output tokens, with a faster variant at $3 / $15. That is much cheaper than the frontier models it is trying to replace for day-to-day coding work.Cursor engineers are reportedly dogfooding Composer 2.5 heavily and rarely switching away. That matters more to me than any single benchmark. If the people building Cursor can use it as a daily driver, that is a very real signal.The wild part is what comes next. Cursor is partnering with SpaceXAI to train a much larger model from scratch using 10x more compute on Colossus 2. Cursor has the workflow data. xAI has enormous compute. If this works, Cursor stops being just the IDE company and becomes a coding-model lab.We've been saying for months that coding agents are the path toward general agents. Anthropic has Claude Code. OpenAI has Codex. Google has Antigravity. xAI has Grok Build. Cursor has Composer. I'm looking forward to seeing how well it performs on our own benchmarks! Anthropic, xAI, Karpathy, and the compute warsThe compute story this week was bonkers.The SpaceX IPO filing reportedly revealed that Anthropic is paying SpaceXAI $1.25B per month for AI compute at the Memphis Colossus facility. Per month. That's about $15B a year, through May 2029, for access to more than 220,000 NVIDIA GPUs including H100s, H200s and GB200s.This is apparently inference compute for Claude Pro, Max and API users, not training. And it explains a lot of the recent quota changes. Anthropic doubled some Claude usage limits, and suddenly the product feels less constrained.Also, can we just acknowledge the comedy here? Elon Musk publicly called Anthropic “misanthropic,”, went off against every competitor to XAI, is now selling spare GPU time to Cursor and Anthropic? Who's next, OpenAI? The bigger point is that the AI capex story is no longer just NVIDIA. It's also whoever owns the data centers, power, cooling, networking, and GPU clusters. Compute is becoming the land under the AI economy.Also, Andrej Karpathy joined Anthropic. Karpathy could work anywhere. He co-founded OpenAI, led Tesla Autopilot vision, taught half the AI world how neural nets work, and now he's going back into frontier LLM R&D at Anthropic.Open source LLMs - Cohere, Qwen, NousOpen source had a strong week too.Cohere released Command A+, a 218B total parameter sparse MoE model with only 25B active parameters per token, under Apache 2.0. This is their first model that unifies reasoning, vision, multilingual, tool use and citations in one package.The hardware story is great: W4A4 quantization can run on 2 H100s or a single B200. Cohere says it supports 48 languages, 128K input context, 64K output, and gets big jumps over Command A Reasoning, including Tau-squared Bench Telecom from 37% to 85% and Terminal-Bench Hard from 3% to 25%.Cohere is one of those labs that doesn't always chase the loudest consumer hype, but they are very serious on enterprise and multilingual. Apache 2.0 makes this one especially useful.Alibaba also dropped Qwen 3.7-Max, positioned as an agentic frontier model. The headline from their testing is wild: 35 hours of continuous autonomous operation with more than 1,000 tool calls. They also showed it controlling a physical robot inside Alibaba offices and finding an umbrella after about 20 minutes of agent interaction.This digital-to-physical bridge is where things start feeling very real. An agent loop that can write code and use tools can also navigate physical tasks if you give it the right robotics stack.And our friends at Nous Research released Lighthouse Attention, a sparse attention method for long-context pretraining. At 512K context, they report a 17x faster forward+backward pass than standard attention on a single B200, and the recovered checkpoints actually beat dense-from-scratch final loss at the same token budget.The clever part is that the selection logic sits outside the attention kernel, so you still use regular FlashAttention on a gathered dense subsequence. No custom sparse kernel nonsense. If this holds up, this could matter a lot for long-context training.Tools and agentic engineering - X subscriptions, Grok Build, Codex MobileOne really practical tool update: Hermes and OpenClaw can now use your X subscription directly.This is more important than it sounds. You can connect your X Premium subscription and get access to semantic X search and Grok-related tooling without using sketchy browser automation or unofficial APIs that might get you banned. Wolfram already used this to have his agent go through his likes and bookmarks from the past week and send me news items for the show. That is exactly the kind of “small but real” agent workflow that becomes addictive.xAI also launched Grok Build, their agentic CLI coding tool, in early beta for SuperGrok Heavy subscribers. Early users are already running parallel Grok Build agents through tmux supervisors and using it for more than coding: fleet data triage, security patching, training label work, and general automation.The pricing being discussed is aggressive, around $1 per million input tokens and $2 per million output tokens for the API. The model version is grok-build-0.1, and folks have already wired it into Hermes with a 256K context window.And then there's Codex Mobile, which OpenAI shipped inside the ChatGPT mobile apps. This is one of those releases that sounds small until you start using it. You can control Codex sessions remotely from your phone, connected to your machine, and because Codex has native connectors to Gmail, Calendar and other surfaces, it sometimes feels faster and more reliable than local CLIs duct-taped to third-party integrations.I ported Wolfred into Codex with skills and everything, and I've been comparing the same tasks in Hermes and Codex. Codex is often faster, not necessarily because the model is always smarter, but because the connectors and harness are cleaner. Harness matters. We keep coming back to this.This Week's Buzz - W&B, CoreWeave, WolfBench and roboticsThis week in the Buzz, Wolfram walked us through a few things from the Weights & Biases / CoreWeave world.CoreWeave is a gold sponsor at ICRA 2026 in Vienna, the International Conference on Robotics and Automation. NVIDIA is also going big there with a keynote on generalist humanoid robots, 17 accepted papers and workshops around sim-to-real, robot foundation models, autonomous driving, manipulation, and physical AI.Wolfram will be there later in the week, after speaking at the AI Developer event in Cologne about WolfBench. If you're in Europe and into robotics or agent evals, find him.We also looked at WolfBench results for Gemini 3.5 Flash, which honestly became one of the more interesting empirical points of the episode. The model looks variable in simple harnesses, but very capable in better agent loops. That's the whole thesis of measuring model + harness together instead of pretending the model card tells the whole story.The water discourse, almonds, and data center realityWe also got into the data center water discourse, because this talking point is everywhere right now.There are real infrastructure questions around AI. Power, land, cooling, grid capacity, permitting, local impact, all of that matters. But the “AI is stealing drinking water” version of the argument is often wildly detached from scale.The stat I brought up on the show: California almonds use roughly 3 to 5.5 million acre-feet of water per year, multiple times more than all North American data centers combined in 2025. Nisten and LDJ added the important cooling nuance: many large data centers use closed-loop cooling, and evaporative cooling is not universal. Some data centers can avoid water use almost entirely, but at the cost of higher electricity usage.This doesn't mean “no concerns are valid.” It means if we're going to regulate or pause data centers, let's be honest about the actual tradeoffs. AI compute is becoming the substrate for medicine, robotics, science, logistics, software, education and every other productivity layer. We should build responsibly, but not based on viral fear math.Closing thoughts - foothills of the singularityDemis closed I/O saying we're in the foothills of the singularity, and I know how that lands when you write it down. But I was in the room, and after the keynote he told me something I haven't been able to shake: he thinks AI is going to be 10x as impactful as the Industrial Revolution, and 10x as fast. Basically 100x. This is the AlphaFold guy. Not someone loose with his words.Then look at the week. A general reasoner cracked an 80-year-old math problem. Cursor is training near-frontier coding models on a fraction of the big-lab budget. Anthropic is paying Elon $15B a year for inference. Karpathy left education to go back into pre-training. Google rolled out an intelligence uplift to a billion people who don't even know a model dropped.If you put that on a whiteboard in 2023, it reads like a sci-fi pitch.LDJ's mathematician friends are asking if they should keep doing their PhDs. My answer hasn't changed: yes, please keep going. The people who combine domain taste with these tools are going to ship more in 5 years than the previous generation did in 50. The tool doesn't replace the taste. It just removes the bottleneck.That's the whole reason ThursdAI exists. Not to hype every drop, not to dunk for engagement, but to give you a shot at being one of the people who knows what's happening, with the receipts.This week, a lot changed.See you next Thursday.TL;DR and Show Notes* Hosts and Guests* Alex Volkov - AI Evangelist at Weights & Biases / CoreWeave, @altryne* Co-hosts: @WolframRvnwlf, @nisten, @ldjconfirmed* Guest: Logan Kilpatrick, MTS at Google DeepMind / AI Studio, @OfficialLoganK* Google I/O 2026* Google went all-in on agents across Search, Gemini, Antigravity, Workspace, Android, Cloud and YouTube (I/O site, Alex thread)* Antigravity 2.0 became the central agentic coding harness across Google (Sundar, Google OS demo)* Gemini 3.5 Flash launched as a fast, determined workhorse model for agentic loops (Logan, Noam Shazeer, Jeff Dean)* Gemini 3.5 Flash is rolling out across the Gemini app, Search AI Mode, Gemini API, Google AI Studio, Antigravity and Gemini Enterprise Agent Platform (Koray Kavukcuoglu)* Google Search is getting new Gemini 3.5 Flash-powered agentic capabilities, including a new AI-powered Search box and background information agents (Sundar)* Gemini Spark was announced as a 24/7 personal AI agent that can proactively work across Google surfaces (News from Google)* Google teased Gemini-powered Android XR smart glasses with eyewear partners Gentle Monster and Warby Parker (Google, Alex live reaction)* Google AI Studio and the Gemini API got major agentic developer updates, including Managed Agents (Google AI Developers)* Vision & Video* Google DeepMind launched Gemini Omni, a “create anything from anything” multimodal model starting with conversational video editing (DeepMind, Google DeepMind on X)* Omni is available in the Gemini app, Google Flow and YouTube, with API support coming soon (Logan, Gemini App, Sundar)* Key distinction: Omni is not just text-to-video, it is an iterative multi-turn video editing model that combines Gemini intelligence, world knowledge, multimodal inputs and generative media (Google)* Big CO LLMs + APIs* OpenAI announced a general-purpose reasoning model made progress on the Erdős planar unit distance problem, challenging an 80-year-old mathematical belief (OpenAI, X)* Cursor launched Composer 2.5, built on Kimi K2.5, with Opus-class coding performance at much lower cost (Cursor blog, X)* Alibaba released Qwen 3.7-Max, an agentic frontier model with long autonomous runs and robotics demos (Qwen blog, X, robot demo)* Andrej Karpathy joined Anthropic to work on frontier LLM R&D (X)* SpaceX IPO filing revealed Anthropic is paying $1.25B/month for AI compute at the Memphis Colossus facility (Axios, Sawyer Merritt)* The jury in Musk v. Altman found Musk's OpenAI claims barred by statute of limitations, with Musk saying he will appeal (Elon Musk, Sawyer Merritt, Max Zeff)* Open Source LLMs* Cohere released Command A+, a 218B MoE model with 25B active parameters under Apache 2.0 (Cohere, Nick Frosst, HF W4A4, HF BF16)* Nous Research released Lighthouse Attention, a sparse attention method for long-context pretraining with major speedups (Blog, X, arXiv, GitHub)* Tools & Agentic Engineering* Google launched Managed Agents in the Gemini API, letting developers spin up hosted Antigravity agents with Linux sandboxes and persistent state (Docs, X)* xAI launched Grok Build, an agentic CLI coding tool in beta for SuperGrok Heavy users (xAI CLI, X)* Hermes and OpenClaw can now use X subscription auth for semantic search and Grok tooling (Alex)* OpenAI Codex Mobile is now available in the ChatGPT mobile apps for remote agent workflows (OpenAI)* Anthropic doubled Claude usage outside peak hours for a limited period, including Claude Code and other Claude surfaces (Claude)* This Week's Buzz - W&B / CoreWeave* Weights & Biases by CoreWeave is at ICRA 2026 in Vienna, with robotics and automation taking center stage (ICRA, W&B event page)* NVIDIA heads to ICRA 2026 with robotics work around generalist humanoids, physical AI and sim-to-real systems (NVIDIA Robotics, NVIDIA ICRA)* Wolfram is speaking about WolfBench at the AI Developer event in Cologne before heading to ICRA in Vienna (Wolfram)* Other Topics* Data center water usage discourse came up again, including why comparisons need real scale and context rather than viral fear math* The broader theme of the week: coding agents are becoming general agents, and the major labs are now competing on the full stack of model, harness, tools, context and compute This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

In the latest episode of Executive Function, Brett sits down with Graham Moreno, Head of GTM at Parallel Web Systems. Before Parallel, Graham scaled Windsurf's GTM organization from three sellers to seventy-five in under a year, served as President through the Cognition acquisition, and earlier built and led enterprise sales teams at Grafana Labs and MongoDB. In this conversation, he unpacks why the AI-era backlash against structured enterprise sales misreads the data, how to design a process that raises the floor for ordinary reps without capping the ceiling for stars, and why selling to AI-native customers compresses an eight-week cycle into five business days. In today's episode, we discuss: Why in-person enterprise rollouts still beat product-led motions Building a robust sales process that still leaves room for unscripted moments Why the three highest-leverage early sales hires aren't sellers at all The case for outsized commission accelerators for star sellers — and the kind of person they attract Why most AI companies are skipping the in-person sales work that enterprise customers actually want References: Ahead: https://www.ahead.com Amazon: https://www.amazon.com Anthropic: https://www.anthropic.com Attio: https://www.attio.com Augment Code: https://www.augmentcode.com/ Cognition: https://cognition.ai Cursor: https://cursor.com Dani McCabe: https://www.linkedin.com/in/danielle-mccabe/ Datadog: https://www.datadoghq.com GitHub Copilot: https://github.com/features/copilot HubSpot: https://www.hubspot.com Jeremy Powers: https://www.linkedin.com/in/jeremypowers/ JPMorgan: https://www.jpmorgan.com Matt McClernan: https://www.linkedin.com/in/mattmcclernan/ MongoDB: https://www.mongodb.com Nicole Rettinger: https://www.linkedin.com/in/nicole-rettinger-23b20465/ Notion: https://www.notion.com OpenAI: https://openai.com Parag Agrawal: https://www.linkedin.com/in/paragagr/ Parallel: https://parallel.ai Snowflake: https://www.snowflake.com University of Chicago: https://www.uchicago.edu Windsurf: https://windsurf.com Where to find Graham: LinkedIn: https://www.linkedin.com/in/grahammoreno/ Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps: 00:00 Introduction 00:32 Has the sales playbook changed in the AI era? 02:13 Why "showing up" beats letting the marketplace decide 06:50 Why great salespeople sell to engineers and executives in one motion 11:37 Selling to AI-native buyers who grew up on ChatGPT 13:49 Same seller, different tempo: 8 weeks vs. 8 business days 15:57 How AI-native buyers handle build vs. buy decisions 17:48 The rep who taught a champion's son guitar over Zoom 19:03 Raising the floor without capping the ceiling 22:09 Why too much process narrows the kind of seller you attract 25:46 The three pillars of GTM excellence 31:00 Building peers who are 80% aligned, not 100% 38:03 Whether AI is changing what good enablement looks like 41:35 Selling against direct and implied competitors at once 42:45 Instrumenting the funnel from stage zero to close 45:57 Why post-sales should always roll up to the revenue leader 48:19 The case for outsized commissions 52:02 The 96 hours of panic before Cognition acquired Windsurf 53:04 How far out should a GTM leader be planning? 57:53 What a normal week looks like in hypergrowth

In this episode of Run the Numbers, CJ sits down with ParkerGale Operating Partner Paul Stansik to break down five ways CFOs can help build a better sales engine: making the budget mean something, improving forecasting, sharpening metrics, getting involved in key RevOps moments, and building real trust with sales.—SPONSORS:Aleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cjBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metrics—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/paulstansik/Company: https://www.parkergale.com/Hello Operator: https://hellooperator.substack.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:Is a weekly martini ARR? | with Dave Kellogghttps://youtu.be/Yb1lUQLJ6qw—TIMESTAMPS:All verified. Here are the timestamps:0:00 Preview and intro2:27 Parker Gale and Paul's role3:52 Topic: how CFOs build a better sales engine6:21 1: Make the budget mean something8:11 Budget segmentation and cleaving the business10:54 Sponsors — Aleph | RightRev | Rillet14:08 2: Help emphasize forecasting17:23 Forecasting as non-threatening co-construction19:37 Sponsors — EY | SpendHound | Brex23:06 3: Lend a hand with data and metrics25:32 Walking sales through NDR levers27:16 Metrics tied to exit readiness28:00 4: Get involved in a few RevOps spots29:04 Pricing, proposals, and quoting31:22 Kill your SKUs32:51 Selling with certainty: quote formatting34:26 CFO letter for enterprise deals37:37 5: Build a great relationship with sales37:59 You can't fix a secret39:23 EQ over IQ for finance leaders40:41 Recap: all five tips42:11 Credits#RunTheNumbersPodcast #CFO #SalesStrategy #FinanceLeadership #RevenueOperations

If your SaaS product delivers genuine value fast, growth takes care of itself. That's the core thesis Sanjay Sarathy has spent 8+ years proving at Cloudinary, where he oversees a self-service business representing nearly a third of the company's revenue across 11,000+ paying customers in 150+ countries — without feet on the ground in most of them.In this episode, Sanjay breaks down what product-led growth actually looks like when it's executed well: not just free trials and clever onboarding flows, but building such a frictionless, valuable experience that developers naturally tell other developers. He shares why Cloudinary invested in technical support before marketing, how they redefined "activation" to mean real value (not just uploading a file), why discoverability is a non-negotiable pillar of their growth strategy, and how they're now rethinking the developer experience for a world where AI agents and LLMs are writing the code.This is a masterclass in developer-led PLG from someone who has lived it at scale.Key Takeaways4:07 — The Growth Levers Have Changed SEO, outbound, and paid are still valid, but word of mouth (especially in developer communities), AEO, and agentic discoverability have become powerful new growth engines — when they're earned as a byproduct of value, not engineered as a primary goal.8:28 — Why PLG Before Enterprise Cloudinary was built by developers for developers. They started with self-service because that's what their founding team would have wanted. Only after PLG proved itself did enterprise customers come knocking — and it was far easier to layer on security, SLAs, and support than to bolt on a product that developers already loved.13:46 — Great Product Isn't Enough Without Distribution Cloudinary is in 150 countries with no boots on the ground in most of them. SEO, developer relations, and a docs site that functions as a discovery engine are what made global reach possible. Distribution and product must go hand-in-hand.15:36 — Discoverability Is a Strategy, Not a Tactic "Discoverability" is a recurring internal theme at Cloudinary — constantly asking how to ensure the right people, in the right context, can find and experience the product's value.16:03 — The Cannibalization Trap Cloudinary made the mistake of launching a new product without considering its impact on existing products — and cannibalized their own business. They now use a two-track product strategy: "mature" products with full go-to-market support, and "invest" products being validated for product-market fit before scaling.19:24 — Invest in Support Before Marketing One of Cloudinary's earliest and most impactful decisions: invest heavily in technical support first. Happy, successful developers become word-of-mouth advocates. That bet paid off across an entire community.21:06 — Developer Experience in the Age of AI Tooling Developer experience today means meeting developers where they work — VS Code, Cursor, Claude, Windsurf. Cloudinary built a VS Code extension and is working to minimize hallucinations by giving LLMs accurate, context-rich instructions for using Cloudinary correctly.24:03 — Redefining Activation Uploading a file to Cloudinary is not activation. Doing something with that file — transforming it, tagging it, delivering it — is activation. Reframing their metric around genuine value changed how they prioritized onboarding.33:25 — The Seven-Day Activation Window Data shows clearly: if users don't activate within the first 7 days, a second surge doesn't come. Most activation happens in the first 4–5 days. This insight shapes everything about how Cloudinary approaches onboarding urgency.27:01 — Speak Use Cases, Not Features "We have automated image optimization" means nothing. "Your images are 40% lighter and you'll save X on bandwidth" means everything. The language of outcomes and use cases is what drives adoption and expansion.36:39 — Pricing Must Communicate Value Cloudinary's self-service pricing has remained largely flat for years while the product has added enormous capability — intentionally improving the value/price ratio over time. They also offer pay-as-you-go flexibility for seasonal businesses.44:28 — The 90-Day PLG Focus: Build Trust For founders building a PLG motion right now, Sanjay's single most important recommendation: engender trust. Do what you say. Follow up when you say you will. Make your product deliver on its promise. Trust is the flywheel.Tweetable Quotes"We never set out to get word of mouth. We set out to create value. Word of mouth was the byproduct." — Sanjay Sarathy"If your product genuinely helps people win, growth becomes a natural byproduct." — Sanjay Sarathy"Distribution is equally as important as the product itself. You can have a great product and go nowhere." — Sanjay Sarathy"Discoverability isn't a campaign. It's a strategy." — Sanjay Sarathy"Uploading a file isn't activation. Doing something valuable with it is." — Sanjay Sarathy"If a developer doesn't activate in the first seven days, don't expect another surge. It won't come." — Sanjay Sarathy"Stop talking about your features. Start talking in the language of your customer's use cases." — Sanjay Sarathy"We're okay with free users who are actively using the product. They pay us back in word of mouth." — Sanjay Sarathy"In a PLG motion, trust is the flywheel. Without it, everything else breaks down." — Sanjay Sarathy"We fell in love with our own capabilities and forgot that customers don't care. Use cases are what drive adoption." — Sanjay SarathySaaS Leadership Lessons1. Build Distribution Like You Build Product Cloudinary reaches 150+ countries without sales reps in most of them — through SEO, developer relations, documentation, and community. Great products disappear without intentional distribution. Your discoverability strategy is a growth strategy.2. Earn Word of Mouth — Don't Engineer It The moment you prioritize getting word of mouth over generating it as a byproduct of genuine value, you've lost the plot. Build something that makes people win, then step back and let them talk. The data will tell you if it's working.3. Start Narrow, Validate, Then Scale Cloudinary's "invest vs. scale" product framework exists because they once cannibalized their own product line by expanding without rigor. Validate product-market fit in a controlled way before committing the full go-to-market machine. Repeatability before scale.4. Redefine Your Activation Metrics Around Real Value Ask yourself: is the action we're measuring actually a moment of value, or just a moment of presence? Cloudinary stopped counting uploads and started counting transformations. The metric you optimize shapes the product you build.5. Invest in Customer Success Before You Think You Need To Cloudinary prioritized technical support ahead of marketing in their early days. Counter-intuitive — and it was exactly right. Successful users become advocates. That investment compounded for years through word of mouth and developer trust.6. Speak the Language Your Customer Thinks In "Automated image optimization via F-Auto" is internal language. "Your images are 40% lighter and your site is faster" is customer language. The translation layer between what your product does and what your customer achieves is where adoption lives or dies. Build that bridge deliberately.Guest Resourcessanjay@cloudinary.comwww.cloudinary.comhttps://www.linkedin.com/in/sanjaysarathy/https://x.com/guffnuffEpisode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

In this episode of Run the Numbers, CJ sits down with Figure Technologies CFO Macrina Kgil to break down how Figure's business model works, why traditional lending remains so bloated, and how speed in origination and funding flows through financial performance. They also cover stablecoins, blockchain as invisible infrastructure, AI in accounting, and scaling finance with fewer than 35 people.—SPONSORS:Brex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cj—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/macrina/Company: https://www.figure.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro3:10 Working for a CEO who's a former CFO5:00 What Figure does and how it makes money6:57 Blockchain's first commercial use case8:17 50%+ margins, path to 60%9:23 Sponsors — Brex | Aleph | RightRev12:56 HELOC origination: 45 days to 5 days14:26 Where the lending system is bloated15:56 Credit and liquidity as core risks17:06 Blockchain makes the marketplace transparent18:10 Risk as a fintech CFO19:49 Sponsors — Rillet | EY | SpendHound22:58 Crypto on the balance sheet24:12 Blockchain becomes invisible like cloud25:44 Stablecoins explained27:47 YLDS: Figure's yield-bearing stablecoin29:02 Crawl, walk, run stablecoin strategy34:32 IPO process: what got easier35:03 What got harder: testing the waters37:12 Blockchain KPIs and investor conversations38:21 Finance team: 130 people down to 3540:00 SEC engagement: storytelling not financials40:49 IPO advice: pick durable KPIs42:05 First earnings after IPO: don't miss43:13 AI automation goal: 60% by 202646:11 Director of Finance Transformation hire47:40 30-60-90 for the transformation role48:58 Long-Ass Lightning Round52:20 Credits

durée : 00:02:06 - Esprit sport - par : Cédric Guillou - Quelles sont les conditions météorologiques idéales pour pratiquer le wingfoil ? Le spécialiste Sam Estève présente les spécificités de cette discipline de glisse accessible, entre technicité et apprentissage en milieu naturel. Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

Cerebras is going public with the largest commercial chip ever built, $510M in 2025 revenue, and a $24.6B backlog mostly tied to OpenAI. CJ breaks down the company's wafer-scale AI bet, why inference changed the story, the strange customer-investor-lender relationships behind the IPO, and the big question: is Cerebras the next NVIDIA-style infrastructure winner, or a concentrated hardware company with a very expensive cloud pivot?—SPONSORS:SpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cjBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartups—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNCJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro0:59 Cerebras: the dinner plate chip3:56 Why chip size matters for AI5:26 Old vs. new AI: inference is the bottleneck7:30 Revenue: 20x in 3 years7:49 Sponsors — SpendHound | Brex | Aleph11:33 Gross margin12:02 Net income: the one-time accounting trick12:41 Operating cash flow whipsaw13:24 RPO: $24.6B backlog13:58 Customer concentration: the UAE entities18:06 Sponsors — RightRev | Rillet | EY21:05 Cloud revenue: the inverse SaaS story22:23 Cloud gross margin collapse23:53 G42 warrants for pennies29:14 The OpenAI warrant: Funky Town31:08 $40B market cap milestone31:36 R&D and S&M breakdown33:15 Balance sheet and cash burn35:59 Red flag 1: accounting weaknesses36:37 Red flag 2: one foundry, no supply deal37:28 Red flag 3: UAE geopolitical risk38:10 Red flag 4: cloud is unproven39:02 Cap table: founders diluted40:43 Voting control: Class A, B, and N41:15 Valuation: 10–13x forward revenue41:48 Peer comparison43:47 CEO's prior issues46:10 CreditsNothing said or created by this podcast is business or investment advice#RunTheNumbersPodcast #IPO #Semiconductors #AIStrategy #FinanceLeadership

durée : 00:01:46 - Esprit sport - par : Cédric Guillou - Le matériel de wing foil est devenu plus compact et technologique. Romuald Mamadou, responsable commerciale France chez Armstrong, explique les étapes de conception d'une aile et les conseils pour s'équiper, du neuf à l'occasion. Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

durée : 00:01:54 - Esprit sport - par : Cédric Guillou - Qu'est-ce que la parawing ? La sportive Manon Dupé expose les caractéristiques de cette pratique en pleine expansion. Entre gain de place et sensations de glisse, elle compare cette discipline au wingfoil traditionnel. Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

durée : 00:01:54 - Esprit sport - par : Cédric Guillou - "Les drones sont devenus un peu indispensables" explique Olivier Sautet, producteur de vidéos et d'images. Car les figures sont réalisées loin des côtes et prendre un bateau pour s'en approcher reste trop mouvant. Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

David Bell, co-founder of Idea Farm Ventures and early investor behind Warby Parker, Harry's, and Diapers.com, and CJ break down how consumer investing works. They cover why durable consumer companies require more than clean unit economics, how to apply SaaS-style thinking to businesses without contracts, and why the best opportunities often live in boring gray space.—SPONSORS:EY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cjBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cj—Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/david-bell-086820/Company: https://www.ideafarmventures.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro3:16 Edge: economics and psychology5:21 Best ideas in boring gray space5:41 Functional, emotional, and symbolic value7:08 Grüns gummies and divisibility9:02 Sponsors — EY | SpendHound | Brex12:31 Ideation: personal pain vs. market analysis13:17 Diapers.com and the Starbucks origin story16:00 Warby Parker: asking why16:33 LTV to CAC in D2C18:02 Retention math: 85 to 90% can double LTV19:00 Milkman and recurring vs. reoccurring20:42 Trust economics22:38 Warby stores boost online sales22:58 Sponsors — Aleph | RightRev | Rillet26:12 Away store as advertising27:46 Warby discovery: dots on a map30:10 Home try-on word of mouth value32:09 D2C unit economics mistakes34:55 Innovating on distribution35:50 Touchland in Sephora: right channel, right signal37:00 Capital allocation: margin and low CAC first39:18 Sequencing: people, brand, then inventory40:58 Product vs. brand: the 8x10 thought experiment42:23 Consumer monetization shifts45:45 The gravity framework50:32 Isolation principle: most underused lever52:36 Working backwards from exit at day zero57:23 What if your business isn't venture scale?59:32 Book plug: Founders Gold1:00:25 Credits

durée : 00:01:58 - Esprit sport - par : Cédric Guillou - Que ce soit en loisir ou en compétition, les disciplines de glisse sur l'eau attirent de plus en plus les adolescents qui jonglent entre scolarité et quête de trophées, comme pour Axel Gérard, 17 ans et grand espoir mondial du wingfoil. - réalisation : Jérôme Val Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

  In this episode, Ray Cochrane leads with Mozilla shipping Firefox 150 with 271 patched bugs found by Anthropic’s Mythos system, the first major real-world deployment of the AlphaGo-Moment cybersecurity tooling. He also covers a 9-year dormant Linux kernel root, a college student stopping Taiwan’s high-speed rail with a software-defined radio, GitHub MCP secret scanning going GA, the NVIDIA NeMo lawsuit surviving its motion to dismiss, the Hugging Face Reachy Mini app store, Anthropic’s Auto Mode for Claude Code, and the 4-gigabyte AI model Chrome silently installed on your computer. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens the show with the AlphaGo Moment moving from theory into production. Mozilla shipped Firefox 150 this week with 271 patched bugs that Anthropic’s Mythos system found. Furthermore, the broader episode threads a clear pattern: AI tooling is reshaping security, developer workflows, and consumer software faster than the surrounding ecosystem can absorb it. The show closes on the four-gigabyte AI model Chrome installed on a billion machines without explicit consent. Mozilla Ships 271 Mythos Bugs in Firefox 150 Mozilla ran Anthropic’s restricted Mythos system against the Firefox 150 codebase before shipping. The result: 271 found bugs (180 high severity, 80 moderate, 11 low) baked into the release. However, the bigger number is the year-over-year jump. April 2026 shipped 423 total Firefox security fixes versus 31 a year prior. The breakdown for April: 271 from Mythos, 41 from external researchers, and 111 from other internal sources. Cochrane is sticking to his guns on calling this the AlphaGo Moment for cybersecurity. Skeptics argue Mythos is industrial-scale fuzzing because most found bugs sit in memory-safety territory. However, his counter is the velocity itself. Furthermore, he frames the resistance as carriage-versus-cars: humans-first research still grounds the tool, but throughput is the win. The Firefox CTO put it directly: defenders finally have a chance to win, decisively. For developers asking whether Mythos changes anything if they already run fuzzers, Cochrane’s answer is yes, and not even close. Additionally, he notes Mythos is restricted-access. The broadly available tier is Claude Opus 4.7, which Mozilla used since February before getting onto the restricted program for the Firefox 150 cycle. Run Opus 4.7 first. Sponsor: GoDaddy GoDaddy has been sponsoring this show for over twenty years. Economy hosting starts at $6.99/month, WordPress hosting at $12.99/month, and domains at $11.99. Use codes at geeknewscentral.com/godaddy for exclusive deals and to directly support the show. Copy Fail: 9-Year Linux Kernel Bug, 732 Bytes to Root A 9-year-old dormant Linux kernel bug got disclosed April 29 as CVE-2026-31431. Researchers published a 732-byte Python script that roots every major Linux distribution shipped since 2017. Additionally, CISA added the CVE to its Known Exploited Vulnerabilities catalog on May 1 with a May 15 federal deadline. The bug lives in the kernel’s crypto socket layer through the AF_ALG AEAD interface, originating in a 2017 in-place crypto optimization that lacked bounds checking. Cloudflare published their post-mortem this week. Their first instinct was to remove the kernel module entirely. However, service dependencies forced a workaround instead. Cloudflare resumed normal patched-kernel reboot automation across their 330-city fleet on May 4, with manual reboots and rollouts continuing after. Taiwan Rail Stopped by a 23-Year-Old With a Software-Defined Radio A 23-year-old Taiwanese university student with the surname Lin spoofed a TETRA general alarm signal on April 5, stopping trains on Taiwan’s high-speed rail. The accomplice supplied the radio parameters. Both were arrested by month-end. Lin posted NT$100,000 bail; the accomplice posted NT$80,000. The incident hit at 11:23 PM during the Qingming holiday weekend, stopping three revenue passenger trains plus one deadhead. Furthermore, the system has been in service for 19 years without rotating its cryptographic parameters once. Cochrane notes this is exactly the type of long-dormant infrastructure flaw that Mythos-class tooling catches, if anyone bothers to point it at the wires we already have. GitHub MCP Secret Scanning Goes GA GitHub’s secret scanning in the MCP server hit GA on May 5, with dependency scanning entering public preview the same day. Both released after a seven-week public preview run starting March 17. Additionally, the feature lets MCP-compatible coding agents (Copilot CLI, VS Code, JetBrains, Claude Code, Cursor, Windsurf) detect exposed secrets before commits or pull requests. Findings are ephemeral. They surface only in the current chat session and don’t persist as GitHub alerts. Sources disagree on scope: GitHub’s GA changelog says repo-level or org-level settings work, while the docs say only org-level applies. Cochrane flags the open question of whether MCP prompt injections could be exploited to send discovered secrets elsewhere. Subquadratic Debuts a 12-Million-Token Context Window Miami-based Subquadratic emerged from stealth on May 5 with a $29 million seed round and a reported $500 million valuation. Their model, SubQ 1M-Preview, runs on a new Subquadratic Sparse Attention architecture (their technical writeup calls it Selective Attention; same acronym, different second word). The headline claim: a thousand-times reduction in attention compute at 12 million tokens versus frontier models. However, that figure is vendor marketing math. There is no peer-reviewed paper, no public weights, and no independent benchmark replication. Researchers are demanding independent proof. Furthermore, CTO Alex Whedon’s pull line, “Retrieval / RAG plumbing is a waste of human intelligence,” signals how aggressively they want to position against retrieval-augmented architectures. ChatGPT Goblins, China’s “Catch You Steadily”: Sycophancy Is Universal Last week’s ChatGPT goblin obsession has a Chinese-language twin. The model overuses a phrase translating as “I will steadily catch you.” Additionally, a new Stanford and CMU study called ELEPHANT shows social sycophancy is universal across all 11 LLMs tested with 2,400-plus participants. Models endorsed users 49 percent more than humans did, and 47 percent even on harmful prompts. Alibaba’s Qwen and DeepSeek topped the rankings. Cochrane notes sycophancy is obvious once you’re aware of it but tricky to dissuade. Even with explicit instructions, longer context windows can reintroduce the behavior as the instructions get diluted. Furthermore, the trap is believing you’ve handled it. Once you think you’ve got it under control, you’re more prone to being influenced because you stopped watching for it. NVIDIA NeMo Lawsuit: Judge Tigar Denies Motion to Dismiss Three authors filed Nazemian v. NVIDIA in March 2024, alleging NVIDIA used The Pile and Books3 (approximately 196,640 pirated books) to train its NeMo AI framework. NVIDIA’s defense relied on the Sony v. Universal Betamax doctrine, arguing NeMo’s training scripts are general-purpose tools like a VCR. This week, Judge Tigar denied NVIDIA’s motion to dismiss in the Northern District of California. The headline quote: NeMo’s training scripts “have no other purpose than to speed up the process of infringement.” Furthermore, the judge rejected the VCR analogy outright. NeMo’s scripts are not general-purpose tools; they were allegedly purpose-built to ingest pirated material. Cochrane reads the Betamax framing as legal-jargon arbitrage rather than honest defense. The Humanoid Robot Market Is Smaller Than the Hype Michael Barnard at CleanTechnica argues that scenario-math against the global labor market puts realistic humanoid TAM at $200 billion to $1 trillion, not $20 trillion. Near-term wins cluster in warehouses, not homes. Additionally, the framework weighs dexterity burden against human-proximity safety burden. Real opportunities cluster where both burdens are low. Cochrane connects this to last week’s reservations about humanoids in the household. Furthermore, the risk profile is the issue: these robots aren’t prepared for every scenario, can’t make dynamic decisions, and one software update can change the definition of “safe.” Hugging Face Launches Reachy Mini App Store Hugging Face launched an open-source app store for the Reachy Mini robot this week, $299 for the Lite tethered version and $449 wireless. There are 200-plus community-built apps at launch from over 150 creators, with nearly 10,000 Reachy Minis cumulative shipped. Additionally, apps are forkable, with the default agent (ML Intern) able to modify, write, test, and ship code on any existing app. Examples at launch include an office receptionist built in under two hours, a Reachy Phone Home anti-procrastination app, baby-monitor-style apps, a cooking assistant, and a 78-year-old Joel Cohen’s voice-controlled CEO peer-group app. Pollen Robotics, the company behind Reachy, was acquired by Hugging Face on April 14, 2025. Bebop the Humanoid Robot Delays Southwest Flight 1568 A 4-foot, 70-pound humanoid robot named Bebop delayed Southwest flight 1568 from Oakland to San Diego by more than 73 minutes on April 30. The crew flagged the lithium battery as oversized. Furthermore, the battery was reportedly four times the cabin limit. Bebop belongs to Dallas-based Elite Event Robotics, which bought a full-price cabin ticket because the robot exceeded checked-baggage weight. Bebop danced for passengers at the gate before boarding. However, Southwest had Elite remove the batteries before departure, and replacements were overnighted to Chicago for the next event. Cochrane flags the obvious: batteries have always been flagged in aviation, so forgetting that with a humanoid robot in tow is a strange miss. Ouster Rev8: Native Color Lidar With Google, Volvo, Skydio Stating Intent Ouster announced the Rev8 OS Family on May 4 in San Francisco. The sensors fuse depth and color via SPAD detectors (single photon avalanche diodes) on Ouster’s custom L4 and L4 Max chips. Google, Volvo Autonomous Solutions, Skydio, Liebherr, Epiroc, and PlusAI have stated intent to adopt, though nothing is formally signed. Specs include 48-bit color, 116 dB dynamic range, and pre-fused 3D colorized point clouds. The OS1 Max gets 500-meter max detection. Available to order today and shipping this quarter, with no pricing disclosed. CEO Angus Pacala in his TechCrunch interview: “The goal is to obviate cameras. There’s no reason that one sensor can’t do both.” TagTinker Lets a Flipper Zero Mess With Electronic Shelf Labels A new Flipper Zero app called TagTinker uses infrared signals to push images and text to electronic shelf labels. Additionally, these are the same kind of price tags grocery chains are starting to use for surveillance pricing. The app and GitHub repo went public this week. Maryland’s HB 895, signed by Governor Wes Moore, takes effect October 1 as the first-in-nation surveillance pricing law. It covers food retailers and third-party food delivery service providers. Furthermore, ESLs use the same IR signaling as TV remotes with weak security. The dev’s disclaimer states it’s strictly for educational research, security curiosity, and displaying digital art on hardware you legally own. Fitbit App Becomes Google Health, Plus Fitbit Air, Plus Google Fit Sunset Google announced May 7 that the Fitbit app becomes Google Health on May 19, rolling through May 26. The launch ships with the new $99.99 Fitbit Air screenless tracker and the long-rumored Google Fit shutdown. Additionally, the four-tab interface (Today, Fitness, Sleep, Health) bundles a Gemini-powered AI Health Coach. Coach is premium-gated at $9.99/month or $99/year. Medical records integration is US-only at launch. The Fitbit Air gets up to one week of battery life and 50-meter water resistance. However, Cochrane flags conflicting privacy framing: Google’s AI summary bullets say “your data stays private,” but the actual document copy says only “committed to not using Fitbit user health and wellness data for Google Ads.” Those are not the same statement. Russinovich on Why Win32 Won and WinRT Didn’t Microsoft Azure CTO Mark Russinovich said via Microsoft Dev Docs video that Win32, the 1995 API, is still foundational to Windows 11. WinRT, the modernization replacement, “didn’t play out the way a lot of people expected.” Mostly clickbait framing per Windows Latest, but the substantive angle is real. Microsoft is pivoting back to native WinUI 3 development after years of pushing developers toward WebView2 and Electron. Additionally, Electron-based apps are known for insane RAM usage, and everyone is hurting for RAM right now. Furthermore, the bigger open question is whether Electron survives the test of time, especially with the React engine reportedly being rewritten in Rust. “Tabula Plena”: The Brain Starts Full, Not Blank A Nature Communications study from the Institute of Science and Technology Austria found that the mouse hippocampal CA3 recurrent network begins densely connected and refines through pruning. ISTA’s press release frames this as “tabula plena,” meaning full slate, counter to tabula rasa. The paper published April 21. First author Victor Vargas-Barroso and senior author Professor Peter Jonas studied mice at three developmental stages. Furthermore, the “starting overloaded enables faster sensory integration” framing is Jonas’s hypothesis from the press release, not a paper conclusion. Cochrane closes on the bigger question: did we have human growth and experience mapped wrong from the start? The Aqueous Battery You Can Pour Down the Drain A Chinese research team led by Professor Chunyi Zhi at City University of Hong Kong built an aqueous battery using a custom organic polymer electrode plus neutral magnesium and calcium salts (food-grade tofu coagulants) as electrolyte. Published in Nature Communications on February 18. Numbers to know: 120,000-plus charge cycles, full-cell energy density of 48.3 watt-hours per kilogram. That’s well below typical lithium-ion. However, post-cycling analysis showed only magnesium, calcium, chlorine, carbon, and copper, with no heavy metals. The cell complies with US RCRA, ISO 14001, and China’s GB 18599-2020 for direct environmental disposal. Additionally, the “300-plus years” framing is journalists extrapolating from the 120,000 cycles, not a paper claim. ResoNix Klippel Tests Expose Car-Audio Spec Lies Nick Apicella, founder of ResoNix Sound Solutions in Stony Point, New York, spent around $23,000 on independent Klippel LSI and TRF testing of 40 subwoofers. He published 21 results showing widespread misrepresentation of Xmax (excursion) and thermal/power-handling claims. Test data published in three batches between December 2025 and January 2026. Specifics: Wavtech thinPRO12 claimed 20 mm of excursion but delivered 8.85 mm, scoring 15 out of 100 on marketing accuracy. One driver hit 44 percent of advertised excursion. Another tripped thermal protection at half its rated power. Additionally, nine of 21 drivers scored below 50 out of 100. Brands tested include JL Audio, Sundown, Focal, Morel, Audiofrog, Adire, Stereo Integrity, and Dynaudio. Conflict-of-interest flag: ResoNix’s own GUS-15, 12, and 10 prototypes conveniently rank one, two, three. JetBrains Opens 2026 Developer Ecosystem Survey JetBrains opened the 10th annual Developer Ecosystem Survey this week. It takes about 30 minutes, with prizes including a MacBook Pro 16-inch and a $1,000 Amazon gift card. Anonymized raw data is published publicly, and cumulative scale is 100,000-plus developers across recent years. Additionally, the survey is going fully anti-AI: “evil bots, dishonest respondents, and AI agents will be excluded from prize distribution.” Cochrane is curious whether TypeScript holds its 2025 crown after knocking Python off, and whether Rust shows real growth given the wave of LLM-driven Rust rewrites in the past few months. Anthropic’s Claude Code Auto Mode Goes Live Anthropic launched Auto Mode for Claude Code roughly six weeks ago. Claude Code’s previous behavior required user approval for most file modifications and command executions, generating heavy approval-fatigue complaints during longer sessions. Auto Mode is the answer: Claude can run multi-step development tasks without per-action approval. Additionally, the architecture is a two-stage classifier, with stage one a fast yes/no filter and stage two doing chain-of-thought on flagged actions. Cochrane runs his own Claude Code in YOLO mode but with custom rejection rules baked into settings to block commands he doesn’t want, even with skip-permissions on. He recommends configuring settings as the actual policy layer rather than relying on classifier judgment alone. Furthermore, recent posts about Claude deleting websites or wiping production databases reinforce why the settings layer matters more than the auto-mode toggle. Chrome Quietly Installed a 4GB AI Model on Your Computer Google Chrome silently downloads on-device AI model weights (Gemini Nano family) to a `weights.bin` file in the OptGuideOnDeviceModel directory, around four gigabytes in Alexander Hanff’s audit. Furthermore, the model re-downloads if you delete it. Hanff timed his own install at 14 minutes 28 seconds on macOS. Affected platforms include Windows, macOS (including Apple Silicon), and Linux. Hanff frames this as a multi-front legal violation: a direct breach of Europe’s ePrivacy Directive, two articles of GDPR, and an environmental harm of a magnitude that would be notifiable under the Corporate Sustainability Reporting Directive. At one billion users, the four-gigabyte distribution represents roughly 240 gigawatt-hours of network and storage energy paired with about 60,000 tonnes of CO2-equivalent emissions. However, no EU regulator action or formal complaint has surfaced as of this episode. The model powers on-device features (email writing, scam detection, summarization, smart paste, tab grouping) but not the visible AI Mode button, which routes to the cloud. To disable, Cochrane recommends Chrome Settings, then System, then On-device AI, toggle to off. Two more paths exist via `chrome://flags` or a Windows registry edit. Cochrane closes the show with show housekeeping: GNC Insider at geeknewscentral.com/insider, email at geeknews@gmail.com, newsletter signup at geeknewscentral.com, and Pocket Casts as a solid modern podcast app pick. Have a wonderful night. The post Mozilla Meets Mythos #1864 appeared first on Geek News Central.

Improve your foiling skills in paradise! Join us in Montanita Ecuador May 23-30, 2026 for a foil drive / tow / prone foil camp with Ecuador Foil, KT Foiling & Julia Castro. Learn MoreOn this episode, Luc Moore sits down with Floris Dielen from the Waterspeed App team to break down the results of the massive Defi 2026 pre-event challenges. Fresh off closing the leaderboard for Defi Kite, Wing, Windsurf, and Foil, they reveal the yellow jersey winners, standout performances, and some wild stats from thousands of logged kilometers.Floris shares the top finishers across disciplines — including dominant runs from Leopold, Martyn, PierrotRapido, Jean Le Gall, Charly Djen, and more — while highlighting incredible dedication like PierrotRapido's 11-hour, 354 km wing session and multi-event warriors logging 19 sessions each. The duo also discusses the exciting new Waterspeed live tracking feature debuting at Defi Wing and Windsurf: real-time session mapping so friends and family can follow competitors from home.From fastest overall times and location breakdowns to the pure stoke and preparation heading into the main event in France, this episode is packed with results, behind-the-scenes nuggets, and practical info for anyone heading to Defi or following along.Episode Highlights:Full Defi Challenge winners across Kite, Wing, Windsurf & FoilRecord-breaking sessions: 11 hours / 354 km, 19 sessions logged, and the fastest single run of the competitionNew Waterspeed live tracking — follow athletes in real time on the big screen and onlineEvent logistics, yellow jersey stories, and what to expect at Defi 2026Tips for participants and how to join the fun at the Waterspeed boothWhether you're competing at Defi, cheering from home, or just love data-driven foiling/windsurf/kite stories, this episode delivers the latest results and serious inspiration ahead of one of the biggest events on the calendar.

CJ turns the mic on the people behind Mostly Media for a special behind-the-scenes episode of Run the Numbers. Michelle, Callie, Sarah, Matthew, Ben, and Steve share what it's like building the company, scaling media, talent, sales, production, and operations, and dealing with CJ's scooter lore, calendar quirks, and chaos along the way.—SPONSORS:Rillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cjBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJ—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNCJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro2:44 Show intro: meet the Mostly Media team3:37 Michelle Finn: accounting and ops4:28 Parts Tech days: CJ's first CFO role7:21 How the newsletter convinced Michelle to join9:02 Sponsors — Rillet | EY | SpendHound12:11 Callie Spillane: talent director13:17 Callie's background: HubSpot, Sneak, Superhuman13:40 Why Callie was hard to hire15:37 Snyk hypergrowth: 150 to 1,500 people16:59 Zero to one vs. one to ten18:20 9 of 20 roles filled: how it's going23:24 Sponsors — Brex | Aleph | RightRev26:58 Sarah Bousquet: media op27:45 Stay at home mom to ops lead33:48 CJ's schedule: American Psycho34:43 Matthew Mozzocchi: sales and partnerships35:41 Going full time with a newborn36:20 Product market fit signal38:07 Fewer, bigger bets on creators41:00 Podcast is the air game, newsletter is the ground game43:56 Ben Hillman and Steve Cerasoli: production47:46 Media in service of a product vs. the product itself48:37 Run the Numbers vs. Mostly Growth51:25 Where are we in three years?52:40 Credits#RunTheNumbersPodcast #CreatorEconomy #MediaBusiness #Entrepreneurship #ContentBusiness

In this episode of Run the Numbers, CJ Gustafson breaks down the diamond industry as a business model: how De Beers controlled supply, engineered demand, and built one of the most powerful pricing machines in history. From the Central Selling Organization to “A Diamond Is Forever” to the rise of lab-grown diamonds, this episode unpacks monopolies, scarcity, pricing power, and what happens when technology forces transparency.—SPONSORS:RightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cjBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/run—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNCJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:Why Uber Drivers Can't Escape the 30% Cuthttps://youtu.be/LpbH9GpBrSY—TIMESTAMPS:All verified. Here are the timestamps:0:00 Blood Diamond1:55 Show intro2:15 Diamond history: ancient India and riverbed origins3:56 Brazil supply shock and the mining era4:39 De Beers: Cecil Rhodes and the PE rollup6:47 Sponsors — RightRev | Rillet | EY9:49 The Central Selling Organization (CSO)10:39 The site system: invite-only, take-it-or-leave-it auctions12:22 Diamonds are more abundant than gold13:01 NW Ayer, Bernays, and engineering demand14:17 "A Diamond is Forever" campaign15:13 Lab grown diamonds: 20% of US purchases16:32 De Beers collapses: $3.1B operating loss17:18 Sponsors — SpendHound | Brex | Aleph21:07 Lesson 1: Rollups are powered by capital21:39 Lesson 2: Distribution beats mining22:11 Lesson 3: Artificial scarcity unravels22:55 Lesson 4: Price opacity is a temporary moat23:39 Lesson 5: Demand engineering24:30 Lesson 6: The real product was risk smoothing26:30 Credits#RunTheNumbersPodcast #BusinessStrategy #FinanceHistory #Pricing #Monopoly

When Jeff Wang stepped into the CEO role at Windsurf, it was not part of some long-term succession plan. It happened in the middle of a full-blown crisis. In this episode of the ProductLed Podcast, Wes Bush and Esben Friis-Jensen sit down with Jeff to unpack the wild chain of events that followed the collapsed OpenAI acquisition, the founders leaving for Google, and the intense 72-hour window Jeff had to help save the company and protect 250 jobs. He shares how Windsurf navigated that moment, how the Cognition deal came together, and what it has been like leading one of the most closely watched teams in AI coding ever since. Jeff also gets into what made Windsurf so strategically valuable in the first place, from shipping early breakthroughs in autocomplete, chat, context engineering, and agent workflows, to building one of the first generally available coding agents on the market. Beyond the origin story, the conversation goes deep on go-to-market strategy, why free products worked early on, how token economics changed the game, and why enterprise AI adoption takes far more than handing teams a tool. They also explore Windsurf 2.0, the shift toward managing multiple agents at once, how Jeff uses AI in his own CEO workflows, and why founders need to obsess over painful problems, customer conversations, and product-market fit instead of flashy demos. Key Highlights: 00:00 - The 72-Hour Crisis That Changed Everything Jeff shares the short version of the OpenAI, Google, and Cognition saga, and what it was like stepping into the CEO role during a company-defining emergency. 01:40 - Why Big Tech Wanted the Windsurf Team A look at the execution speed, product breakthroughs, and agent innovations that made Windsurf one of the most valuable teams in AI coding. 04:10 - The Future of Coding Is Multi-Agent Jeff explains why developers are moving from one-on-one AI assistance to managing many agents at once, and how Windsurf 2.0 is built for that shift. 08:54 - How Free Became Their Growth Wedge From free autocomplete to on-prem enterprise deals, Jeff walks through Windsurf's early PLG motion and how it created awareness and pipeline. 13:10 - The Hard Truth About AI Pricing A candid discussion on token costs, self-serve subsidies, pricing pressure, and why raising prices can reveal whether you truly have product-market fit. 16:13 - Why Enterprise AI Sales Are Top-Down Jeff shares how Windsurf sells into large companies by focusing on transformation, adoption, security, and measurable outcomes instead of seat counts. 20:51 - What It Takes to Drive Real AI Adoption Why playbooks, training, and solving a meaningful first use case matter more than just rolling out a shiny new tool to an engineering team. 24:40 - Jeff's AI Workflows as CEO Jeff reveals how he uses AI and custom playbooks for go-to-market research, outreach preparation, and spotting product trends before opening dashboards. 32:32 - Jeff's Advice for Every Product Founder Build around painful problems, talk to hundreds of prospects, and learn to enjoy rejection because that is often where the real insight comes from. Resources:

Episode 2 - NEWS from the windsurfing World

Hey ya'll, Alex here with your weekly AI news catch up. It's one of those Thursday's where no matter how well I prep, the big AI labs are hell bent to show up before each other. Alibaba dropped Qwen 3.6 with Apache 2, confirming their commitment to Open Source, then Anthropic released Claude Opus 4.7 (not quite Mythos) and OpenAI followed with a huge Codex update that includes Computer Use among other things. The highlight of Computer User is the background usage, more on that below. This is all just from today!Previously in the week we had 2 incredible 3D world generators, Lyra 2.0 from Nvidia and HYWorld 2 from Tencent, Windsurf dropping 2.0 version with Devin integration and Google releasing a Gemini TTS, with over 90+ languages support and incredible emotions range, and Baidu open sources Ernie Image, rivaling Nano Banana. Today on the show we had 3 awesome guests, Theodor from Cognition joined to cover the new Windsurf, Kwindla is back on the show to talk about “the side project that escaped containment” Gradient-Bang, a multi agent, voice based space game and Trevor from Marimo joined to talk about pairing your agents with a Marimo notebook. Let's dive in!

Get Sam & Shaan's pro-level biz resource vault (free): https://clickhubspot.com/kgcm Episode 805: Sam Parr ( https://x.com/theSamParr ) and Shaan Puri ( https://x.com/ShaanVP ) talk to the branding genius behind BlackBerry, Sonos, Vercel and Swiffer about how to create a billion-dollar brand name.  — Show Notes:  (0:00) Nothing will be used more than your name (2:01) Windsurf (3:50) Swiffer (10:06) Naming game: Fiber startup (17:14) quantity leads to quality (29:46) Problem solving propositions (32:40) Power letters (35:04) How Sam names a company (40:48) Rate this brand (1-10)  (47:50) Blackberry (48:24) When to change a name (50:47) Presidential slogans (51:43) Recommended reading (53:39) How David thinks about AI — Check Out Shaan's Stuff: • Shaan's weekly email - https://www.shaanpuri.com  • Visit https://www.somewhere.com/mfm to hire worldwide talent like Shaan and get $500 off for being an MFM listener. Hire developers, assistants, marketing pros, sales teams and more for 80% less than US equivalents. • Mercury - Need a bank for your company? Go check out Mercury (mercury.com). Shaan uses it for all of his companies! Mercury is a financial technology company, not an FDIC-insured bank. Banking services provided by Choice Financial Group, Column, N.A., and Evolve Bank & Trust, Members FDIC • I run all my newsletters on Beehiiv and you should too + we're giving away $10k to our favorite newsletter, check it out: beehiiv.com/mfm-challenge — Check Out Sam's Stuff: • Hampton - https://www.joinhampton.com/ • Ideation Bootcamp - https://www.ideationbootcamp.co/ • Copy That - https://copythat.com • Hampton Wealth Survey - https://joinhampton.com/wealth • Sam's List - http://samslist.co/ My First Million is a HubSpot Original Podcast // Brought to you by HubSpot Media // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano /

Es wird mal wieder Zeit dem Winter zu entfliehen. Steigt mit uns in den prall gefüllte Billigflieger und entdeckt den Bei allen Wind- und Wellensportlern beliebten Spot El Medano auf Teneiffa. Zusammen mit Windsurf- und Wingfoil Spezi Philipp Kümpel entdecken wir diese kleine Bucht und ihren Vibe. Wer nebenbei noch was über Filmmusik lernen will, ist hier auch an der richtigen Stelle.

The State of JavaScript 2025 survey results are in this week, and there's some givens and some surprises this year. Givens: Vite's still a favorites and devs want more native TS features. Surprises: ChatGPT usage declined, no one's using Windsurf, and Bun is the third most-used JS runtime. And thanks for making us the most written-in podcast of the survey! We appreciate it!An 8 month study conducted by the Harvard Business Review reports AI tools don't shrink work for employees, they actually intensify it. Employees work faster, take on more tasks, and work longer hours, which can lead to burnout, cognitive fatigue, and lower quality work over time. And there's yet another new React2Shell flaw that's being exploited by the ILOVEPOOP toolkit to scan and target vulnerable Next.js and RSC environments. Patch your React apps, folks.Timestamps:0:59 - State of JS survey results23:53 - Harvard Business Review's report on how AI is changing work35:30 - React2Shell exploits and ILOVEPOOP40:08 - The largest domain name purchase ever43:00 - Adobe Acrobat can turn PDFs into podcasts46:48 - We hit 100k downloads!47:55 - What's making us happyNews:Paige - State of JavaScript survey results 2025Jack - React2Shell exploitsTJ - HBR reports AI didn't shrink work for employees, it intensified it and the burnout is getting realLightning News: Thanks for helping us reach 100k downloads!The largest publicly disclosed domain name purchase everAdobe Acrobat can turn PDFs into podcastsWhat Makes Us Happy this Week:Paige - San Diego Zoo Safari ParkJack - Bambu Lab H2CTJ - Ubuntu OSThanks as always to our sponsor, the Blue Collar Coder channel on YouTube. You can join us in our Discord channel, explore our website and reach us via email, or talk to us on X, Bluesky, or YouTube.Front-end Fire websiteBlue Collar Coder on YouTubeBlue Collar Coder on DiscordReach out via emailTweet at us on X @front_end_fireFollow us on Bluesky @front-end-fire.comSubscribe to our YouTube channel @Front-EndFirePodcast

From building internal AI labs to becoming CTO of Brex, James Reggio has helped lead one of the most disciplined AI transformations inside a real financial institution where compliance, auditability, and customer trust actually matter.We sat down with Reggio to unpack Brex's three-pillar AI strategy (corporate, operational, and product AI) [https://www.brex.com/journal/brex-ai-native-operations], how SOP-driven agents beat overengineered RL in ops, why Brex lets employees “build their own AI stack” instead of picking winners [https://www.conductorone.com/customers/brex/], and how a small, founder-heavy AI team is shipping production agents to 40,000+ companies. Reggio also goes deep on Brex's multi-agent “network” architecture, evals for multi-turn systems, agentic coding's second-order effects on codebase understanding, and why the future of finance software looks less like dashboards and more like executive assistants coordinating specialist agents behind the scenes.We discuss:* Brex's three-pillar AI strategy: corporate AI for 10x employee workflows, operational AI for cost and compliance leverage, and product AI that lets customers justify Brex as part of their AI strategy to the board* Why SOP-driven agents beat overengineered RL in finance ops, and how breaking work into auditable, repeatable steps unlocked faster automation in KYC, underwriting, fraud, and disputes* Building an internal AI platform early: LLM gateways, prompt/version management, evals, cost observability, and why platform work quietly became the force multiplier behind everything else* Multi-agent “networks” vs single-agent tools: why Brex's EA-style assistant coordinates specialist agents (policy, travel, reimbursements) through multi-turn conversations instead of one-shot tool calls* The audit agent pattern: separating detection, judgment, and follow-up into different agents to reduce false negatives without overwhelming finance teams* Centralized AI teams without resentment: how Brex avoided “AI envy” by tying work to business impact and letting anyone transfer in if they cared deeply enough* Letting employees build their own AI stack: ChatGPT vs Claude vs Gemini, Cursor vs Windsurf, and why Brex refuses to pick winners in fast-moving tool races* Measuring adoption without vanity metrics: why “% of code written by AI” is the wrong KPI and what second-order effects (slop, drift, code ownership) actually matter* Evals in the real world: regression tests from ops QA, LLM-as-judge for multi-turn agents, and why integration-style evals break faster than you expect* Teaching AI fluency at scale: the user → advocate → builder → native framework, ops-led training, spot bonuses, and avoiding fear-based adoption* Re-interviewing the entire engineering org: using agentic coding interviews internally to force hands-on skill upgrades without formal performance scoring* Headcount in the age of agents: why Brex grew the business without growing engineering, and why AI amplifies bad architecture as fast as good decisions* The future of finance software: why dashboards fade, assistants take over, and agent-to-agent collaboration becomes the real UI—James Reggio* X: https://x.com/jamesreggio* LinkedIn: https://www.linkedin.com/in/jamesreggio/Where to find Latent Space* X: https://x.com/latentspacepodFull Video EpisodeTimestamps00:00:00 Introduction00:01:24 From Mobile Engineer to CTO: The Founder's Path00:03:00 Quitters Welcome: Building a Founder-Friendly Culture00:05:13 The AI Team Structure: 10-Person Startup Within Brex00:11:55 Building the Brex Agent Platform: Multi-Agent Networks00:13:45 Tech Stack Decisions: TypeScript, Mastra, and MCP00:24:32 Operational AI: Automating Underwriting, KYC, and Fraud00:16:40 The Brex Assistant: Executive Assistant for Every Employee00:40:26 Evaluation Strategy: From Simple SOPs to Multi-Turn Evals00:37:11 Agentic Coding Adoption: Cursor, Windsurf, and the Engineering Interview00:58:51 AI Fluency Levels: From User to Native01:09:14 The Audit Agent Network: Finance Team Agents in Action01:03:33 The Future of Engineering Headcount and AI Leverage Get full access to Latent.Space at www.latent.space/subscribe

From building internal AI labs to becoming CTO of Brex, James Reggio has helped lead one of the most disciplined AI transformations inside a real financial institution where compliance, auditability, and customer trust actually matter. We sat down with Reggio to unpack Brex's three-pillar AI strategy (corporate, operational, and product AI) [https://www.brex.com/journal/brex-ai-native-operations], how SOP-driven agents beat overengineered RL in ops, why Brex lets employees “build their own AI stack” instead of picking winners [https://www.conductorone.com/customers/brex/], and how a small, founder-heavy AI team is shipping production agents to 40,000+ companies. Reggio also goes deep on Brex's multi-agent “network” architecture, evals for multi-turn systems, agentic coding's second-order effects on codebase understanding, and why the future of finance software looks less like dashboards and more like executive assistants coordinating specialist agents behind the scenes. We discuss: Brex's three-pillar AI strategy: corporate AI for 10x employee workflows, operational AI for cost and compliance leverage, and product AI that lets customers justify Brex as part of their AI strategy to the board Why SOP-driven agents beat overengineered RL in finance ops, and how breaking work into auditable, repeatable steps unlocked faster automation in KYC, underwriting, fraud, and disputes Building an internal AI platform early: LLM gateways, prompt/version management, evals, cost observability, and why platform work quietly became the force multiplier behind everything else Multi-agent “networks” vs single-agent tools: why Brex's EA-style assistant coordinates specialist agents (policy, travel, reimbursements) through multi-turn conversations instead of one-shot tool calls The audit agent pattern: separating detection, judgment, and follow-up into different agents to reduce false negatives without overwhelming finance teams Centralized AI teams without resentment: how Brex avoided “AI envy” by tying work to business impact and letting anyone transfer in if they cared deeply enough Letting employees build their own AI stack: ChatGPT vs Claude vs Gemini, Cursor vs Windsurf, and why Brex refuses to pick winners in fast-moving tool races Measuring adoption without vanity metrics: why “% of code written by AI” is the wrong KPI and what second-order effects (slop, drift, code ownership) actually matter Evals in the real world: regression tests from ops QA, LLM-as-judge for multi-turn agents, and why integration-style evals break faster than you expect Teaching AI fluency at scale: the user → advocate → builder → native framework, ops-led training, spot bonuses, and avoiding fear-based adoption Re-interviewing the entire engineering org: using agentic coding interviews internally to force hands-on skill upgrades without formal performance scoring Headcount in the age of agents: why Brex grew the business without growing engineering, and why AI amplifies bad architecture as fast as good decisions The future of finance software: why dashboards fade, assistants take over, and agent-to-agent collaboration becomes the real UI — James Reggio X: https://x.com/jamesreggio LinkedIn: https://www.linkedin.com/in/jamesreggio/ Where to find Latent Space X: https://x.com/latentspacepod Substack: https://www.latent.space/ Chapters 00:00:00 Introduction 00:01:24 From Mobile Engineer to CTO: The Founder's Path 00:03:00 Quitters Welcome: Building a Founder-Friendly Culture 00:05:13 The AI Team Structure: 10-Person Startup Within Brex 00:11:55 Building the Brex Agent Platform: Multi-Agent Networks 00:13:45 Tech Stack Decisions: TypeScript, Mastra, and MCP 00:24:32 Operational AI: Automating Underwriting, KYC, and Fraud 00:16:40 The Brex Assistant: Executive Assistant for Every Employee 00:40:26 Evaluation Strategy: From Simple SOPs to Multi-Turn Evals 00:37:11 Agentic Coding Adoption: Cursor, Windsurf, and the Engineering Interview 00:58:51 AI Fluency Levels: From User to Native 01:09:14 The Audit Agent Network: Finance Team Agents in Action 01:03:33 The Future of Engineering Headcount and AI Leverage

Aaron and Brian review the Year in AI, hand out AI awards, and discuss the biggest AI trends from 2025. Maybe a few predictions will be made as well.SHOW: 987SHOW TRANSCRIPT: The Cloudcast #987 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW SPONSORS:SHOW NOTESCLOUD & AI NEWS OF THE MONTH - NOV 2025 (show)CLOUD & AI NEWS OF THE MONTH - OCT 2025 (show)CLOUD & AI NEWS OF THE MONTH - SEPT 2025 (show)CLOUD & AI NEWS OF THE MONTH - AUG 2025 (show)CLOUD & AI NEWS OF THE MONTH - JUL 2025 (show)CLOUD & AI NEWS OF THE MONTH - JUN 2025 (show)CLOUD & AI NEWS OF THE MONTH - MAY 2025 (show)CLOUD & AI NEWS OF THE MONTH - APR 2025 (show)CLOUD & AI NEWS OF THE MONTH - MAR 2025 (show)CLOUD & AI NEWS OF THE MONTH - FEB 2025 (show)CLOUD & AI NEWS OF THE MONTH - JAN 2025 (show)2025 AI YEAR IN REVIEWThe Year of OpenAIThe Year of NVIDIAThe Year of MicrosoftThe Year of GoogleThe Year of OracleThe Year of China AIThe Year of AppleThe Year of Coding Agents (Anthropic, Cursor, Windsurf, CLIs, etc..)The Year of Data CentersAI Highlights and Lowlights (Corporate Layoffs, Acquihires, Funding, etc..)2026 AI DraftFEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

Did AI end up being a political force this year?

Glean started as a Kleiner Perkins incubation and is now a $7B, $200m ARR Enterprise AI leader. Now KP has tapped its own podcaster to lead it's next big swing.From building go-to-market the hard way in startups (and scaling Palo Alto Networks' public cloud business) to joining Kleiner Perkins to help technical founders turn product edge into repeatable revenue, Joubin Mirzadegan has spent the last decade obsessing over one thing: distribution and how ideas actually spread, sell, and compound. That obsession took him from launching the CRO-only podcast Grit (https://www.youtube.com/playlist?list=PLRiWZFltuYPF8A6UGm74K2q29UwU-Kk9k) as a hiring wedge, to working alongside breakout companies like Glean and Windsurf, to now incubating Roadrunner which is an AI-native rethink of CPQ and quoting workflows as pricing models collapse from “seats” into consumption, bundles, renewals, and SKU sprawl.We sat down with Joubin to dig into the real mechanics of making conversations feel human (rolling early, never sending questions, temperature + lighting hacks), what Windsurf got right about “Google-class product and Salesforce-class distribution,” how to hire early sales leaders without getting fooled by shiny logos, why CPQ is quietly breaking the back of modern revenue teams, and his thesis for his new company and KP incubation Roadrunner (https://www.roadrunner.ai/): rebuild the data model from the ground up, co-develop with the hairiest design partners, and eventually use LLMs to recommend deal structures the way the best reps do without the Slack-channel chaos of deal desk.We discuss:* How to make guests instantly comfortable: rolling early, no “are you ready?”, temperature, lighting, and room dynamics* Why Joubin refuses to send questions in advance (and when you might have to anyway)* The origin of the CRO-only podcast: using media as a hiring wedge and relationship engine* The “commit to 100 episodes” mindset: why most shows die before they find their voice* Founder vs exec interviews: why CEOs can speak more freely (and what it unlocks in conversation)* What Glean taught him about enterprise AI: permissions, trust, and overcoming “category is dead” skepticism* Design partners as the real unlock: why early believers matter and how co-development actually works* Windsurf's breakout: what it means to be serious about “Google-class product + Salesforce-class distribution”* Why technical founders struggle with GTM and how KP built a team around sales, customer access, and demand gen* Hiring early sales leaders: anti-patterns (logos), what to screen for (motivation), and why stage-fit is everything* The CPQ problem & Roadrunner's thesis: rebuilding CPQ/quoting from the data model up for modern complexity* How “rules + SKUs + approvals” create a brittle graph and what it takes to model it without tipping over* The two-year window: incumbents rebuilding slowly vs startups out-sprinting with AI-native architecture* Where AI actually helps: quote generation, policy enforcement, approval routing, and deal recommendation loops—Joubin* X: https://x.com/Joubinmir* LinkedIn: https://www.linkedin.com/in/joubin-mirzadegan-66186854/Where to find Latent Space* X: https://x.com/latentspacepodFull Video EpisodeTimestamps00:00:00 Introduction and the Zuck Interview Experience00:03:26 The Genesis of the Grit Podcast: Hiring CROs Through Content00:13:20 Podcast Philosophy: Creating Authentic Conversations00:15:44 Working with Arvind at Glean: The Enterprise Search Breakthrough00:26:20 Windsurf's Sales Machine: Google-Class Product Meets Salesforce-Class Distribution00:30:28 Hiring Sales Leaders: Anti-Patterns and First Principles00:39:02 The CPQ Problem: Why Salesforce and Legacy Tools Are Breaking00:43:40 Introducing Roadrunner: Solving Enterprise Pricing with AI00:49:19 Building Roadrunner: Team, Design Partners, and Data Model Challenges00:59:35 High Performance Philosophy: Working Out Every Day and Reducing Friction01:06:28 Defining Grit: Passion Plus Perseverance Get full access to Latent.Space at www.latent.space/subscribe

Glean started as a Kleiner Perkins incubation and is now a $7B, $200m ARR Enterprise AI leader. Now KP has tapped its own podcaster to lead it's next big swing. From building go-to-market the hard way in startups (and scaling Palo Alto Networks' public cloud business) to joining Kleiner Perkins to help technical founders turn product edge into repeatable revenue, Joubin Mirzadegan has spent the last decade obsessing over one thing: distribution and how ideas actually spread, sell, and compound. That obsession took him from launching the CRO-only podcast Grit (https://www.youtube.com/playlist?list=PLRiWZFltuYPF8A6UGm74K2q29UwU-Kk9k) as a hiring wedge, to working alongside breakout companies like Glean and Windsurf, to now incubating Roadrunner which is an AI-native rethink of CPQ and quoting workflows as pricing models collapse from “seats” into consumption, bundles, renewals, and SKU sprawl. We sat down with Joubin to dig into the real mechanics of making conversations feel human (rolling early, never sending questions, temperature + lighting hacks), what Windsurf got right about “Google-class product and Salesforce-class distribution,” how to hire early sales leaders without getting fooled by shiny logos, why CPQ is quietly breaking the back of modern revenue teams, and his thesis for his new company and KP incubation Roadrunner (https://www.roadrunner.ai/): rebuild the data model from the ground up, co-develop with the hairiest design partners, and eventually use LLMs to recommend deal structures the way the best reps do without the Slack-channel chaos of deal desk. We discuss: How to make guests instantly comfortable: rolling early, no “are you ready?”, temperature, lighting, and room dynamics Why Joubin refuses to send questions in advance (and when you might have to anyway) The origin of the CRO-only podcast: using media as a hiring wedge and relationship engine The “commit to 100 episodes” mindset: why most shows die before they find their voice Founder vs exec interviews: why CEOs can speak more freely (and what it unlocks in conversation) What Glean taught him about enterprise AI: permissions, trust, and overcoming “category is dead” skepticism Design partners as the real unlock: why early believers matter and how co-development actually works Windsurf's breakout: what it means to be serious about “Google-class product + Salesforce-class distribution” Why technical founders struggle with GTM and how KP built a team around sales, customer access, and demand gen Hiring early sales leaders: anti-patterns (logos), what to screen for (motivation), and why stage-fit is everything The CPQ problem & Roadrunner's thesis: rebuilding CPQ/quoting from the data model up for modern complexity How “rules + SKUs + approvals” create a brittle graph and what it takes to model it without tipping over The two-year window: incumbents rebuilding slowly vs startups out-sprinting with AI-native architecture Where AI actually helps: quote generation, policy enforcement, approval routing, and deal recommendation loops — Joubin X: https://x.com/Joubinmir LinkedIn: https://www.linkedin.com/in/joubin-mirzadegan-66186854/ Where to find Latent Space X: https://x.com/latentspacepod Substack: https://www.latent.space/ Chapters 00:00:00 Introduction and the Zuck Interview Experience 00:03:26 The Genesis of the Grit Podcast: Hiring CROs Through Content 00:13:20 Podcast Philosophy: Creating Authentic Conversations 00:15:44 Working with Arvind at Glean: The Enterprise Search Breakthrough 00:26:20 Windsurf's Sales Machine: Google-Class Product Meets Salesforce-Class Distribution 00:30:28 Hiring Sales Leaders: Anti-Patterns and First Principles 00:39:02 The CPQ Problem: Why Salesforce and Legacy Tools Are Breaking 00:43:40 Introducing Roadrunner: Solving Enterprise Pricing with AI 00:49:19 Building Roadrunner: Team, Design Partners, and Data Model Challenges 00:59:35 High Performance Philosophy: Working Out Every Day and Reducing Friction 01:06:28 Defining Grit: Passion Plus Perseverance

Improve your foiling skills in paradise! Join us in Montanita Ecuador May 23-30, 2026 for a foil drive / tow / prone foil camp with Ecuador Foil, KT Foiling & Julia Castro. Learn MoreWelcome to the very first episode of the Foil Life Travel Show! Your go to series series dedicated to the world's best foil destinations, schools, accommodations and hidden gems. We're kicking off the journey in style with Carl from The Launch Pad Windsurf & Foil Academy on his private island in Georgian Bay, Ontario, Canada — hands-down one of the most stunning and unique teaching spots on the planet.In this premiere episode you'll discover:- Why a 45-year windsurfing veteran bought his own island and turned it into a full-time foil & windsurf academy- How Georgian Bay delivers Caribbean-looking water and surprisingly consistent summer breeze (thanks to Canadian Shield thermals + the biggest wind farm in Ontario nearby)- Carl's insanely effective (and fun) teaching system: the world's wobbliest land simulator, BB Talkin' headsets, tow-foiling, dry-land drills, and post-session island beers- Lessons in windsurfing, wind-foiling, and wing-foiling — with wing-foiling now ~50% of bookings- Crazy traveler-friendly pricing: only CAD $175 for a 2-hour private lesson (everything above costs gets donated to Toronto Windsurfing Club)- Water-access-only adventure: boat rentals, water taxis, nearby lodges & campsites all linked- Duotone & North-supported gear fleet including the magical iRig for kids and beginnersIf you've ever wanted to learn to foil (or level up) surrounded by crystal-clear Canadian wilderness that feels like the BVI but with zero crowds… this is your spot.Book your island foil adventure at: https://www.launchpadwindsurfacademy.ca/View Listing: https://foillifepodcast.com/the-launch-pad-windsurf-foil-academyNext stop… who knows? Drop your dream foil destination in the comments ⬇️#FoilTravelShow #001 #WingFoil #WindFoil #Windsurf #GeorgianBay #CanadaFoiling #LearnToFoil #PrivateIslandVibes #TheLaunchPad

This episode is brought to you by Villa Carina Apartments in beautiful Bonaire. Welcome to the very first episode of the Foil Life Travel Show! Your go to series series dedicated to the world's best foil destinations, schools, accommodations and hidden gems. We're kicking off the journey in style with Carl from The Launch Pad Windsurf & Foil Academy on his private island in Georgian Bay, Ontario, Canada — hands-down one of the most stunning and unique teaching spots on the planet.In this premiere episode you'll discover:- Why a 45-year windsurfing veteran bought his own island and turned it into a full-time foil & windsurf academy- How Georgian Bay delivers Caribbean-looking water and surprisingly consistent summer breeze (thanks to Canadian Shield thermals + the biggest wind farm in Ontario nearby)- Carl's insanely effective (and fun) teaching system: the world's wobbliest land simulator, BB Talkin' headsets, tow-foiling, dry-land drills, and post-session island beers- Lessons in windsurfing, wind-foiling, and wing-foiling — with wing-foiling now ~50% of bookings- Crazy traveler-friendly pricing: only CAD $175 for a 2-hour private lesson (everything above costs gets donated to Toronto Windsurfing Club)- Water-access-only adventure: boat rentals, water taxis, nearby lodges & campsites all linked- Duotone & North-supported gear fleet including the magical iRig for kids and beginnersIf you've ever wanted to learn to foil (or level up) surrounded by crystal-clear Canadian wilderness that feels like the BVI but with zero crowds… this is your spot.Book your island foil adventure at: https://www.launchpadwindsurfacademy.ca/View Listing: https://foillifepodcast.com/the-launch-pad-windsurf-foil-academyNext stop… who knows? Drop your dream foil destination in the comments ⬇️#FoilTravelShow #001 #WingFoil #WindFoil #Windsurf #GeorgianBay #CanadaFoiling #LearnToFoil #PrivateIslandVibes #TheLaunchPad

Tomer Cohen is the longtime chief product officer at LinkedIn, where he's pioneering the Full Stack Builder program, a radical new approach to product development that fully embraces what AI makes possible. Under his leadership, LinkedIn has scrapped its traditional Associate Product Manager program and replaced it with an Associate Product Builder program that teaches coding, design, and PM skills together. He's also introduced a formal “Full Stack Builder” title and career ladder, enabling anyone from any function to take products from idea to launch. In this conversation, Tomer explains why product development has become too complex at most companies and how LinkedIn is building an AI-powered product team that can move faster, adapt more quickly, and do more with less.We discuss:1. How 70% of the skills needed for jobs will change by 20302. The broken traditional model: organizational bloat slows features to a six-month cycle3. The Full Stack Builder model4. Three pillars of making FSB work: platform, agents, and culture (culture matters most)5. Building specialized agents that critique ideas and find vulnerabilities6. Why off-the-shelf AI tools never work on enterprise code without customization7. Top performers adopt AI tools fastest, contrary to expectations about leveling effects8. Change management tactics: celebrating wins, making tools exclusive, updating performance reviews—Brought to you by:Vanta—Automate compliance. Simplify security: https://vanta.com/lennyFigma Make—A prompt-to-code tool for making ideas real: https://www.figma.com/lenny/Miro—The AI Innovation Workspace where teams discover, plan, and ship breakthrough products: https://miro.com/lenny—Transcript: https://www.lennysnewsletter.com/p/why-linkedin-is-replacing-pms—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/180042347/my-takeaways-from-this-conversation—Where to find Tomer Cohen:• LinkedIn: https://www.linkedin.com/in/tomercohen• Podcast: https://podcasts.apple.com/us/podcast/building-one-with-tomer-cohen/id1726672498—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Tomer Cohen(04:42) The need for change in product development(11:52) The full-stack builder model explained(16:03) Implementing AI and automation in product development(19:17) Building and customizing AI tools(27:51) The timeline to launch(31:46) Pilot program and early results(37:04) Feedback from top talent(39:48) Change management and adoption(46:53) Encouraging people to play with AI tools(41:21) Performance reviews and full-stack builders(48:00) Challenges and specialization(50:05) Finding talent(52:46) Tips for implementing in your own company(56:43) Lightning round and final thoughts—Referenced:• How LinkedIn became interesting: The inside story | Tomer Cohen (CPO at LinkedIn): https://www.lennysnewsletter.com/p/how-linkedin-became-interesting-tomer-cohen• LinkedIn: https://www.linkedin.com• Cursor: https://cursor.com• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• Devin: https://devin.ai• Figma: https://www.figma.com• Microsoft Copilot: https://copilot.microsoft.com• Windsurf: https://windsurf.com• Building a magical AI code editor used by over 1 million developers in four months: The untold story of Windsurf | Varun Mohan (co-founder and CEO): https://www.lennysnewsletter.com/p/the-untold-story-of-windsurf-varun-mohan• Lovable: https://lovable.dev• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (co-founder and CEO): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• APB program at LinkedIn: https://careers.linkedin.com/pathways-programs/entry-level/apb• Naval Ravikant on X: https://x.com/naval• One Song podcast: https://podcasts.apple.com/us/podcast/%D7%A9%D7%99%D7%A8-%D7%90%D7%97%D7%93-one-song/id1201883177• Song Exploder podcast: https://songexploder.net• Grok on Tesla: https://www.tesla.com/support/grok• Reid Hoffman on X: https://x.com/reidhoffman—Recommended books:• Why Nations Fail: The Origins of Power, Prosperity, and Poverty: https://www.amazon.com/Why-Nations-Fail-Origins-Prosperity/dp/0307719227• Outlive: The Science and Art of Longevity: https://www.amazon.com/Outlive-Longevity-Peter-Attia-MD/dp/0593236599• The Beginning of Infinity: Explanations That Transform the World: https://www.amazon.com/Beginning-Infinity-Explanations-Transform-World/dp/0143121359—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

AI Assisted Coding: Building Reliable Software with Unreliable AI Tools In this special episode, Lada Kesseler shares her journey from AI skeptic to pioneer in AI-assisted development. She explores the spectrum from careful, test-driven development to quick AI-driven experimentation, revealing practical patterns, anti-patterns, and the critical role of judgment in modern software engineering. From Skeptic to Pioneer: Lada's AI Coding Journey "I got a new skill for free!"   Lada's transformation began when she discovered Anthropic's Claude Projects. Despite being skeptical about AI tools throughout 2023, she found herself learning Angular frontend development with AI—a technology she had no prior experience with. This breakthrough moment revealed something profound: AI could serve as an extension of her existing development skills, enabling her to acquire new capabilities without the traditional learning curve. The journey evolved through WindSurf and Claude Code, each tool expanding her understanding of what's possible when developers collaborate with AI. Understanding Vibecoding vs. AI-Assisted Development "AI assisted coding requires judgment, and it's never been as important to exercise judgment as now."   Lada introduces the concept of "vibecoding" as one extreme on a new dimension in software development—the spectrum from careful, test-driven development to quick, AI-driven experimentation. The key insight isn't that one approach is superior, but that developers must exercise judgment about which approach fits their context. She warns against careless AI coding for production systems: "You just talk to a computer, you say, do this, do that. You don't really care about code... For some systems, that's fine. When the problem arises is when you put the stuff to production and you really care about your customers. Please, please don't do that." This wisdom highlights that with great power comes great responsibility—AI accelerates both good and bad practices. The Answer Injection Anti-Pattern When Working With AI "You're limiting yourself without knowing, you're limiting yourself just by how you formulate your questions. And it's so hard to detect."   One of Lada's most important discoveries is the "answer injection" anti-pattern—when developers unconsciously constrain AI's responses by how they frame their questions. She experienced this firsthand when she asked an AI about implementing a feature using a specific approach, only to realize later that she had prevented the AI from suggesting better alternatives. The solution? Learning to ask questions more openly and reformulating problems to avoid self-imposed limitations. As she puts it, "Learn to ask the right way. This is one of the powers this year that's been kind of super cool." This skill of question formulation has become as critical as any technical capability.   Answer injection is when we—sometimes, unknowingly—ask a leading question that also injects a possible answer. It's an anti-pattern because LLM's have access to far more information than we do. Lada's advice: "just ask for anything you need", the LLM might have a possible answer for you. Never Trust a Single LLM: Multi-Agent Collaboration "Never trust the output of a single LLM. When you ask it to develop a feature, and then you ask the same thing to look at that feature, understand the code, find the issues with it—it suddenly finds improvements."   Lada shares her experiments with swarm programming—using multiple AI instances that collaborate and cross-check each other's work. She created specialized agents (architect, developer, tester) and even built systems using AppleScript and Tmux to make different AI instances communicate with each other. This approach revealed a powerful pattern: AI reviewing AI often catches issues that a single instance would miss. The practical takeaway is simple but profound—always have one AI instance review another's work, treating AI output with the same healthy skepticism you'd apply to any code review. Code Quality Matters MORE with AI "This thing is a monkey, and if you put it in a good codebase, like any developer, it's gonna replicate what it sees. So it behaves much better in the better codebase, so refactor!"   Lada emphasizes that code quality becomes even more critical when working with AI. Her systems "work silently" and "don't make a lot of noise, because they don't break"—a result of maintaining high standards even when AI makes rapid development tempting. She uses a memorable metaphor: AI is like a monkey that replicates what it sees. Put it in a clean, well-structured codebase, and it produces clean code. Put it in a mess, and it amplifies that mess. This insight transforms refactoring from a nice-to-have into a strategic necessity—good architecture and clean code directly improve AI's ability to contribute effectively. Managing Complexity: The Open Question "If I just let it do things, it'll just run itself to the wall at crazy speeds, because it's really good at running. So I have to be there managing complexity for it."   One of the most honest insights Lada shares is the current limitation of AI: complexity management. While AI excels at implementing features quickly, it struggles to manage the growing complexity of systems over time. Lada finds herself acting as the complexity manager, making architectural decisions and keeping the system maintainable while AI handles implementation details. She poses a critical question for the future: "Can it manage complexity? Can we teach it to manage complexity? I don't know the answer to that." This honest assessment reminds us that fundamental software engineering skills—architecture, refactoring, testing—remain as vital as ever. Context is Everything: Highway vs. Parking Lot "You need to be attuned to the environment. You can go faster or slow, and sometimes going slow is bad, because if you're on a highway, you're gonna get hurt."   Lada introduces a powerful metaphor for choosing development speed: highway versus parking lot. When learning or experimenting with non-critical systems, you can go fast, don't worry about perfection, and leverage AI's speed fully. But when building production systems where reliability matters, different rules apply. The key is matching your development approach to the risk level and context. She emphasizes safety nets: "In one project, we used AI, and we didn't pay attention to the code, as it wasn't important, because at any point, we could actually step back and refactor. We were not unsafe." This perspective helps developers make better judgment calls about when to accelerate and when to slow down. The Era of Discovery: We've Only Just Begun "We haven't even touched the possibilities of what is there out there right now. We're in the era of gentleman scientists—newbies can make big discoveries right now, because nobody knows what AI really is capable of."   Perhaps most exciting is Lada's perspective on where we stand in the AI-assisted development journey: we're at the very beginning. Even the creators of these tools are figuring things out as they go. This creates unprecedented opportunities for practitioners at all levels to experiment, discover patterns, and share learnings with the community. Lada has documented her discoveries in an interactive patterns and anti-patterns website, a Calgary Software Crafters presentation, and her Substack blog—contributing to the collective knowledge base that's being built in real-time. Resources For Further Study Video of Lada's talk: https://www.youtube.com/watch?v=_LSK2bVf0Lc&t=8654s Lada's Patterns and Anti-patterns website: https://lexler.github.io/augmented-coding-patterns/ Lada's Substack https://lexler.substack.com/ AI Assisted Coding episode with Dawid Dahl AI Assisted Coding episode with Llewellyn Falco Claude Flow - orchestration platform   About Lada Kesseler   Lada Kesseler is a passionate software developer specializing in the design of scalable, robust software systems. With a focus on best development practices, she builds applications that are easy to maintain, adapt, and support. Lada combines technical expertise with a keen eye for clean architecture and sustainable code, driving innovation in modern software engineering.   Currently exploring how these values translate to AI-assisted development and figuring out what it takes to build reliable software with unreliable tools.   You can link with Lada Kesseler on LinkedIn.

Micky and I rank the top vibe coding apps in 2025, from Cursor and Claude Code to Lovable, V0, Bolt, Windsurf, and emerging mobile-focused platforms. They break down which tools work best for technical developers versus non-technical builders, discuss the trust and ecosystem factors that matter when choosing a platform, and share hard-won lessons about the mindset shift required to build software with AI assistance. Timestamps 00:00 – Intro 01:11 – Windsurf 03:46 – Cursor 06:33 – Lovable, v0, Bolt 10:17 – Mobile vibe coding: Rork, VibeCode App, Anything 15:04 – Codex 16:52 – Claude Code 18:28 – Replit 20:23 – Chef by Convex 21:39 – Advice for Vibe Coders Key Points Community size and tutorial availability matter as much as technical capability when choosing a vibe coding platform For technical developers, Cursor and Claude Code dominate; for non-technical builders, V0 offers the best balance of power and accessibility Mobile vibe coding platforms (Rourke, Vibe Code App, Anything) represent a new wave of opportunity, especially for consumer apps monetizing through TikTok discovery Non-technical builders need a mindset shift: building real software takes time, testing, and iteration—not five prompts Betting on a platform means betting on the team and founder behind it; follow their vision to choose the right tool The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ Boringmarketing - Vibe Marketing for Companies: boringmarketing.com The Vibe Marketer - Join the Community and Learn: thevibemarketer.com Startup Empire - get your free builders toolkit to build cashflowing business - https://startup-ideas-pod.link/startup-empire-toolkit Become a member - https://startup-ideas-pod.link/startup-empire FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND MIC ON SOCIAL X/Twitter: https://x.com/rasmickyy  Youtube: https://www.youtube.com/@rasmic

Sora 2's new Character Cameo update brings your pets to AI video while OpenAI settles the whole non-profit mess. Oh, and Sam Altman says in 2028 AI will make itself. WHAT? Plus, 1x Robotics's new Neo robot is causing a big AI controversy. For now, it's all remote operated but WILL be autonomous. Are you ok with people watching you? Also: new AI video & audio tools from Cartesia & Odyssey and we get Cursor 2.0. And much more AI news. WE'RE NOT REMOTE OPERATED. UNFORTUNATELY.   Get notified when AndThen launches: https://andthen.chat/ Come to our Discord to try our Secret Project: https://discord.gg/muD2TYgC8f Join our Patreon: https://www.patreon.com/AIForHumansShow AI For Humans Newsletter: https://aiforhumans.beehiiv.com/ Follow us for more on X @AIForHumansShow Join our TikTok @aiforhumansshow To book us for speaking, please visit our website: https://www.aiforhumans.show/   // Show Links // Sora 2 Character Cameos https://x.com/OpenAI/status/1983661036533379486 Sora Names For Kevin & Gavin's Pets: OlliePurcell & NotKevin.DrWes OpenAI IPO at 1 TRILLION DOLLARS?!? https://www.reuters.com/business/openai-lays-groundwork-juggernaut-ipo-up-1-trillion-valuation-2025-10-29/ Meanwhile, Sam Altman says that by 2028 we might get Full AI Engineers https://openai.com/live/  Bernie Sanders Says OpenAI Should Be Broken Up https://www.reddit.com/r/ChatGPT/comments/1ohd8lb/bernie_says_openai_should_be_broken_up_ai_like_a/ 1x Tech Neo Pre-Sale Video https://youtu.be/LTYMWadOW7c?si=7j8pZAM87pV-_Tnd WSJ Reporter Joanna Stern's More Realistic Look at It https://youtu.be/f3c4mQty_so?si=KvUPplgng2oN77yH Meanwhile, in China… Unitree G1 Pulls a Car https://www.reddit.com/r/robotics/comments/1oi0lil/researchers_at_beijing_academy_of_artificial/ Adobe MAX "Sneaks" https://www.youtube.com/live/_SBn0Iu3K-U?si=XUeD1NQlwZ8FUPIs Udio Partners With UMG But… No More Downloads For You https://www.udio.com/blog/a-new-era Cartesia Sonic-3: Very Good New Audio Model https://x.com/krandiash/status/1983202316397453676 Cursor 2.0 / Windsurf launch their OWN models… https://cursor.com/blog/2-0 Extropic  https://x.com/Extropic_AI/status/1983579587649904960 https://extropic.ai/ Odyssey-2 Live Real Time Video Gen https://x.com/odysseyml/status/1982856110290939989 https://experience.odyssey.ml/ iHollywood GLIF Agent https://x.com/fabianstelzer/status/1983155521705447850 Bots In The Hall - AI-created animation series https://www.youtube.com/watch?v=FtwWzhwQ2vs What if Michael Jackson Trained Anakin Skywalker? https://youtu.be/wVllBYOtELA?si=_Y0KzCY1TysRDU81