Podcasts about UI

If you aren't a tech nerd like us, you probably didn't even know Computex and Microsoft Build were happening. And that's alright. Everyone besides Apple is trying to get their announcements out there before WWDC kicks off next week. Watch on YouTube! - Notnerd.com and Notpicks.com INTRO (00:00) WWDC next Monday at 10! (03:30) MAIN TOPIC: NVIDIA, Microsoft, and More drop their news before Apple (04:45) Computex 2026: All the news and announcements What we learned at Microsoft Build: Autopilots, MAI-Thinking-1, and Nvidia RTX Spark Dell stock skyrockets 32% for its best day ever as AI server revenue soars Microsoft is killing Office 2019 for Mac and iPhone, and you can't do much about it DAVE'S PRO-TIP OF THE WEEK: Rule of Thirds! (19:55) JUST THE HEADLINES: (27:20) Perfect randomness realized for the first time A fundamental principle of aeronautical engineering has been overturned Meta AI support bot helped hackers hijack Instagram accounts Roku updates its UI for the first time in a decade Something made Earth's molten core reverse direction in 2010 YouTube to automatically detect, label AI-generated videos Google requests permission to release 32 million mosquitoes in California and Florida LISTENER MAIL: From Hey Grandma - The Year 2038 problem (31:05) WITHIN REACH! Dave 8-6, Round 14, Nate goes first (35:25) TAKES:  Facebook Plus, Instagram Plus subscriptions launch for $3.99/month (42:15) Nintendo's new Pictonico iOS game turns your photos into minigames (46:45) Not news: iOS 28 will reportedly be 'far more significant' than iOS 27 (48:20) BONUS ODD TAKE: Magnified Sand (50:15) PICKS OF THE WEEK:  Dave:  PUGG Wall clock, stainless steel, 12 ½" (54:25) Nate: Upgraded 67mm Phone Lens Filter Adapter Mount for iPhone 17 16 15 14 Pro Pro Max Plus Air, Double-Sided Magnetic Phone Lens Filter Ring with 1/4-20" & Cold Shoe Pull-Out to fit 17 Pro Max (No Filters (58:20)

Sure, you didn't miss Anthropic's BIG Opus 4.8 drop.

Valve hiked Steam Deck prices by up to $300 as RAMageddon hits consumer electronics. Bloomberg detailed Apple's Siri overhaul ahead of WWDC, Meta rolls out subscription plans for Instagram, Facebook, and WhatsApp, and Oura unveils a 40% smaller Ring 5. Valve hikes the Steam Deck OLED's prices due to "rising memory and storage costs": from $549 to $789 for the 512GB model and from $649 to $949 for the 1TB model (The Verge) Illustrations based on sources detail Apple's Siri overhaul, including a new UI, a chatbot-style app, and other major iOS 27 changes, ahead of WWDC on June 8 (Bloomberg) Meta rolls out Plus plans for Instagram, Facebook, and WhatsApp globally, and tests $7.99/month and $19.99/month Meta AI plans, and a $49.99/month creator plan (TechCrunch) Reactor, which says its AI platform can generate video in real-time with near-zero latency, emerges from stealth with a $59M Series A led by Lightspeed (Variety) Oura unveils the Oura Ring 5, with a 40% smaller form factor, improved sensing, and repositioned LEDs, on sale from June 4 for $399, up from the Ring 4's $349 (Bloomberg) Learn more about your ad choices. Visit megaphone.fm/adchoices

Benjamin and Chance discuss the illustrations Bloomberg published depicting iOS 27 and the new Siri interface. Also, we talk about rumors of a revamped AirPods settings UI, phone snatching detection in iOS 26.6 code, and Digitimes says the Apple Watch Ultra 4 will feature a significant redesign. Also, has AirDrop got worse? And in Happy Hour Plus, Apple showcases the iPhone 17 Pro by producing and broadcasting the first full live sports game using ‘just' iPhones. Subscribe at 9to5mac.com/join. Sponsored by Bartender: Bartender Pro is a new option for users who want to take things up a notch. Visit macbartender.com/happyhour to check it out. Sponsored by Copilot Money: Get two months free with code 9TO5MAC at copilot.money/9to5mac. Sponsored by Shopify: See less carts go abandoned and more sales. Sign up for a $1 per month trial at shopify.com/happyhour. Hosts Chance Miller @ChanceHMiller on Twitter @ChanceHMiller on Instagram @ChanceHMiller on Threads Benjamin Mayo @bzamayo on Twitter @bzamayo@mastodon.social @bzamayo on Threads Subscribe, Rate, and Review Apple Podcasts Overcast Spotify 9to5Mac Happy Hour Plus Subscribe to 9to5Mac Happy Hour Plus! Support Benjamin and Chance directly with Happy Hour Plus! 9to5Mac Happy Hour Plus includes:  Ad-free versions of every episode  Pre- and post-show content Bonus episodes Join for $5 per month or $50 a year at 9to5mac.com/join.  Feedback Submit #Ask9to5Mac questions on Twitter, Mastodon, or Threads Email us feedback and questions to happyhour@9to5mac.com Links iOS 27 leak reveals new Siri design, Camera app, more Report: iOS 27 to revamp the AirPods settings UI Report: watchOS 27 to improve heart-rate tracking; AI health coach may not debut at launch Apple Intelligence image models to boast 'major' visual upgrades in iOS 27: report Apple Watch Ultra 4 getting two major new upgrades, per report Apple Watch could soon gain new high blood pressure feature iOS 26.6 adds new alert when you try blocking too many contacts Apple working on iPhone anti-snatching feature that locks the device automatically New Oura Ring 5 unveiled with dramatically smaller design, hypertension detection, more Apple TV to air first major live sporting event shot entirely on iPhone 17 Pro How Apple Shot an Entire MLS Game Using Only iPhone | PetaPixel Eddy Cue named 2026 Cannes Lions Entertainment Person of the Year

Mikayla Maki, software engineer at Zed, digs into what makes this Rust-built code editor tick... from GPUI, their GPU-accelerated UI framework with a Tailwind-inspired API, to CRDTs powering real-time live collaboration without merge conflicts. She talks about the Zed 1.0 release, their approach to AI, how the team builds popular features directly into core instead of relying on extensions, and why Rust might be the best language for agentic coding. Plus: native app comeback, GPUI on mobile, and where the framework is heading. Links LinkedIn: https://www.linkedin.com/in/mikayla-maki Bluesky: https://bsky.app/profile/rad.gendervibes.online GitHub: https://github.com/mikayla-maki Resources Zed 1.0 announcement: https://zed.dev/blog/zed-1-0 DeltaDB / Sequoia Series B post: https://zed.dev/blog/sequoia-backs-zed ACP overview: https://zed.dev/acp GPUI engineering post: https://zed.dev/blog/leveraging-rust-and-the-gpu-to-render-user-interfaces-at-120fps Builder.io "Is Zed ready for AI power users in 2026?": https://www.builder.io/blog/zed-ai-2026 Mikayla's RustConf 2025 talk: https://www.youtube.com/watch?v=rpEU9DNbXA4 filtra.io interview with Mikayla: https://filtra.io/rust/interviews/zed-aug-25 We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabeth.becz@logrocket.com, or tweet at us at PodRocketPod. Check out our newsletter! https://blog.logrocket.com/the-replay-newsletter/ Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form, and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. Chapters

Scott and Wes sit down with Alex Sexton and Amadeus De Marzi from Pierre Computer to dig into the gnarly performance challenges behind building blazing-fast code review tools, covering virtualization, progressive rendering, and why GitHub's UI feels so sluggish. They also chat about how major AI coding tools like Claude, Codex, and Cursor are adopting Pierre's diffs library, plus the role of web components, benchmarking, and what it takes to build “VS Code 2.0.” Show Notes 00:00 Welcome to Syntax! 04:00 The Need for Better Infrastructure 05:53 Understanding Diffs and Trees diffs.com Trees by the Pierre Computer Co 08:16 Performance Challenges in Code Review 10:49 Virtualization Techniques for Smooth Scrolling 15:04 In-Page Find and Virtualization Limitations 17:00 Browser Limitations and Content Visibility 19:29 Progressive Rendering and Syntax Highlighting 23:05 Tools and Techniques for Performance Testing 33:35 Optimizing Performance with AI 36:31 Mastering Auto Research for Efficiency 42:00 Exploring Web Components and State Management 44:05 Innovations in Rendering and Virtualization 49:12 Business Insights and Future Directions 53:58 Sick Picks Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

In this episode, we debrief Telehash #4 and dig into the open-source future of Bitcoin mining. We share behind-the-scenes metrics from HydraPool's six-and-a-half–hour live stress test, including 30.8 zettahashes processed, an average of 1.32 EH/s, a peak of 2.495 EH/s, 2,231 workers, 59 unique users, and an impressively low ~1% server CPU under >2,000 connections. We explain why rejection rates under ~2% matter, how stale and “difficulty too low” shares differ in solo vs pooled mining, and how Stratum “suggest difficulty,” plus our d= and h= password parameters, help right-size starting difficulty—making Telehash inclusive for both exahash renters and single-chip Bitaxe miners. We also touch on leaderboards, loyalty uptime rules, and shout out supporters like Elektron Energy, Compass, Saaz Mining, and Abundant Minds. From hardware to policy, we discuss Bitaxe UX updates (LVGL, Figma-driven UI, external display/knob), DOOMAXE fun, and industry standardization—from firmware and pools to racks, cooling, and power—arguing that open reference designs cut costs and risk for everyone. We cover GridPool's “winners list” approach to decentralized variance smoothing, the Patoshi/extra nonce story, vardiff dynamics, and privacy-conscious VPN mining. We reflect on immersion's decline versus hydro, ASIC roadmap realities and slowing efficiency gains, the supply-chain and security stakes (FCC Wi‑Fi moves, vendor backdoors), and why nonprofit coordination via the 256 Foundation matters for open firmware, dev kits, and reference designs. We close with community invites, next steps for Telehash #5, and a call for ASIC makers and big miners to collaborate on open standards that benefit small and large operators alike.

Wes and Scott talk about the foundational decisions that make AI-assisted coding actually work—database schemas, validation, routing, CSS structure, and more. They explore why consistency matters more than specific tools, and how a little upfront planning can keep agents from turning your codebase into chaos. Show Notes 00:00 Welcome to Syntax! 03:19 Planning your database schema before AI touches it 06:08 Picking a validation strategy that won't drift 07:18 Mapping your routing structure and auth flow 08:48 Brought to you by Sentry.io 10:52 Locking in your CSS methodology and UI framework 13:31 Choosing how your client and server communicate 15:03 Creating a folder structure agents can follow 16:16 Don't be afraid to switch up your AI setup later Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

Brent's been hacking smart speakers, Wes has a surprise, and Chris gives up on OpenClaw.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:ConnecTen Internet — Get $35 off your order total with Jupiter35

This week on More or Less, the crew unpacks IPO speculation around OpenAI and SpaceX, debates whether AI's economics ultimately favor recurring API spend or owning infrastructure outright, questions if Google's distribution advantage is enough to win the AI race despite muddled product execution, and wrestles with whether today's AI valuations are driven by real fundamentals or pure mimetic momentum, alongside broader debates on broken AI user experience, data center concentration risk, agentic search killing SEO, and whether skilled trades like plumbing may ultimately prove more durable than many white-collar jobs in the AI era.Chapters:01:35 — Brit's fish disaster story + the fish microbiome economy04:50 — Dell World, AI PCs, and the tokenomics debate (API spend vs. owning infrastructure)11:00 — Google I/O recap: agentic search, generative UI, Android glasses, and whether Google is actually back16:30 — Google's UX problem: why AI still feels broken for normal users20:00 — Anthropic vs. Google: focused monolith vs. sprawling empire22:10 — OpenAI IPO speculation + Anthropic's mega-round: what do you have to believe at $1T valuations?29:30 — The “hate invest” thesis: public sentiment, retail risk, and crypto déjà vu34:20 — Portfolio debate: SpaceX vs. Anthropic vs. OpenAI35:00 — Sam builds an AI token pricing dashboard live + how companies actually burn $30K/month on tokens37:00 — What's next in AI research? Memory, world models, and where infra plays went42:30 — White House AI model oversight rumors + OpenAI's Elon legal update50:10 — AI side projects, kids learning to code, and why plumbers may win the AI eraWe're also on ↓X: https://twitter.com/moreorlesspodInstagram: https://instagram.com/moreorlessSpotify: https://podcasters.spotify.com/pod/show/moreorlesspodConnect with us here:1) Sam Lessin: https://x.com/lessin2) Dave Morin: https://x.com/davemorin3) Jessica Lessin: https://x.com/Jessicalessin4) Brit Morin: https://x.com/brit

Episode 95: Emily Vincent - Usability & UI Design in Board Games, 10+ Hidden Small Box Gems, Libraries, and cats calling in the SpringPlease come join me and meet this wonderful human and talented designer, Emily Vincent. We connect about cats, what makes a sandwich, and fonts. We discuss how UI design and accessibility design concepts are implemented in board games. Then we talk about 10+ small box games that we think fell under the radar or don't get enough love. And end with cats, libraries, and hearing about her upcoming games.00:00:00 Intro00:00:31 Who is Emily Vincent?00:06:35 Playtesting GamesBoston Game Makers' GuildProtoSpiel OnlineBreak My Game DiscordUnPub00:07:41 Rapid Fire Questions00:11:49 Applying Usability / UI Design Concepts to Game Design00:36:31 Exploring our Top 5 Hidden Gems in Small Box Games00:38:33 Newfoundland Jam00:41:38 Cat Rescue00:45:10 No Regerts: The Game of Art and Poor Life Choices00:47:46 Big SurStrange Worlds Above the Clouds00:50:54 Tootin' & Hollerin'00:55:04 Espresso00:57:57 Pass the Buck: A Game of Corporate Responsibility Management01:02:26 Lunar Skyline01:06:14 Holiday Hijinks #1: The Kringle Caper and series01:10:23 Wok and Roll01:12:47 Honorable Mentions01:13:17 Trickadee01:14:13 Bandido01:16:06 Neko Syndicate01:20:53 Moments of Positivity01:28:49 Where can you find Emily?Instagram @ Pink Hawk GamesWebsite: http://pinkhawkgames.com Discord: EmilyKeepsKittiesBuilding the Game podcast01:32:12 Outro(Please note that these time stamps might not be accurate due to the use of dynamic ads.)You can flip your game night by picking up DNUP.https://www.dnup.game/en/ Register for Brave & Bold Learn-to-play events at Gen Conhttps://resurrection.games/products/brave-bold-bag-building-combat-game?variant=48030270587112&UTM_medium=referral&UTM_source=bggpodcast&UTM_campaign=gencon26Web: https://boardgamegeek.com/YouTube: https://www.youtube.com/@boardgamegeekTwitter: https://twitter.com/BoardGameGeekEmail: podcast@boardgamegeek.com

This show has been flagged as Explicit by the host. WARNING AI GENERATED NOTES AHEAD YMMW Here is a summary of the recorded training session regarding Android hacking from Hacker Public Radio, including web references for the main topics discussed. Overview The recording features a security consultant performing a live assessment of an Android application. The consultant uses a custom tool suite called "Jamboree" and various other utilities to test a location-sharing and vehicle management app. The session highlights the increasing complexity of mobile app security, specifically dealing with SSL pinning, encrypted traffic, and anti-tampering mechanisms 1 . Environment and Tools The assessment is conducted on a rooted Android emulator. The speaker utilizes several tools to set up the environment and intercept traffic: Jamboree : A custom automation tool developed by the speaker over six years to handle rooting, proxy setup, and app installation within minutes 1 . Burp Suite : The primary interception proxy used to analyze traffic between the app and the production server 1 . Frida : Used to bypass anti-root detection and SSL pinning 1 . Ghidra : A decompiler used to analyze the app's code, specifically helpful for patching the Flutter-based application 1 . Android Debug Bridge (ADB) : Used for troubleshooting, debugging, and analyzing logs ( logcat ) to extract user IDs and location data 1 . Technical Challenges: SSL Pinning and Flutter The target application is built using Flutter and implements rigorous security controls, including SSL pinning, which prevents standard Man-in-the-Middle (MitM) attacks. The app's HTTP client ignores system and user-installed certificates, and it does not respect device Wi-Fi proxy settings 1 . To overcome this: Traffic Redirection : The speaker uses iptables commands to force all HTTP and HTTPS traffic through the proxy's IP address at the network layer, bypassing the app's proxy ignorance 1 . Patching with AI : The speaker leverages AI (specifically mentioning Claude and access to "Kuro") to assist in patching the APK. The AI helped navigate Ghidra and generate Python scripts to bypass the app's protections, allowing the modified APK to trust the auditor's certificate 1 . Frida Scripts : "Frida anti-root SSL pinning" scripts are executed to further mitigate detection mechanisms 1 . Key Vulnerabilities Identified 1. Geolocation Spoofing The consultant successfully spoofed the device's GPS location using emulator settings (e.g., setting the location to Puerto Rico or Costa Rica). The application accepted this falsified location data as valid, indicating a lack of server-side verification for location origin 1 . 2. Insecure Direct Object Reference (IDOR) / Broken Access Control The most critical finding involves the app's user tracking feature. The consultant discovered that the API allows querying a user's location via a user_id . By intercepting traffic and analyzing adb logcat logs, the consultant extracted their own user_id and the user_id of a second test account 1 . While authenticated as one user, the consultant was able to send a request substituting the user_id with the target's ID. The server responded with the target's GPS coordinates. This confirms that an authenticated user can track any other user's real-time location if they possess the target's ID 1 . Proof of concept was created by copying the request as a curl command to demonstrate the exploit 1 . 3. Potential Information Disclosure The consultant began testing a feature that allows users to add vehicles by license plate. The concern is that querying a license plate might return excessive PII (Personally Identifiable Information), such as VIN numbers or registration details, beyond what the UI strictly requires (least privilege issue) 1 . 4. Access Control (Calendar Feature) The consultant tested whether calendar events could be accessed by switching user_id parameters. This test resulted in a "401 Unauthorized" error, indicating that this specific endpoint had proper access control in place 1 . Web References and Resources Below are references for the main tools and concepts discussed in the training: Hacker Public Radio : https://hackerpublicradio.org/ Burp Suite (Web Security Testing) : https://portswigger.net/burp Frida (Dynamic Instrumentation Toolkit) : https://frida.re/ Ghidra (Software Reverse Engineering) : https://ghidra-sre.org/ Android Debug Bridge (ADB) : https://developer.android.com/tools/adb OWASP Mobile Top 10 : https://owasp.org/www-project-mobile-top-10/ OWASP Testing for Insecure Direct Object References (IDOR) : https://owasp.org/www-project-web-security-testing-guide/v42/4-Web_Application_Security_Testing/04-Authorization_Testing/04.1-Testing_for_Insecure_Direct_Object_References Flutter (UI Toolkit) : https://flutter.dev/ Provide feedback on this episode.

David Daiches: Inside INSHUR — From Manhattan Uber Rides to Insuring Autonomous Fleets In this episode of Scouting for Growth, Sabine VanderLinden speaks with David Daiches, co-founder and COO of Insure, about building insurance solutions for the on-demand economy. The conversation traces Insure's origins to a simple yet powerful insight: traditional insurance models were not designed for gig workers like Uber drivers, who operate entirely on their smartphones and cannot afford downtime. David explains how Insure addressed this gap by creating flexible, usage-based insurance embedded directly into platform ecosystems. They explore the importance of “fluency over features,” emphasizing that successful insurtechs solve real operational problems rather than just showcasing technology. A central theme is that claims, not policies, define the true value of insurance, leading Insure to bring claims in-house to improve customer experience and data insights. The discussion also looks ahead to emerging challenges, including electric vehicles and autonomous mobility, where insurance must evolve to cover complex ecosystems of software, hardware, and data. Finally, David shares candid lessons on scaling, partnerships, and the growing role of AI, highlighting the need for adaptability, continuous learning, and strong teams in building resilient insurtech businesses.   KEY TAKEAWAYS What stands out most is the importance of starting with the problem, not the technology. David and his team didn't build Insure by showcasing features; they immersed themselves in the daily realities of gig workers and platform operators. That mindset shaped everything, from product design to partnerships. It reinforces my belief that fluency in a partner's business model is far more valuable than any standalone innovation. Another key insight is how insurance must adapt to changing customer behaviors. The on-demand economy is no longer a niche; it supports millions of people. Traditional annual policies simply do not fit this model. By aligning insurance coverage with actual usage, Insure has shown how to close protection gaps while improving affordability and access. What resonated deeply with me is the idea that claims are the product. Customers only truly experience insurance when something goes wrong. Investing in claims operations, empathy, and responsiveness is therefore not optional; it is the core value proposition. I was also struck by the operational lessons. Scaling too quickly, hiring without enough rigor, and taking partnerships for granted are common pitfalls. Building a strong, empowered team and maintaining close alignment with partners is essential for long-term success. Finally, the future of mobility and insurance will require entirely new thinking. Autonomous vehicles, AI, and data-driven ecosystems are reshaping risk. The winners will be those who can navigate this complexity while staying grounded in customer needs.   BEST MOMENTS “Claims is the product. Everything else just gets us to that point.” – David Daiches “We didn't just sell insurance, we solved problems in the platform's business model.” – David Daiches “People are not interested in a fancy UI when something goes wrong. They want a product that is there at the moment they need it the most.” – Sabine VanderLinden “Make yourself easy to do business with.” – David Daiches “The best insurtech founders aren't selling insurance, they are removing friction from someone else's business model.” – Sabine VanderLinden “If you're not spending time learning AI now, you risk being left behind.” – David Daiches   ABOUT THE GUEST David Daiches is the co-founder and Chief Operating Officer of Inshur, a digital-first managing general agent focused on the on-demand economy. With a background in technology and retail, he entered the insurance industry over 15 years ago and identified significant opportunities for digital transformation.  At Inshur, David has led the development of embedded, usage-based insurance solutions for platforms such as Uber, Amazon, and DoorDash. David is particularly focused on innovation in mobility insurance, including the future of autonomous vehicles and AI-driven claims and underwriting.   ABOUT THE HOST Sabine VanderLinden is a corporate strategist turned entrepreneur and the CEO of Alchemy Crew Ventures. She leads venture-client labs that help Fortune 500 companies adopt and scale cutting-edge technologies from global tech ventures. A builder of accelerators, investor, and co-editor of the bestseller The INSURTECH Book, Sabine is known for asking the uncomfortable questions—about AI governance, risk, and trust. On Scouting for Growth, she decodes how real growth happens—where capital, collaboration, and courage meet. If this episode sparked your thinking, follow Sabine VanderLinden on LinkedIn, Twitter, and Instagram for more insights. And if you're interested in sponsoring the podcast, reach out to the team at hello@alchemycrew.ventures

JPEG Store, long the biggest NFT marketplace in the Cardano ecosystem, is shutting down, which means NFT holders and project teams need to decide where their listings go next. In this episode, Peter walks through a practical migration from JPEG Store to WayUp and shows what the process looks like using a real wallet and live listings.The tutorial covers why this migration matters, how WayUp pulls in existing JPEG Store listings, what the wallet transaction is doing behind the scenes, and how to verify the contract movement on Cardanoscan. Peter also shares an important clarification on timing: even after the JPEG Store interface goes down, smart contract level migration may still be possible, but it is better to test before the UI disappears in case edge cases need manual relisting.Key Takeaways:- JPEG Store is shutting down, so NFT holders should review and migrate active listings before the interface goes offline.- WayUp offers a migration feature that can pull in existing JPEG Store listings and move them into a new marketplace contract.- The migration requires a wallet signature, so users should review the transaction addresses and metadata before confirming.- Cardanoscan can be used to verify that assets moved from the JPEG Store ask contract into the WayUp contract.- Supporting active marketplaces helps keep NFT trading infrastructure alive during a weak market cycle.- The JPEG Store website going offline does not necessarily mean NFTs are lost, because the assets remain in smart contracts.- There may still be contract mismatch edge cases, so testing the migration before the UI shutdown is the safest approach.Links & References:- Cardano Apps Directory: Wallets, DEXes, NFTs & More | Cardano: https://link.learncardano.io/JIJdb3- Add your Application | Cardano: https://link.learncardano.io/W52NlV- Cardano (ADA) Blockchain Ecosystem and Project Explorer: https://link.learncardano.io/k9zcxs- Adastack Ecosystem Explorer: https://link.learncardano.io/gdYxSdWebsite: https://link.learncardano.io/bQ68RcX/Twitter: https://link.learncardano.io/3a1QtvDisclaimer: This content is for educational purposes only. Nothing constitutes financial advice.DISCLAIMER: This content is for informational and educational purposes only and is not financial, investment, or legal advice. I am not affiliated with, nor compensated by, the project discussed—no tokens, payments, or incentives received. I do not hold a stake in the project, including private or future allocations. All views are my own, based on public information. Always do your own research and consult a licensed advisor before investing. Crypto investments carry high risk, and past performance is no guarantee of future results. I am not responsible for any decisions you make based on this content.

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.“The end of localhost” has been Ivan Burazin's obsession for more than a decade.Something that is all too familiar…Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.The thesis was directionally right, but the market wasn't ready yet.However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.Daytona isn't just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan's original localhost thesis.In this episode, Daytona's CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.We go deep on the new agent compute market: Daytona's hard pivot from human dev environments to AI sandboxes, the New Year's Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.We discuss:* How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis* Why Daytona pivoted from human dev environments to AI sandboxes* Why agents need composable computers instead of disposable code execution boxes* The New Year's Eve MVP that customers chased API keys for* Why Daytona chose bare metal, stateful snapshots, and its own scheduler* How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds* Why Daytona's biggest customer runs ~850,000 sandboxes a day* How RL/eval workloads create zero-to-100,000 CPU spikes* Why RL workloads went from 0% to roughly 50% of Daytona usage* Why customers compare Daytona against EKS/GKS and say they're “never going back”* Why every AI agent may need a computer, including Windows and macOS environments* The Apple licensing constraints that make macOS sandboxes hard* Why CLI gives agents more power than MCP* How open source helps agents integrate Daytona* Why agent-generated PRs may break today's CI/CD assumptions* Why AI SaaS companies reselling tokens may face a cold shower* Why the AI cloud may look more like Stripe than AWSIvan Burazin* LinkedIn: https://www.linkedin.com/in/ivanburazin* X: https://x.com/ivanburazinDaytona* Website: https://www.daytona.io* X: https://x.com/daytonaioTimestamps* 00:00:00 Hook* 00:01:12 Introduction* 00:03:15 CodeAnywhere, Shift, and the end of localhost* 00:05:58 What Daytona is: composable computers for AI agents* 00:08:07 The pivot from dev environments to AI sandboxes* 00:10:17 The New Year's Eve MVP and customers begging for API keys* 00:12:56 Bare metal, stateful sandboxes, and Daytona's scheduler* 00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs* 00:21:53 Spiky RL/eval workloads and the new agent infra problem* 00:28:12 RL workloads, Kubernetes pain, and dynamic resizing* 00:33:31 Why every AI agent needs a computer* 00:38:48 macOS sandboxes and Apple's licensing problem* 00:44:28 Why CLI may matter more than MCP* 00:48:11 Open source, GitHub stars, and agent integration* 00:53:11 Git, CI/CD, and agent collaboration bottlenecks* 00:58:15 Founder life and building a 25-person infra company* 01:02:44 AI SaaS, token resale, and API-first business models* 01:06:10 GPU sandboxes, data centers, and compute growth* 01:09:48 Why the AI cloud may look more like Stripe than AWS* 01:11:26 Closing thoughtsTranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.Ivan [00:00:07]: Thanks for having me, man.Swyx [00:00:08]: Ivan, you and I go back.Ivan [00:00:10]: Way back.Swyx [00:00:11]: How I don't even know how, you found, did you reach out or, for Shift.Ivan [00:00:17]: I reached out to you. The reason was you - we were just - we were thinking about I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of, localhost should die. And you had this article.Swyx [00:00:29]: End of localhost.Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.Swyx [00:00:51]: I don't remember.Ivan [00:00:52]: I remember because I was with my then I'm thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great, and I was late ‘cause we were, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.Swyx [00:01:10]: The reason I'm nice is because I'm also late to other people, so it's like, who's, who's without sin here, yeah, so I have to, for those who don't know, InfoBip Shift, there's this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should've took the advisory shares. So I'm sorry, dude. But anyway.Swyx [00:01:43]: We're not, we're not venture backed.Ivan [00:01:44]: No, it doesn't matter.Swyx [00:01:45]: It's Yeah, anyway, so I think what's impressive about you is that CodeAnywhere is the thing that you've been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I've said this multiple times, it's like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It's not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, at a foundational level, and that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.Swyx [00:02:55]: There was Cloud9.Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out when we stopped doing it, Replit came out, and they have been successful since then, which is great. There was Nitrous.io. There was quite a few that existed at the time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, there was no Kubernetes, and Docker had just started when we Or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves and that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used CodeAnywhere. It was slightly, it was angel-backed more than venture-backed. We ended up paying everyone back because it didn't have that sort of scale. But, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.Swyx [00:04:01]: Historic pivot, yeah, and, it's one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I'm like, “F**k.”Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn't have done it.Swyx [00:04:18]: No way.Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don't invest.”Ivan [00:04:29]: That's because it was your quote. It's like we.Swyx [00:04:30]: Yeah. It's the end of localhost.Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.Swyx [00:04:34]: No, that's like, I put, I wrote that blog post, and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and, talk it, talk through it with me.Ivan [00:04:47]: It's finally happening though.Swyx [00:04:48]: It was really super interesting.Ivan [00:04:48]: It's finally happening.Swyx [00:04:49]: It's finally happening.Ivan [00:04:49]: Yeah, it's finally.Swyx [00:04:49]: It's finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You're wearing the shirt. Yes,.Swyx [00:04:59]: It says, I think your branding is very good. Like, it's very consistent. It runs AI code. Like, it cannot be simpler.Ivan [00:05:05]: Exactly, but we're gonna probably have to change that.Swyx [00:05:07]: Oh, s**t.Ivan [00:05:07]: It's also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we've given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn't really market about us.Swyx [00:05:21]: Yeah, Daytona's on the back.Ivan [00:05:22]: It markets the back. It markets to the person itself, so I think we did a really good job on that one. But it is also a subset of what we do, because people, when they think about Run AI Code, they just think about these small, let's call it isolates, code execution boxes that, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes which can be misleading.Swyx [00:05:44]: All these things. All these things on.Ivan [00:05:45]: Yeah, exactly, ‘cause it can be misleading ‘cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different computers or different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we offer that basically through an API.Swyx [00:06:19]: Yeah, to give people - I'm trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it's just been growing for a while. Like, it's been going like this. And every single - It's not just you guys. It's every single.Ivan [00:06:41]: Everyone, yeah.Swyx [00:06:42]: Sort of, compute provider. I don't know if you agree with me saying compute provider or not.Ivan [00:06:48]: It's fine.Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?The Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is the quarter before that, so end of 2024, when we had - Basically, we did a demo with - I don't I think we discussed this as well, Devin was not public. You actually gave me access to Devin at that time. So Devin.Swyx [00:07:25]: I did?Ivan [00:07:26]: Yeah, you gave me access.Swyx [00:07:26]: I don't think I was supposed.Ivan [00:07:27]: Yeah, exactly.Swyx [00:07:28]: Yeah, I.Ivan [00:07:28]: So it doesn't matter. You.Swyx [00:07:29]: Yeah. I gave like three friends access.Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn't matter. but OpenDevin was available, which is now called OpenHands. And so we're like, “Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, OpenDevin and launch that as a SaaS.” And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, “Hey, my agent needs a compute sandbox runtime,” whatever you wanna call it. I forgot what it was called at that point. And then we were like, “Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go.” And what we found really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, the sandbox we're building for agents. People were like, “Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things.” But we saw that everyone we gave it to, it was like 20, 30 people, they all said, “No.” Like, “This is not what we need. This sort of breaks.” And basically, me and my co-founder not knowing a lot about - ‘cause we're infra people. We're not AI people. So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get, understand what's going on.Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.Ivan [00:08:49]: Generally we -, I looked at There's a few of podcast, different segments and different types. So there's you guys, No Priors, Bill Gurley's was great while.Swyx [00:09:04]: VG2, yeah.Ivan [00:09:05]: Yeah, while it was around. So there's a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.Swyx [00:09:14]: We're not really about the compute market.Ivan [00:09:15]: It was also already - Sorry?Swyx [00:09:16]: You're, you want - You're looking at the agent infra market.Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what the perception, and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year's, and go back to just, doing this. And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, “This is absolute garbage.” “Do not show this to anybody at all, but the idea is good.” And so he took two weeks, and he rebuilt it.Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. But it was like a very - It was a simplistic view of what it should be. Like, it worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed it to everyone. And all the calls went long, every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, “We need, we want access.” There was no login, just an API key, ‘cause it was just a beta or an alpha. And they said, “Oh, we want access.” And we're like, “Sure, yeah. Okay, thank you very much.” But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, “Where is my API key?” Like everyone wanted it. We're like, “S**t.” Like this is it. Like I've never felt So one, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never seen, I've never experienced - I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. the thing that they want doesn't seem to exist, or they have not found it, and they really want what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, “We are all in on this.” And so that is where we made sort of the cut between the old product and the new one.Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn't composable at the time?Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things. It was just the, just the first sort of variation of that, yeah.Swyx [00:12:22]: Was it bare metal from the start?Ivan [00:12:24]: It was bare metal from the start. And so the interesting thing that we thought about right away, so our.Swyx [00:12:29]: Which, give people the background, what is the normal path?Ivan [00:12:32]: Yeah, so, basically most providers run this on top of VMs. And also.Swyx [00:12:37]: Firecracker.Ivan [00:12:38]: Yeah, they run on Firecracker and VM. And so we also fire - We can get - We have multiple isolation layers and we can do that. But the common way to do it is that they, one, that the state of the machine, or the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible, like they can There's a time that they can live. And so our thought was when we were going into this is, agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state. So you - Agents would want that, like the pause and come back. They want those two things. But also agents really want speed, right? Can they get it? So when we thought about it's like we need something insanely fast, how to make it fast, how to make it long-running, and stateful. And so those two things, it's like combining a Lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it, ‘cause we didn't know too that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't wasn't good enough for that. We looked at Nomad, it didn't enable that. And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, “Oh, the learnings from there,” and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, “Dude, what is this? This is like 2008.” Like, we went back in time, and he's like, “Exactly.” And so the reason why Daytona is like super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler, we use the underlying, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, you're essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine, and it's local. There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with, and that's what we created.Benchmarks, 60ms Startup, and 50,000 SandboxesSwyx [00:15:02]: Yeah. I should maybe, I don't know if you endorse this, but there's someone that does compute SDK, you guys do very well on there, with like the TTI, right? I. is this a, is this a is this a relevant benchmark for you guys? I don't know.Ivan [00:15:16]: I don't know, and it changes every day. So today RKL is.Swyx [00:15:18]: I don't know what RKL is. Never heard of it.Ivan [00:15:20]: Yeah. RK, yeah, so it is there.Swyx [00:15:22]: You are, at least a third of the next tier of performance, and then, there's a lot of other better-known names that are very slow to start.Ivan [00:15:31]: Yeah. We've been the number one by far for a long time, and now there's different, there's different definitions also of sandboxes, different isolation patterns, different other things. So RKL runs it literally on the S3, the data, so it's very different, and they spin up a sandbox, spin up a container for that, so it's a different type of thing. So the definition of a sandbox is something that we can all, we all need to get along with. But yeah, we're insanely fast on getting these things, up and running. And so you can see even there that it's a zero point 0.10 to 0.11, so.Swyx [00:16:03]: Close enough. Yeah. what else do you need, right?Ivan [00:16:05]: Yeah. So the benchmarks itself, so, in this, in I don't think the benchmarks equate to market ownership or revenue or anything like that. and I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.Swyx [00:16:20]: It's table stakes. It's just like.Ivan [00:16:21]: Exactly. But it doesn't hurt.Swyx [00:16:22]: Just roughly check.Ivan [00:16:22]: Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things like how many can you spin up consecutively? There's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, on the benchmarks.Swyx [00:16:40]: How many people do people spin up consecutively?Ivan [00:16:43]: So we have.Swyx [00:16:43]: Or concurrently, is the Concurrency, right?Ivan [00:16:45]: There's three metrics that we look at. And so one is like time to spin up one, and so our time to spin up one is 60 milliseconds with network latency. So request, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you wanna spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the so it is speed of one, speed of like multiple, and then how many can you consistently have up and running. And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they're, where they're just shy of a million every single day that they're running, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.Swyx [00:17:44]: They pay by like vCPU seconds.Ivan [00:17:47]: By seconds, yeah.Swyx [00:17:47]: Or whatever. Yeah. Okay, and so and then, and the other thing is, the sleeping and the resuming, ‘cause it's all the stateful resumption of all these things, how, what kind of workload are people putting through this, right? Like how is it Do we measure by gigabytes in memory, gigabytes in storage? I don't In like network attached storage. I, what are the costly ones of, out of all these features?Workload Economics: CPU, RAM, Network, and StorageIvan [00:18:15]: The most expensive thing are CPU.Swyx [00:18:18]: Okay. Yeah, of course.Ivan [00:18:18]: The second one, yeah Then it's RAM, then it's disk. We actually don't charge.Swyx [00:18:22]: Which is snapshotting, right?Ivan [00:18:23]: No, it's actually the, snapshotting's part of it, but basically the size of your hard disk, of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the transference of that. Right now, currently we don't charge for, network at all at Polychron.Swyx [00:18:37]: Oh, you gotta, yeah, you gotta fix.Ivan [00:18:38]: Yeah. It is very much a it's a larger and larger part of our bill, so we're working around, that part there. Obviously, that is the least, expensive, so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, ‘cause we don't charge the customer, and then hard disk, is how it's split up. But there's also different types of workloads, so we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents. and the other is, basically RLs and evals, which I put sort of together. And so they have very different patterns of usage, and if you look at the usage of a background And I'll just name names of companies, not specifically.Background Agents vs. RL/Evals: Two Usage ShapesSwyx [00:19:21]: Yeah, open, all hands.Ivan [00:19:23]: Yeah. So like a background agent's a Cognition, a Lovable, a like all these things are Harvey. These are all long-running, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest, and the midnight is the lowest, and then weekends are lower. weekday is higher.Swyx [00:19:42]: Yeah, that's a fun question. How global is it? Is it very US-centric or?Ivan [00:19:46]: The US is a large part, but we have currently, we have Asia, Europe, and the US regions.Swyx [00:19:52]: So it's quite global.Ivan [00:19:53]: Yeah, it's quite global. We have it all over. It's interesting that our I talked to you a bit about this. Our number one city by user.Swyx [00:20:01]: Hmm.Ivan [00:20:02]: Is Singapore.Swyx [00:20:04]: Oh, wow. Amazing.Ivan [00:20:05]: Which is an interesting one, right? Not by revenue, just by just like by individual head count.Swyx [00:20:09]: Really?Ivan [00:20:09]: Just like an interesting thing.Swyx [00:20:10]: Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, seven, eight million population. And it's like keeps showing up.Ivan [00:20:20]: No, it's quite interesting. We were quite shocked, and I was like, “Oh, this is interesting.” And also one that's up there.Swyx [00:20:24]: There's a reason I'm doing AI using Singapore. it's because I'm from there.Ivan [00:20:27]: We're there. We're gonna, we're gonna be there as well. and it's interesting that Japan is in the top or like Tokyo's in the top, which is in all the tech cycles it has never been. It has never been, so it's quite interesting that they're.Swyx [00:20:39]: I think the Japanese just love AI. Yeah. It's that, and then it's Brazil. That's it.Ivan [00:20:44]: Brazil has always been in.Swyx [00:20:45]: I think.Ivan [00:20:46]: Even when I look, if you look at like GitHub's data and ask historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in sort of that top, that top.Swyx [00:21:01]: Yeah. Weird pockets.Ivan [00:21:01]: Weird. Yeah, so it's very global.Swyx [00:21:02]: Okay, so actually that, but that's helps you to distribute your load through, all time?Ivan [00:21:08]: The interesting thing is like we have those kind of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs at ARMb, when they fire off a run, it's just 100%. And then it just runs, and then it stops. So it's very, the usage pattern is squares basically, right? And it's also not follow the sun, because people will fire it off at midnight before they go to sleep but then wake up and so it's very unpredictable, so you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun, even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that, because it's sort of, it grows in a, in a way you can project. When you have companies doing sort of like evals and RL, they're super spiky. So they're gonna come in, it's like, “We're gonna use nothing, then can we have 100,000?” Right? And then go back down. And then 100,000, go back down. So it's very different, right? And.Swyx [00:22:09]: Do you want to lock them into commits so.Ivan [00:22:11]: Yeah, we do.Swyx [00:22:12]: Yeah, okay.Ivan [00:22:12]: We so we have to lock them into some sort of commits to have that capacity, because we have to have, basically we have to have the capacity for peak. Right? And so right now, Daytona's mean utilization is 15%, 1-5.Swyx [00:22:25]: Oh my God.Ivan [00:22:26]: So it's very low.Swyx [00:22:27]: Because it's very spiky.Ivan [00:22:27]: It's very spiky, but we get up to 90%. so we have these things. And so what we're, what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agent where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, as a compute provider in the agent space. And when we were doing the conference recently, and so we talked to like Nikita from Neon and.Swyx [00:22:57]: I should bring it up.Ivan [00:22:58]: Parag from Parallel and whatnot, everyone has the same problem. Whereas the usage is super spiky, and this is something that has not happened before, that you have these types of like it was always, it the amplitudes were not this high, right? So it's quite interesting use case and problem solve.Compute Conference and Spiky Agent InfrastructureSwyx [00:23:12]: Yeah, I don't know if we're gonna bring this up again, but let's just talk about the conference, you had like 1,000 something people at the Warriors game, at the Sorry, where is it? What's.Ivan [00:23:22]: Chase Center.Swyx [00:23:23]: Chase Center.Ivan [00:23:23]: Chase Center.Swyx [00:23:24]: I went. It was, it was very impressive. Obviously, you can, how to throw a conference, what did you learn? you put, you pulled together all these impressive names.Ivan [00:23:33]: What I.Swyx [00:23:34]: What were you looking for?Ivan [00:23:35]: My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents, and so we can do that. And what I found, this was a theory, it wasn't proven, is that we all have these problems, as I touched onto. And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, in human, compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking.Swyx [00:24:20]: Lynn. Nikita.Ivan [00:24:21]: Lynn, Nikita. Lynn especially, I was talking to her the other day as well. Like the It is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos, and basically, as users work in different places, and depending on your tier, they can move you around the geos. And so that how, that's how they get the higher utilization. But you can sort of predict these, and it's If it's something in You'll rarely get a spike that is 10 orders of magnitude. Like you'll get a like let's say one of your customers has some like an exponential curve. What is that to I'm using Cloudflare as an example. 10%, 20%, whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10x, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So we have.Swyx [00:25:11]: She also has the same thing.Ivan [00:25:12]: Yeah, I know specifically that like Neon had that issue as well. Like how are we solving these spiky loads and things like that ‘cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, which is quite.Swyx [00:25:28]: Let me let me double-click on this. Okay. So for example, Neon, I happen to know that they're very sort of S3 oriented, right? so they're just like fully bet on S3. And you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And, for listeners, we did an episode with her, one and a half years ago. And you have to care. But like, right?Ivan [00:25:54]: Parag cares for sure, and Nikita.Swyx [00:25:58]: And Parag is C of, Parallel.Ivan [00:25:59]: Parallel, yeah.Swyx [00:26:00]: Former CTO of Twitter.Ivan [00:26:01]: Twitter, yeah.Swyx [00:26:02]: They are the search.Ivan [00:26:03]: Yeah, they're search, yeah.Swyx [00:26:03]: I You and I know but the listeners don't know.Ivan [00:26:08]: Yeah, we can put it down in the screen, and so ‘cause we, when we were talking.Swyx [00:26:11]: I'll put it up on the, on the screen.Ivan [00:26:12]: Yeah, right.Swyx [00:26:12]: People can look it up if they need.Ivan [00:26:14]: Look it up. And, yes, but they still have CPU and RAM, allocation that you have to have up and running. And so CPU and RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over-provision and you can handle the bursts, or two, you basically have, I don't know if this is a term, just-in-time compute, which is like as your load becomes, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them up and running.Swyx [00:26:43]: This is if you go above 100%, right?Ivan [00:26:45]: Yeah, this is.Swyx [00:26:46]: Like your overflow.Ivan [00:26:46]: If your overflow, like spillage or whatever you do.Swyx [00:26:48]: You probably lose money on it, but it doesn't matter, right?Ivan [00:26:50]: It, not Well, you might, you might not That is a more cost-effective way to do it but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just-in-time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine, and you can do that. But if your customer, and especially for, let's say, the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at, what, 100% the entire time. And so when you're running runs on CPUs, when the when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down, right? And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.RL Workloads, Declarative Images, and Kubernetes ReplacementSwyx [00:27:43]: Yeah, let's talk about the different workload, right? You said that, what was it? A few months ago, you had zero RL workload and now it's 50%.Ivan [00:27:52]: It will be this one, 50%, yeah.Swyx [00:27:54]: Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here, it's probably all the same code. You're just doing parallel runs or something, I don't know.Ivan [00:28:05]: Yeah. So you'll have multiple Depends on the like for each run, you'll have a snapshot. And they, for the most part, they actually do use our declarative image builder, which is like, “Oh, we, the agent wants these dependencies, these env vars.”Swyx [00:28:17]: These ones, yeah.Ivan [00:28:18]: Yeah, the declarative image builder, it.Swyx [00:28:20]: Which is a very modal like thing that they.Ivan [00:28:22]: Yeah. And so we build it on the fly and then we propagate that snapshot, and you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also be automated. It's like, “Oh, now for the next run, we need to install these things or remove these things or whatever to get, a task done,” and then it goes off and runs that. So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. So EKS, GKE, whatever. That is what the vast majority run on. And anyone that has tried Daytona versus GKE, EKS is like, “I'm never going back.” That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM, or out of memory, our sandboxes, because we can dynamically on the fly.Swyx [00:29:48]: Resize.Ivan [00:29:49]: Resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, the Terminal Revenge team is, brought us actually. So thank you, Alex and the team, that brought us into this whole space.Swyx [00:30:05]: It's just very rare that, a framework would just say, “Guys, just use Daytona.”Ivan [00:30:11]: Yeah, I think it says it somewhere. Yeah.Swyx [00:30:13]: Yeah. I was like, “What is this?”Ivan [00:30:15]: There's all, there's multiple there, but they also mention a few other places. and so Daytona specifically-We have, the, just jumping on themes here We, I don't know where it says Data Center.Swyx [00:30:27]: I, there.Ivan [00:30:27]: Doesn't matter.Swyx [00:30:28]: There's a very strong recommendation, which is, very unusual. Which is, it's.Ivan [00:30:33]: We do not pay them for this, just.Swyx [00:30:34]: I know, yeah. They just like you.Ivan [00:30:35]: Yeah, they like us. yeah, and also a thing, so, Data Center has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, that's hardened with Sysbox. So it's Docker's, isolation that is a security equivalent to a VM, but it's still a container. And that is the default, and they, especially in these training workloads, really like that as an interface to be able to use just a basic Docker container, and we enable Docker and Docker. Which for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3S inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that, through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the general venture people.Swyx [00:31:28]: Those people, yeah.Ivan [00:31:29]: And Harbor people.Swyx [00:31:29]: Harbor people, do are they, are they a company yet?Ivan [00:31:33]: As far, I do not know.Customer Pull, Slack Connect, and the Computer Use BetSwyx [00:31:35]: Okay. All right. Yeah. It's like super obvious that like, there's a lot of excitement and success around these things, okay, so yeah, tell us more, right? Like, this is an exploding workload, Harbor adopted you, which helped speed things along. But what are you learning as this new workload comes online?Ivan [00:31:53]: There's a couple things that we learned, which we chat about in the beginning. We, and this has led our story, as we mentioned, we like talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And it's interesting that And I think it's that the ecosystem is so small and/or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week. It's like very bizarre. It happens so many times, which is.Swyx [00:32:27]: Because they're all friends.Ivan [00:32:28]: Sorry?Swyx [00:32:28]: They all, they're all friends. They're all in the same group chat.Ivan [00:32:30]: Yeah, probably, yeah. ‘Cause and they're like, “Oh, can you do this?” And I'm like, “Okay, this is interesting. We'll put it on a feature request.” And then the next one's like, “Oh, can you do this?” “Okay.” It's all the same, right? It's always the same. And so what we try to do, and I personally try to do, I try to be on as many call, quote-unquote “sales calls” I can. I'm in every Slack channel. We literally have about 1,000 Slack Connect channels, something like that. It's an interesting, there's so many interesting things you find out when you have all the Slack channels. You can also see where people, transfer between companies. You see leave Slack channel, enter Slack channel. It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be. You have a list.Swyx [00:33:08]: LinkedIn charges you to, use your own connections, but Slack doesn't, right? Slack is like, do it for free. It's more lock-in. It's great.Ivan [00:33:15]: Yeah. It's amazing. Yeah. It's one of the reasons.Swyx [00:33:17]: You're gonna pay Slack for life.Ivan [00:33:18]: Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we were talking about earlier is we made a big bet and put a lot of investment on computer use. that is not seen publicly the light of day. We haven't GA'd that yet, but we have.Swyx [00:33:32]: Is there a thing I can pull up?Ivan [00:33:33]: There is computer use there. It's right up a bit.Swyx [00:33:36]: Oh, yeah. Okay.Ivan [00:33:38]: What we have, what we talked about and what we've seen publicly is there's this theme now about, the human emulator where And Elon from XAI has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, terminal or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US 50 trillion worldwide.Swyx [00:34:24]: Wow.Ivan [00:34:25]: Something like that. And if we look at, the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very long time. Like, people just won't invest in that. How much of it? our assumption is the following: if, in the RPA market, which is similar market, well, not the same 25% of, these white collar, workers', work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, 40%, right? And so if you take 40% of that, you get to essentially, $10 trillion a year.Swyx [00:35:17]: That's a TAM.Ivan [00:35:18]: That is a that is a TAM. So that's the TAM of the models, right? That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, either Mac or Windows or Linux. Linux we also obviously have and others have. But Windows specifically is something very new, and the only option right now is an EC2 with, Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, point in time snapshots, you have, forking, you have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's been our big push and bet, but we've sort of, kept our ear to the ground. What is sort of the next things in the market?RPA Returns: Why Agents Still Need ComputersSwyx [00:36:06]: Yeah, knowledge work, and building, and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing. And it was, a very hot Isn't it, Eastern European?Ivan [00:36:20]: It is, Romanian.Swyx [00:36:21]: Romanian?Yeah, it might be the only Romanian, big unicorn okay, yeah. This I don't I don't, I don't have like a I think there's, I think there's a stage being set for the resurgence of RPA, ‘cause everyone understands that, yeah, no one wants to deal with these shitty apps and no one's gonna rewrite them. Like, you just have to do, a remote operation and programmatic operation of them.Ivan [00:36:45]: If you wanna unlock it, my own setup was basically the following. So I was doing a board deck recently, last month, whatever, and I'm like, “Okay, let's just, let's just do automated.” So, all our data's in, ClickHouse and PostHog and QuickBooks, where everyone else's is, and I'm basically, connected that all to, my Cloud code, like go off and go Cloud code whatever. Go off and, here's the integrations, go do that. It pulled out the first report, which was great. It connected to Brex and all these things, pulled it, which was great, and then I say, “Okay, now pull out this, and this,” and I kept getting, really well McKinsey-style design reports, but the data said partial data. all the missing data, partial data. Like, it can't access all the things, and I got so frustrated, and so I got, I got, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company. And so I would say, “Now go and do this report,” and it would get the same, or like, “I can't via the MCP or the API or whatever. I can't get all the information.” I'm like, “Go log in.” And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I to get value, I get immense value right now, but it has to be a computer usage, unfortunately, and so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools, still needs a computer agent what hope does, Goldman have to have a headless, right?Swyx [00:38:22]: Yeah, what a - Why isn't Microsoft doing this?Ivan [00:38:27]: I'm pretty sure, Satya had a post yesterday.Swyx [00:38:29]: Oh, okay. I see.Ivan [00:38:29]: Which was like, “Every agent needs a computer.”Swyx [00:38:31]: I see, I see.Ivan [00:38:32]: So they have launched something recently.Swyx [00:38:34]: Yeah, they have Microsoft Power Automate, I'm sure, I'm sure, they're gonna have their version.macOS Sandboxes, Apple Constraints, and the Windows OpportunityIvan [00:38:39]: Version of that, yeah.Swyx [00:38:39]: You're gonna try to do yours, and it - I always know there's always demand for Mac, but I know it's, tricky to host, macOS sandboxes.Ivan [00:38:49]: We will have macOS sandboxes fairly soon. The problem with macOS, OS sandboxes is, I'm deep in this, I don't know how much interesting is.Swyx [00:38:55]: No, it's.Ivan [00:38:56]: MacOS has this problem.Swyx [00:38:57]: It's a licensing thing, right?Ivan [00:38:58]: Licensing thing. So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically, if I wanna charge you per second and I charge you one second, I have to have it idle for the rest of the day. I can't have anyone else doing that. So the pricing will be different in the sense that I will have to - we would have to charge for 24 hours, and that's not even, that's not even the most difficult thing. But the, thing above that is, from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine. And so what you can do in, Windows world or Linux world is that I can move in the background, your snapshot from one to the other and manage load, right? Here, if you wanna do that, you essentially have to have your.Swyx [00:39:49]: Yeah, snapshots. Yeah.Ivan [00:39:50]: Your.Swyx [00:39:51]: It's like.Ivan [00:39:51]: Physical machine.Swyx [00:39:52]: You can't break it up.Ivan [00:39:53]: You can't, you can't move things around that, and all of that is, that part is, from a security standpoint, if it is written. Like, I understand the security aspect of that, but it disables you from doing these agentic, like really scalable agentic workloads.Swyx [00:40:08]: You need to do a vibe-coded, clean room implementation on macOS that you can then - That's like Clean OS or something. I don't know.Ivan [00:40:17]: So. We have.Swyx [00:40:18]: ‘cause like Linux was originally like a clean room rewrite of Unix.Ivan [00:40:21]: Okay. Yeah.Swyx [00:40:21]: Or something like that, right? Like same thing to macOS. Someone needs to do it.Ivan [00:40:25]: Someone will do that, and someone will have some long-running agents for a few days to figure this stuff out. But yeah. So definitely we - we're really close to offering something ‘cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.Swyx [00:40:38]: Yeah, nobody's gonna use this. like, the labs, the labs will because they want to automate macOS.Ivan [00:40:42]: They have to do RL. They have to do RL again. But even if you The - So the point is with the RL part, if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if they would just enable a concurrency model similar to what you can get on a Windows and a, and Linux.Swyx [00:41:17]: Yeah. Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's And maybe they will change their mind with the new CEO.Ivan [00:41:24]: Yeah. We'll see.Swyx [00:41:25]: We'll see.Ivan [00:41:25]: High hopes.Swyx [00:41:26]: High hopes.Ivan [00:41:26]: High hopes.Swyx [00:41:27]: Okay. But I, it's very clear the market opportunity is huge in Windows, and you can go for a long time on just Windows, but your customers are gonna want both. and I think, it is interesting to me that, this is the sort of God application of agents, right? Like, I don't It was - How big was OpenClaw for you guys? Like, was it, was there, a significant bump.OpenClaw, Agent Labs, and the B2B2C Sandbox MarketIvan [00:41:54]: Not for us because we.Swyx [00:41:54]: Because you already.Ivan [00:41:55]: We're kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. Sort of it's either B2B or B2B2C. So, in the researcher world, it's B2B, so you're selling to, labs and neo labs and things like that. But on the long-running agents, it's mostly, from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you at a big scale.Swyx [00:42:26]: Like a Manus. Yeah.Ivan [00:42:28]: Like a Manus Lovable type of thing.Swyx [00:42:31]: Yeah. I think that's the question of, well how, um-Uh, yeah, B2B to C is basically to me what I've been calling an agent lab, which is kind of like you're not in a model lab, but you're making a very good wrapper that is a platform that other people can sign up so they don't have to code those things. Yeah, it sound, it sounds like a much better market than the direct OpenClaw market.Ivan [00:42:56]: I've like - We I've done multiple things. So the CodeAnywhere's part of our career path R in the calendar, was very much an end user developer product. And so that is great. It You can get a lot of developer love, and I feel that we do as a company have a bunch of developer love. But it's a different type, where it's people building these things. Again, it's more akin to a Twilio because you don't really run - As a person, you wouldn't run Twilio. I don't know how many people remember. It was like ask your developer billboard and whatnot. And people really love Twilio, but they only used it inside of like, “Oh, I'm building this app or service for thing.” And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, in my DNA.Swyx [00:43:35]: People don't know InfoBip is that big.Ivan [00:43:38]: Yeah, it's.Swyx [00:43:39]: Because.Ivan [00:43:40]: It's a billion euro.Swyx [00:43:40]: They're all American. They're like, “Whatever's in Europe doesn't matter to me.” But like it's the, it's the same size or bigger? Same size?Ivan [00:43:46]: It's about half the size.Swyx [00:43:47]: Half the size?Ivan [00:43:48]: Yeah, about half the size.Swyx [00:43:48]: It's like, yeah.Ivan [00:43:48]: Still huge. Multiple billions a year. Yes.Swyx [00:43:51]: That's crazy.Ivan [00:43:51]: Exactly, and so that - These are like really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the - When your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very around that. And there's very It's very hard to scale. Your cap is the number of people that are willing to spin up - First of all, wanna spin that up, and then spin up multiple of these. Whereas if you're in the enterprise one, like we know everyone's talking about like how many tokens they're spending, I'm spending. Like a lot of companies today are like, “If this is our company, spend as much as you can.” Like basically that is where we're going. And so if you think about that paradigm, where you're selling to companies that say, “Spend as much as you can to generate, productivity,” versus, “Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever.” Like it is a different, it's a different go-to-market, I think, strategy.MCP, CLIs, and Sandboxes as the Agent RuntimeSwyx [00:44:50]: Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.Ivan [00:45:10]: Those things you - work well in our favor, to your point just because every.Swyx [00:45:13]: They're kind of drop in the bucket, right?Ivan [00:45:15]: I think it's like sort of all the things come together. And so there's so many things that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, from Anthropic, so or Cloud Claude Code was very interesting. The reason why it was interesting is that a lot of, let's call them app I don't know what to call them, app layer agent companies, essentially they are like, “Oh, I can create this new app, this new agent. All I need, I just use Claude Code, and I throw it into a sandbox, and then I have my interface to the human to that.” And so that enabled so many more companies to actually offer this, and then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP, versus the CLI, the MCP is an interface against an API, whereas the CLI is like you can actually go do things. Like this is it. The difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very well enables the agent to do more things, and it's because that people will invoke a sandbox, they'll run it in the CLI, and but it'll do anal-analysis on that data and then give you an actual result versus just, pulling data from an API source.Swyx [00:46:29]: Yeah, it's a layer of indirection basically, it's the same thing as agentic search versus RAG, which where you're.Ivan [00:46:34]: Exactly, yeah.Swyx [00:46:34]: Just like you just win whenever people put more agents into their workflow. And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, “Oh yeah, this is another sandbox use case. Oh yeah, that's another one.” Am I, am I missing any big ones?Ivan [00:46:51]: The thing, the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is, and to your point, we've talked to so many people over the last year. It's like, “Oh, like why do you need a sandbox? Why do you need this? Why this?” And to your point, it's like, “Oh, I need sandbox for this. I need sandbox for that. I need sandbox-” It's like, “Oh, I need it for every single thing.” And so basically what I, what I - and it sounds like a broken record, it's like you use a laptop every single day, right? And you are n of one. It's just you. But now imagine how And by the way, the laptop, the computer PC market, the PC market is about equal to the cloud market in total. So it's about 150, 180 billion a year. Something like that. It's about roughly the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, It's a little bit less, but it's sort of like that. And now imagine And that's just like, so how big is the addressable market? What, how many people are there in the world now? What's the last data?Swyx [00:47:45]: Let's call it eight billion.Ivan [00:47:46]: Eight billion. And so let's say you can have two computer, like you have one personal and one business, whatever. Like so it's double that, right? and so that's 16 billion, right? How many agents are gonna be running in two years, in 10 years, in 100 years? Like And for every single task, they will need one of these. And so how big is that? That market is essentially quote unquote “infinite”. You will get to the point, and Dylan Patel was at the conference talking about, from SemiAnalysis, that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint. You won't be able to grow, or we won't be able to have enough of these because there won't be enough CPUs to basically do.Swyx [00:48:23]: Yeah. Well, I actually had a really good podcast with Doug Oliphant, who, which was his president at SemiAnalysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs.Ivan [00:48:35]: CPU, yeah.Swyx [00:48:35]: It-What's next? So networking. So, networking actually has been in shortage for a while if you're looking at, just GPU networking. But, yeah, it's really crazy the amount of computer use that's going on, yeah, cool. I, other questions are, just the one very big part is the open sourceness which you didn't have to do, your competitors don't do, like it's not, a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.Open Source, Trust, and Enterprise ProcurementIvan [00:49:15]: Yeah. There's a bunch. So we the original product that we did was open source.Swyx [00:49:19]: Yeah. CodeAnywhere.Ivan [00:49:20]: So doing that was actually very good for us. There's basically a saying of, What's the saying? Like, companies that are, that are doing really well, measure themselves against, free cashflow, that are kinda okay, it's EBITDA, then, it's, it goes all the way down.Swyx [00:49:36]: The worst is like GitHub stars.Ivan [00:49:37]: GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars. That's what we talked about, we're at the point we're talking about revenue, so we're we've gone up the stack on that. And so we started.Swyx [00:49:47]: No, profit.Ivan [00:49:48]: Yeah. We haven't, we're, we'll get there. We'll get there. But basically at that point we did stars and GitHub and it was useful, and the original variation that we did, it we split the core into its own repo and it was Apache 2.0, so very, permissive. And then we basically would bundl

Welcome to Omni Talk's Retail Daily Minute, sponsored by Duvo and Mirakl.In today's Retail Daily Minute, Omni Talk's Chris Walton discusses:Google unveils its biggest Search overhaul in 25 years at Google I/O, replacing the traditional link-based results with AI agents, generative UI, and conversational experiences.Klarna launches a Shopping Search app inside ChatGPT, connecting over 100 million products to AI-powered conversations and staking a position at the top of the retail funnel.Target reports its first positive same-store sales comp in five quarters, beating Wall Street estimates across the board and raising its full-year outlook under new CEO Michael Fiddelke.The Retail Daily Minute has been rocketing up the Feedspot charts, so stay informed with Omni Talk's Retail Daily Minute, your source for the latest and most important retail insights.

Is Google deliberately obscuring its AI search mechanics to protect its 20-year monopoly? In this episode of Digital Marketing From The Coalface, we dive into the smoke and mirrors of the latest Google AI guidelines, which frustratingly claim that nothing has actually changed. We also explore the widening divide between building websites for human journeys versus AI machine crawlers, and try to bridge the gap between marketing strategy and web engineering as we discuss why older, structurally simple websites might perform perfectly fine, while modern JavaScript-heavy, lazy-loading pages can be entirely invisible to AI agents. We also unpack the shift toward headless CMS to resist the heavy load of AI crawlers and deliver lightning-fast web pages, and we contrast human-centric design, which prioritises branding and UI, with agent-centric design that demands clear structure, entities, and metadata. Finally, we cover the topic of deep integration between CRM and Google Ads to track meaningful B2B conversions. The reality of keeping up with AI search is an exhausting marathon, but endurance is key.

The Great Talent Redistribution: Where is Talent Actually Going in 2026 and beyond?  Is the start-up compensation model broken? How about big Big Tech? How about non-tech small & medium businesses? What is happening to talent, going forward? This and many other topics in this episode of Tech Deciphered. Navigation: Intro The Broken Contract? The Great Unbundling The Three (?) Destinations Alternative Cap Tables, Alternative Compensation Models Investor Landscape Fragmentation Operator Playbook and Predictions Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show: Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Nuno Goncalves Pedro Introduction Welcome to episode 77 of Tech Deciphered. This episode will focus on the great talent redistribution. Where’s talent actually going in 2026 and beyond? The Silicon Valley deal of the last 30 years, very low salary, stock options, you will either sell for a ton of money or IPO, and everyone gets rich, is seemingly broken. Or is it really? The dominant narrative says the tech middle class is dying. We disagree. There is obviously a lot of stuff going on whereby big tech is partially barbelling. There’s a superstar concentration on the top. There’s a bit of a seemingly allowing of the belly. We’ll come back to that. We don’t quite believe that is totally true. There’s a collapse at entry level. The belly is migrating into three, potentially even more, very different destinations: AI native startups, human-verified premium businesses, and the read the industrialized middle of the S&P 500 and SMB world. Each has its own cap table, each will have its own compensation model, and each will have its own investor profile. In some ways, this is the third episode in our Reset trilogy. We started with episode 75 on the SaaS-apocalypse. We talked about the great private capital reset in episode 76, and now we talk about talent redistributions. Bertrand, exciting times, not always positive times.   Bertrand Schmitt Yeah, it’s exciting times because it’s a time of change. Of course, we have the doomsayers. If you listen to Dario Amodei of Anthropic, every white-collar job on Earth is going to disappear. I think I strongly disagree, and I suppose you too as well, we strongly disagree. It’s going to be more of a redistribution. If you look at the history of technology, this is what always happened. We forget how many jobs have disappeared over the past 150 years. We move from a time of 150 years ago. People were mostly in agriculture. Then you had a lot of weird jobs that disappeared from people transporting water to people bringing ice from the pools to people doing the job of computers. People forget that computer was a title given to human beings. We’re doing calculations. Then, of course, secretory jobs in the ’80s, ’90s, where suddenly anyone can type using a word processor, the rise of Excel, that sort of stuff. Many things have changed. Some jobs have indeed disappeared. Some jobs have totally transformed. Where you do these jobs have changed. I think we are at a similar stage where, thanks to AI, and I would say for now, or at least the rise of AI coding, there is a dramatic change happening. I don’t think it means that people will be without a job. It just means, from my perspective, that jobs are changing. You are not just doing a lowly coding level task that actually indeed could be replaced, but you are going to have more of builder type of mindset, a product manager type of mindset going forward. We also expect that the distribution of jobs, depending on the type of business, will be quite different.   Nuno Goncalves Pedro The Broken Contract? Maybe let’s reset a little bit to the broken contract, or if it’s really a broken contract. There’s been this image in technology and tech that basically you get paid very little to work in tech. You get a bunch of stock options. The earlier you are in the company, the higher the level of stock option grants you get. Then you make a ton of money at some point because the company will either sell or IPO, and that’s heard of it. Obviously, there’s a lot of movements happening right now that are changing how these dynamics work. The first part is obviously AI, and in some ways, AI is shrinking companies. It’s not unheard of that companies with as little as four or five people reach 50 million in ARR. There’s companies with one person that have gotten bought for hundreds of millions of dollars or billion of dollars. Obviously, things are moving very, very fast, and therefore, there isn’t a large employee cap table. How would you share the upside? Would you actually give a couple of percentage points to an early employee rather than your 0.2-0.5% kind of thing for early employees? The second part is a little bit the other side of the table, which is the IPO market is seemingly in a drought. There’s not much happening in IPOs. Maybe 2026, at some point, there will be an unlock, but right now, it’s seemingly difficult to get your upside. Even if you’re an employee, you have to wait a long time. The median time of IPO has climbed over 10, 11 years, the longest in over a decade. Basically, not only you have to wait a long time as if there is an IPO drought, like we might be going through right now, when do I actually get my cash back? Unless the company gets bought, maybe there are secondary transactions along the way, maybe there’s something else. But obviously there’s a little bit of a reduction and lowering of the upside seemingly for this contract and for this place. The easy conclusion that I think many are taking is, because of all of this and all the layoffs that are happening, even in big tech, that serve the tech middle class is dying, that basically AI screwing the workers, et cetera, there’s also a lot of discussion that even it might be affecting the entry-level jobs as well. Everyone coming out of undergrad right now can’t get a job, et cetera. There’s this doomsday scenario that you’re alluding to that everything is changing. We have a slightly different perspective. We think there’s a realignment of market. In layoffs, there was a lot of layoffs that were warranted. Big tech, in particular, had actually hoarded a lot of engineering capacity over the last decade or so. There’s a little bit of a realignment that needed to happen in any case. When everyone’s saying, “Well, AI is compressing everything,” well, it’s compressing right now, but we don’t think actually it’s going to compress over time. You’ll still need engineering and science talent to come on board for you to be able to scale up. It’s not like AI is going to take care of everything and teams are going to be five people for companies that are worth a trillion dollars. That’s not happening. Today’s thesis, I think a little bit of this doomsday scenario needs to be seen with a more nuanced lens. I think that’s how we’re framing today’s episode, that there’s a bit of a nuance, there are some extremes happening. We’re going to talk about those extremes, but ultimately, it’s not quite as simple as saying that the tech middle class is disappearing in early jobs are going to be a thing of the past.   Bertrand Schmitt At the same time, what you started with is true. I mean, that 50 million ARR company, just five people. At a bigger scale, that’s exactly the matrix for Anthropic. They have reached a stage where they are at a range of 12 million ARR per staff per employee. It’s metrics that are definitely never seen before. I don’t think any company raised to this level. Best in class, best run companies, one, two million per employees. I mean, that was your target if you can make it. We are definitely in a different game. But I think what matters at the end of the day, and that’s what we’re arguing, is that you have to see the big pictures. Yes, some positions might disappear inside some companies, but some other positions will be created in other companies. Usually, what people do is keep talking about the jobs who disappear and not looking at the bigger picture of jobs that are being created as well. What is true, and I think you alluded to that, is that the big tech the past 10, 15 years had some strategy of hoarding talent in a war where having the best talented people will make the difference in numbers, will make the difference between winning or losing. The Google of the world, the Microsoft of the world, the Amazon of the world, they were hoarding talent. They would try to make sure that they might not have such needs in talented number of people. But if they have the talent, it means their competitors didn’t have the talent. It means that the startup trying to reach scale couldn’t pay the giant salaries that the Google of the world were paying. There was definitely some hoarding. But it went so far in the 2020, 2021, that I think since then there has been a coming back to normal. There is also now in 2026, the recognition that it’s not true anymore. Yes, talent can be very valuable, but there is now a bigger and bigger gap between the extremely talented versus the rest that are merely talented because of AI. AI is able to replace at scale your software engineers, your software managers. I would say it’s quite new. I don’t think it was true a year ago. We’re really talking about a recent dramatic change in what can be achieved thanks to AI. We can see most of the big AI companies are moving to coding. It was started by Anthropic as a trend, OpenAI has followed through. Obviously, the Cursor of the world existed before, but they were not as successful. All the Chinese open-source models are moving very fast to coding optimization the past few weeks. It’s quite an incredible change. I think there is that dramatic change, recognition that coding can be done differently. As a result, we are going to see change in the distribution of jobs. I think it will start from the top because we see the news of the big Google, Microsoft, Amazon, and others who used to hold talented software developers to a change in realization that no, we actually need to invest in AI. We need to invest in compute because compute is going to do the job of most of these people. Therefore, we can’t pay for both at the same time, even us with all our money, we cannot. Wall Street is not going to let us do that. They start by removing a lot of position. I think we see that accelerating, quite frankly. We have only seen the beginning, but in the next 2 years, we see a dramatic shift. But I think my position, I guess yours, and you know as well, is that there will be a lot more opportunities created as well, probably by also entities.   Nuno Goncalves Pedro The Great Unbundling Yeah, there will be more opportunities created. The hoarding is just taken also a little bit of a different view. To your point, there’s hoarding of resources, compute, et cetera. But there’s also hoarding of top talent. We are seeing people getting paid, packages all in that could run up to 100 million, in some cases even over 100 million over several years. This is unheard of. I mean, an officer of Meta would make, I don’t know, maybe 20, 25 million a year. It’s like now there are people that are on the top end of AI researchers that are getting paid around that amount just to join some of these companies. There’s a little bit of a different hoarding. It’s very selective hoarding of certain talent. We’ve seen some acqui-hires. We’ve talked about it in previous episodes that are just literally about getting one or two people specifically to come on board. Alexander Wang, again, going to Meta to lead their intelligence labs there. I feel, I don’t know what you feel, but I feel this is a transition moment where there is overpaying for certain talent on the top of the market. At some point, this will stabilize. You can’t keep paying people 100 million over 4 years or something like that across the board. To your point, a lot of this is actually going to scale up quickly also on the AI side. There’s a little bit of a different hoarding happening on the top end, not just the resources, but also of people, which seems to give further this notion of barbell, that there’s two extremes, the haves and have-nots, the super-duper talented people that get paid a ton of money, tens of millions of dollars a year at the very least. Then the emptying of the middle where there’s a ton of tech layoffs going on in some ways, the belly, as they would call it, is being expelled. The middle market, the managers are being fired because there’s nothing to manage. There’s a lot of positions going away. In some cases, you might keep some of the more junior talent, but with a little bit of experience. But even the talent coming out of colleges is not getting hired either. It’s a little bit of a weird thing where there’s hoarding at the top, there’s an emptying of the belly, the middle, and then the early, early, early is also not getting recruited. It’s like what gives? How is this going to look in the future? I agree fully with you, Bertrand, that there’s a migration of this talent, not only to other companies, but also to other jobs. There will be new jobs that will emerge out of this. The DevOps, dev tools market didn’t exist until maybe 20 years ago at scale, and it got created. In some ways, we’re seeing there will be new markets, there will be new roles and new jobs that will be created around engineering teams going forward. We can’t anticipate all of them. But basically, the emptying of the belly is true as it’s happening right now. The low hiring on the early and the top end, getting tons of money. We think this is a transition to something else. There’s the hoarding of engineering in general is coming to an end at momentum. Now it’s time to rightsize teams, to get the right at the table, et cetera, and start figuring out what works and what doesn’t work. We’ve already had some horror stories coming out even from Amazon where they were breaking systems with their use of AI tools, and I’m sure it’s happening across the board. I’m on a board of a company and been tremendously affected by Meta and its algorithms, where basically because of advertising, there have been people served with ads for this specific company where the ad doesn’t match the company, so basic stuff like that. It’s been actually very, very difficult because in some ways, the company goes back to Meta. It’s like, “Hey, dudes, you guys are serving ads that are not even our ads with our copyright and stuff. How does this work?” They’re like, “Oh, it’s AI.” It’s like, “Well, it’s AI but can you give me my money back?” They’re like, “No, we won’t give you money back.” This creates huge issues for companies, for example, that are very dependent on advertising, which obviously there’s a lot of industries that are. They’re actually in production systems at scale. Meta is, I think now, the largest digital advertising in the world. I think they outgrew Google in one of the last quarters. Basically, this has a tremendous effect that systems that are in production at scale are getting inputs and changes driven by AI tooling, and somehow nobody can say what the hell is happening. Again, there will be a reckoning, there will be a redistribution, there will be a rightsizing of teams and an adequacy of teams going forward. I personally think this is a transition period.   Bertrand Schmitt I think we are moving from hoarding or software engineering to hoarding the top of the top scientists in AI and hoarding of GPUs, GPUs/data center. For me, it was quite interesting to see the deal of Cursor with xAI, where basically they couldn’t get access to computing resources to run their model. But xAI had, I forgot the exact numbers, but close to half a million GPUs that no one, I mean, “no one was using” because their services are not so successful yet in terms of AI chatbot and the like. Basically, suddenly they are like, “You know what? We control access to resource.” But the new resource is, again, a mix of extremely talented AI engineering or AI scientists versus GPUs/data center. There is this race of controlling boss and everything else is going to be collateral damage. Some examples, I think, are quite interesting. You talk about some example of Amazon, even some production issues. I remember reading a quick post-mortem of one of the issues, and the conclusion was it was AI, definitely part of the issue. But the other part of the issue was AI used by junior engineers. For me, it’s interesting. It shows that actually junior plus AI is actually a danger zone. That’s why many companies are going to be way more careful. “Why do we need the junior people if they are just playing with fire?” I think we go back to that situation of barbell, as you call it. The top talents are extremely valuable because they know how a production system works. They are here to develop better AI systems. But the junior guys playing with fires, yeah, maybe it’s cute in startups, but in a big time production environment, a different story.   Nuno Goncalves Pedro There will be a barbell with top-end talent super-mega paid and then mid-level talent that is individual contributors still doing a lot of great work, et cetera. Along the way, a lot of emptying of entry, a lot of emptying of the middle. Where does the talent go? The Three (?) Destinations I think we could say there’s three destinations for this talent. Maybe there’s four, maybe there’s more. Three that we can immediately identify. One is the AI native startup piece, where we have smaller teams that potentially get to a lot of revenue or top line over time, and where the Series Seed is the primary round, where we’re seeing Series Seed being raised of tens of millions of dollars, actually even hundreds of millions of dollars in Series Seed. In some ways, the stars there can get incredible compensations in terms of stock. They will stay for private and selling in secondaries later down the road because there’s so much capital at the table. Actually, in some ways, salaries are very high as well in some of these companies. It’s not like you’re trading off anything. You can get paid a lot of money. If your company at Series Seed for 10 or 15 employees has raised 50-$100 million, you can pay great salaries. In some ways, this is the extreme destination. The AI native startups that can make it is the extreme destination. Now, there aren’t a ton of AI native startups that can raise 50-100 million to 400 million in Series Seed, just to be clear. There’s a handful of hot deals in that space, but that’s one clear destination for top-end talent going through that. In that market, I think that’s one of the destinations. The second one is more what we would call the human-verified premium. It’s more of a play of companies that has still the need of human in the loop, either in terms of development, also in terms of activity, either because go-to markets are very intensive, and so therefore you need to have sales forces, partnership teams, et cetera. Or on the engineering side, it needs to have a lot of customization, integration. Companies are not just going to the, “Oh, you can come in and just apply your AI tooling and somehow magically the systems all work.” there needs to be quite a lot of and work and high touch work in getting stuff done. A significant part of that market, I’m not sure, is super VC investible. Maybe it’s a hybrid of private equity in VC, more PE style in many cases. It’s a PE-hold, sell to someone else market. As we’ve discussed in a previous episode on the SaaS-apocalypse, that hasn’t quite worked out for PEs. Question marks on how that human-verified premium market is going to evolve. But obviously, there’s a lot of work still to be done there, even on the engineering and science side. That’s the second potential destination. Then the third more aggressive destination is the reindustrialized middle companies that have a lot of specificity in going after small and medium businesses, local or regional affectations like ERPs or CRMs for specific markets, et cetera. Those are the three natural destinations. I would add the fourth, which is big tech. I mean, big tech doesn’t magically disappear, and I don’t think it fits neatly into any of these three markets. In some ways, big tech is now looking at the extreme for top talent a little bit like the AI native startup because they can pay. They can pay the 100 million every four years, et cetera. I do think it will typify taxonomically into a fourth type emerging, where, as we discussed, you’ll have top-end individual contributor talent. You’ll have the absolute top-end of the market because they can get paid. Then you’ll start having the emergence of earlier talent that is highly capable, et cetera. That will go back to a bit of a normal distribution in terms of talent on big tech. For me, those are the four destinations that I would put at the table.   Bertrand Schmitt For me, big tech moving to big tech, I’m not sure if it’s really a destination. I mean, yes, in some ways it’s a reshuffle between the big tech companies. They are definitely all fighting in some ways for some of the same people. I can see that dramatic shift where big tech has to remove a lot of positions in order to replace by AI. Again, I think at this stage, it’s mostly driven by AI coding. We are still at the beginning because this is brand-new phenomenon that AI coding is so successful at its task. I don’t think it was true even 6 months ago. Some companies, take Anthropic, take OpenAI, are definitely there or close to be there in terms of no more writing of a single line of code by a human, zero. This is, again, 6, 12 months ago. Not true. But now it’s true in a few top companies. Take OpenClaw as well, most successful GitHub project of all time, not a single line written by its author. It would have been impossible. We’re talking about hundreds of thousands of line of code in a few months. It’s impossible to achieve that manually. If you look at the other big tech companies, the Google of the world, the Meta of the world, the Microsoft of the world, they are absolutely not there yet. They are going to be there because they have no choice. It’s you either go fast there or you die. You are not going to be able to survive competitors that are shipping 10, 50, 100 times faster than you are shipping. It’s a life and death situation. All the big tech companies are going to move, and mark my word, in the next 2 years from 10, 20% of AI-written code to 100%. During that transition, the next 2 years max, if you don’t do it in 2 years, you are going to die. Your stock price is going to crash. Then, of course, you will have to make changes. You will have to invest more in GPUs. You will have to invest less in your standard typical software engineer employees. Like you, I’m very optimistic that there are new buckets. AI-native startups definitely will be there. It will be transformational. Human-verified premium, very interesting category. In a way, it will be businesses that are inevitably less scalable through AI, and there is definitely a spot from there. I think the biggest would be the reindustrialized middle SMBs. Most of S&P 500 type of business are going to dramatically offer new software opportunities, new opportunity story to talented software employees because they will need to implement AI in everything they do. They will do it. They will need people who have software engineering knowledge in order to implement these systems. For them, what’s changing dramatically really is that thanks to much cheaper cost as thanks to AI coding, a lot of software projects that they couldn’t afford to do, that they couldn’t imagine doing by themselves, they are able to do it. They will invest in a lot more software capabilities than ever before. That will be a big game changer. And software, very tuned to their business model. There might be less buying of your traditional off-the-shelf SAF software and a lot more investment in a highly custom software by their own team, assisted with AI. I think that would be the part that is most transformed by all of this in a positive way.   Nuno Goncalves Pedro Alternative Cap Tables, Alternative Compensation Models This will lead to a very fundamental shift, right back to the broken contract. What does the new contract look like? It looks like alternative cap tables depending on which bucket are you transitioning into. If you’re going into your AI-native bucket, and you’re a top-end talent, you’re like, “Dude, I’m worth 100 million over 4 years, so just compensate me accordingly with a mix of options in the company plus my salary.” If you’re top 1%, you can probably get away with salaries that you’d get anyway at mid-level from 300K, 400K and above, and you can get actually a lot of options already in the company. A lot of this is happening right now. There’s a premium for AI, we know that. There’s a premium for AI at the top end of AI researching, in particular on companies that are doing hardcore research on staff AI engineers, so companies that require actual AI engineering. There is a premium that is significant. It could be as high as 18% over non-AI peers, and it widens actually with seniority, shockingly enough. This is more of an average than anything else. Now, for me, and it’s for debate, but the perspective is this extreme comp will need to compress at some point. There will still be the haves and have-nots paid much better than the have-nots, so to speak, but there will be a compression. The variance can’t be the variance we’re seeing today for absolute top-end talent. That said, there will be variants. We know that big tech for over a decade, decade and a half, for example, in the Bay Area, has been paying a lot of money for director and above levels that used to be the VPs, so a million, a million and a half a year, all in compensations. It’s not unheard of that this will actually increase after this stage. That said, I do think that the compensation extreme that we’re in will get diluted down the middle. It will actually come down at some point. It’s part of where we are today. As we know, it is still a bubble.   Bertrand Schmitt Yeah, it’s an interesting point. I think it’s possible. At the same time, that compression coming 2, 3, 5 years. At the same time, we have examples where there is no such compression. Take the top sports players in the world, golfing, basketball, NBA players. There has not really been any compression at all. For me, it’s interesting. If you look at the big tech companies, each being one of this top NBA team, why would such compression happen? As long as they are competing against each other and generating plenty of cash, I think there will be some fair question. We will see. I don’t have a strong opinion, but for me, it’s not a total given.   Nuno Goncalves Pedro For me, the shocking thing is the faster AI becomes better, the more that compression will happen, because at some point, it’s like, why do you need the top talent as well? I don’t know. It feels like you’re trying to evolve a system that’s there to replace you. It’s like, “Okay, I’m getting paid 100 million over the next 4 years”, and then you develop something that’s so good that replaces you. Thank you. That’s cool.   Bertrand Schmitt That’s a total possibility, yes, because we are in that very unusual market where the game is to only replace yourself and people like yourself. At some point, it is a possibility, I guess this one. Right now, we’re talking about replacing your “average software talent”. In 2 years, could we absolutely replace the absolute best top experts in the world? Probably. I think it’s just that at some point we’ll be reaching the stage where we strictly have no control anymore on our AI systems because no human is able to challenge and understand what’s produced. It’s not just a question of scale anymore. We’re talking about a gap in IQ, basically.   Nuno Goncalves Pedro Exactly. It will happen at some point in history. We don’t know exactly when. For the second bucket, the human-verified premium bucket, it’s difficult to see how an HVAC company or an HVAC roll-up of scale or a regional health care platform or high touch go-to-market, B2B, SaaS play, et cetera, for a vertical will compete. At the same end, they have to compete and they will compete. There will be more and more jobs, we believe, for engineering talent in these companies. They’ll have to be more and more AI-enabled themselves. The cash salaries will have to be competitive within the local markets, not necessarily with Silicon Valley. There will be potentially profit sharing and revenue sharing and actual dividends played at the table. The model there on the cap table needs to change a little bit, needs to be probably propped up more on salary and on some way of doing profit sharing or actually having dividends paid to employees and figuring out employee to equity in a more aggressive manner. This is the market that probably was already very attacked, so to speak, or let’s say, occupied by private equity firms. There are still obviously part of that model that would work well. There needs to be a fundamental shift, certainly on the quantum of salary compensation, dividend compensation, profit sharing, and all of that. Then last but not the least, obviously, we had the bucket around basically the reindustrialization of the middle, so everything else, which will take most of the belly that we were talking about. This is probably a poor analogy, the belly fat. It’s not belly fat, it’s people that were doing their jobs that now are getting disrupted. In some ways, that bucket will absorb a lot of that belly, will absorb a lot of talent. The small and medium businesses that Bertrand was saying will need to crucially become more AI, software-enabled by themselves, even with some core stuff and underpinnings that actually might not even require AI in terms of infrastructure platforms. There, you need to get properly paid. Again, how many people do you need in your engineering team if you’re a small business? Probably not a lot. It’s maybe you need one or two people and that’s it. They’ll need to be very nicely paid because they’re running the stuff in the rails. This is probably a market that over time, as AI gets more and more competent, will also be disrupted, but let’s not talk about the disruption to the disruption because otherwise, we’ll stay here the whole day, but certainly a market that has a lot of potential to shift and to absorb a lot of the moments that we’re seeing in terms of layoffs happening in the US in particular.   Bertrand Schmitt This category was a category that historically could not compete with Silicon Valley salaries, could not attract the most talented engineers. It’s not a category that didn’t want to bring these people on board. It’s a category that just couldn’t afford to bring this talent on board, typically. I think it would be a dramatic shift for them when suddenly there are opportunities to hire these people. There is an opportunity to hire them at maybe more reasonable prices from this company’s perspective. You talk about small companies, the great thing is that there are millions of small companies at some point. I think things could be truly transformational. Of course, some of these engineers, software engineers, might decide to become entrepreneurs on their own. Solo entrepreneurs, small businesses, build their own, easier to build their own product to market so to serve other companies. I think there will be quite dramatic changes because not all companies will be disrupted by AI as much, but not every company will benefit from improving processes, improving software through AI. At least early on, you will need this human touch to make it work inside a business. Interestingly enough, I was hearing that some companies like IBM were hiring more younger people to do the work of going to the client, understand their needs, propose implementation plans. That forward deployed engineer, those positions, I think there will be more and more available.   Nuno Goncalves Pedro Investor Landscape Fragmentation What happens to investor into the landscape? We already had an episode, the previous one, Episode 76, where we talked quite a lot about the big capital reset on the private equity and private reset, including venture capital. Just maybe to summarize, how does it align with the buckets that we’ve just been discussing? I think the AI-native bucket clearly is going to be the key bucket. There, we’re going to see two movements. One movement, which is the mega funds, as we discussed in the last episode, are no longer just VC funds. They’re really mostly multi-asset private equity funds, maybe even private equity hedge funds in some cases. Those funds will be all over the high-growth AI-native companies and will be pouring money into companies that are scaling really, really quickly. The early stage, so to speak, VCs, the actual VCs that will stay in the market will be the guys probably identifying the next big wave of AI-native companies. We’ve discussed that as well in the last episode, some research that we did at Chamaeleon that I shared in episode 76. We’ll see that as emerging. What happens to the second bucket, the bucket around human premium, human in the loop? Likely we’ll have more and more private equity capital going into it and the large-scale VC guys, the Thrives of the world, they’ve just announced Thrive Holdings, and others going after those markets as well. It’s trying to converge into the private equity market, which aligns with the point we made in the previous episode that the VC mega funds are no longer VC, that they are private equity, multi-asset class. They’re going after a bunch of things. There’s a conversion happening from VC into private equity. It was going to happen anyway because the private equity guys were coming into VC as well and the hedge funds were coming to VC as well. There’s a convergence in the middle of very, very large funds and large assets under management happening to go after some of these opportunities, certainly in Bucket B. Then this Bucket C, so to speak, the bucket of reindustrialization, as Bertrand was saying, very well, likely will be self-funded for a significant period of time. Will self-fund with their own cash flow. Doesn’t need to have a ton of capital intensity. Maybe you need one or two engineers to do stuff, but that’s it. You don’t need tons of capital. You didn’t need in the past, you won’t need it today. Not sure there’s going to be a fundamental shift to that market.   Bertrand Schmitt Yes, I certainly, overall, agree with you. That last pocket, probably little change to the capital and capital structure. Again, I see that as the biggest opportunity for a lot of people who might be less needed by big tech and also top tech companies. What is sure for the first category, the high native startups? I would say more overall in the VC ecosystem, there is no space left for SaaS anymore. I think SaaS, as we used to know it, is dead in some ways in the sense that new pure SaaS software startup are definitely out. Existing ones that are critical to run your infrastructure, the Salesforce of the world, I think they’re in a decent spot. Actually, interestingly, they changed their pricing model to now sell to AI agents, not just per seat. There is a change in pricing there. But this day and age of funding a pure SaaS software startup through VC money, no way. VC money going to AI-native startups, AI-focused startups, to biotech, to deep tech, to defense tech, yes. SaaS as a fundable category early on, I think it’s over.   Nuno Goncalves Pedro I’m a bit more nuanced as we shared in The SaaS Apocalypse episode. We can call it whatever we call. It’s applied AI is the new SaaS thing. Horizontal applied AI is the new horizontal SaaS or vertical applied AI is the new vertical SaaS. I agree in common with your point that very specific point solutions around SaaS will be disrupted by nature with all the easy stuff you can do today with AI. It will take a while. This is not something that’s going to happen this year. It’s going to happen over the next years. Maybe interesting to also talk about the exit markets. I think the IPO market, as we’ve also discussed in the past, there is, in my view, going to be a reopening of the IPO market, I think this year, probably later in the year, third or fourth quarter. The median time to IPO actually is going to be really weird because there’s going to be potentially some companies in the current landscape, bubble or no bubble, that are going to IPO, the OpenAIs of the world, Anthropics of the world, et cetera. There will be more and more aggression, I think, on M&A. Big tech has already shown it, that they want to buy into markets. Large non-tech companies have also started doing acquisitions in space. To prop up their IT teams, their engineering teams with this world that we’ve also discussed in previous episodes that I’m going to own my own engineering stack for now. As we see, that normally doesn’t withstand the test of time. At some point it will get unbundled and served by someone else. Then finally, the secondary market is very hot right now. Obviously, there’s heavy discounting on some areas, high premiums on others. The exit market, strangely enough, is going to be propped up, in my opinion, over the next year to 2 years, dramatically. Then we’ll see if there’s a big reckoning around the bubble that we are clearly in or not, if it’s a soft landing or hard landing. Definitely, there’s going to be a lot of exit paths over the next year to 2 years.   Bertrand Schmitt Concerning the “bubble”, I have two perspectives on this. One is it’s a bubble in the sense that money is going to a lot of players and some players are going to blow it up. There will be a concentration of players at the end, like it usually happens. If you look at, for instance, long time ago, the railway revolution, there was that intense influx of capital. At the end of the day, there was a dramatic change in transportation in the US and a complete railway system put in place. Yes, some investors lost money, some companies went bankrupt, but the transformation was fully real. There were a lot of top leaders at the end of this revolution. The change after that only happened, we guess, post-World War II, with the construction of the highway system and the rise of airlines and plane transportation overall. Here I feel it’s similar in the sense that, yes, there is a lot of money going in. Some players are going to blow it. They will misuse the money in different ways, but that’s part of dynamic allocation of capital. Of course, you make mistakes. That’s what happens. At the same time, I feel it’s a similar level in the sense of this is a dramatic change in the US infrastructure. This buildup of AI data centers filled with GPUs, integrated at scale with some of the best software in the world and running it, supported by a dramatic shift in energy infrastructure. This is for me similar to the Railroad Revolution. Some players might not own the data center they build because they didn’t manage well their debt, they didn’t manage to run proper software. You know what? They will get acquired by somebody else. I think we are at this level of fundamental transformation. The fact that in a matter of maybe 2 years, the move from 0% of code written by AI to 100 % written by AI is an insane dramatic shift. Just to be clear, when you move from manually coded to AI coded, we’re talking about a 100X difference in terms of speed at similar, if not better level of quality. The shift is dramatic, and on top of it, you don’t pay salaries anymore to achieve that. You pay CapEx, and with GPUs and OpEx with electricity. It’s a very big shift, positive shift in business model. New unions, no management over it, AI working 24/7. Personally, I think for me, bubble has a bad connotation in the sense of it was all for a waste. I don’t think it’s all for a waste. I think we are witnessing a dramatic revolution of our lifetimes, quite frankly, bigger than SaaS, bigger than mobile. From my perspective, it’s exciting times.   Nuno Goncalves Pedro Operator Playbook and Predictions Let’s move to if you are this person, what would you do in the future? Let’s start with two extremes and go from there. One is you’re non-tech, so you’re not an engineer, et cetera. You’re trying to figure out, how do I scale my activity? Maybe physical labor is where I want to go. It’s not, “Go west” anymore. Definitely not necessarily go west. You should go to, I guess, the states that have no sales tax with very cheap energy because that’s where the data centers are being built if you want to be in that market. Obviously, there’s a lot of stuff that needs to be done: HVAC, electricity work, et cetera. Don’t go west. Go low sales taxes, low cost of energy. That’s likely where the data centers are being built. You probably can just follow. There’s, I’m sure, some way for you to follow where the data centers are being built, but that’s next, I think on that extreme of the table. The other extreme of the table, let’s say you are super ambitious, maybe you’re no longer an engineer, but you’re a product manager in your prompt engineering. You could do prompt engineering all day long. You’re 28, 29-year-old superstar. What do you go and do? Likely either you start your own thing, start your own company because you’re so good at prompt engineering, you probably can do a lot of the code yourself, particularly if you have an engineering background, or you go and join very early an AI-native startup that you think has the chance of going through the roof, and you take a pretty good salary early on, a ton of upside on the company because guess what? Companies like that need product managers. They need people to figure out UX, UI. It’s not going to be, at least for now, yet AI figuring that out for you. Those are two extremes, just to give two of the extremes, like engineering, product management persona, and physical labor at the other extreme, non-tech, et cetera.   Bertrand Schmitt In some ways, every software engineering job is going to become the equivalent of a software engineering manager or a product manager, because suddenly you don’t have to do the coding anymore. You’re managing AI that is coding for you. Either you start to have some manager hat, but we saw the humans, so it’s a very different type of manager, obviously, or you are going to be really an empowered product manager. You’re skipping the middleman. You’re skipping the traditional engineering organization because your engineering organization is AI running and doing the work for you. I still believe that it requires some serious skills. I don’t believe in the vibe coder type of value proposition. I don’t believe in the prompt engineer becoming suddenly super incredible, able to manage that. I still think it requires some serious chops to do the best from all of this and to do it in a safe and sane way. It’s very easy to have poor taste, make mistakes. I don’t know you, but keep reading these stories on the heads of companies who lost everything because of the AI agents. That deleted stuff in production, and they had no backups or the backups weren’t deleted as well. Crazy situation. You cannot run companies like this if you let your agents running wild. You could argue it’s the early days. I would argue it that that issues would be there for a while. You need to have some engineering discipline at core in the company running the business to make sure things don’t go sideways because it would be easy for things to go sideways.   Nuno Goncalves Pedro I totally agree. If you’re thinking, Oh, should my kid go into science and engineering and computer science, et cetera? Absolutely, still, because of everything that Bertrand just said. You need to understand actually what code does and what technology does and what all of that does. That’s still a skill of the future. It’s not a skill of the past. In some ways, it’s still a skill of the future very much. Maybe let’s try two more extremes. Around the same level, the person that decided to do an AI native company bootstrapped initially, having difficulty raising a mega round, but could probably get away with raising a 2-3 million seed round, et cetera. Is that still viable? The answer is yes. There’s tremendous capital efficiency right now happening in the market still, 10 plus higher than if you were doing a SaaS company, and you were a founder in 2019 or something like that. That capital efficiency is going to reverberate. You can run a tighter team, smaller team. Actually, you don’t need that many salaries. If you’re a decent engineer as a founder or if you understand enough as a product manager to just generate that code, you can do a lot of stuff yourself, can bring in maybe one or two technical elements to the team early on as you would have done if you were bootstrapped anyway. There’s obviously a path for that. The other extreme is you’re in big tech, you’re level five, individual contributor, making a ton of money, or you were a manager, and you’re now out of a job, where do you go? You can go to a big company that is non-tech, S&P 500 company that’s non-tech, something like that. You join the company, you’ll probably get paid pretty well, maybe not as high as you were paid in big tech. There’s some stock at the table, but guess what? You’ll have probably more work-life balance than you ever did. That’s the trade-off. You’ll have a better job. On the upside, you can transform the company. You can help and be part of transforming a company from non-AI to AI-first or AI-enabled in the future, whatever BS that will look like in terms of the argumentation to the board. You can actually create tremendous productivity enhancements in a big non-tech company if you come with that background. Again, you’ll have certainly a better work-life balance, so not a bad deal, to be honest.   Bertrand Schmitt Also, to be clear, I talk a lot about AI coding because it’s truly transformational. You could argue that it’s going to be self-improving. We are in the situation of a self-improving AI that keeps improving itself thanks to automated coding. It’s a dramatic, virtuous loop. Obviously, AI is also going to improve everything else. It’s going to improve your marketing, it’s going to improve your search process, it’s going to improve your DNA. Improvements will be everywhere. It’s just that right now we are at a point in the quote-unquote revolution where there is one clear piece of the puzzle that is moving faster than the rest.   Nuno Goncalves Pedro Bertrand, the senior executives at non-tech don’t know anything about that. It could be just a great prompt engineer. That’s the only job you do. “I’m the chief marketing officer. I have someone below me that’s doing the whole work.” Nobody knows. Nobody’s the wiser, I guess. I’m being facetious, but not fully.   Bertrand Schmitt Yeah. There would be a transition period where what you described happen. I want to say, going back to AI coding, I think that the part of AI that as of today has reached a stage of limited AGI. We have reached, from my perspective, a limited type of AGI for coding. If you take coding as a discipline today, I think we reach AGI. If you go beyond coding, that’s true. If we are talking about coding, leveraging the latest LLMs: OPUS 4.7, ChatGPT 5.5, combined with Claude Code, Codex, and OpenCode for harness, I think we’ve reached AGI in the context of coding. I’m not sure everyone fully realize that and the consequence of that. I think the rest is going to come as well. We are going to see that category by category, usually categories that are more scientific in nature, where you can replicate, where you can test easily, where you can create clear success. Metrics will be the “easiest” to follow in that direction of self-improvement. I just want to highlight that this part is truly transformational, the root cause of everything we’re talking about today. At the same time, it’s coming beyond coding.   Nuno Goncalves Pedro I think it is true. There are a couple of markets where that might not hold true, which is maybe the final path. If you’re thinking of starting your own business in plumbing and in HVAC maintenance and installation, this is a pretty good time for the reasons we already said before. There’s a lot of buildup of data centers and all that stuff, but also for other reasons, because it’s an activity that won’t be disrupted by AI yet. You need them embodied AI. You need physicality to AI to do stuff like actually fixing pipes.   Bertrand Schmitt Until Optimus replace you.   Nuno Goncalves Pedro Yeah, but if we’re 3, 4 years out in terms of a lot of these optimizations that we’re talking about at the software layer, we’re 10 years plus out on embodied AI, right?   Bertrand Schmitt Oh, yeah, it’s 10 years.   Nuno Goncalves Pedro We’ll probably be optimistic as we speak. That’s a nice business. I’m thinking of starting to go into that market. If you guys are interested in listening to this, just reach out to me. What’s the angle? I think there’s a lot of stuff you can do in the buildup of some of these businesses, plumbing, HVAC, all sorts of maintenance. There are markets that are just totally messed up. Handyman market in the US is totally messed up. There’s a bunch of companies out there that try to go after it with marketplaces and stuff. I honestly just start something from scratch, a small business, and go from there.   Bertrand Schmitt Yes. They’re an interesting middle. Think about accounting firms, consulting firms. I think they are not as easy to replace, but at the same time, there is no way on what they do is not going to be dramatically changed with AI. I don’t know if it’s 50, 80, 90% of the job, but this is changing quite dramatically, would be my expectation in the coming few years. Conclusion Thanks for listening episode 77 of Tech Deciphered about that great talent redistribution. As you heard it from us, we believe there is a dramatic change in play, enabled by AI coding, and that ultimately a lot of the big tech companies are changing their employee distribution, way more focused on the top talents and bringing more GPUs. As a result, we will see a change in their staffing. Some of this change will benefit AI-focused startups, but probably more likely will benefit the bigger SMBs, the S&P 500 companies of the world that will finally be able to bring inside and afford some of the talent that were in some ways trapped by the top 5, 10, 20 software companies of the world. Thank you, Nuno.   Nuno Goncalves Pedro Thank you, Bertrand

A bi-weekly news show informing you on the latest in Bitcoin, privacy and open source tech hosted by Ungovernables, Max and Q. AOBAll aboard the vibe trainFTF with Max TQ got some holidays coming upKeonne appealNEWSBisq v1 trade protocol exploit: 11.59 BTC drained, fully reimbursed, hardening shipped in 1.10.0 (bisq.community PSA, Bisq on X, reimbursement plan on GitHub)Disclosed: 2026-05-01Bisq's v1 trade protocol had a missing validation check on taker-side input. Because maker and taker were supposed to use the same miner fee, a malicious taker could push a bad fee value through the transaction math and shrink the multisig output to 0.001 BTC while sweeping the rest into the taker's change. Attacker drained 11.59 BTC from 10 users, all on altcoin trades. Maintainer Henrik Jannsen filed a reimbursement plan on GitHub on May 3, payouts in BTC (with BSQ as optional), DAO vote scheduled around May 25. The hotfix landed as Bisq 1.10.0 on 2026-05-16 with broader hardening: trade protocol checks, network message validation, release verification, supply-chain hardening. The Bisq team explicitly flagged the incident as a likely AI-assisted exploit, though they did not detail how AI was used.Sterlingov Appeal: The Criminalization of Privacy (therage.co)Published: 2026-05-12The appellate court reviewing Roman Sterlingov's Bitcoin Fog conviction openly suggested that mixers remain "legal in theory but not practice" once criminals use them. Judges questioned whether running an internationally accessible service forces compliance with every jurisdiction's licensing regime.Pro-law-enforcement CLARITY Act advances out of Senate Banking (therage.co)Published: 2026-05-15The Digital Asset Market Clarity Act passed committee with expanded surveillance provisions: Bank Secrecy Act integration sixteen times over, new PATRIOT Act special measures. Privacy advocates flagged the breadth of data collection on Americans who haven't done anything.CVE-2024-52911 disclosed in Bitcoin Optech #405, fix has been in Bitcoin Core 29.0+ since release (https://bitcoinops.org/en/newsletters/2026/05/15/)Published: 2026-05-05Use-after-free in parallel script validation between Bitcoin Core 0.14.0 and 28.x. Required attacker-supplied proof-of-work, so practical attack window was narrow, but the bug sat unannounced across many versions.Bitcoin Knots 29.3 enables BIP-110, fork-off countdown started (release notes) + Lopp's countdownPublished: 2026-05-09 (release)Knots 29.3 ships RDTS soft-fork enforcement on by default. Nodes running Knots with this flag set will fork off the network in August unless they change behaviour. Lopp set up a countdown.Bybit exploit post-mortem (Blockstream): enterprise multisig + hardware wallets did not save them (blog.blockstream.com)Published: 2026-05 (week of 5-12)$1.5B drained despite multisig and hardware. Failure was process, not key custody, a UI / signing-flow compromise.Poland passes EU MiCA-aligned crypto bill while Zondacrypto fraud probe deepens (bitcoinmagazine.com)Published: 2026-05-15Polish lawmakers ratified the MiCA framework ahead of the July EU deadline. The vote landed alongside an investigation into Zondacrypto's collapse, roughly $96M of user losses, with Prime Minister Tusk floating possible foreign-influence angles.Claude helps retrieve lost 5BTCX user 'CPRKRN' has Claude check over whole file system and match a wallet file to an old passwordSpiral and Block ship Loupe, an AI-powered vulnerability scanner for open-source Bitcoin (spiralbtc.substack.com)Published: 2026-05-12Uses LLMS to surface security weaknesses in code repositories and requires demonstrable test cases for any vulnerability report so false positives are minimised. Spiral and Block are funding scans themselves; reports go to maintainers confidentially before any public disclosure.RELEASESBitcoin Core 31.0 (release index entry) — 2026-05-12Operator review required before production rollout. Major version landing.Bitcoin Knots v29.3.knots20260508 — 2026-05-09RDTS soft-fork enforcement on by default, fork-off risk in August. New configuration changes, bug fixes.Core Lightning v26.06rc1 — 2026-05-12Adds graceful command for clean shutdown, new sendamount RPC, BOLT12 payer-proof support, plus 211 commits since v26.04.Bitkey App 2026.9.1 — 2026-05-15Security patch from Block.Trezor Suite v26.5.1 — 2026-05-15Legacy labeling migration, WalletConnect insufficient-balance warnings, side-by-side trade comparisons, new DeFi Tokens section.BitBoxApp v4.51.0 — 2026-05-12Bundles BitBox02 firmware v9.26.1, address formatting in 4-char groups, iOS haptic feedback on charts, account-summary perf.Ledger Live Desktop 4.4.0 — 2026-05-13Hardens Live App handling of external-protocol URLs (itms-apps:, ms-word:, file:, etc.) across Chromium navigation vectors.Ledger Live Mobile 4.4.0 — 2026-05-13Adds an addresses section to asset detail screens, device-card management menus with removal confirmations.Bull Bitcoin Mobile v6.10.1 — 2026-05-18Onboarding redirect fix on wallet creation failure.Bull Bitcoin Mobile v6.10.0 — 2026-05-11Major release: Ledger hardware-wallet integration, FSS hybrid storage strategy, real-time WebSocket notifications, new onboarding wizard, Payjoin privacy enhancements, 11 new translations.Bull Bitcoin Mobile v6.9.101-Internal-Release (display name v6.9.108-Internal) — 2026-05-09Pre-6.10.0 testing build, Android migration / startup wizard / secure storage fixes.Bitcoin Safe 2.0.0rc0 — 2026-05-17Comprehensive redesign of the wallet setup wizard, added support for Coldcard mk5 and Trezor 7, plugin architecture via external repos, fiat-balance category column.Sparrow Frigate 1.5.0 — 2026-05-14Low-latency mempool ingestion via Bitcoin Core's ZMQ sequence publisher, auto-discovers the bitcoind ZMQ endpoint when unconfigured. Useful for operators running Sparrow Frigate alongside Core.Blockstream Green iOS release_5.4.0 — 2026-05-11Aggregate fiat balance across all wallet assets, updated Send flow for Lightning, migrates Lightning backend from Breez to Greenlight (Blockstream's own LSP).Blockstream Green Android release_5.4.0 — 2026-05-08Same redesign as iOS: aggregate fiat balance, redesigned Send flow (recipient → asset → account), transaction pagination, also the Breez-to-Greenlight migration.Blockstream Green Desktop 3.3.0 — 2026-05-06Total fiat balance in wallet header, AMP ID exposed in settings, GDK 0.77.3, Qt 6.11.0, Wayland fixes.Peach Bitcoin 0.69.0 (build 346) — 2026-05-06Signature validation for backed-up payment details, encrypts custom refund addresses, removes invalid backed-up data.Peach Bitcoin 0.69.0 (build 345) — 2026-05-05Percentage filtering on offers, encrypted server backup syncing for payment methods, advanced offer-creation options, GrapheneOS camera-permission fix, Buy Offer creation restricted to experienced users.ZEUS v13.0.2-rc3 — 2026-05-18Third RC for 13.0.2. New RGS server at rgs.zeusln.com providing graph updates every 15 minutes instead of every three hours. Clipboard and NFC UX improvements.ZEUS v13.0.1 — 2026-05-07Stable release: fixes recovering Embedded LND wallets from seed (was stalling out), payment retry logic, false-positive offline detection. Cashu token sweeping to self-custody continues to land.Alby Hub v1.22.2 "Marc Horowitz" — 2026-05-11Adds Core Lightning support (their most-requested feature), new AI & Agents page, integrated on-chain wallet mode, custom transaction labels, redesigned settings, improved budget selection for app connections.Boltz Backend 3.13.0 — 2026-05-08Full Arkade swap support, EVM commitment-swap lockup flow, multi-LND support in backend and sidecar.Boltz Client 2.12.0 — 2026-05-12Final removal of the GDK wallet library.Arkade arkd v0.9.5 — 2026-05-11Client-lib wallet interface updates, breaking-changes documentation, single-key wallet signing fixes.Arkade TS SDK v0.4.25 — 2026-05-07Maintenance bump for the Arkade JavaScript SDK.NodeGuard 0.24.2 — 2026-05-14Fixes invoice-expiry calculation in rebalance flows. Check logs if rebalance operations have been timing out.ThunderHub v0.18.3 — 2026-05-15Bug-fix release in the 0.18.x line. (Subsequent 0.18.1-0.18.3 are CI/docker polish after the headline 0.18.0.)ThunderHub v0.18.0 — 2026-05-05Adds Taproot Assets support to the dashboard. The actual show story for ThunderHub this fortnight.Blink Mobile 2.4.44 — 2026-05-06Upgrades protobufjs (CVE-2026-41242 mitigation). Security patch.Fedimint SDK canary release — 2026-05-14React Native transport fix, persistent callback, RPC payload flattening. Canary channel.umbrelOS 1.7.3 — 2026-05-12DirtyFrag security patches: CVE-2026-43284 + CVE-2026-43500 in the Linux kernel. Mandatory.umbrelOS 1.7.2 — 2026-05-05CopyFail patch: CVE-2026-31431 in the Linux kernel. Mandatory.Tails 7.7.3 — 2026-05-12Emergency release: critical Linux kernel CVE fix (kernel 6.12.86 ships the Dirty Frag fix), plus Tor Browser and Tor client security fixes.Whirlpool Observer…

Explore the biggest announcements from Microsoft Ability Summit and Google I/O 2026, including breakthroughs in accessibility, AI-powered video editing, and Google's new intelligent audio glasses. Discover how screen readers like Narrator are evolving and how AI agents are reshaping the way blind and low-vision users interact with technology. The discussion covers: Microsoft's focus on Narrator improvements, including Braille support and enhanced voices. Team Gleason's partnership with Microsoft to create custom voices for people with ALS and similar conditions. GitHub CoPilot's role in accessible video editing, enabling blind users to create professional content with AI assistance. Broader reflections on the evolution of accessible screen readers and the importance of uniformity in UI behaviours. The conversation then shifts to Google I/O 2026: Launch of Google and Samsung's intelligent audio glasses with turn-by-turn navigation, voice interaction, and Gemini AI integration. Gemini 3.5 Flash and Gemini Omni models, including multimodal AI with video generation and live editing capabilities. Gemini Spark, Google's persistent AI agent for email, calendar, and task automation. Universal Shopping Cart and proactive AI agents that monitor, purchase, and track items on your behalf. The hosts discuss how these innovations could transform accessibility, daily productivity, and creative opportunities for blind and low-vision users. ----Follow on:YouTube: https://www.doubletaponair.com/youtubeX (formerly Twitter): https://www.doubletaponair.com/xInstagram: https://www.doubletaponair.com/instagramTikTok: https://www.doubletaponair.com/tiktokThreads: https://www.doubletaponair.com/threadsFacebook: https://www.doubletaponair.com/facebookLinkedIn: https://www.doubletaponair.com/linkedinSubscribe to the Podcast:Apple: https://www.doubletaponair.com/appleSpotify: https://www.doubletaponair.com/spotifyRSS: https://www.doubletaponair.com/podcastiHeadRadio: https://www.doubletaponair.com/iheartAbout Double TapHosted by the insightful duo, Steven Scott and Shaun Preece, Double Tap is a treasure trove of information for anyone who's blind or partially sighted and has a passion for tech. Steven and Shaun not only demystify tech, but they also regularly feature interviews and welcome guests from the community, fostering an interactive and engaging environment. Tune in every day of the week, and you'll discover how technology can seamlessly integrate into your life, enhancing daily tasks and experiences, even if your sight is limited."Double Tap" is a registered trademark of Double Tap Productions Inc. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Host Matt Paige records a special Talking AI episode live from Google I/O with AI creators Kushank Aggarwal, Marcin Teodoru, and Jay Enrique, discussing Google's biggest announcements and what will matter in real use.They argue Google's edge is distribution—bringing AI to existing Search users—positioning Gemini as an intelligence layer across products like Search, YouTube, Gmail, Docs, Chrome, Android, and shopping.They highlight rapid growth in token usage, Search's new AI mode and generative UI/dashboard experiences, and YouTube features that jump to relevant video moments, potentially improving discoverability for creators and local businesses.They debate Gemini Spark's agentic approach, prepackaged agents like Daily Brief, and enterprise “agent garden” concepts, then cover Omni as a broader “world model” play, Pix/NanoBanana-style editing and image workflow improvements, and a glasses demo featuring translation, Gemini Live, and impressive audio.--Key Moments:00:54 Gemini Everywhere Strategy02:09 Search Gets Agentic03:47 Generative UIs for All06:48 YouTube as Action Engine08:21 Gemini Spark Agents10:10 Adoption and Standards13:55 Omni World Model17:13 Pix Editing Workflow19:06 Omni Platform Take19:53 Fire Round Highlights22:05 Glasses Demo Reactions24:02 Wrap Up and Where to Follow--Key Links:DigitalSamaritanConnect with Kushank on LinkedInRoboNuggetsConnect with Jay on LinkedInAI BuildersConnect with Marcin on LinkedIn

One thing that I don't like about Claude is that you get into this weird mental state: oh, I think I trust the model. Let's do the slot machine. Hit click, which puts you in an inactive mode of thinking.  Maybe it's better to use a worse model….Vincent Warmerdam, senior data professional and prolific open-source maintainer (some packages with over a million downloads), now Engineer at marimo, joins Hugo to talk about how the Python notebook is evolving from a static scratchpad into a working agent harness, and what it takes to stay in the loop as a developer when agents are writing most of the code. This episode was originally a livestream Q&A with the Vanishing Gradients audience.We Discuss:* Shared Notebook Canvas: Notebooks act as a shared memory space where agents and humans co-exist, enabling real-time visual feedback by direct manipulation of global state and UI elements;* Speed-of-Thought Models: Faster, open-weight models like Kimi K2 enhance exploratory flow by keeping humans more alert to the code, unlike frontier models that can induce passive thinking;* Pi as a Harness: Vincent favors an agent harness where agents extend themselves rather than reach for MCP, and where hooks can rigidly constrain which files an agent is allowed to read or touch;* Why PRDs Don't Fit Notebooks: Notebook work is fundamentally exploratory, so the discipline that works for shipping web apps does not transfer cleanly; the one exception is reproducing a paper;* Interactive Code Review: Interactive UIs (e.g., dragging integers) transform code into a physical object, incentivizing developers to actively review and understand agent logic;* Modular “Lego” Components: Provide agents with high-level, well-tested components (”Lego” code) instead of raw boilerplate, creating systems that are easier to debug and modulate;* Algorithm-Driven Visualization: Let the algorithm dictate the visualization needed, rather than choosing visualizations first, revealing the most interesting structures within the data;* Don't Outsource the Thinking: Pen and paper architectural planning, walks away from the keyboard, and protecting calm remain the most effective ways to keep producing good ideas in the age of AI-generated software.* Agent Auto-Healing: A marimo-specific linter solved 60% of agent errors overnight by letting agents diagnose and fix their own “slop” without complex prompt engineering;* Incremental Generation: Avoid monolithic LLM outputs; generate code one to two cells at a time to prevent laziness and ensure human oversight and learning;Vincent closes on the idea that calm, not the latest frontier model, is the most underrated tool for building well, and that we should study LLM output the way chess players studied the engines that beat them.Vincent gives several live demos toward the end of the episode. He describes them well enough to follow on audio, but the visuals are worth seeing, so check out the YouTube version here.You can also find the full episode on Spotify, Apple Podcasts, and YouTube.You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!

In this episode, Peter breaks down one of the first real decisions Cardano developers face when building a dApp, choosing between Mesh SDK and Evolution SDK. Both libraries cover the off-chain essentials like transaction building, wallet integration, provider support, smart contract interoperability, and governance-era transactions, but they make different trade-offs depending on the kind of app you want to ship.The episode walks through practical decision points instead of abstract theory. Peter explains when Mesh makes more sense for React-based apps, production-ready smart contract templates, Hydra support, and AI-assisted development workflows, and where Evolution can be the better fit for Cloudflare Workers, edge runtimes, or teams that prefer stronger type safety and functional programming patterns. He also shows live examples from his own Mesh-based projects, including a bounty platform, a Cardano-wide leaderboard, and a governance dashboard, to make the comparison concrete.Key Takeaways:- Mesh SDK and Evolution SDK are both TypeScript-first Cardano off-chain libraries that support transaction building, wallet integration, multi-provider workflows, and governance-era transactions.- Mesh is generally the stronger choice for React-based dApps, teams that want ready-made smart contract templates, Hydra integrations, and developers leaning on AI coding tools.- Evolution SDK is often the better fit for Cloudflare Workers, edge deployments, WASM-hostile runtimes, and codebases that prioritise functional programming and strict type safety.- Teams migrating from Lucid or Lucid Evolution have a more natural upgrade path into Evolution SDK because it is the direct successor.- For NFT marketplace-style builds, Mesh offers practical advantages through its existing contract templates and developer tooling.- Mesh includes features such as social sign-in and custody wallet creation that can reduce onboarding friction for mainstream users entering a Cardano application.- The best SDK choice depends less on ideology and more on deployment target, UI framework, developer workflow, and how much prebuilt infrastructure a team wants.Links & References:- Mesh SDK vs Evolution SDK: Which off-chain library? - Learn Cardano: https://learncardano.io/comparison/mesh-vs-evolution-sdk/Website: https://learncardano.ioX/Twitter: https://x.com/LearnCardanoDisclaimer: This content is for educational purposes only. Nothing constitutes financial advice.DISCLAIMER: This content is for informational and educational purposes only and is not financial, investment, or legal advice. I am not affiliated with, nor compensated by, the project discussed—no tokens, payments, or incentives received. I do not hold a stake in the project, including private or future allocations. All views are my own, based on public information. Always do your own research and consult a licensed advisor before investing. Crypto investments carry high risk, and past performance is no guarantee of future results. I am not responsible for any decisions you make based on this content.

For episode 730 of the BlockHash Podcast, host Brandon Zemp is joined by Stepan Uherik, CFO of Trezor.Trezor is a pioneering brand of cryptocurrency hardware wallets. Known for launching the world's first hardware wallet, Trezor provides "cold storage" solutions and physical devices that keep private keys offline to protect digital assets from online hackers, malware, and data leaks. 

Today we are talking about The Open Web, What it means, and Why it's important with guest Alex Moreno. We'll also cover AI Schema.org JSON-LD as our module of the week. For show notes visit: https://www.talkingDrupal.com/553 Topics Defining the Open Web Drupal in a Bubble Marketing and PR Challenges AI Bias Against Drupal Why AI Won't Recommend Drupal Is Drupal AI Native Marketing Against Giants Local Evangelism Push Funding Outreach Trips Drupal CMS PR Gap Templates Lower Barriers Need a Drupal Onramp Speaking Beyond Drupal Web Summit Lessons Sell Problems Not Drupal Rethinking DrupalCon Camps and New Audiences Marketplace Ecosystem Idea Wrap Up and Contacts Resources Drupalcamp Grenoble 2026 - Bursting the bubble Drupal Iberia keynote Schema dot org Drupal is Great! Its Perception Might Not be TD Cafe - Caching   Guests Alex Moreno - alexmoreno Hosts Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi Bernardo Martinez - bernardm28 MOTW Correspondent Jacob Rockowitz - jrockowitz.com jrockowitz Brief description: The AI Schema.org JSON-LD module provides a straightforward way to send a prompt — including a webpage's content and data, along with instructions and requirements — to an AI provider and receive a response containing valid Schema.org JSON-LD for saving and embedding in a webpage. It's a "glue module" that combines AI Automators, Field Widget Actions, and JSON Field to create an AI-powered Schema.org JSON-LD field for content entities. Module name/project name: AI Schema.org JSON-LD Brief history How old: Created in April 2026 by jrockowitz (Jacob Rockowitz) of The Big Blue House Versions available: 1.0.0-alpha1 (requires Drupal ^11.3); 1.0.x-dev branch also available Maintainership Actively maintained Yes — updated as recently as April 30, 2026 Security coverage No — not currently covered by Drupal's security advisory policy; use at your own risk Test coverage The module notes that all contributed code must include test coverage, though it is early alpha Documentation Yes — the project page includes setup instructions, implementation guidance, philosophy, and a 2-minute demo video on YouTube Number of open issues: 0 open issues, 0 of which are bugs against the current branch Usage stats: 1 site currently reporting use of this module Module features and usage Adds a native JSON "Schema.org JSON-LD" field to content entities (nodes, media, taxonomy terms) Field is populated via an AI automator triggered by a Field Widget Action, keeping a human in the review loop before saving Stores Schema.org JSON-LD as native JSON data, creating a fully queryable knowledge graph for the site Works with complex nested content structures (paragraphs, components) by having AI parse and generate the structured data Includes an optional sub-module for logging prompts and AI responses for human and AI review and iterative improvement Configurable per entity type/bundle via UI, Drush, or Drupal recipe Philosophy: "Use AI to build a tool that helps AI understand your website while always keeping a human in the loop" Built using AI coding agents (Claude and Codex), with community contributions encouraged — especially around crafting and sharing optimal prompts

Lower League DegensJimbo is joined by Calvinator & RootCoors for an unfiltered verdict on MFL 2.0 and everything coming in Season 14.The new UI is live. Retirements are looming. S14 is here.

In this special episode of In Touch With iOS, Dave Ginsburg and Jeff Gamet welcome Don McAllister to discuss the history of Screencasts Online, his transition to semi-retirement, and his new app Wedding Player. Don shares how the app was inspired by his daughter's wedding and explains its offline Apple Music integration, ceremony-focused controls, AI-assisted development process, and future plans for iPhone, iPad, Android, and professional wedding venues. The show notes are at InTouchwithiOS.com  Direct Link to Audio  Links to our Show Give us a review on Apple Podcasts! CLICK HERE we would really appreciate it! Click this link Buy me a Coffee to support the show we would really appreciate it. intouchwithios.com/coffee  Another way to support the show is to become a Patreon member patreon.com/intouchwithios Website: In Touch With iOS YouTube Channel In Touch with iOS Magazine on Flipboard Facebook Page BlueSky Mastodon X Instagram Threads Summary In this special episode, Dave Ginsburg is joined by co-host Jeff Gamet for an in-depth conversation with Don McAllister, the founder of Screencasts Online and creator of the new app Wedding Player. Don reflects on the origins of Screencasts Online back in 2005, explaining how it began as a hobby after he originally planned to launch a Mac audio podcast. Instead, he discovered the power of video tutorials and quickly found an audience eager to learn more about the Mac ecosystem. He shares stories about the early days of podcasting and online video creation, describing how he slowly transformed the project from free tutorials into a successful premium service while still maintaining a passion for helping Apple users learn new technology. The discussion then shifts to the transition of Screencasts Online to Lee Garrett. Don explains why it was important for him to hand the platform to someone who already understood the community and culture that had been built over nearly two decades. He talks about how Lee's experience, technical background, and long history creating tutorials made the transition smooth for both subscribers and contributors. Don also reveals he still contributes occasional tutorials and monthly articles while remaining available behind the scenes for advice and support. A major focus of the episode centers on Don's newest project, Wedding Player. The idea came directly from helping organize music for his daughter Nicola's wedding. Frustrated with the limitations of existing music playback solutions and concerned about reliability during a live ceremony, Don decided to create his own dedicated app. He explains how Wedding Player organizes music into "moments" for different sections of a ceremony, supports fade controls between songs, includes loop functionality, and uses simplified "Go Live" controls designed to prevent mistakes during high-pressure events. Don also details the extensive safeguards built into the app. Wedding Player works fully offline, integrates with Apple Music through MusicKit, supports local audio files, and includes pre-flight checks to verify downloaded tracks, airplane mode settings, and device readiness before a ceremony begins. He discusses the importance of reliability and explains how the app was intentionally designed to reduce the chance of user error. The conversation also highlights the inclusion of royalty-free "Wedding Player Originals" tracks for users who do not subscribe to Apple Music. Jeff and Dave explore how Don used AI tools, particularly Claude, to accelerate development of the app and even build an Android version after discovering significant demand from wedding professionals using Android devices. Don shares how AI dramatically shortened the development timeline and how he now sees AI as a powerful platform for building software and systems rather than simply a chatbot. The panel also discusses accessibility improvements, large-text UI challenges, venue support, future iCloud syncing plans, and professional features designed for wedding venues and officiants. Finally, Don shares pricing details, future roadmap ideas, and where listeners can find Wedding Player online. Links mentioned Wedding Player https://weddingplayer.app/our-story/ https://weddingplayer.app/blog/ https://weddingplayer.app/support/ ScreenCastsOnline ScreenCastsOnline - About Us Announcements Macstock X is here celebrating its 10th anniversary ! Dave, Chuck, Jeff, Marty, and Jill are all speaking this year!. With Three Full Days of expert-led Presentations and Workshops, Macstock's sessions are crammed full of productivity-enhancing content. NEW this year is a partnership with sponsor Ecamm. Ecamm Creator Camp: Mac Edition on July 9, 2026 there are only 100 tickets available for the bundle. There are 2 passes available: Macstock weekend pass July 10,11,12, 2026 or the Macstock Ecamm Bundle starting July 9 (only 100 tickets available)  Come join us. Register HERE and use our offer code INTOUCH to save $50 Our Host Dave Ginsburg is an IT professional supporting Mac, iOS and Windows users and shares his wealth of knowledge of iPhone, iPad, Apple Watch, Apple TV and related technologies. Visit the YouTube channel https://youtube.com/intouchwithios follow him on Mastodon @daveg65, , BlueSky @daveg65  and the show @intouchwithios   Our Guest Don McAllister is founder of Screencasts Online and developer of the new app Wedding Player, You can find him on social media at X @donmcallister Our Co-Host Jeff Gamet is a podcaster, technology blogger, artist, and author. Previously, he was The Mac Observer's managing editor, and Smile's TextExpander Evangelist. You can find him on Mastadon @jgamet Pixelfed @jgamet@pixelfed.social and Bluesky @jgamet.bsky.social‬ Podcasts The Context Machine Podcast  Retro Rewatch Retro Rewatch His YouTube channel https://youtube.com/jgamet

节目提要：1、理想L9 Livis到底是不是全新换代？2、预售55.98万，到底贵在哪？3、普通版L9马上也要来了，到底该等谁？4、四颗激光雷达，实际体验优势在哪？5、800V主动悬架和蔚来48V到底谁更高级？6、L9 Livis适合什么样的人，对比竞品能不能打？【本期高光】Part 1 理想L9发布与市场冲击：价格、配置与行业秘辛00:00:12 理想L9车型发布：顶配与特别版双亮相！00:00:50 价格惊喜：L9立减5万，最终售价超出预期！00:04:14 保密协议罚款高达1000万，行业最严？00:09:27 800伏主动悬挂：开起来如履平地，体验震撼！00:15:13 涨价十万因专属配置：Livis尾标、4颗激光雷达！00:18:43 李想的管理术：Token无限量，与高用量员工每月聊！00:21:03 智能体推行：高管从抵触到自费购买，技术魅力尽显！00:27:13 市场转折：四五十万预算，中国男人不再只选BBA！Part 2 技术深探与用户体验：悬挂、智能与成本博弈00:24:52 主动悬挂的代际差：驾乘体验如同坐上「时光机」！01:13:27 技术竞速前沿：M9新版本或全系标配800伏全主动悬架！01:16:09 800伏悬架 vs 48伏：高压电池直供，性能悬殊！01:21:20 沙地脱困如跳舞：800伏全主动悬挂，车轮独立调节！01:24:46 毫秒级响应：48伏系统每秒上千次调节，底盘工作「无感化」！00:55:58 成本控制极限：通过同质化与大批量生产压榨成本！01:00:12 车机UI设计：风格各异，土的、高级的，情绪价值是关键！Part 3 用车哲学与行业展望01:03:55 车内设计哲学：营造松弛居家感，让你想「葛优躺」！01:29:31 购车终极建议：问问老车主，比看100个帖子更靠谱！01:30:51 未来消费图景：期待孩子上大学后，买辆宽敞的自驾游车！【本期主播】三刀：自称“别人研究车，而我研究人”的汽车KOL。2006年从事汽车销售，2013年成立播客工作室，靠一支麦克风从播客做到抖音、B站，小红书、微博等平台。节目里既聊车，也聊人间冷暖，刀友们口中的“老大哥”。抖音丨快手丨小红书丨视频号：三刀侃车汽车之家丨懂车帝丨bilibili丨公众号：百车全说微博：百车全说三刀欢迎在苹果播客、小宇宙、喜马拉雅、网易云音乐、qq音乐、蜻蜓FM、微博音频、微信视频号搜索【百车全说】，马上订阅节目，不错过每次更新。加入听友社群，微信号：46415254想与三刀1对1交流，扫码加入知识星球：

Apple lleva cuatro años construyendo la infraestructura para que eso ocurra: App Intents desde iOS 16, Assistant Schemas desde iOS 18, y ahora una Siri completamente rediseñada en iOS 27 que Gurman describió esta semana como "un agente siempre activo que toma acción en todas las apps". En este episodio explico qué significa eso en términos técnicos reales, por qué Apple lleva dos años sin conseguir que funcione bien, y cómo el acuerdo con Google y la destilación del modelo Gemini cambia la ecuación. Pero además: Google presentó ayer en el Android Show previo al Google I/O exactamente la misma arquitectura. Las dos plataformas móviles más grandes del mundo, la misma semana, construyendo lo mismo de forma independiente. En este episodio también hablo del HomePad, las gafas N50, el pendant y el robot de sobremesa: el ecosistema de hardware que hace inevitable que el agente de IA —Siri, Gemini, Claude— se convierta en la interfaz que gobierna todos tus servicios instalados. Y lo anclo en algo que ya está pasando hoy: llevo meses usando Linear sin haber abierto su app. Igual que mucha gente usa Notion, Obsidian o el correo a través de agentes sin tocar su interfaz. El agente es la nueva UI. El App Store no muere: crece en una nueva dimensión.

We know why you're here every week. To answer that one burning question...  Where does Microsoft Solitaire Collection sit on the chats?   Now on with The Regular Show  

Benjamin and Chance discuss changes to the Apple education store, the cool new Apple Developer icon, rumors about some design changes for iOS 27 and macOS 27, and whether we can think of anything compelling AirPods with cameras could be used for.  And in Happy Hour Plus, we discuss the state of iOS keyboard autocorrect and dictation accuracy.. Subscribe at 9to5mac.com/join. Sponsored by Copilot Money: Get two months free with code 9TO5MAC at copilot.money/9to5mac. Sponsored by Framer: The only free design tool that brings your ideas to the web. Visit framer.com/happyhour for 30% off a Framer Pro annual plan. Sponsored by Quince: Refresh your wardrobe with Quince. Visit quince.com/happyhour for free shipping on your order and 365-day returns. Hosts Chance Miller @ChanceHMiller on Twitter @ChanceHMiller on Instagram @ChanceHMiller on Threads Benjamin Mayo @bzamayo on Twitter @bzamayo@mastodon.social @bzamayo on Threads Subscribe, Rate, and Review Apple Podcasts Overcast Spotify 9to5Mac Happy Hour Plus Subscribe to 9to5Mac Happy Hour Plus! Support Benjamin and Chance directly with Happy Hour Plus! 9to5Mac Happy Hour Plus includes:  Ad-free versions of every episode  Pre- and post-show content Bonus episodes Join for $5 per month or $50 a year at 9to5mac.com/join.  Feedback Submit #Ask9to5Mac questions on Twitter, Mastodon, or Threads Email us feedback and questions to happyhour@9to5mac.com Links Apple Developer app gains Liquid Glass design and WWDC 2026 iMessage stickers Apple now requires verification for Education Store, adds Apple Watch with discounts iOS 26.5 adds end-to-end encryption for RCS messaging, rolling out now iOS 26.5 now available: Here are all the new iPhone features Apple hits milestone in development of AirPods with cameras: report Report: macOS 27 to feature UI tweaks to address some Tahoe design complaints Apple Plans Customizable iPhone Camera App, Siri Overhaul: iOS 27 - Bloomberg iOS 27's upgraded Camera app will be ‘fully customizable,' per report iOS 27 to make key design changes to ‘streamline' Liquid Glass: report iOS 27's ‘completely rebuilt' Siri will include a new system-wide search gesture: report Gemini Intelligence brings gen UI, Gboard 'Rambler' to Android Gemini Intelligence brings proactive AI to Android

Yoni Avital lives in Tel Aviv, Israel, with his wife and 3 older children. The oldest kid is a boy, so Yoni and he try to see as many football games as possible. He enjoys shopping with this girls, though it's cause he is their Dad, not because he enjoy shopping. He likes to travel, hike, and enjoys a nice white wine in warmer weather. His most memorable hike was at Yosemite, when he started at 4 am and came across a lot of wildlife.Yoni was in the virtual desktop space in the past. What he and his team realized was that troubleshooting these virtual experiences were incredibly complicated. They started to build an enterprise task manager, to centralize a task management UI to control the endpoints. When customers started asking to use it daily, Yoni figured out they had something unique.This is the creation story of ControlUp.SponsorsUnblockedTECH DomainsMezmoBraingrid.aiLinkshttps://www.controlup.com/https://www.linkedin.com/in/yoavital/Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://click.cash.app/ui6m/mt82fpxl #CashAppPod. Cash App is a financial services platform, not a bank. Banking services provided by Cash App's bank partner(s). Prepaid debit cards issued by Sutton Bank, Member FDIC. See terms and conditions at https://cash.app/legal/us/en-us/card-agreement. Cash App Green, overdraft coverage, borrow, cash back offers and promotions provided by Cash App, a Block, Inc. brand. Visit http://cash.app/legal/podcast for full disclosures.* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

It's not just Recall: Security vulnerabilities that require you to sign into an account on your PC are not necessarily vulnerabilities. Also, Windows 11 gets its first big feature updates in this week's Patch Tuesday releases. Snapseed 4.0 comes to Android/iOS, and Claude FM is great for relaxing or getting coding/work done. Plus, the Helium browser has emerged as a favorite with 2 notable caveats: No online settings sync and no mobile client. Windows 25H2/24H2: Xbox Mode, Agents on the Taskbar, more 26H1: Smart App Control improvements, other things we saw previously (26H1 is like the stable version of Canary, it seems) Microsoft used a new Mythos-like model called MDASH to find vulnerabilities this month, so expect the numbers of fixed bugs to jump in coming months A low-latency profile for Windows will let it optimize for app/UI launch performance just like mobile platforms already do New builds across most channels with two major changes: Touchpad improvements in Experimental and free upgrade path to Pro for education users in Experimental Beta. A new threat emerges Google announces Googlebook, an Android-based laptop platform with Google Intelligence Some morning-after thoughts, including Microsoft promising AI and that Copilot will be the new Start, while Google delivers AI and is remaking the laptop as an intelligent device AI Microsoft Edge gets big AI and productivity updates on desktop and mobile An Anthropic engineer argues that AI should use HTML for output, not Markdown. He's right. About that 4 GB Gemini Nano model that Chrome secretly downloads OpenAI brings Codex to Google Chrome Security A Bitlocker concern emerges Microsoft Edge loads all saved passwords into plain text when it launches, Microsoft says this is as intended Mozilla patched 423 vulnerabilities in Firefox during April, most courtesy of Anthropic Mythos 465 million Amazon customers have enrolled in passkeys Xbox & gaming Xbox Insider Program: New build for console with previously announced new boot animation, tiered Gamerscore badges, new filters in Game Library Forza Horizon 6 leaks on Steam, those who play it early will be banned until the sun swallows the earth Discord Nitro now has an Xbox Game Pass Starter Edition perk Mojang will host a special MINECRAFT LIVE event on May 30 Sony sold just 1.5 million PS5s in most recent quarter, its lowest number yet Nintendo sold just 2.49 million Switch 2s in quarter, lowers annual estimates Supreme Court gives Apple the

It's not just Recall: Security vulnerabilities that require you to sign into an account on your PC are not necessarily vulnerabilities. Also, Windows 11 gets its first big feature updates in this week's Patch Tuesday releases. Snapseed 4.0 comes to Android/iOS, and Claude FM is great for relaxing or getting coding/work done. Plus, the Helium browser has emerged as a favorite with 2 notable caveats: No online settings sync and no mobile client. Windows 25H2/24H2: Xbox Mode, Agents on the Taskbar, more 26H1: Smart App Control improvements, other things we saw previously (26H1 is like the stable version of Canary, it seems) Microsoft used a new Mythos-like model called MDASH to find vulnerabilities this month, so expect the numbers of fixed bugs to jump in coming months A low-latency profile for Windows will let it optimize for app/UI launch performance just like mobile platforms already do New builds across most channels with two major changes: Touchpad improvements in Experimental and free upgrade path to Pro for education users in Experimental Beta. A new threat emerges Google announces Googlebook, an Android-based laptop platform with Google Intelligence Some morning-after thoughts, including Microsoft promising AI and that Copilot will be the new Start, while Google delivers AI and is remaking the laptop as an intelligent device AI Microsoft Edge gets big AI and productivity updates on desktop and mobile An Anthropic engineer argues that AI should use HTML for output, not Markdown. He's right. About that 4 GB Gemini Nano model that Chrome secretly downloads OpenAI brings Codex to Google Chrome Security A Bitlocker concern emerges Microsoft Edge loads all saved passwords into plain text when it launches, Microsoft says this is as intended Mozilla patched 423 vulnerabilities in Firefox during April, most courtesy of Anthropic Mythos 465 million Amazon customers have enrolled in passkeys Xbox & gaming Xbox Insider Program: New build for console with previously announced new boot animation, tiered Gamerscore badges, new filters in Game Library Forza Horizon 6 leaks on Steam, those who play it early will be banned until the sun swallows the earth Discord Nitro now has an Xbox Game Pass Starter Edition perk Mojang will host a special MINECRAFT LIVE event on May 30 Sony sold just 1.5 million PS5s in most recent quarter, its lowest number yet Nintendo sold just 2.49 million Switch 2s in quarter, lowers annual estimates Supreme Court gives Apple the

It's not just Recall: Security vulnerabilities that require you to sign into an account on your PC are not necessarily vulnerabilities. Also, Windows 11 gets its first big feature updates in this week's Patch Tuesday releases. Snapseed 4.0 comes to Android/iOS, and Claude FM is great for relaxing or getting coding/work done. Plus, the Helium browser has emerged as a favorite with 2 notable caveats: No online settings sync and no mobile client. Windows 25H2/24H2: Xbox Mode, Agents on the Taskbar, more 26H1: Smart App Control improvements, other things we saw previously (26H1 is like the stable version of Canary, it seems) Microsoft used a new Mythos-like model called MDASH to find vulnerabilities this month, so expect the numbers of fixed bugs to jump in coming months A low-latency profile for Windows will let it optimize for app/UI launch performance just like mobile platforms already do New builds across most channels with two major changes: Touchpad improvements in Experimental and free upgrade path to Pro for education users in Experimental Beta. A new threat emerges Google announces Googlebook, an Android-based laptop platform with Google Intelligence Some morning-after thoughts, including Microsoft promising AI and that Copilot will be the new Start, while Google delivers AI and is remaking the laptop as an intelligent device AI Microsoft Edge gets big AI and productivity updates on desktop and mobile An Anthropic engineer argues that AI should use HTML for output, not Markdown. He's right. About that 4 GB Gemini Nano model that Chrome secretly downloads OpenAI brings Codex to Google Chrome Security A Bitlocker concern emerges Microsoft Edge loads all saved passwords into plain text when it launches, Microsoft says this is as intended Mozilla patched 423 vulnerabilities in Firefox during April, most courtesy of Anthropic Mythos 465 million Amazon customers have enrolled in passkeys Xbox & gaming Xbox Insider Program: New build for console with previously announced new boot animation, tiered Gamerscore badges, new filters in Game Library Forza Horizon 6 leaks on Steam, those who play it early will be banned until the sun swallows the earth Discord Nitro now has an Xbox Game Pass Starter Edition perk Mojang will host a special MINECRAFT LIVE event on May 30 Sony sold just 1.5 million PS5s in most recent quarter, its lowest number yet Nintendo sold just 2.49 million Switch 2s in quarter, lowers annual estimates Supreme Court gives Apple the

It's not just Recall: Security vulnerabilities that require you to sign into an account on your PC are not necessarily vulnerabilities. Also, Windows 11 gets its first big feature updates in this week's Patch Tuesday releases. Snapseed 4.0 comes to Android/iOS, and Claude FM is great for relaxing or getting coding/work done. Plus, the Helium browser has emerged as a favorite with 2 notable caveats: No online settings sync and no mobile client. Windows 25H2/24H2: Xbox Mode, Agents on the Taskbar, more 26H1: Smart App Control improvements, other things we saw previously (26H1 is like the stable version of Canary, it seems) Microsoft used a new Mythos-like model called MDASH to find vulnerabilities this month, so expect the numbers of fixed bugs to jump in coming months A low-latency profile for Windows will let it optimize for app/UI launch performance just like mobile platforms already do New builds across most channels with two major changes: Touchpad improvements in Experimental and free upgrade path to Pro for education users in Experimental Beta. A new threat emerges Google announces Googlebook, an Android-based laptop platform with Google Intelligence Some morning-after thoughts, including Microsoft promising AI and that Copilot will be the new Start, while Google delivers AI and is remaking the laptop as an intelligent device AI Microsoft Edge gets big AI and productivity updates on desktop and mobile An Anthropic engineer argues that AI should use HTML for output, not Markdown. He's right. About that 4 GB Gemini Nano model that Chrome secretly downloads OpenAI brings Codex to Google Chrome Security A Bitlocker concern emerges Microsoft Edge loads all saved passwords into plain text when it launches, Microsoft says this is as intended Mozilla patched 423 vulnerabilities in Firefox during April, most courtesy of Anthropic Mythos 465 million Amazon customers have enrolled in passkeys Xbox & gaming Xbox Insider Program: New build for console with previously announced new boot animation, tiered Gamerscore badges, new filters in Game Library Forza Horizon 6 leaks on Steam, those who play it early will be banned until the sun swallows the earth Discord Nitro now has an Xbox Game Pass Starter Edition perk Mojang will host a special MINECRAFT LIVE event on May 30 Sony sold just 1.5 million PS5s in most recent quarter, its lowest number yet Nintendo sold just 2.49 million Switch 2s in quarter, lowers annual estimates Supreme Court gives Apple the

Ken Liu (Computer Science PhD at the Stanford AI Lab) and Erik Chi (CS PhD at UMich) are the Creators of the Open Anonymity Project, which lets people prove things about themselves online without revealing their identity. In this episode we explore what it means for AI systems to "know" you; why today's so-called privacy modes fall short; and how the next generation of AI systems could be built with privacy as a default, rather than an afterthought. Key Takeaways:  What "unlinkable inference" means and why it changes the privacy model of AI chat tools What actually happens to your data the moment you hit "send" in a typical AI system Why incognito mode in AI tools is largely a UI illusion, rather than a real privacy protection The role of metadata in identifying and profiling users, and how "secretary models" could enable personalization without sacrificing privacy How anti-censorship and privacy intersect in a future dominated by agentic AI systems Why now is the time to rethink assumptions about privacy in AI tools Guest Bio: Ken Liu is a Computer Science PhD student at the Stanford AI Lab, advised by Percy Liang and Sanmi Koyejo. His research focuses on foundation models and data/user privacy, and the intersection between the two. His recent work studies the privacy properties of AI (such as membership, memorization, and unlearning), and various AI privacy tools (such as anonymization, differential privacy, and federated learning). His papers have earned spotlights at top venues, and his findings have been deployed at scale on Android. Ken also led a team to a 1st-place win at the US-UK PETs Prize sponsored by the White House OSTP and the UK Government. Previously, Ken spent time at Google DeepMind, Carnegie Mellon University, Meta, Apple, and Amazon. Erik Chi is a CS PhD at UMich, advised by J. Alex Halderman. His research focuses on security and privacy, particularly network security and anti-censorship. He worked on a new standard for implementing and distributing censorship circumvention protocols—a standard that's now being adopted by VPN vendors to help millions of users access the free Internet. He also did content moderation (surveillance) and recommendation systems at ByteDance before realizing how censors will evolve in the AI era. ---------------------------------------------------------------------------------------- About this Show: The Brave Technologist is here to shed light on the opportunities and challenges of emerging tech. To make it digestible, less scary, and more approachable for all! Join us as we embark on a mission to demystify artificial intelligence, challenge the status quo, and empower everyday people to embrace the digital revolution. Whether you're a tech enthusiast, a curious mind, or an industry professional, this podcast invites you to join the conversation and explore the future of AI together. The Brave Technologist Podcast is hosted by Luke Mulks, VP Business Operations at Brave Software—makers of the privacy-respecting Brave browser and Search engine, and now powering AI everywhere with the Brave Search API. Music by: Ari Dvorin Produced by: Sam Laliberte  

It's not just Recall: Security vulnerabilities that require you to sign into an account on your PC are not necessarily vulnerabilities. Also, Windows 11 gets its first big feature updates in this week's Patch Tuesday releases. Snapseed 4.0 comes to Android/iOS, and Claude FM is great for relaxing or getting coding/work done. Plus, the Helium browser has emerged as a favorite with 2 notable caveats: No online settings sync and no mobile client. Windows 25H2/24H2: Xbox Mode, Agents on the Taskbar, more 26H1: Smart App Control improvements, other things we saw previously (26H1 is like the stable version of Canary, it seems) Microsoft used a new Mythos-like model called MDASH to find vulnerabilities this month, so expect the numbers of fixed bugs to jump in coming months A low-latency profile for Windows will let it optimize for app/UI launch performance just like mobile platforms already do New builds across most channels with two major changes: Touchpad improvements in Experimental and free upgrade path to Pro for education users in Experimental Beta. A new threat emerges Google announces Googlebook, an Android-based laptop platform with Google Intelligence Some morning-after thoughts, including Microsoft promising AI and that Copilot will be the new Start, while Google delivers AI and is remaking the laptop as an intelligent device AI Microsoft Edge gets big AI and productivity updates on desktop and mobile An Anthropic engineer argues that AI should use HTML for output, not Markdown. He's right. About that 4 GB Gemini Nano model that Chrome secretly downloads OpenAI brings Codex to Google Chrome Security A Bitlocker concern emerges Microsoft Edge loads all saved passwords into plain text when it launches, Microsoft says this is as intended Mozilla patched 423 vulnerabilities in Firefox during April, most courtesy of Anthropic Mythos 465 million Amazon customers have enrolled in passkeys Xbox & gaming Xbox Insider Program: New build for console with previously announced new boot animation, tiered Gamerscore badges, new filters in Game Library Forza Horizon 6 leaks on Steam, those who play it early will be banned until the sun swallows the earth Discord Nitro now has an Xbox Game Pass Starter Edition perk Mojang will host a special MINECRAFT LIVE event on May 30 Sony sold just 1.5 million PS5s in most recent quarter, its lowest number yet Nintendo sold just 2.49 million Switch 2s in quarter, lowers annual estimates Supreme Court gives Apple the

If you're hoping that adding AI to your analytics product or capabilities is going to unlock new revenue, sales, and greater user adoption, but you're not sure what's involved in this transformation, this episode is for you! Today, I'm talking with Juan Sequeda today, an expert in knowledge graphs and ontologies who most recently was Head of the AI lab at data.world, which was recently acquired by ServiceNow. Juan and I met while speaking at CDOIQ a few years ago, and after being on his former podcast “Catalogs and Cocktails.” (With a name like that, I naturally had him out to my local tiki bar while visiting Cambridge!) Talk-to-your-data products – effectively next-gen business intelligence applications – are a hot topic right now, and this has made much of Juan's PhD work in semantics highly relevant right now as companies try to make analytics more user-friendly via natural language.  Juan is clear that the starting point for this transformation isn't the model or the UI, but actually the customer's workflow—and that was like music to my ears! Analytics only matters when it drives action, so the real challenge is not answering more questions, but enabling better decisions and outcomes. A key theme is semantics, which, in product design language, I think of as making users' mental models of their business or domain map logically to system and data models so that AI produces the right answers in the right context. Juan outlines a practical path to getting started with this: strong data modeling, a well-defined semantic layer, buy-vs-build considerations, and throughout, a constant focus on what the customer's workflow and problem is. Highlights/ Skip to:   Juan Sequeda's background (2:14)  Is AI for BI the way to go for proprietary analytics products? (4:30) Bolted-on AI versus transformational AI, and what customers are doing with current reporting (8:26) Knowing your product's boundaries and when extending into adjacent customer workflows stops making strategic sense (14:46) Setting proper expectations for non-technical founders around what AI can “answer” with analytics (18:43) The role of customer problems in informing the prerequisite technology and data decisions (24:37) What's the actual lift to add chat-with-your-data capabilities to a SaaS product: data foundation, semantic layer, and the build-vs-buy call (33:38) Why Juan thinks every company should become “AI-native” (41:20) AI might theoretically make for a better analytics UX, but are users ready to change their behavior or abandon the analytics tools they use now? (46:00) How to follow Juan Sequeda (49:03) Links Catalogs & Cocktails Podcast Juan Sequeda's LinkedIn  Juan Sequeda's Substack

Listen to a recap of the top stories of the day from 9to5Mac. 9to5Mac Daily is available on iTunes and Apple's Podcasts app, Stitcher, TuneIn, Google Play, or through our dedicated RSS feed for Overcast and other podcast players. Sponsored by Bitwarden: Make your life easier with Bitwarden, featuring a secure, open source password manager with end-to-end encryption and seamless autofill across all your devices. New episodes of 9to5Mac Daily are recorded every weekday. Subscribe to our podcast in Apple Podcast or your favorite podcast player to guarantee new episodes are delivered as soon as they're available. Stories discussed in this episode: iOS 27 adding new way to manage your Safari tabs, per report Report: macOS 27 to feature UI tweaks to address some Tahoe design complaints Apple and Intel have reached a deal to produce future chips: report Listen & Subscribe: Apple Podcasts Overcast RSS Spotify TuneIn Google Podcasts Subscribe to support Chance directly with 9to5Mac Daily Plus and unlock: Ad-free versions of every episode Bonus content Catch up on 9to5Mac Daily episodes! Share your thoughts! Drop us a line at happyhour@9to5mac.com. You can also rate us in Apple Podcasts or recommend us in Overcast to help more people discover the show.

Today we are talking about The Midwest Open Source Alliance, What they do, and How they support Drupal with guests April Sides & Tearyne Almendariz. We'll also cover Canvas Field Component as our module of the week. For show notes visit: https://www.talkingDrupal.com/552 Topics Congratulations to April as the 2026 Aaron Winborn award! What is MOSA, and what gap in the Drupal ecosystem was it created to fill? How did MOSA get started, and who were the key people behind its formation? MOSA acts as a fiscal sponsor—what does that actually mean in practice for Drupal events and initiatives? What are some of the projects or camps MOSA currently supports? How does MOSA help sustain and grow regional Drupal communities over time? What does membership in MOSA look like, and who should consider getting involved? How does MOSA balance local community focus with broader, national or global Drupal efforts? What are the biggest challenges MOSA faces as a nonprofit supporting open source communities? How has MOSA evolved in recent years, and what's different today compared to when it launched? Looking ahead, what's the long-term vision for MOSA and its role in the Drupal ecosystem? Resources MOSA Website MOSA Drupal Project Aaron Winborn Handbook Moline, Illinois Guests Tearyne Almendariz - nlbcworks.com NineLivesBlackCat April Sides - weekbeforenext Hosts Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi MOTW Correspondent Martin Anderson-Clutz - mandclu.com mandclu Brief description: Have you ever wanted to place Drupal-rendered fields into your Drupal Canvas templates? There's a module for that. Module name/project name: Canvas Field Component Brief history How old: created in Apr 2026 by me! With some help from a couple of AI models Versions available: 1.0.0, which works with Drupal 11.2 or newer Maintainership Actively maintained Security coverage Test coverage Documentation - a README, but is designed to be narrow in scope Number of open issues: technically 5 open issues, but all marked as fixed Usage stats: 41 sites Module features and usage By design, when using Drupal Canvas to create templates for content types, the idea is to map field values to properties in the template's components That is a new system, however, so site builders may find there are gaps in terms of available mappings for field types they need to use, or may want to draw on mature formatting options such the responsive image definitions that come with Drupal CMS With the Canvas Field Component module installed, you'll find a new "Field display" option available in your Canvas component library. When you drag that into a Canvas template layout, you can choose which field from the content type you want to display, and the formatter to use That, in turn, will expose all settings for the chosen formatter, as well as any third-party settings available, for example if using Date Augmenters with Smart Date fields Those settings will be reflected in real-time inside the Canvas UI preview, and then on rendered content once the template changes are published This module started as a simple idea, based on my own experience using other UI-based Drupal solutions for laying out content type templates, like Layout Builder or Acquia Site Studio. Over the years, I've come to appreciate the flexibility of being able to place Drupal-rendered fields into templates, so you can mix-and-match existing, robust formatting options with flexible ways of pulling field values into layouts that also include more bespoke elements. Or, just use this as a way to add more layout flexibility to Drupal's default, linear display controls. That's what I do on my own blog, where I use Layout Builder but don't have a single custom layout on the site. It's only used for enhancing the layout of structured content. Full disclosure: I also used the idea for Canvas Field Component as the impetus to venture into vibe coding, inspired by the conversations happening in the AI Learners Club, which listeners will hear more about in an upcoming episode.

Mycket står på spel när världens två mäktigaste ledare ska ses i Peking. Tullstriden, kampen om chipen, Taiwan, global säkerhet och stormakternas framtoning. Lyssna på alla avsnitt i Sveriges Radios app. Vi listar fem avgörande punkter och ger en utförlig guide till allt som står på spel i stormaktskampen mellan Kina och USA. Donald Trump väntas i veckan besöka Kina som första amerikanska president på nästan tio år, och mötet kan få stora följdverkningar för hela världen. En av de mest brännande frågorna i stormaktskampen mellan länderna är handelskriget. Moa Kärnstrand beskriver stämningen i Guangzhou där vi hör om vad kinesiska företag säger om tullstriden, och vi reder ut vem som skadas mest av dem. En annan avgörande punkt är kampen om chipen – halvledare och sällsynta jordartsmetaller. Kommer Kina fortsatt använda exportkontroller som motdrag till Trumps tullpolitik?Världsordningen, Taiwan och Kina-bildenSäkerhetsläget i världen, och inte minst då kriget i Iran, står också högt på agendan. Samtidigt är Taiwan krutdurken som präglar säkerhetspolitiken i Östasien, med potential att påverka världen långt bortom regionen. Världsordningen och frågan om vem som sätter spelreglerna är avgörande för både Xi Jinping och Donald Trump. En sista avgörande punkt när ledarna möts handlar också om image och framtoning. Här har Kina enligt många just nu ett övertag som global trendsättare. Hur kommer ledarna utnyttja mötet för att vinna omvärldens förtroende? Till vår hjälp för att beskriva mötets ingångsvärden har vi med oss röster från ett flertal Kinakännare och experter som vi mötte på en Kinakonferens hos Utrikespolitiska institutet häromveckan. Medverkande: Hanna Sahlberg, Ekots Kinareporter. Moa Kärnstrand, Sveriges Radios Kinakorrespondent.Programledare: Björn DjurbergProducent: Therese Rosenvinge och Oskar SellströmI avsnittet hörs också: Björn Jerdén, chef för Nationellt kunskapscentrum om Kina vid UI. Hannes Lenk, utredare Kommerskollegium. Ola Wong, redaktör Kvartal. Christopher Weidacher Hsiung, Kinaanalytiker på FOI. Ruthger de Vries, senior rådgivare Scania. Kristina Sandklef, oberoende Kinaanalytiker. Källor ljudklipp: Forbes.

Steve finally fixed phillycocoa.org, and the journey from broken CircleCI pipelines and hijacked S3 buckets to a blazing-fast Cloudflare Pages site took one Side Project Saturday and an embarrassing number of Codex tokens. Then The Trio turns to the AI hype machine, and they're tired: tired of opaque token costs, tired of reviewing generated code that complicates everything it touches, and tired of an industry that mistakes syntax speed for software engineering. Fred Brooks called it in 1986, and The Trio is calling it now.## Chapters00:00 Introductions01:47 The Journey of Updating the Website06:38 Challenges with CircleCI and S3 Buckets09:23 Exploring Cloudflare Pages11:14 Navigating Cloudflare's User Interface14:22 Setting Up Automatic Deployments17:35 Managing DNS and SSL with Cloudflare23:07 LLM Development Fatigue26:15 Navigating Concerns and Costs in AI Usage29:11 LLMs are No Silver Bullet31:57 The Exhaustion of Code Review and Architectural Decisions36:25 Token Management and Cost Awareness in AI Tools40:07 The Economics of AI and Software Development42:45 The Hype vs. Reality of AI Tools46:34 Future Prospects of LLMs and Universal UI50:16 The Future of Edge Computing with LLMs53:08 The Evolution of Software Development and AI Integration54:17 AI in Sci-Fi: Myths vs. Reality57:54 The Challenges of Local Models and Hardware Limitations01:03:21 Outro & Upcoming Event01:09:21 Tag## Show Notes- Steve spent Side Project Saturday migrating phillycocoa.org from a broken CircleCI/S3 setup to Cloudflare Pages, burning his entire weekly Codex token budget in about three hours.- Cloudflare Pages handles Hugo builds automatically and manages SSL and CDN without manual config, all on a free tier that's plenty for the site.- Cloudflare's UI hides the Pages "Get Started" link below giant worker buttons, which Kotaro calls "the weirdest dark pattern."- Steve argues that syntax generation was never the real bottleneck in software engineering, citing Fred Brooks' 1986 essay "No Silver Bullet."- Aaron is worn out from reviewing AI-generated code and still having to make every architectural decision himself.- LLM costs are nearly impossible to forecast: a single prompt can burn a significant chunk of your plan, depending on model, tool calls, and context.- The Trio sees firms rushing to adopt LLM tooling before the ROI math makes sense, driven by hype rather than evidence.- ThePrimeagen's recent take on the shifting AI economy lines up with what Steve sees at work: token-based billing is starting to expose the real cost.- The Trio agrees local models running on personal hardware are the interesting long-term play, but RAM shortages make even basic setups expensive.- Kotaro closes with a dad joke: he thought his LLM skills landed him his current job, but it turns out...## Links**PhillyCocoa.org Update**Website: https://phillycocoa.org**Articles & Essays**"Let's talk about LLMs" by James Bennett: https://www.b-list.org/weblog/2026/apr/09/llms/"No Silver Bullet" by Fred Brooks: https://www.cs.unc.edu/techreports/86-020.pdf**Videos**"The AI economy is about to change" by ThePrimeagen: https://youtu.be/_Q-e_nczWqM**One More Thing**"Beyond the Simulator: Perspectives on Modern App Development": https://luma.com/i00ll61z**PhillyCocoa:** https://phillycocoa.orgIntro music: "When I Hit the Floor", © 2021 Lorne Behrman. Used with permission of the artist.

An Adaptive Cyber Analytics UI for Web Honeypot Logs https://isc.sans.edu/diary/An%20Adaptive%20Cyber%20Analytics%20UI%20for%20Web%20Honeypot%20Logs%20%5BGuest%20Diary%5D/32962 Ivanti May Patchday https://hub.ivanti.com/s/article/May-2026-Security-Advisory-Ivanti-Endpoint-Manager-Mobile-EPMM-Multiple-CVEs Redis Security advisory: [CVE 2026 23479] [CVE 2026 25243] [CVE-2026-25588] [CVE 2026 25589] [CVE-2026-23631] https://redis.io/blog/security-advisory-cve202623479-cve202625243-cve-2026-25588-cve202625589-cve-2026-23631/ @sans_edu research paper: Marcio Enriquez [link will be added once the paper has been published]

Nicolas Alejandro Bogliolo is the AI PM at Despegar, the largest online travel agency in Latin America, and the engineer-product-hybrid behind Sofia, the GenAI travel concierge that beat most of the OTA world to a working multi-agent system. Before MCP was a standard and before LangChain was widely adopted, his team had already shipped their own orchestration layer and tool protocol in production. This conversation is a rare look at what it takes to build an agentic system that actually books trips, runs on WhatsApp, and keeps adding capabilities without falling over.What we cover:- Chappi, the brain of Sofia: how Despegar built an internal orchestration layer when there was nothing off the shelf- Building "MCP before MCP": the custom tool-calling protocol that predated the Anthropic standard- Multi-agent architecture by vertical: flights, hotels, activities, and cars each own their own flow- Decentralized agent ownership: how any squad in the company can build a flow with central supervision- Sofia on WhatsApp: making messaging the consumer control center, the way Slack became it for the enterprise- The five-phase travel arc Sofia covers: dreaming, planning, anticipation, in-trip, and post-trip- KPI evolution: why "in-scope conversation rate" topped out near 96 percent and what they measure now- The flight-delay-claim use case and why filing claims through a chatbot is a perfect agent task- Group trip planning in WhatsApp groups: the next frontier for travel agents- Sofia as channel of choice: the WeChat-style vision for an agent that handles your entire trip- Why Despegar held off on giving Sofia the ability to bargain with customers, for now. Whether you are building production agents, running an OTA, or just curious about how an AI travel concierge actually works under the hood, this episode is full of grounded, in-production lessons from a team that had to invent the patterns the rest of us are now adopting.Links and Resources:- Despegar: https://www.despegar.com- Sofia announcement: https://investor.despegar.com/news-presentations/news-releases/news-details/2024/Despegar-revolutionizes-the-tourism-industry-introducing-the-regions-first-Generative-AI-Travel-Assistant- Sofia coverage on PhocusWire: https://www.phocuswire.com/despegar-debuts-genai-travel-assistant-remembers-previous-interactions- MLOps Community: https://mlops.community- Subscribe for more agent and AI infra deep divesTimestamps 00:00 - Intro: Nicolas, Sofia, and Despegar in LatAm01:30 - Chappi as the brain of Sofia and the squad model04:00 - Anyone in the company can build a flow07:00 - Why airline check-in still exists and what agents could replace09:30 - The flight-delay refund story and the chatbot gap13:00 - File-the-claim-for-me as a perfect agent use case16:00 - The dreaming phase: helping users who do not know where to go yet19:00 - In-scope conversation KPI hitting 96 percent and what comes next22:00 - Beating the traditional flight search UI with conversation25:00 - WhatsApp group trip planning and the ski trip example28:00 - Personalization at scale and the new gateway to the internet31:00 - WhatsApp as the consumer control center, like WeChat in China34:00 - Sofia as gateway: complaints, customer service, and verticalized agents37:00 - Building MCP before MCP and the custom orchestration layer40:00 - Why Sofia does not negotiate prices, yet#AIAgents #MCP #AgenticAI

Google Chrome engineer Adam Argyle breaks down why AI is bad at frontend development and CSS in particular. From LLM training data problems to the fact that LLMs can't see, the issues run deep. But it's not all doom! Adam shares a game-changing technique for getting creative AI generated UI by asking for low probability outputs, introduces tools like Impeccable and V0 for AI assisted CSS editing, and dives into agentic loop engineering with auto research, an overnight AI workflow he uses to ship improvements while he sleeps. Links Website: https://nerdy.dev/ YouTube: https://www.youtube.com/channel/UCBGr3ZMcV5jke40_Wrv3fNA Twitter: twitter.com/argyleink Github: github.com/argyleink Linkedin: https://www.linkedin.com/in/adamargyle Resources Why AI sucks at frontend: https://nerdy.dev/why-ai-sucks-at-front-end We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabeth.becz@logrocket.com, or tweet at us at PodRocketPod. Check out our newsletter! https://blog.logrocket.com/the-replay-newsletter/ Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form, and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. ChaptersSpecial Guests: Adam Argyle and Jack Herrington.

While B2B companies debate whether to "embrace AI," one of the fastest-growing HR platforms in the world is already asking its designers to ship production code, run their own research, and rethink what a UI is in the first place.In this episode, Avi Ashkenazi — Senior Product Design Director at Deel, where 97 product designers work without a physical office, operate directly with the CEO, and are expected to unblock themselves — breaks down what actually separates designers who thrive in high-velocity environments from those who flame out. He makes the case that taste, devotion, and the willingness to stay opinionated are what AI can't automate, and explains why "vibe coding" will always hit a wall the moment a client asks for something exact.We also cover:Why Deel built a "Delight" team — and what it tells you about the ROI of caring about the emotional texture of your product, not just its functionalityThe agentic design system Deel is actively building, where interfaces construct themselves on demand and no two users ever see the same pageHow to run a 120-person distributed design org with no office and quarterly themes that actually move everyone in the same direction

Most teams are sitting on a stack of Zoom recordings (webinars, customer trainings, all-hands sessions) that nobody ever turns into anything.Doing anything with them seems too... messy, and a lot of people don't realize that there's an asset there in the first place.In this episode, Matt sits down with Carson Vestergaard, Instructional Designer at TechSmith, who breaks down why the easiest training video you'll ever ship is often one that already exists in your meeting recordings folder, and how Camtasia's new Zoom integration is making that possible.Carson's team on TechSmith's customer education side runs this workflow every week. They pull a Zoom recording into Camtasia, and the integration automatically splits the speaker from the screen.From there, Audiate's text-based editing changes how the cleanup feels. What used to be an afternoon of manual work becomes a read-through.Beyond the Zoom integration, the conversation gets into Sync Audio (Camtasia's new feature that auto-aligns multi-mic recordings without the manual clap-and-spike trick), AI noise removal that handles the leaf blower outside the window without breaking voice clarity, and the screenshot-overlay trick Carson leans on to keep tutorials current long after the original UI has moved on.Carson also shares a few insider tricks for keeping the viewer's eye where you want it, from cursor zooms to on-screen highlights.Learning points from the episode include:00:00 – 00:57 Intro00:57 – 02:34 Carson's career path:02:34 – 03:54 The most underrated editing skill03:54 – 06:47 Inside Camtasia's new Zoom integration06:47 – 10:12 Cleaning up a webinar with Audiate's text-based editing10:12 – 14:18 Layouts, the cursor caveat, and why this is Zoom-only14:18 – 17:38 Sync Audio and AI noise removal in loud rooms17:38 – 20:43 Multi-take editing and why videos are easier to update than you think20:43 – 22:30 Carson's favorite tools22:30 – 23:02 OutroImportant links and mentions:Connect with TechSmith on LinkedIn: https://www.linkedin.com/company/techsmith/Learn more about Camtasia: https://www.techsmith.com/camtasia/Explore Snagit: https://www.techsmith.com/snagit/Learn more about Audiate: https://www.techsmith.com/camtasia/audiate/

We dig into the Copy Fail vulnerability and test a proof-of-concept against our own box. Plus, Jon Seager, VP of Engineering at Canonical joins us, and we kick off the BSD Challenge!Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:

"Not a creative"?