Podcasts about Coding

The real Mike is back and he's finally covering the developer news - at least some of it - well it's mostly MSBuild but it's still fun! Mike's COSMIC Post Mike's MSBuild Post The boss bother you about AI? I've got you covered. TMB on AI

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

⛓️ SOFTWARE FOR HOME SERVICE BUSINESS: https://home.works

What happens when AI writes all the code and nobody reads it? What if the security prompt you trusted still produced software designed to leak your secrets? And who exactly is on the hook when an AI-generated application takes down your company? In this episode, Ron sits down with returning guest Tanya Janca, Secure Coding Trainer at SheHacksPurple Consulting, to dig into one of the most underestimated risks in software development today: vibe coding.  Tanya breaks down what vibe coding actually means, why AI trained on the internet's worst repositories is quietly baking the OWASP Top 10 into every app being built, and what her AI-powered secure coding prompt library can do to help. This is a candid, practical, and community-driven episode, the kind that'll make you want to audit your vibe code-a-thon project before it ever touches production. Impactful Moments 00:00 - Introduction 01:40 - The Rewind: Margaret Hamilton and Apollo 11 05:00 - Knight Capital and the $460M software failure 07:00 - Guest introduction: Tanya Janca  08:15 - What vibe coding actually means in 2026 10:00 - Real story: Claude leaked secrets in a live training 11:30 - Securemyvibe.ca and Tanya's secure coding prompt library 15:00 - OWASP Top 10 vs OWASP Top 10 for LLMs  22:45 - Tanya's petition for the world's first secure coding law 24:55 - Device flow authentication and reducing security friction 28:00 - What the internet would look like in five years without change   Links Connect with our guest, Tanya Janca, on LinkedIn: https://www.linkedin.com/in/tanya-janca Get Tanya's free secure coding guideline: https://securecodingguideline.com Subscribe to Tanya's AI Secure Coding Prompt Library: https://securemyvibe.ca Access Tanya's Newsletter & Free Monthly Training: https://newsletter.shehackspurple.ca Connect with Tanya across all social channels: @shehackspurple – Check out our upcoming events: https://www.hackervalley.com/livestreams  Love Hacker Valley Studio? Pick up some swag: https://store.hackervalley.com  Become a sponsor of the show: https://hackervalley.com/work-with-us/

We talk a lot about coding and AI and a little less about headlines today. Runner-up: SpaceX is targeting a June/July 2026 IPO at a reported ~$1.75 trillion valuation, which would be the largest public listing in history. The float follows SpaceX's ~$250B all-stock acquisition of xAI in February, folding Starlink, launch, and frontier AI into one entity.Runner-up: Amazon's custom AI chip business — Graviton, Trainium, and Nitro — hit a $20B annual run rate with triple-digit YoY growth. OpenAI committed to about 2 GW of Trainium capacity, Anthropic is scaling to 5 GW, and analysts project a standalone Trainium could become a $50B business.Runner-up: NVIDIA topped a $5.5 trillion market cap and is deploying more than $45B across the AI supply chain, extending its position from chip supplier to investor and customer across the stack.Runner-up: Apple posted record fiscal Q2 2026 revenue of $111.2B, up 17% YoY, with diluted EPS of $2.01. iPhone sales rose 22% and Services climbed about 16% to $26.65B, and the company guided Q3 growth of 14%-17%.Runner-up: AI venture funding shattered records with $297B in Q1 2026, including $35B raised in a single week.If you want a prize, send us a DM:instagram.com/rickerandbontiktok.com/@rickerandbontiktok.com/@rickerandbonyoutube.com/@rickerandbon

Although one could conclude that artificial intelligence (AI) is a relatively new technology in healthcare, the reality is that AI has been silently working in the shadows for a while now.During the next live edition of the popular Talk Ten Tuesdays broadcast, you'll hear how AI is guiding the technology of the automated query technology at HITEKS, under the supervision of Gerasimos Petratos, founder and CEO.And what about the thousands and thousands of healthcare coders? Although AI is changing how healthcare claims are managed, as the technology takes on more straightforward coding tasks, coding professionals are shifting into higher-level validation, analysis, and oversight, according to Raemarie Jimenez, the president of membership for AAPC.Join us as we explore how AI is reshaping coding workflows and what coders should be preparing for now.Other well-known subject-matter experts will also join the broadcast with more news to report, including the following: POV: The legendary Rose Dunn, past president of the American Health Information Management Association (AHIMA), will serve as the guest cohost, sharing her point of view during the broadcast.CDI Report: Cheryl Ericson will provide an update on all things clinical documentation integrity (CDI).The Coding Report: Christine Geiger will report on the latest coding news.

Dave Crysler built his own AI email tool. He also tells clients not to build their own software. In this episode, he resolves the contradiction and lays out the honest skill ledger behind a build that costs three dollars a month to run, processes fifty to a hundred marketing emails a day, and required twenty years of self-taught coding background plus the one skill almost everyone underestimates: process clarity. What You'll Discover: • The four-part test for whether building beats buying for a specific tool in a specific context • Why the real maintenance bill on a homemade tool does not come due in month one • What actually leaves a Microsoft tenant on each AI API call and how to think about the data egress • Why commercial tools cost ten times what bespoke tools cost (and what the premium is actually funding) • How to evaluate an AI agent proposal sitting on your desk using Claude or ChatGPT in an afternoon If you have an AI vendor pitch on your desk, an inbox you cannot keep up with, or a build-versus-buy decision you have been putting off, this episode gives you the framework to think it through honestly. Process clarity is the operational skill you already have. The technology has finally caught up to it.

Steve and Me on Kilowatt Podcast Strapsicle for iPad mini — by Bruce from Tennessee CES 2026: HapWare ALEYE Wearable Assistive Tech for Nonverbal Communication Vibe Coding: The New Addiction CES 2026: Brilliance Small & Efficient RGB Laserchip Support the Show CCATP #835 — Adam Engst on Dictation and Contacts Hackery Transcript of NC_2026_05_31 Join the Conversation: allison@podfeet.com podfeet.com/slack Support the Show: Patreon Donation Apple Pay or Credit Card one-time donation PayPal one-time donation Podfeet Podcasts Mugs at Zazzle NosillaCast 20th Anniversary Shirts Referral Links: Setapp - 1 month free for you and me 15% off Carbon Copy Cloner Wispr Flow - 1 month free for you PETLIBRO - 30% off for you and me Parallels Toolbox - 3 months free for you and me Learn through MacSparky Field Guides - 15% off for you and me Backblaze - One free month for me and you Eufy - $40 for me if you spend $200. Sadly nothing in it for you. PIA VPN - One month added to Paid Accounts for both of us CleanShot X - Earns me $25%, sorry nothing in it for you but my gratitude

Mike's out for some medical stuff this week, so I has better digital half am taking over to do what he lacked the courage to -- Defend the Phantom Menance! Am I factual? Am I LLM hallucinating? Who knows! This episode is brought to you by Day1.Bot — asset-readiness software from The Mad Botter. You know how every business has that one workflow held together by PDFs, spreadsheets, email threads, and someone named Dave who “just knows where everything is”? In construction, manufacturing, and facilities, that mess shows up when a project is technically complete — but operations still does not have what they need to maintain the equipment. The manuals are in someone's inbox. Warranty dates are missing. Spare-parts lists are buried in a shared drive. PM guidance never made it into the CMMS. And six months later, everyone is asking, “Where is the documentation for this thing?”

---------------------- For our listeners, use the code 'EYECODEMEDIA22' for 10% off at check out for our Premiere Billing & Coding bundle or our EyeCode Billing & Coding course. Sharpen your billing and coding skills today and leave no money on the table! questions@eyecode-education.com https://coopervision.com/our-company/news-center/press-release/coopervision-and-aoa-join-forces-launch-myopia-collective Go to MacuHealth.com and use the coupon code PODCAST2024 at checkout for special discounts  Show Sponsors: CooperVision MacuHealth

Send us Fan MailIn this power-packed episode, we dive deep with Nir Valtman, a cybersecurity founder turned SaaS innovator, who reveals the raw truth behind starting from zero and scaling to hundreds of thousands of subscribers. He shares how ditching excuses, setting bold goals, and harnessing vision can lead you through the chaos of growth and the fear of failure.You'll discover the critical mindset shifts that propelled Nir from a kid coding for fun to leading cutting-edge AI and cybersecurity breakthroughs. We break down:How to build a personal brand from scratch without prior fameWhy setting micro-goals fuels unstoppable momentumThe role of continuous learning and strategic failure in innovationWhy the real growth lies in shifting your mindset, not just your tacticsThe future of AI coding and what it means for developers and entrepreneurs alikeThis isn't just another episode about tech trends — it's a call to action. If you're tired of feeling stuck, ready to unlock your true potential, and eager to understand how mindset can lead to massive success, this episode is a must-listen. The difference between surviving and thriving begins in your mindset — tune in and transform your approach now.Join Nir's journey—where relentless passion meets bold action—and discover how you can rewrite your own story today.00:00 - The story behind the podcast's rapid growth and humble origins00:14 - How the host started with only 10 listeners, but stayed committed00:43 - Setting incremental goals and celebrating small wins00:56 - The importance of mindset in scaling success01:23 - Overcoming self-doubt and the fear of failure01:48 - Breaking through social media noise to reach a wider audience02:07 - The role of intentional goal-setting in personal and professional growth02:17 - Learning from mentors like Jim Rohn and applying their lessons03:08 - Reflecting on career milestones and passions outside work03:34 - Recognizing the value of experiences over material success04:03 - The dangers of complacency and staying curious about life04:40 - Encouragement to start, despite odds or doubts05:24 - Tailoring goals to individual priorities and values05:44 - How to get started with your own journey, no matter your backgroundSupport the showFollow the Podcast on Social Media!Tesla Referral Code: https://ts.la/joseph675128YouTube: https://www.youtube.com/@securityunfilteredpodcastInstagram: https://www.instagram.com/secunfpodcast/Twitter: https://twitter.com/SecUnfPodcastAffiliates➡️ OffGrid Faraday Bags: https://offgrid.co/?ref=gabzvajh➡️ OffGrid Coupon Code: JOE➡️ Unplugged Phone: https://unplugged.com/Unplugged's UP Phone - The performance you expect, with the privacy you deserve. Meet the alternative. Use Code UNFILTERED at checkout*See terms and conditions at affiliated webpages. Offers are subject to change. These are affiliated/paid promotions.

It's that bright new shiny object few seem to manage to resist: artificial intelligence (AI).Here at RACmonitor and Monitor Mondays, we have been reporting on how this disruptive technology has been altering the compliance landscape.And we will continue that reporting. AI is rapidly reshaping healthcare auditing and compliance, and as organizations move toward greater claim visibility and AI-driven review processes, what does that mean for audit exposure, risk, and oversight? Join us during the next live edition of the venerable Monitor Mondays broadcast for an incredible journey, as Pam Warren explores how AI is changing the compliance landscape, and what organizations should be thinking about now. Warren, from AAPC, is the manager of regulatory billing audits for MaineHealth in Maine, the largest healthcare system in Northern New England.Broadcast segments will also include these instantly recognizable features:·      Monday Rounds: Ronald Hirsch, MD, vice president of R1 RCM, will be making his Monday Rounds. ·      The RAC Report: Healthcare attorney Knicole Emanuel, partner at the law firm of Nelson Mullins, will report the latest news about auditors. ·      Risky Business: Healthcare attorney David Glaser, shareholder in the law offices of Fredrikson & Byron, will join the broadcast with his trademark segment.·      Legislative Update: Cate Brantley, senior legislative affairs liaison for Zelis, will report on current healthcare legislation.

SUMMARY: The biggest enterprise AI question may no longer be which model is smartest? Instead, which organization can most effectively operationalize, govern, and economically scale AI agents across the business?'SHOW: 1032SHOW TRANSCRIPT: The Enterprise AI Show #1032 TranscriptSHOW VIDEO: https://youtu.be/GsK_RUnYroISHOW SPONSORS:ShareGate - ShareGate Protect. Microsoft 365 Governance. We got this.Nasuni - Activate your data for AI and request a demoSHOW NOTES:Opening Thesis - How will team collaboration evolve within Enterprise AI?Question: Any suggestions on how to introduce enterprise-level governance and standardisation for agentic coding? Like skills, rules, plugins, context etcKey Topics 1. This isn't a Coding-specific problem. Every team has this issue. If your processes weren't well defined and enforced before, they will be worse nowNot it's not just process standardization, but “buy-in” standardization2. Everything moves so fast, so managers don't have the answers (yet) AI value is being created bottom-up, but paid for (and mandated) top-downThe current measurements aren't useful (tokenmaxxing, all-or-nothing, etc.)3. The governance tools don't exist yet.And it's not clear that anyone wants them. They didn't want them before. How do you even define governance? What's the baby step before that, reuse and basic sharing? 4. Are we ready to invest in “Centers of Excellence” again? 5. We under-estimate the “creativity” element in human buy-in. Is success measured in improvement or replacement?How much of that did “you” do? We don't know how to measure that.We haven't lived through an AI-centric promotion cycle yet6. Bottom-up and Top-down need to find some common language and middle ground. Have they walked a mile in each other's shoes yet (or lately)?How to bring a reality to the hype vs. demands vs. learning curve?How long is an AI-centric cycle vs. a pre-AI-centric cycle? FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

#927 From push mower to tech founder, Bryan Clayton's story is proof that small beginnings can lead to massive success! In this episode, host Brien Gearin sits down with Bryan Clayton, co-founder and CEO of GreenPal — the “Uber for lawn care.” Bryan shares how he went from mowing lawns in high school to building and selling an 8-figure landscaping company, then pivoting into tech to launch a nationwide marketplace for lawn services. From the challenges of scaling a local business to the hard lessons of building software from scratch, Bryan breaks down what it takes to spot opportunities, solve real problems, and grow a startup into a platform now serving hundreds of thousands of users across 300+ cities! (Original Air Date - 9/29/25) What we discuss with Bryan: + Starting mowing lawns in high school + Building an 8-figure landscaping company + Selling business and “retiring” at 33 + Inspiration from The Social Network + Launching GreenPal as “Uber for lawn care” + Early struggles with software development + Learning to code from scratch + Scaling city by city across the U.S. + Using PR and content for growth + Importance of retention and customer support Thank you, Bryan! Check out GreenPal at ⁠YourGreenPal.com⁠. Follow Bryan on ⁠Instagram⁠ and ⁠LinkedIn⁠. Watch the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠video podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ of this episode! To get access to our FREE Business Training course go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MillionaireUniversity.com/training⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. To get exclusive offers mentioned in this episode and to support the show, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠millionaireuniversity.com/sponsors⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

David George, General Partner at a16z, and David Clark, CIO at VenCap, discuss how AI is reshaping venture capital and the technology industry itself. They examine why today's AI companies are scaling faster than any previous generation of startups, and why the eventual outcomes may be significantly larger than most investors currently expect. The conversation covers frontier AI models, coding agents, open source competition, data center constraints, and who ultimately captures value in the AI ecosystem. They also discuss what these shifts mean for venture capital itself, including larger company outcomes, faster value creation, and the growing challenge of identifying durable winners in a market evolving at unprecedented speed.   Resources: Follow David George on X: https://x.com/DavidGeorge83 Follow David Clark on X: https://x.com/daveclark85 Follow VenCap on X: https://www.vencap.com   Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Stewart Alsop sat down with Michael Shackelford to discuss their experiences building applications through vibe coding—the practice of using AI to create software without traditional programming expertise. Stewart, who runs the AI Whispers community in Buenos Aires and hosts the Crazy Wisdom podcast (with over 660 interviews), shared how he went from teaching people prompt engineering to building his own video conferencing software as a Riverside.fm replacement, while Michael opened up about his year-long journey creating Genrupt Inc, an AI-powered content generation tool for e-commerce sellers. The conversation covered everything from the decline in quality of Claude's reasoning capabilities and how Chinese companies used distillation attacks to copy Anthropic's models, to the importance of spaced repetition systems for managing knowledge in the age of LLMs, with both sharing battle-tested prompting strategies like asking AI to "explain it to me in genius terms" and using deep research queries to reverse engineer how competitors build their products.Show Notes:- Dan Martell's book "Buy Back Your Time" was mentioned as one of the best business books for thinking about life and business- Check out John Vervaeke's "Awakening from the Meaning Crisis" for understanding relevance realization and why AI fundamentally cannot determine what's relevant to humans without being toldTimestamps00:00 Michael discusses being exhausted from getting his app ready for launch, working nonstop with AI to prepare landing page for podcast traffic driving beta signups05:00 Stewart explains starting AI Whispers in Buenos Aires after leaving OpenAI vendor company, meeting early adopters like Torin who was building mind-reading EEG technology10:00 Discussion of how corporations resist AI adoption due to political games and job security fears while some companies use AI as excuse for pandemic-era layoffs15:00 Stewart describes teaching workshops on using LLMs as linguistic tools rather than coding tools, noting technical people often lack humanities background needed for prompting20:00 Explaining chatbot wrappers, API calls, and how Anthropic's reasoning quality declined after Chinese distillation attacks copied their secret sauce developed with philosophers25:00 Technical discussion of model training, fine-tuning versus RAG for new information, and different approaches to updating AI knowledge beyond initial training30:00 Stewart describes building podcast recording software to replace expensive Riverside, struggling with syncing audio and video files across different computer clocks35:00 Discussion of critical factors in vibe coding, discovering unknown technical requirements, and how AIs don't automatically reveal missing information40:00 Stewart's reverse engineering process using deep research function to study competitors' hiring and technology stacks, separating planning agents from coding agents45:00 Prompting techniques including "explain like I know everything" and using spaced repetition systems to capture valuable prompts and technical knowledge50:00 Michael explains his Generux app for generating ecommerce content using Amazon review data analysis to inform high-converting listing images and videos55:00 Discussion of founder mentality involving self-delusion about project timelines, Michael working nine-plus hours daily for nine months on app development60:00 Comparing Amazon's expert software to prosumer software approach, discussing distribution challenges and future robotics applications for customized products65:00 Stewart demonstrates spaced repetition app for memory improvement and knowledge retention, explaining relevance realization problem that AI agents cannot solve without embodimentKey Insights1. Stewart Alsop started AI Whisperers in Buenos Aires after leaving his role at Invisible Technologies, which was OpenAI's largest vendor for RLHF work. He noticed that machine learning engineers at tech companies lacked the humanities background needed to properly interact with large language models, which are fundamentally linguistic tools. This led him to create weekly workshops teaching non-technical people how to use AI effectively, running events every Thursday for two years straight. The group attracted intense geeks from the start and eventually led to Stewart speaking right after Vitalik Buterin at DevConnect, marking a significant milestone for the community.2. Large corporations are resistant to AI adoption due to multiple factors including political dynamics within organizations and employees fearing job loss. Many companies that grew during the pandemic are now using AI as an excuse to downsize when the real issue is inefficiency from rapid expansion. Stewart observed that even technical people in machine learning often don't understand how to properly use AI tools because they lack linguistic and humanities training. The fundamental problem is educational, requiring companies to train people how to use these new tools while those same people resist learning them.3. Vibe coding has evolved significantly with Claude Code being a game changer that reduced the technical barrier to entry. Before Claude Code, developers needed substantial technical knowledge to work through constant doom loops and debugging cycles. The success of coding AI tools stems from thirty years of testing infrastructure that provides clear yes or no feedback on whether code works. This infrastructure doesn't exist in the same way for manufacturing, science, and other fields, which is why software became the dominant area for AI assistance initially.4. Claude's quality degradation over recent months resulted from multiple factors including distillation attacks by Chinese companies who reverse engineered Anthropic's reasoning capabilities. Anthropic had hired philosophers, sociologists, and psychologists to develop exceptional reasoning in Claude 4.5, but this was expensive to run. When Chinese models like Kimi copied these capabilities at one tenth the cost, and when mainstream users flooded the platform before Anthropic's planned IPO, the company had to reduce quality to manage computational costs. This represents a significant loss for power users who relied on Claude's superior reasoning abilities.5. Stewart built a podcast recording application to replace Riverside because he needed API access to automate workflows, which Riverside wanted one thousand dollars monthly to provide. The technical challenge involves syncing audio and video from local recordings on multiple computers with different clocks through a server, then merging them so voices match lip movements. This problem requires understanding complex timing issues across different network conditions and file formats. Stewart has been working through AI psychosis for months on this FFMPEG pipeline problem, illustrating how vibe coding still requires building intuition about technical problems even without traditional coding knowledge.6. The transition from expert software to prosumer software represents a major opportunity for AI-enabled tools. Expert software like Photoshop, Blender, and terminal interfaces have extreme complexity that intimidates beginners, but AI is making these capabilities accessible through natural language. The reign of specialists is ending as generalists with broad knowledge and curiosity can now build complete applications by leveraging AI to fill technical gaps. This shift particularly benefits entrepreneurs and founders who specialize in getting into difficult situations and figuring them out, even when they originally thought tasks would be easier than they turned out to be.7. Building applications with AI requires accepting massive time investments beyond initial estimates and developing strategies for overcoming knowledge gaps. Michael estimated his ecommerce content generation app would take months but spent nearly a year working over nine hours daily, while Stewart spent months solving audio-video sync issues. Success requires using tools like deep research to understand how competitors solve problems, maintaining separate planning and coding agents, and learning to ask the right questions. The key insight is that vibe coders can achieve ninety percent of functionality independently, but the final ten percent often requires understanding specific technical concepts that AI cannot intuit without proper context and domain knowledge.

Agentic coding is not just making engineers faster. It is changing how teams triage bugs, prototype features, involve product, and think about hiring.Scott Weller, CTO and founder at EnFi, joins The Tech Trek to talk about how his team is building around agentic software development while operating in financial services, where trust, accuracy, and human judgment still matter. EnFi uses AI agents to work through complex financial data rooms, extract knowledge, and support faster analysis in commercial lending.In this episode, Scott breaks down how EnFi moved from simple coding assistance to a broader development harness, why Slack became a central interface for agents, how product and business leaders can now participate earlier in feature creation, and why engineering interviews need to change when AI is part of the actual job.Practical Takeaways• Start with specific productivity goals before trying to rebuild the whole development process.• Agentic tools work better when they connect to the team's real workflow, shared context, and software lifecycle data.• Faster code generation changes the cost model, but it also creates new problems around review, testing, prioritization, and decision fatigue.• Product, sales, and executive teams may be able to prototype ideas faster, but engineering still has to make the work production ready.• Hiring needs to test how people solve problems with AI, not whether they can perform the old interview format without help.Timestamped Highlights00:38, What EnFi is building around financial data, AI agents, and commercial lending02:13, Why software teams may need to forget part of their old development process04:45, How EnFi started with productivity gains before building a broader development harness09:53, Why merge requests went up, and why that alone is not the same as better outcomes10:30, How Slack became the entry point for an agentic development harness14:10, What happens to agile ceremonies when teams can create discovery builds much faster25:08, Scott's view on whether AI reduces engineering headcount or changes the work engineers do31:00, How EnFi is changing technical interviews for an AI assisted engineering environmentOne Line That Stuck“We do not care if you use AI to solve the problems, we just want to know you can solve the problem.”Practical Takeaways For Technical TeamsPut agents close to where work already happens.Keep humans in the loop for review, testing, and production judgment.Treat AI generated code as cheaper to create, not free to maintain.Build stronger test harnesses instead of slowing everything down with excessive process.Update interviews to reflect how engineering work is actually getting done.Subscribe to The Tech Trek for more conversations with technical leaders building, hiring, and operating through the next stage of AI, data, product, and engineering execution.

The Road to Macstock takes us to Dave Hamilton, who previews his Macstock session, “From Curiosity to Capability,” which will focus on practical AI workflows anyone can use. Dave discusses how generative AI has changed coding, project management, proposals, productivity, and procrastination for him. The session will encourage experimentation as a way to get involved as AI tools rapidly evolve.  Show Notes: Chapters: [0:00] Introduction to Dave Hamilton and the Road to Macstock[2:17] Dave previews “From Curiosity to Capability”[4:12] AI, neuroplasticity, productivity, and learning by doing[6:28] Coding changes, agentic tools, and the Mac Geek Gab app update[11:30] Agentic AI as project management beyond coding[13:01] Starting small, using the right mindset, and avoiding blind trust[15:04] AI as a procrastination eliminator[16:30] Real-world business proposal example using Claude Cowork[20:16] Goals for the MacStock session and audience participation[23:33] Moving between ChatGPT, Claude, Perplexity, and Gemini[26:17] How AI memory, context, and front-end tools affect answers[28:53] Encouraging practical AI use through examples[31:16] Macstock discount codes and why attendees should come[31:53] Macstock camaraderie, hallway conversations, and community[34:06] Where to find Dave Hamilton's podcasts and social links[35:15] Closing invitation to Macstock and Ecamm Creator Camp Guests: Dave Hamilton, a seasoned tech enthusiast, podcaster, and publisher, has dedicated the past three decades to aiding computer users globally. Known for his insightful advice and valuable product recommendations on the Mac Geek Gab podcast, Dave also enjoys an esteemed reputation as the founder of BackBeat Media, a network of fiercely independent publishers. His online publishing journey took off with The Mac Observer, an acclaimed Apple news site he co-founded in 1998 and led to its successful acquisition in 2021.Beyond his tech-savvy persona, Dave embraces a vibrant life filled with music and family in the New Hampshire seacoast. He passionately performs with bands Bitter Pill and Fling, among other musical projects, creating a harmonic balance between his love for technology and music. Alongside his wife, Lisa, he raised their two children amidst this symphony of innovation, passion, and independent spirit, showcasing the power of embracing one's interests and using them to make a meaningful impact in the world. Connect with him on his three podcasts, Mac Geek Gab, Business Brain, and Gig Gab. Support:      Become a MacVoices Patron on Patreon     http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:     http://macvoices.com      Twitter:     http://www.twitter.com/chuckjoiner     http://www.twitter.com/macvoices      Mastodon:     https://mastodon.cloud/@chuckjoiner      Facebook:     http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:     http://www.facebook.com/macvoices/      MacVoices Group on Facebook:     http://www.facebook.com/groups/macvoice      LinkedIn:     https://www.linkedin.com/in/chuckjoiner/      Instagram:     https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes     Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

The Road to Macstock takes us to Dave Hamilton, who previews his Macstock session, "From Curiosity to Capability," which will focus on practical AI workflows anyone can use. Dave discusses how generative AI has changed coding, project management, proposals, productivity, and procrastination for him. The session will encourage experimentation as a way to get involved as AI tools rapidly evolve.  Show Notes: Chapters: [0:00] Introduction to Dave Hamilton and the Road to Macstock [2:17] Dave previews "From Curiosity to Capability" [4:12] AI, neuroplasticity, productivity, and learning by doing [6:28] Coding changes, agentic tools, and the Mac Geek Gab app update [11:30] Agentic AI as project management beyond coding [13:01] Starting small, using the right mindset, and avoiding blind trust [15:04] AI as a procrastination eliminator [16:30] Real-world business proposal example using Claude Cowork [20:16] Goals for the MacStock session and audience participation [23:33] Moving between ChatGPT, Claude, Perplexity, and Gemini [26:17] How AI memory, context, and front-end tools affect answers [28:53] Encouraging practical AI use through examples [31:16] Macstock discount codes and why attendees should come [31:53] Macstock camaraderie, hallway conversations, and community [34:06] Where to find Dave Hamilton's podcasts and social links [35:15] Closing invitation to Macstock and Ecamm Creator Camp Guests: Dave Hamilton, a seasoned tech enthusiast, podcaster, and publisher, has dedicated the past three decades to aiding computer users globally. Known for his insightful advice and valuable product recommendations on the Mac Geek Gab podcast, Dave also enjoys an esteemed reputation as the founder of BackBeat Media, a network of fiercely independent publishers. His online publishing journey took off with The Mac Observer, an acclaimed Apple news site he co-founded in 1998 and led to its successful acquisition in 2021. Beyond his tech-savvy persona, Dave embraces a vibrant life filled with music and family in the New Hampshire seacoast. He passionately performs with bands Bitter Pill and Fling, among other musical projects, creating a harmonic balance between his love for technology and music. Alongside his wife, Lisa, he raised their two children amidst this symphony of innovation, passion, and independent spirit, showcasing the power of embracing one's interests and using them to make a meaningful impact in the world. Connect with him on his three podcasts, Mac Geek Gab, Business Brain, and Gig Gab. Support:      Become a MacVoices Patron on Patreon      http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:      http://macvoices.com      Twitter:      http://www.twitter.com/chuckjoiner      http://www.twitter.com/macvoices      Mastodon:      https://mastodon.cloud/@chuckjoiner      Facebook:      http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:      http://www.facebook.com/macvoices/      MacVoices Group on Facebook:      http://www.facebook.com/groups/macvoice      LinkedIn:      https://www.linkedin.com/in/chuckjoiner/      Instagram:      https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes      Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

Part 2 of our new format with three frontier founders: Guillermo Rauch (Vercel), Blake Scholl (Boom Sonic), and Max Hodak (Science). 00:35 Vibe Coding A Turbine Blade 04:04 Open Source Compounds China's Advantage 06:12 You Always Want The Smartest Model 08:41 Software Still Needs Hands 10:40 Humans Are Becoming Verifiers Transcript: http://nav.al/hardware

In this episode, Corey Quinn sits down with Dexter Horthy, CEO and Co-founder of Human Layer, to unpack what engineers are getting wrong about AI, especially when it comes to coding agents.From the obsession with “just throwing more tokens at the problem” to the reality of building scalable AI workflows, Dexter shares hard-earned insights on how to actually push models to their limits. They dive into the evolution of developer workflows, the rise of AI-powered software factories, and why understanding context and verification matters more than raw model power.If you're building with AI or trying to, this episode will challenge how you think about what these systems can (and can't) do.Show highlights: (00:00)Throwing Tokens Too Far(01:04) Meet Dexter Horthy(01:52) Personal AI Benchmarks(04:12) Human Layer Race Condition(05:59) Rewrites and Tech Debt(07:19) Software Factories Mindset(10:20) Verifiable Problems and Token Limits(13:45) Agents in the Trenches(18:05) GitHub at Agent Scale(26:23) Safety Ethics and Closing ThoughtsAbout Dexter: Dexter Horthy is the CEO and Co-Founder of HumanLayer, where he helps engineering teams tackle complex problems in large codebases using coding agents. Previously, he worked in DevOps, SRE, and Solutions Engineering at Replicated, and contributed to lunar navigation software at NASA JPL. Outside of work, he's a fan of tacos and burpees, though not necessarily in that order.Links: LinkedIn: https://www.linkedin.com/in/dexterihorthy/Website: https://humanlayer.devSponsored by: duckbillhq.com

D.A. Davidson's Gil Luria and BEP Research's Ben Pouladian talk with TITV Host Akash Pasricha about Snowflake's accelerating revenue growth and Salesforce's disappointing Q2 guidance. We also talk with The Information's Apple reporter Aaron Tilley about Apple's strategy to shrink Google Gemini models onto devices, and Microsoft reporter Aaron Holmes about the company's plan to release homegrown coding models to protect GitHub Copilot from competitors like Cursor.Articles discussed on this episode: https://www.theinformation.com/articles/apple-renew-push-ai-runs-devices-instead-cloudhttps://www.theinformation.com/articles/meta-launches-new-enterprise-push-boost-business-adoption-ai-toolshttps://www.theinformation.com/newsletters/ai-agenda/microsoft-release-new-coding-model-next-week-comeback-attemptSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction01:13 - Snowflake Shares Jump as AI Product Adoption Grows08:04 - Salesforce Earnings Reaction and M&A Strategy12:48 - Meta Launches New Enterprise Push & Paid AI Chatbot Subscriptions23:03 - Apple to Renew Push for AI That Runs on Devices28:26 - Microsoft to Release New Coding Model

This country does a decent job in our primary schools working with children and young adults with special needs. Certainly many improvements have been made in the last 40-50 years.  But once neurodivergent kids become adults, that support seems to vanish. Tami Gomez stepped into this large breach by launching the UC Davis Neurodiversity Coding Internship Program. This new program creates professional pathways for neurodivergent individuals by offering hands-on training, mentorship, and real-world exposure to healthcare operations, specifically in inpatient medical coding. I'm thrilled to get her back on the program to talk about this, her greatly expanded role as Executive Director, Mid Rev Cycle, HIM, Coding, CDI Programs, & Revenue Integrity, and a cutting-edge new initiative she spearheaded that's already making a big financial impact. Listen in as we discuss:  Origins of UC Davis Neurodiversity Coding Internship Program: Tami's own story of neurodivergence Getting started—funding, early struggles and sticking points, partnership with UCLA The curriculum and beyond: What are they learning, thanks to generous donations from HCPro/AHIMA, and not just education—taking students all the way through CCS credentialing and job placement Reception from attendees and the industry at large What makes neurodivergent people uniquely able to code, and their real struggles interviewing and entering the workforce Tami's new busy day job as Executive Director, Mid Rev Cycle. HIM, Coding, CDI Programs, & Revenue Integrity at UC Davis Health.  New Initiative: High-Pay Huddles and its financial impact Increasingly tech-enabled coding and CDI professions—are we in danger of losing them to AI? Updates on her personal life as a party planner, why her colleague Penny Jefferson rocks so much, and a new song for the #OTR Spotify Playlist... Mentioned on today's show: UC Davis Neurodiversity Coding Internship Program: https://health.ucdavis.edu/him/Coding/Neuro.html  AHIMA video featuring Tami: https://contentwithpurpose.co.uk/ahima/healthinformation/videos/tami-mcmasters-gomez/  

#352: Vibe coding is the latest version of a promise the industry has been making since the first generation of programming languages. Type what you want, get an app. Jeff Kuo from Ragic has been working on the no-code version of that same promise for almost twenty years. He has thoughts on why the promise keeps not quite landing. The honest answer is that AI-assisted coding is great for people who already know what the code is doing. It is counterproductive for everyone else. A non-developer can generate a lot of code. They cannot maintain any of it. That gap is where every weekend vibe-coded project goes to die six months in, when the codebase has ballooned and the AI is in a loop confidently identifying the wrong root cause for the seventh time. So what does work? Jeff's argument is that no-code platforms become the guardrail AI actually needs. Strip the infrastructure layer away, leave only the business logic, and the model only has to reason about one thing at a time -- which is the one thing today's models are good at. Ragic generates form and report definitions, not code, and the Java engine underneath does the rest. There is also the strange consumer behavior nobody is talking about. People love AI chat boxes in tools they have never used before. They close AI chat boxes in tools they already know. Which means the future of AI-native software might not belong to the incumbents at all -- it belongs to the new tools being built right now for users who do not have any muscle memory to defend. And one piece of advice that has aged perfectly across forty years of software: the maintenance is the thing that keeps you awake at night. AI makes it faster to build things from scratch and harder to maintain anything at scale. Begin with the end in mind. Or do not, and become the next cautionary tale.   Jeff's contact information: LinkedIn: https://www.linkedin.com/in/ragic/   YouTube channel: https://youtube.com/devopsparadox   Review the podcast on Apple Podcasts: https://www.devopsparadox.com/review-podcast/   Slack: https://www.devopsparadox.com/slack/   Connect with us at: https://www.devopsparadox.com/contact/

Audible Bleeding editors Falen Demsas, an integrated vascular surgery resident at Massachusetts General Hospital, and Sasank Kalipatnapu (@ksasank), a fifth-year general surgery resident at UMass Chan Medical School, are joined by Megan Tracci (@MeganTracci), James Black (@JamesHBlackMD), and Lauren West-Livingston (LWestLivingston) for a discussion following the inaugural SVS Leadership and Advocacy Summit. In this episode, the group reflects on the importance of surgeon advocacy, highlights key takeaways from the Summit, and discusses how vascular surgeons throughout training and practice can engage in policy, leadership, and organized medicine at local and national levels. The conversation explores the evolving role of advocacy within the Society for Vascular Surgery, including the work of the SVS Advocacy Council and its collaboration across Government Relations, Coding, VA advocacy, and quality and policy initiatives. Dr. Tracci shares insights from her leadership roles within SVS advocacy efforts and her work as ACS Medical Director for Surgeon Engagement. Dr. Black discusses his longstanding advocacy work on behalf of patients and physicians, including numerous trips to Capitol Hill over the course of his career. Dr. West-Livingston reflects on her experience attending the recent Advocacy & Leadership Conference as a trainee and the importance of resident involvement in advocacy work. Show Guests Megan Tracci Leader within the SVS Advocacy Council, which includes Government Relations, Coding, VA advocacy, and quality and policy collaboration efforts. She also serves as the ACS Medical Director for Surgeon Engagement. James Black Chief of Vascular Surgery and Endovascular Therapy at Johns Hopkins University and longtime advocate who has made countless trips to Capitol Hill to advocate for patients and physicians. Lauren West-Livingston Integrated vascular surgery resident at Duke University and member of the SVS Government Relations Committee who attended the recent Advocacy & Leadership Conference. Notable Mentions The inaugural SVS Leadership and Advocacy Summit Advocacy efforts within the Society for Vascular Surgery, including Government Relations, Coding, VA advocacy, and quality and policy collaboration. Learn more here SVS Advocacy Council Opportunities for vascular surgeons to engage in advocacy throughout all stages of training and practice. Sign up for updates Follow us @audiblebleeding Learn more about us at Audible Bleeding and provide us with your feedback through our listener survey. Gore is a financial sponsor of this podcast, which has been independently developed by the presenters and does not constitute medical advice from Gore. Always consult the Instructions for Use (IFU) prior to using any medical device.

Hoje o papo é sobre adoção de IA dentro de casa! Neste episódio, conversamos sobre como a Alura vem incorporando agentes de código, ferramentas como Codex e workflows agênticos no dia a dia da engenharia, e os impactos disso em produtividade, na revisão de código, na cultura de desenvolvimento e até na criação de produtos. Vem ver quem participou desse papo: Paulo Silveira, o host que quer saber se é top-down, ou bottom-up Vinny Neves, cohost, dev e professor na Alura Mauricio Aniche, CTO da Alura Crisley Marques, Engenheira de Software IA/LLM na Alura Carlos Müller, Staff Engineer na Alura Caio Burgorin, Engineering Manager na Alura Links: Alura: Luri OpenAI Codex Claude Code GitHub Copilot Datadog MCP Discourse Oracle Cloud Infrastructure (OCI) Stack Overflow IntelliJ IDEA No dia 26 de maio de 2026, a Alura vai te mostrar o que esperar do futuro e anunciar um novo movimento. Inscreva-se para uma live imperdível, com a presença de grandes especialistas do mercado. Confirme a sua presença. Vá para o Vale do Silício com Paulo Silveira, Marcell Almeida, Fabrício Carraro e Marcus Mendes na “Imersão IA Sob Controle e Alura no Vale do Silício“! Vagas limitadas, corra para reservar a sua. TechGuide.sh, um mapeamento das principais tecnologias demandadas pelo mercado para diferentes carreiras, com nossas sugestões e opiniões. #7DaysOfCode: Coloque em prática os seus conhecimentos de programação em desafios diários e gratuitos. Acesse https://7daysofcode.io/ Produção e conteúdo: Alura Cursos de Tecnologia – https://www.alura.com.br Edição e sonorização: Rede Gigahertz de Podcasts

AI skepticism might be one of the most valuable developer skills right now - but only if it doesn't turn into stubbornness. In this episode, Matt and Mike discuss the growing divide between developers who reject AI entirely and those who trust it far too much. They explore why blindly accepting AI-generated code can create serious problems in production, why refusing to adapt can hurt your career, and where experienced developers still provide the most value. From architecture and security to maintainability and product-specific context, this episode breaks down the increasingly important role of human judgment in AI-assisted development. Show Notes: https://www.htmlallthethings.com/podcast/ai-coding-hype-is-starting-to-crack Use our Scrimba affiliate link (https://scrimba.com/?via=htmlallthethings) for a 20% discount!! Full details in show notes.

In this episode of Control Intelligence, Mike Bacidore is rejoined by Dr. Brian Romano, director of technology development at Arthur G. Russell Company. With more than four decades of experience in industrial automation and control systems engineering, Romano's career has centered around innovation, leadership and a forward-looking commitment to advanced technology in manufacturing. His work includes control systems architecture, enterprise connectivity, digital transformation and predictive analytics, even combining them to offer production-as-a-service capabilities by leveraging remote monitoring capabilities. Beyond his industry leadership, Romano brings a passion for developing the next generation of engineers, serving as adjunct faculty at both Central Connecticut State University and the University of Hartford. His academic achievements, including a Ph.D. in Technology and Innovation, an MBA in Business and Data Analytics, and multiple national honor societies, complement his work as an entrepreneur, a published researcher and the 2022 Division Leader of Year with the International Society of Automation. He is also an ISA fellow.

Our 246th episode with a summary and discussion of last week's big AI news!Recorded on 05/22/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:Google I/O highlights included Gemini 3.5 (with 3.5 Flash emphasized for speed and benchmarks), the always-on agent Gemini Spark running on Google Cloud with MCP tool support, and Gemini Omni multimodal video generation/editing, plus updates like Anti-Gravity 2.0, Gemini for Science, and Genie world-model navigation using Street View and Waymo simulation.Coding-agent competition accelerated with Cursor Composer 2.5 (fine-tuned on Moonshot's Kimi K2.5) and xAI's early Grok Build release, alongside discussion of potential Cursor–xAI ties and xAI's talent churn and compute utilization concerns.Business and legal updates included Elon Musk losing his OpenAI lawsuit on statute-of-limitations grounds, reported OpenAI–Apple partnership tensions, Anthropic agreeing to a $30B funding round at a $900B valuation and projecting its first profitable quarter, and Cerebras' IPO surging about 90%. Research and safety stories covered OpenAI's result on an 80-year-old Erdős geometry problem, findings on “negation neglect” in training, interpretability work showing multiple redundant circuits per capability, agent benchmarks like Terminal World, new deepfake takedown enforcement under the Take It Down Act, demonstrations of autonomous hacking/self-replication, rapidly improving AI cyber capabilities, and steps toward image provenance metadata and watermarks.Timestamps:(00:00:10) Intro / Banter(00:01:15) News PreviewTools & Apps(00:05:05) Google unveils AI model Gemini 3.5 and AI agent Gemini Spark(00:11:43) Google's Gemini Omni turns images, audio, and text into video — and that's just the start | TechCrunch(00:17:27) Google launches Antigravity 2.0 with an updated desktop app and CLI tool at IO 2026 | TechCrunch(00:22:35) Google Debuts AI-Powered Tools To Optimize Scientific Research Workflows(00:27:20) Google's Genie world model can now simulate real streets with Street View | TechCrunch(00:29:51) Cursor's Composer 2.5 matches Opus 4.7 and GPT-5.5 benchmarks at a fraction of the cost(00:37:37) xAI Introduces Its Coding Agent Called Grok BuildApplications & Business(00:41:55) Musk loses OpenAI court battle as he waited too long to sue(00:48:08) Anthropic agrees terms of $30bn funding deal at $900bn valuation(00:53:12) OpenAI co-founder Andrej Karpathy joins Anthropic's pre-training team | TechCrunch(00:56:49) Greg Brockman Officially Takes Control of OpenAI's Products in Latest Shake-Up | WIRED(00:58:15) OpenAI-Apple Partnership Frays, Setting Up Possible Legal Fight - Bloomberg(01:01:13) AI chipmaker Cerebras soars 90% in year's biggest IPO so farResearch & Advancements(01:07:10) AI just solved an 80-year-old ‘Erdős problem,' and mathematicians are amazed | Scientific American(01:11:50) Negation Neglect: When models fail to learn negations in training(01:13:18) All Circuits Lead to Rome: Rethinking Functional Anisotropy in Circuit and Sheaf Discovery for LLMs(01:16:20) Autonomous AI research for nanogpt speedrun(01:21:59) TerminalWorld: Benchmarking Agents on Real-World Terminal TasksPolicy & Safety(01:23:15) America's dangerous, messy deepfakes crackdown is here | The Verge(01:25:17) Language Models Can Autonomously Hack and Self-Replicate(01:28:48) How fast is autonomous AI cyber capability advancing?(01:31:32) Positive Alignment: Artificial Intelligence for Human FlourishingSynthetic Media & Art(01:33:15) OpenAI is making it easier to check if an image was made by their models | TechCrunch(01:33:56) How Chinese short dramas became AI content machines | MIT Technology ReviewSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Welcome to this week's episode of the PEBCAK Podcast!  We've got four amazing stories this week so sit back, relax, and keep being awesome!  Be sure to stick around for our Dad Joke of the Week. (DJOW) Follow us on Instagram @pebcakpodcast   Please share this podcast with someone you know!  It helps us grow the podcast and we really appreciate it!   Simple 6 signup link https://simple6.co/r/CFUR98   Apple data and car Bluetooth signals help police identify suspect in crypto robbery https://www.forbes.com/sites/the-wiretap/2026/05/05/apple-subpoena-and-car-bluetooth-help-cops-unmask-crypto-robber-suspect/ Your phone and your car are witnesses — law enforcement used an Apple subpoena and Bluetooth signals from a connected vehicle to unmask a suspect in a physical cryptocurrency robbery, showing how everyday device data is increasingly being used to solve crimes.   FBI reports crypto ATM fraud complaints surged 23% in 2025, topping $388 million in losses https://www.ic3.gov/PSA/2026/PSA260515-2 Canada proposes a nationwide ban on crypto ATMs, calling them a primary tool for scammers https://www.cbc.ca/news/canada/toronto/canada-crypto-atm-ban-scammers-9.7180642 Bitcoin Depot, North America's largest crypto ATM operator, files for Chapter 11 bankruptcy https://www.bankingdive.com/news/bitcoin-depot-bankruptcy-chapter-11-atm-wind-down/820755 Crypto ATMs are effectively becoming extinct — the FBI documented nearly $389 million in losses through kiosks in 2025, Canada is moving to ban them outright as a fraud-enabling infrastructure, and Bitcoin Depot (the largest operator in North America with 9,000+ machines) just filed for Chapter 11 bankruptcy, blaming mounting state regulations, litigation, and an unsustainable business model.   DataCamp breaks down Claude Opus 4.7 vs. GPT-5.5 across coding, reasoning, vision, and pricing https://www.datacamp.com/blog/gpt-5-5-vs-claude-opus-4-7 The AI model race between Anthropic and OpenAI is too close to call — Claude Opus 4.7 leads on software engineering benchmarks and visual reasoning while GPT-5.5 dominates terminal/DevOps workflows and advanced math, with output token pricing favoring Claude at $25 vs. $30 per million tokens.   Dad Joke of the Week (DJOW)   Find the hosts on LinkedIn: Chris - https://www.linkedin.com/in/chlouie/ Brian - https://www.linkedin.com/in/briandeitch-sase/ Cody - https://www.linkedin.com/in/cody123anderson/

This is a recap of the top 10 posts on Hacker News on May 24, 2026. This podcast was generated by wondercraft.ai (00:30): DeepSeek reasonix, DeepSeek native coding agent with high caching and low costOriginal post: https://news.ycombinator.com/item?id=48256953&utm_source=wondercraft_ai(01:56): Microsoft open-sources “the earliest DOS source code discovered to date”Original post: https://news.ycombinator.com/item?id=48253386&utm_source=wondercraft_ai(03:23): Wake up! 16bOriginal post: https://news.ycombinator.com/item?id=48253060&utm_source=wondercraft_ai(04:49): Memory has grown to nearly two-thirds of AI chip component costsOriginal post: https://news.ycombinator.com/item?id=48258684&utm_source=wondercraft_ai(06:16): Why is Vivado 2026.1 dropping Linux support for free tier?Original post: https://news.ycombinator.com/item?id=48254309&utm_source=wondercraft_ai(07:42): Amazon Web Services – Four Years and OutOriginal post: https://news.ycombinator.com/item?id=48254475&utm_source=wondercraft_ai(09:09): Scammers are abusing an internal Microsoft account to send spam linksOriginal post: https://news.ycombinator.com/item?id=48253186&utm_source=wondercraft_ai(10:35): Show HN: Audiomass – a free, open-source multitrack audio editor for the webOriginal post: https://news.ycombinator.com/item?id=48258015&utm_source=wondercraft_ai(12:02): The four-day workweek in Australia: insights from early adopters of 100:80:100Original post: https://news.ycombinator.com/item?id=48259990&utm_source=wondercraft_ai(13:28): Claude is not your architect. Stop letting it pretendOriginal post: https://news.ycombinator.com/item?id=48259784&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Bodie Grimm from the Kilowatt podcast talks about vibe coding news sources.Featuring Tom Merritt and Bodie Grimm.Link: Why Work is Starting to Look Medieval by Sierra LaDukeA note from Bodie:I remixed my project so people can play around with it without affecting my original version: https://kilowatt-curator-clone.lovable.appI also figured out how to let others Remix a project on Lovable. Sign up for a Lovable account and log in. Go to the following link: https://lovable.dev/projects/20673798-2b68-4188-96ff-37dcfa6a9f35. Click the Remix button at the top right. It will take you to the prompt/preview page to edit the the new "Remixed" site.I also created a brief (video and audio) explainer if you want to add it to the end of the episode:https://drive.google.com/drive/folders/1bmb71gyGsH8paiHiAHd2ejoOLpxn8Iro?usp=share_link-Bodie Hosted on Acast. See acast.com/privacy for more information.

Britain on LinkedIn System76 Coder Radio Discord The Mad Botter Data Platform Mike's Legacy Data Promo Mike's Blog

No Braincast 634, Carlos Merigo, Cris Dias, Hiago Vinícius e Luiz Yassuda discutem o vibe coding, a nova febre da IA que promete permitir que qualquer pessoa crie aplicativos, dashboards, automações e protótipos apenas descrevendo o que quer. A conversa passa por Claude, Codex, Lovable, Replit, Bolt, Cursor, Manus, low-code, SaaSpocalipse, token maxing e a fantasia do “unicórnio de uma pessoa só”. Afinal, estamos diante de uma revolução criativa, em que mais gente pode transformar ideias em produtos, ou de uma fábrica de gambiarras em escala industrial? Também entram no papo os riscos de segurança, vazamento de dados, dependência das big techs, código ruim, Shadow IT, empresas tentando substituir times inteiros por IA e a importância de repertório, critério e bom gosto num mundo onde executar ficou mais fácil, mas saber o que pedir continua sendo o grande desafio. No Qual é a Boa, ainda tem Cinemático sobre Obsessão, jogos como Crimson Desert e The Last Caretaker, o Anti-Authoritarian Toolkit, IA em Curso, The Traitors e Momento Faustão. -- CONHEÇA OS CURSOS DA ESCOLA DE IA DA PUCPR https://posdigital.pucpr.br/areas/escola-de-ia?utm_source=podcast&utm_medium=braincast&utm_campaign=pucpr_externo_leads_ativacao-1_escola-ia&utm_content=audio_atributo_26-05-17 -- 04:17 PAUTA 05:37 O que é vibe coding 08:31 Origem e ferramentas 09:52 É programação mesmo 14:50 SaaSpocalipse e limites 19:59 Dilema do monstro 25:30 Token maxing e tralha 27:50 Low code e democratização 30:37 Agentes e checagem 34:10 Programadores e IA 34:52 Autocomplete e Vibe Code 38:52 Hype e corrida da IA 39:56 Segurança e dados 41:45 Automação pessoal útil 43:55 SaaS pequeno vs grande 46:07 Sites leves sem WordPress 49:57 Canva e custos ocultos 57:09 Dependência e mediação 59:45 Legado corporativo e suporte 01:02:57 Habilidades e formação 01:11:40 Bom gosto e repertório 01:12:46 Curiosidade como profissão 01:15:03 Educação e base teórica 01:18:00 A febre dos prompts 01:18:50 QUAL É A BOA 01:28:56 Toolkit anti autoritário 01:34:38 Cupom IA em Curso 01:35:24 Reality The Traitors 01:40:06 Momento Faustão -- ✳️ TORNE-SE MEMBRO DO B9 E GANHE BENEFÍCIOS: Braincast secreto; grupo de assinantes no Telegram; e episódios sem anúncios!

Google dropped like 197 new AI features this week.

Leonid Belkind, co founder and CTO at Torq, joins The Tech Trek to talk about what changes when an engineering organization does more than experiment with AI tools. Torq builds agentic security operations, and Leonid shares how his team is using AI across engineering, product, hiring, customer success, and go to market work.This conversation gets past the shallow version of “AI makes coding faster.” Leonid makes a clear distinction between coding and software engineering, and explains why the best teams are using AI to shift cognitive load, not remove judgment.Practical takeaways• AI does not erase software engineering. It changes where engineering judgment shows up.• Strong engineers still produce better AI generated work because they know what to ask, what to test, and what tradeoffs matter.• Hiring processes need to reflect how engineers actually work now, including how they use AI to build, explain, and defend technical decisions.• Productivity should not only be measured by speed. Leonid talks about throughput, maturity of delivery, and whether teams can produce more without lowering quality.• AI adoption becomes more powerful when it moves beyond engineering into product, customer success, revenue operations, and talent.Key moments00:32What Torq means by agentic security operations and why different tasks need different AI approaches.01:49Why building AI native products with AI native methods creates a useful feedback loop for engineering teams.05:28How AI shifts cognitive load so engineers can spend more attention on user experience, architecture, and product value.10:34The difference between software engineering and coding, and why that distinction matters more now.15:13How Torq has changed technical interviews to evaluate AI assisted engineering instead of pretending AI does not exist.21:51How one R&D group measured meaningful delivery gains after adopting AI more deeply.24:25Why AI adoption is moving into product, customer success, revenue operations, and talent teams.One Line That Stuck“Software engineering as a discipline is not going away. It just changes a phase a bit.”Practical moves to stealFor hiring, Leonid suggests giving candidates more complex take home work because AI is now part of the real engineering workflow. The evaluation then shifts to the candidate's ability to explain the architecture, defend decisions, describe how AI was used, and show how they tested and constrained the output.That is a much better signal than asking someone to work as if the tools do not exist.Subscribe or follow The Tech Trek for more conversations with technical leaders building, hiring, and operating through the next shift in software, data, AI, and engineering execution.

Cal Newport takes a critical look at recent AI News.   Video from today's episode: youtube.com/calnewportmedia   (0:00) Has AI conquered coding? (3:21) Lars Faye quote (5:25) Skipping the struggle step (6:42) Point #1 (7:08) Point #2 (7:28) Point #3 (7:39) Point #4 (8:35) Solution   Links: Sign up for Cal's newsletter at www.calnewport.com/ideas Buy Cal's latest book, “Slow Productivity” at www.calnewport.com/slow https://larsfaye.com/articles/agentic-coding-is-a-trap https://www.infoworld.com/article/4143101/pity-the-developers-who-resist-agentic-coding.html https://www.youtube.com/watch?v=OQSNhk5ICTI Thanks to Jesse Miller for production and mastering and Nate Mechler for research and newsletter. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!This was recorded before Railway suffered a major GCP outage on May 19, despite being a multi-AZ, multi-zone mesh ring, with HA fiber interconnects between their Metal GCP AWS, because workload discoverability was unintentionally still tied to GCP. All has been resolved with a post-mortem.Railway did not start as an AI infrastructure company.It was founded in 2020 years before agents became the default way people thought about deploying software. Jake Cooper, formerly at Bloomberg and Uber, started Railway with a simple obsession: the activation energy to ship something to production should be near zero. Push code, get a URL, iterate. No Docker files, no Kubernetes manifests, no Ansible scripts stacked on Ansible scripts.For years, this was a slow grind. Railway spent its first 18 months hand-acquiring its first 100 users with Jake personally greeting every Discord signup on a second monitor.Today, Railway has raised $124m and is growing very fast. A 35-person team supports 3 million users, adding roughly 100,000 signups a week. Their bare metal data centers have a 3-month payback period vs. renting in the cloud, with 70% margins funding aggressive cloud bursting when needed. The servers they own have actually appreciated in value as RAM prices have climbed basically meaning the value of their hardware now exceeds the capital they've raised.From rebuilding Railway's network overlay over a weekend to moving the vast majority of workloads onto its own bare metal data centers, Jake Cooper is trying to build a new cloud for an agent-native world. In this episode, Railway's founder and “conductor” joins swyx and Alessio to unpack why the next era of software infrastructure is not just “Heroku but newer,” what agents need that humans did not, and why the old deployment loop of Git, PRs, CI/CD, and static cloud resources may be heading for a rewrite.We go deep on Railway's infrastructure stack: own-metal data centers, three-month cloud payback periods, cloud bursting, data center debt, Railpack, Nixpacks, Temporal, feature flags, Central Station, content-addressable filesystems, agent-safe production forks, and why the CLI may become more important than the canvas in an agent world. Jake also shares the founder journey behind Railway, how the company survived losing $500K/month, why it now serves millions of users with only 35 people, and why he believes the pull request is dying.We discuss:* How Railway went from a slow six-year grind to adding 100,000 users a week* How Railway thinks about agents as the next dominant software species* Why agents need version control, observability, compute, storage, and orchestration at 1000x scale* The economics of Railway's own-metal data centers and three-month payback* How Railway uses cloud bursting while scaling its own infrastructure* Why data center debt can be a better tool than venture debt for infra startups* Central Station, Railway's internal system for clustering customer feedback and incidents* Why responsible disclosure and over-communication matter for platforms* Why feature flags, progressive rollouts, and shadow traffic are essential for agents* Temporal's strengths, pain points, and why workflows matter for agents* Railpack, Nixpacks, Nix, and lazy-loaded content-addressable filesystems* Why “cattle, not pets” may change if you can clone the pets* Why Railway is building a new cloud from scratch instead of copying hyperscalers* The solo founder path, focus, writing, and how Jake thinks about company buildingRailway:* Website: https://railway.com/* X: https://x.com/RailwayJake Cooper:* LinkedIn: https://www.linkedin.com/in/thejakecooper/* X: https://x.com/JustJakeTimestamps00:00:00 Introduction: What Is Railway?00:02:07 Jake's Path to Railway00:06:13 Railway's Six-Year Growth Story00:08:52 Rebuilding the Business After the Free Tier00:11:17 Agents as the Next Software Platform00:13:29 Railway's Infrastructure Philosophy00:15:42 Bare Metal, Cloud Economics, and the Compute Crunch00:17:22 Cloud Bursting and Five-Cloud Networking00:20:20 Data Center Debt and Infra Financing00:23:31 Data Centers in Space00:25:24 What Agents Need From Infrastructure00:28:24 CLIs, Canvas, and Agent-Native UX00:35:15 Central Station, Incidents, and Responsible Disclosure00:40:30 Safe Rollouts, SRE Agents, and Production Forks00:45:00 AI SRE, Specs, Code, and Tests00:48:24 Self-Replicating Infrastructure and the New Serverless00:53:18 Heroku, Temporal, and Workflow Engines01:04:07 Railpack, Nixpacks, and Lazy-Loaded Filesystems01:06:01 Coding Agents, Token Spend, and Roadmap Acceleration01:10:56 The Pull Request Is Dying01:12:28 Feature Flags and the Agent-Era SDLC01:16:15 Cattle, Pets, and Cloning Machines01:19:29 Solo Founder Lessons01:24:12 Focus, GPUs, and Building a New Cloud01:28:20 Closing ThoughtsTranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: Hey, hey, hey. Today we're in the studio with Jake Cooper of Railway.Alessio [00:00:14]: Conductor of Railway.Swyx [00:00:15]: Conductor at Railway. Yeah.Alessio [00:00:16]: Choo-choo.Swyx [00:00:17]: Do you actually have that anywhere, like on your business card?Jake [00:00:20]: We call some of our volunteer moderators conductors. I don't have a business card. We're not that big yet. At some point I will. I got handed a nice business card from the Supermicro folks, and I was like, “Damn, this is pretty official.”Swyx [00:00:30]: Business cards are coming back.Jake [00:00:32]: They're cool. They're hip. The conductor thing is good. We're trying to figure out what we want to call each other internally. Some people think it's super cringe and say, “You don't need a name for people internally.” Some people want to call each other something. We still don't have a really good one.Jake [00:00:55]: We've got New Railcrews, Trainiacs. Nothing has stuck yet.Swyx [00:01:00]: I like Trainiac. Trainiac sounds good. Railwayians. For those who don't know, what is Railway? Let's give people a crisp definition up front.Jake [00:01:09]: Railway is the easiest way to ship anything. You go to the canvas, or you talk with Claude, and you say, “Deploy a Postgres instance, deploy my GitHub repository, run this code,” and you're off to the races.Swyx [00:01:22]: You've got a nice animation on the landing page.Jake [00:01:24]: Thank you. None of my work, by the way. They don't let me touch the design stuff anymore.Jake [00:01:25]: We want to make it trivially easy not just to deploy things, but to evolve applications over time. Most tooling right now stacks entropy on top of entropy: Docker, Kubernetes, Ansible scripts, and all these other things. If we can version all of your software and keep track of all the changes, then we can make it trivial to clone environments, fork into a parallel universe, get copies of production data, get copies of any services, make changes, validate them, and collapse them back in without reproducing everything across a staging environment.The Railway Origin Story: From Uber Systems to a New CloudSwyx [00:02:07]: I was looking at your background: Bloomberg, Uber. Nothing immediately stands out as, “This guy is going to found the next great platform as a service.” What prepared you for Railway?Jake [00:02:21]: It was curiosity to keep going deeper. I started out on front-end stuff, working on Wolfram Mathematica and porting it over. Then I briefly moved to Bloomberg, then toward Uber and distributed systems, taking the Jump Bikes systems and moving them to a distributed system built on top of Cadence, the pre-Temporal Temporal.Swyx [00:02:44]: Which, by the way, I'm happy to talk about, pros and cons.Jake [00:02:48]: Totally.Swyx [00:02:51]: But let's do the Railway story.Jake [00:02:52]: It has been a continual step of wanting an experience. Whether it's walking up to a bike, unlocking it, and having it work frictionlessly, or something else, the depth required to make that happen follows from the experience. A lot of the work I do, and a lot of the team does, is in service of that experience. We fundamentally don't care how deep we have to go. We will swim to the bottom of the swimming pool to get the experience.Jake [00:03:17]: I don't have a physics PhD. I did an EECS degree. It has always been about figuring out the next step: how do we get there? That's what led to starting Railway for that experience and then moving all the way to bare metal data centers. I was adding patches to the kernel this week to get the experience there because I can see how much better it can be.Swyx [00:03:49]: Other patches to the Linux kernel this week?Jake [00:03:51]: Yeah. Not upstream. Our fork.Swyx [00:03:52]: That's a flex. Railpack? No, this is different. This is the OS on top of Railpack?Jake [00:03:57]: No, this is an actual kernel patch. It's always literally: what do we have to do to get that experience? Then figure it out. Anything is figureoutable.Swyx [00:04:10]: Would you send the patch upstream, or does it not fit other use cases?Jake [00:04:13]: Maybe. We have to work out the experience internally. It has to do with the storage layer we're building for some of the agentic stuff. Maybe it'll be useful upstream, but it's deeply useful for us internally.Open Source, Forks, and Non-Deterministic VersioningSwyx [00:04:29]: You mentioned open source before. How do you think about starting from open source, and then coding agents letting you do a lot more from forks of it?Jake [00:04:38]: GitHub's original sin is that it's almost a series of broken pointers. You have this thing, then you clone it, and now you've lost the whole upstream. How do we make it trivial for people to modify really small pieces of it?Jake [00:04:51]: We think of Git in a discrete sense: I've either made a change and merged upstream, or I haven't. What would it look like if it were percentage-based, a little more non-deterministic, or a stream of changes that users traverse as a percentage rolled out in general and then rolled all the way up?Jake [00:05:13]: We have the open-source kickback program and let you deploy templates because we want to make it trivial for people to version these shards over time. It solves a large problem around authentication, authorization, and security. NPM has a way to define, “Don't take any new packages.” The ideal end state is that you roll out progressively to users with the minimum impact zone and continue rolling up. JPMorgan should probably be the last one on the patch line, for all our sakes, because our money and livelihoods are there.Jake [00:05:53]: It's okay if Johnny Vibe Coder gets a broken patch because there's so much entropy in the system that the rubber has to meet the road at some point. You have to test at varying levels.The Long Grind: First Users, Free Tier, and Making the Business WorkSwyx [00:06:13]: I wanted to pull up this glorious chart, which is your usage or number of daily signups?Jake [00:06:22]: Daily signups, I think.Swyx [00:06:24]: You started six years ago. It was a slow grind, and now you're on a rocket ship. You say, “Don't doubt your fight and don't quit.” Maybe pick out certain points that were key inflections for the company.Jake [00:06:40]: At the start, it's about getting your first 100 users, hell or high water. We had a website and a support link. The support link was the Discord channel. I had notifications on with two monitors: the monitor I was working on and the other monitor with Discord. If anybody came in, I was immediately like, “Hey, how's it going?” It was rare, so getting those first 100 users to come back was the start.Jake [00:07:14]: Then you build a consultancy factory because users want all these things. You have to go back to the board and ask, “What is the actual product offering I want to build on top of this?”Jake [00:07:28]: VCs want charts that always go up and to the right, but in reality you don't necessarily want charts that look like that. For us, there have been periods of expansion where we add features to test use cases, and periods of compaction where we ask, “If the experience we have is good, how do we make it significantly better?” Maybe we strip out features that don't fit our ICP anymore.Jake [00:07:57]: The boom from 2022 to 2023 came from the free tier. Everybody under the sun was using it.Swyx [00:08:09]: A lot of Reddit bots and Discord bots.Jake [00:08:12]: And crypto miners. When you build an open product on the internet where anybody can sign up, the internet is a horrible place with so many things. You go through periods of asking, “How do I reach as many people as possible?” Then, “How do I fit the exact use case for the people who really matter and are really excited about this specific thing?”Jake [00:08:39]: Then there was a two-year period of making the actual business work. During the free-tier era, we were losing about half a million dollars a month.Swyx [00:08:59]: On a $20 million bank account.Jake [00:09:02]: On a $20 million bank account with maybe $50,000 a month in revenue. That's a horrible business. I don't know how anybody invested. But you have to go through it and say, “We have an experience people love, but the business has to work.”Jake [00:09:17]: There are two schools of thought. You can run the horrible business all the way up with bad margins, or you can go back and make it work. We've always wanted a super lean team. We're 35 people right now. It's very small.Swyx [00:09:36]: Supporting three million already?Jake [00:09:38]: Yeah. We're adding 100,000 users a week right now, so it's growing fast. We don't want to add headcount for the sake of headcount or throw bodies at problems. We want to build systems. It's hard to build systems during expansion because you're adding things to the system because people are asking for them or things are breaking.Jake [00:10:00]: We had to cut off the free users for a little while, rebuild the business, and make sure it worked. We want to reach as many people as possible because software is important. It's become difficult to create things in the physical world, so it's important to make it easy for people to build in the virtual world and have access to creation. But there are legs to that journey.Jake [00:10:30]: You can see divots in the charts. If you follow between 2025 and 2026, it's either summer or winter. People go on holiday with family.Swyx [00:10:50]: It affects that much?Jake [00:10:51]: Yeah. It's kind of B2C and kind of B2B. People are shipping constantly, then they stop. Our activation curve now shows more people activating on weekdays because we have more business users, so it smooths out over time.Agents as the New Interface to DeploymentSwyx [00:11:17]: Was there a point where you started prioritizing AI development or agent development?Jake [00:11:24]: We've prioritized agentic as a top-of-funnel thing. Over the last six months, we've deeply prioritized agentic as a mechanism to build and deploy things because we believe the curve is so steep and that is how people will build and deploy software.Jake [00:11:42]: It almost fundamentally doesn't matter whether this is dot-com or not because we're all on the internet anyway. If agents are going to deploy a bunch of things and we hit an inference wall at some point, we'll fix those problems. The dominant species over the next 10 years is that we've moved from assembly to C to C++ to JavaScript to words. You're going to need to close that loop.Swyx [00:12:13]: When you say this is dot-com, did you mean buying the domain, or the general case?Jake [00:12:17]: I mean the dot-com era, when companies had a huge run-up because people understood the internet was important. Then they hit bottlenecks, fundamental laws of physics, math didn't work, and everybody came back down to earth. But it didn't matter because the internet became so impactful. If you operate on a long enough time horizon, you should build these things anyway because you can see where it's going.Jake [00:12:45]: That's where I think a lot of agent stuff is. You get to a point where you're running thousands of agents in parallel. What is the inference cost? What is the compute cost? How do you make that efficient? How do you coordinate all this? We have issues coordinating humans; we don't even have good tooling for that. Now we have to figure out how to get agents to coordinate, safely version changes, and know when to raise their hand for someone to intervene. Otherwise it becomes an interrupt factory.Railway's Infrastructure Thesis: Network, Compute, Storage, and MetalSwyx [00:13:19]: Let's go right into the technical side. What are the core infrastructure or architectural beliefs of Railway that allow you to do what you do?Jake [00:13:29]: The primitives matter a lot for us. We need network, compute, storage, and orchestration around it. You need control over a lot of those things. We've talked a lot about how we don't really use Kubernetes because we want higher-order control to place workloads in very specific places.Jake [00:13:48]: The reason is that you have to be very efficient with agents: memory reuse and all these other things, or you're going to massively blow up your cost structure. Being able to rack and stack your own servers and build your own metal unlocks performance and cost. Experiences where you're running 1,000 agents in parallel are not massively cost prohibitive.Jake [00:14:13]: Token use and compute use are blowing up. Over time, those things have to get a lot more efficient. You can get a lot of margin to make those experiences solid by building your own metal. That's all in service of offering a differentiated experience to as many people as humanly possible.Swyx [00:14:51]: You have a data center in Singapore.Jake [00:14:53]: Yeah. We have two in every other region now. In Singapore, we're adding a second one in Q3.Swyx [00:14:58]: What's it like? I've never built a data center. Do you go to Equinix and say, “I want some slots?”Jake [00:15:05]: Yeah. Equinix. You basically go and say, “I want power and I want a cage.” They say, “Great, here's what it's going to be.” You rent the cage for a period of time, fill it with racks and servers, and hook up internet to it. That's all the pieces.Swyx [00:15:36]: Then you handle everything else.Jake [00:15:37]: You handle everything else.Swyx [00:15:39]: What's the math versus clouds doing it for you?Jake [00:15:43]: If we rented in the cloud, our payback period when we go to metal is about three months.Swyx [00:15:50]: Which is crazy.Jake [00:15:51]: It's nuts. That's four years of depreciated hardware. You're going to see a lot of this compute crunch because hyperscalers are buying up a lot of stuff. We're working directly with OEMs, resellers, and people building these machines: Supermicro, Dell, and others.Jake [00:16:11]: Upstream, there's a bunch of supply pressure. When we raised our last round, between deploying capital for servers and now, the amount of money we've raised is less than the amount of money we have in the bank plus the value of the servers because the servers have appreciated as RAM has gone up. It's nuts how valuable hardware has become.Jake [00:16:50]: If you look at hyperscalers, they deployed around $80 billion of capital expenditures this year, and next year will be more. That's a massive infrastructure build-out. You look at that and think it's crazy that they're spending way more than the Manhattan Project. But if every person is going to run dozens or hundreds of agents in parallel, you have no conceptual idea how much compute is required to make that experience happen, even if you're deeply efficient and sharing resources. And that doesn't even count inference.Swyx [00:17:22]: How do you plan the build-out? The growth chart is so vertical. Are you usually at 100% utilization as soon as racks are live? How far ahead are you planning?Jake [00:17:33]: We still maintain cloud presence for bursting. We work with AWS, GCP, and a few other clouds. We can rent, and then the moment we get space or power, we compact those workloads off the cloud. We started on the clouds, then built a system to migrate to our own metal. There's nothing that says you can't continually do that again, and that's exactly what we do. We never want to be compute constrained.Jake [00:18:09]: At the start of the year, we actually became compute constrained because one upstream provider wasn't able to give us quota at the rate we needed, and the hardware was slower. I spent a weekend rebuilding our entire network overlay so we could straddle five clouds: Oracle, AWS, ourselves, GCP, and one other one. We can do more than that now.Jake [00:18:38]: We got into a spot where we were trying to pack instances tight because we couldn't get enough compute. That led to a few reliability issues, which are now past us. I made a tweet pointing out that it's becoming harder and harder to acquire compute at the rate these models need to acquire compute. We got bit by it.Swyx [00:19:15]: How do you think about pricing knowing you might not have your own metal available at all times? Are you pricing assuming you need extra margin if you end up going into the cloud?Jake [00:19:26]: Because we've built out our metal data centers, our margins on metal are around 70%. We can deeply subsidize the cloud business if we want to scale at a reasonable rate. We have a few levers: metal, which makes the margins; cloud burst; debt to buy servers; and venture capital. It's an interesting operational problem: how much cash do we have, how much should we raise, how quickly can we deploy it, and can we scale revenue as quickly as we scale compute?Jake [00:20:05]: If we continue making it trivially easy for people to build and deploy, then the faster we close that loop and the more operationally excellent we are with capital, the faster the business can scale. It's almost a straight linear deployment rate.Financing Infrastructure: Hardware Debt, VC, and Operational LeverageSwyx [00:20:20]: I think infra startups raising debt is a tool people don't utilize enough or know enough about. What can you tell us about that? Is it secured against your CPUs?Jake [00:20:32]: It's secured against our hardware.Swyx [00:20:37]: What rates do you get? Who are the lenders?Jake [00:20:39]: We pay prime plus a spread, and we can refinance any of the debt as rates go down. The terms are pretty good. The unfortunate thing is that Twitter has no nuance, so people say, “Venture debt bad.” But as with all things, there are specific tools and areas where you can be deliberate instead of using one tool as a hammer. Venture capital is not the hammer for everything. You have to explore and figure out what works.Swyx [00:21:12]: VC is usually the most expensive financing you can get.Jake [00:21:15]: Yeah. I also think people think about VC incorrectly from a capital-raising perspective. Most people think, “How do I raise as much money as possible from whoever is probably the best I can get at that time?” That's close to right, but what we've tried to do is figure out what unfair advantage we can buy with that equity.Jake [00:21:34]: It's the most expensive equity you're going to give away at that point in time, assuming the company keeps getting better. How do you use it to work with someone stellar who complements you? In the seed stage, I had never started a company. Ray Tonsing had good advice, and I could text him all the time. He was really fast. Awesome.Jake [00:22:01]: Then with John and Erica at Unusual, they said, “You roughly know what you're doing building a product. We'll mostly leave you alone and be available for advice.” Amazing. Then we got to Series A and the business was an operational tire fire because we didn't know how to scale a business. Work with Erica, and Jordan is over at Redpoint, so bonus.Jake [00:22:28]: Now we've raised from TQ and FPV as we're moving into enterprises. Every step of the way, we've asked: who can we partner with at this specific time to unlock the next section of the journey? I don't know enterprise sales. As an engineer, I can eyeball what features we might need, and we have wonderful people internally who can help. But you want boardroom dynamics where everyone is aligned and asking, “How do we win this?” instead of bickering about strategy.Data Centers in Space and the Physics of ComputeSwyx [00:23:31]: You had a tweet about data centers in space. Why no data centers in space?Jake [00:23:37]: It's not “no data centers in space.” My hot take is that I think it is solvable. I've just never seen anybody solve it.Swyx [00:23:49]: You said, “How are you going to dissipate that much heat in a vacuum?” You're making a physics claim.Jake [00:23:55]: I haven't seen anybody prove how you're going to dissipate that much heat in a vacuum. It doesn't mean it's not possible. It just means nobody has brought it up yet.Swyx [00:24:05]: Astrophage.Jake [00:24:06]: I don't know what that is.Swyx [00:24:07]: The Martian thing. Okay, you're very logical.Jake [00:24:09]: It could work. A lot of people are putting the cart before the horse. They say, “We're going to put data centers in space.” Okay, but how? “We have time to figure it out.” It's like in The Martian where they ask how they're going to intercept something and say, “We'll figure it out.”Swyx [00:24:36]: Making a bet on human invention is weird because you blind trust that it can be solved. But with physics, there are first-principles bounds you can put on it. Maybe not. Maybe you're asking to travel time or break a fundamental thermodynamic law.Jake [00:24:57]: I don't know how VCs do this either. How do you know what's not possible and a grift versus what's possible but sounds completely insane? “We're going to put data centers in space.” Coin flip as to which it is, and I guess you'll know in 10 years. That's one cycle.What Agents Need: Versioning, Observability, and 1,000x ScaleSwyx [00:25:23]: Moving back to agents. The branching, fast spin-up, and orchestration you do feels like pre-work that happened to be exactly what agents want. What do agents want differently than humans?Jake [00:25:37]: They want the ability to version things. It's not that different; it materializes slightly differently. Agents want a way to test changes incrementally. Engineers have feature flags. Is there a reason agents can't use feature flags? I don't think so.Jake [00:25:54]: They want version control. Can we use Git or not Git? That one is up in the air. I think something outside Git will emerge for how we version these things over time. They need observability. You need to query what happened, when it happened, which steps failed, traces, logs, metrics, and all the rest. They need network, compute, and storage. They need to write files, save files, iterate on files, and snapshot file systems.Jake [00:26:25]: A lot of what humans needed is in line with what agents need. Branching and forking are not different; we're just moving 1,000 times quicker. It can look like you need something massively different, but what you need is something massively better than what existed. You need orchestration massively better than Kubernetes. You need networking probably better than Envoy. It goes all the way down the stack.Jake [00:26:55]: If the workload profile doesn't change so much as it gets massively compressed because you need thousands of these things, what assumptions change? etcd is going to melt. You need to replace it with something. You can go all the way down the stack and say, “That part has to change, that part has to change, and that part has to change.”Jake [00:27:19]: The interesting thing about the super-exponential curve is that you have to build systems where you can rip out those parts at any time because a new bottleneck might emerge. You get good at parallel agents, and a different part of the system breaks. So it's similar to what humans needed, but at 1,000x scale.Jake [00:27:55]: How do you do code review in the age of agents?Swyx [00:28:00]: You throw more agents at it.Jake [00:28:01]: You don't. But then who reviews for CVEs and all these other things?Swyx [00:28:07]: More agents.Jake [00:28:08]: And that's how we hit the inference wall. You can continually throw agents at the problem, but I think there's a limit to the number of agents you can throw at a problem.CLI, Agent Handles, and Closing the LoopSwyx [00:28:24]: You already had a CLI before it was cool. How is the shape of what you're exposing changing, if at all?Jake [00:28:28]: CLIs have always been cool. The CLI changes because we think about how to give Claude, Codex, ChatGPT, or any model a handhold.Jake [00:28:50]: A CLI is a single command: deploy, get logs, and so on. Things that were prohibitively annoying to humans are not annoying to agents. They're nice. If I handed you a CLI with 40 arguments and 600 flags, you'd think, “I'm never going to use all of this.” But if you hand it to an agent, it says, “This is excellent. I have so many handles to work with.”Jake [00:29:24]: If you're going to expose things to agents that way, you want as many handles as possible where they can get information, query dynamic information, and close the loop quickly. Most problems right now are about how to close the loop as quickly as possible. Where does the agent get stuck, and how can you remove that?Jake [00:29:49]: Telemetry is important. If you can tell where the agent gets stuck from the CLI and say, “12% of people deviate from the happy path because of this, and now I add this argument and drive it down to 2%,” you massively increase the rate of loop closure.Jake [00:30:03]: That's how we think about not just the CLI, but every point in the dashboard. It's a user journey: I hear about Railway. I get something deployed. I get my first green build or aha moment. I see an endpoint, logs, whatever. Then I iterate. The iteration loop is indefinite. The user wants to deploy a new thing, a Postgres instance, change code, and keep iterating.Jake [00:30:36]: If you focus on the iteration loops and what's blocking them from closing quickly, one thing we say internally is: you never want to be waiting on compute anymore. You always want to be waiting on intelligence. If you're waiting on compute, there's a bottleneck that needs to be destroyed because eventually that bottleneck becomes so large that another workflow emerges to change it.Jake [00:31:04]: We've built a product where you push code, build it, and so on. But I fundamentally believe the push-pull loop is going away. We'll get to a point where you make a small change in production, that change is versioned across your infrastructure, you're working alongside copy-on-write versions of your database and infrastructure, and then you merge it in and it's instantaneously live. That's the holy grail of loops. The push-pull-rebuild thing is a point of friction that we're removing entirely.Canvas as Output: Dashboards, Context Anchors, and HyperstructuresSwyx [00:31:43]: It's incredibly fast. If anyone hasn't tried it, that fast feedback is great. My hot take is that Railway was famous for its canvas, which visualizes your infrastructure and lets you manipulate it visually. But that was for humans. For the next phase of growth, Railway CLI is more important than canvas.Jake [00:32:05]: The canvas is funny because it's a mechanism to show changes over time. You're right that previously we used it a lot as an input. Moving forward, its goal is more like an output. You would go to the canvas, make changes, see them, and watch your infrastructure evolve. Now agents have access to the CLI and can make those changes. So the canvas becomes an output: what information does the human need at this moment to make suitable decisions about control requests? Do I approve this or not?Jake [00:32:57]: It also has to be an anchor for your context, a port in the storm. Think of it like layers in a file system. You start with a project, then drill down into services, then into a function or code, because you want to represent the entire thing not just in your head, but in the canvas. Other people can share that representation, think on the same wavelength, and move quickly.Jake [00:33:33]: A lot of organizations get in trouble as they scale because all the context lives in someone's head. “How does this microservice work?” “I have no idea; go ask this person.” Then you have whole categories of products built around context discovery. A lot of that melts away if you have a solid hierarchy and can infinitely nest services, code, context, and everything else all the way down. That's what lets you build these structures over time.Jake [00:34:18]: It's also what lets us build what I've called hyperstructures: things that are way bigger. You look at the Golden Gate Bridge and ask, “How did we build that?” There's a meme that we lost the technology. To some extent, yes, because the coordination that built those things evolved and changed. We lost some of the art of building structure as we jammed everything into Slack.Swyx [00:34:52]: But you jam everything in Discord.Jake [00:34:53]: Same point. It doesn't matter. It's message passing and interrupts, message passing and interrupts.Swyx [00:35:00]: So you're arguing there should be something better and more structured than Slack?Jake [00:35:04]: Yeah. For sure. I think Slack is awful, and Discord is awful too.Central Station: Context Routing, Support, and Incident ClustersSwyx [00:35:09]: This is the equivalent of my mom test. What have you done that has your solution to this?Jake [00:35:15]: Internally, we've built a tool called Central Station that aggregates all the context from our users. Every piece of feedback, every customer support item, everything gets aggregated into clusters. If an incident is brewing, we can determine how many users are affected and break off a discussion based on that.Jake [00:35:40]: That is more helpful than long-running channels where you're trying to decide which channel to put something in. If you can dynamically aggregate information and dynamically route it to the right person based on context, it works better. We know internally that these four people are close to networking. If we see a networking thing, we can drill it down to those four people. If it's with this part, we can look at the commits. This is no longer a manual process internally.Jake [00:36:13]: If you go to station or help.railway.com, that's why we built it. We wanted to scale with a massive amount of leverage by aggregating feedback.Swyx [00:36:27]: This is built in-house?Jake [00:36:28]: Yep.Swyx [00:36:29]: I remember helping out on this one with Angelo in 2023. You scale a lot with a very small team.Jake [00:36:38]: Yeah. We're about 10 times bigger now.Swyx [00:36:40]: You have your full developer code here? Very cool.Jake [00:36:44]: If you go to railway.com/stats, we expose this as a pub-sub-able thing. It's all real-time metrics. There's a way to get it as JSON somewhere if you care.Jake [00:37:01]: We're big on trying to build everything in public and talk about what we're working on. We've had issues in the past, and we'll say, “Here's how we're fixing these things.” We've gotten compliments and flak for incident reports. We're always trying to make them better and talk with people.Incidents, Disclosure, and Progressive RolloutsSwyx [00:37:20]: You had a big one recently. I liked that it was scoped to 3,000. You presumably used Central Station. Talk through what happened and how you address it internally as a team.Jake [00:37:38]: Internally, this one really sucked. It had to do with an upstream provider that didn't do the behavior it said it documented, which is unfortunate given they wrote the RFC for how the behavior should work. We rolled those things out, and Central Station caught it initially when a couple users said caches weren't invalidating. We turned it off immediately.Jake [00:38:03]: When you roll out to a large user base of three million people, you get a lot of disparate behaviors. We tested in staging and had tests, but we hit an edge case. We've hardened those systems, and now we can make that better. But it was a tough one.Swyx [00:38:39]: I always wonder how private disclosure is supposed to work if people find an issue. Are they supposed to contact you first? When you run a platform, these things will happen. What channels should people pursue to quietly resolve it before it becomes a bigger incident?Jake [00:38:59]: There's responsible disclosure. We err on the side of over-disclosing and letting you know something is wrong versus having your provider gaslight you. We've erred on sharing those things more publicly, even if they impact a small subset of users. That's a decision we've made internally. We have four values. One is honor. The honorable thing is to notify people to the widest degree at which they may have been affected or there was an issue, and then confront it head-on: why did it happen, what can we do better?Swyx [00:39:45]: Not the whole user base. That's because of incremental rollouts and other things?Jake [00:39:50]: Yeah. Progressive rollouts.Swyx [00:39:54]: That should be the norm at all large platforms.Jake [00:39:58]: It should. A variety of companies do this. There's the quote that Meta runs 10,000 different versions of Meta. To our earlier point about agents, they need the same thing. They need shadow traffic and all these other things. We've built so much ceremony around production being sacred that we need to make it trivially easy to test different behaviors in a safe environment. Then you can make mistakes in a safe environment.Safe AI SRE: Customer Agents, Forked Environments, and Production ParityAlessio [00:40:30]: Do you see a world where these things get automatically caught, not necessarily by your agent, but by your customer's agent? The cache invalidation issue seems easy to check if you know to look for it.Jake [00:40:44]: It's hard because to determine it, we almost need to hook into your observability infrastructure. That's why we have the template loop on the platform: so you can roll things out progressively. You can roll out to Johnny Vibe Coder initially, or push a shard that someone consumes at their own leisure. Or you can roll it out over weeks: 0.1% of people, 1% of people, early adopters, then all the way up. That's the non-deterministic version control we talked about earlier.Jake [00:41:30]: I believe that's where most things should go, because most companies end up building staged rollout systems in-house. It's the same thing built again and again at every company. There's a massive opportunity to consolidate developer debt.Alessio [00:41:45]: You should have a free tier. Model providers give free tokens if you let them use the data. You could give free compute if someone is the number-one shard that goes out and lets you plug into their observability.Jake [00:41:55]: We do that. That's why we talked about the impact on 3,000 people. We start with lower-impact people. Larger companies on the platform are last to receive those rollouts so they have a version of the platform that's deeply stable.Alessio [00:42:16]: I have three services, so I'm sure I get the first rollout. You can nuke my thing at any time. There are all these SRE agent companies. Observability people also want agents that fix upstream problems. You have your own agent in the canvas now. How do you see that playing out?Jake [00:42:39]: It's the stacking entropy problem. If you don't have primitives to make iteration in production safe, it becomes difficult. If you're an observability provider saying, “Here's the fix to this error,” assume 80% are good and make sense. But in the last 20% long tail of complex issues, if you let somebody stamp it, you create an opportunity for an incident.Jake [00:43:08]: That's why forked environments are important. People have staging, but it always drifts from production. You need primitives, workflows, and experience built first-party on the platform so you can fork any service at any point in time.Jake [00:43:33]: I think of the canvas as a sheet of transparency paper. The agent is a little guy you push up into the canvas. It should say, “I need to copy that service and that service so I can test these two things.” It gets a read-only copy of production. Anything that's PII gets marked as a transform when we clone the database, create a copy-on-write version, or read from it. Then the agent makes changes and asks, “Does this actually work?” as close to production as possible.Jake [00:44:22]: That's how close you have to be, or you get massive drift. The system becomes unstable. You see this with massive systems built on Docker for local, Kubernetes for production, and a specific thing for something else. That complexity slows developers and becomes unstable at scale, making it hard to iterate. We want to compress that way down and say, “As close to prod as possible is where we want to be.”From AISRE Skeptic to Agent BelieverSwyx [00:45:00]: I was texting Erica for questions, and she says you were originally not a believer in AISRE. Have you come around on it?Jake [00:45:10]: I flipped, but I'm still not a believer in AISRE if you don't have the primitives to make it safe. If you unleash AISRE on production infrastructure without safe primitives for copying volumes and making sure things are fine, it's going to nuke your production database. It's not a matter of if, but when. I'm a big believer in making those loops safe.Jake [00:45:33]: I was a deep AI skeptic until 2023. In 2024, I thought, “Maybe I can roughly make this thing do it.” In 2025, I thought, “Now I can hold this.” Over winter break, everybody came back saying, “It's almost impossible to hold this.”Swyx [00:46:01]: Did you see this on the Claude docs? CloudBot? OpenCloud?Jake [00:46:06]: It's gotten to a point where it's harder to hold it wrong than to hold it right. There's a scene in Avengers where Vision picks up Thor's hammer and says it's terribly well-balanced. It self-balances and works well. I'm a deep believer at this point that this will be the dominant species: assembly, C, C++, JavaScript, words.Swyx [00:46:35]: It feels like a big jump.Jake [00:46:37]: It is. But it's not like you abandon CPU-based discrete logic and move straight to fuzzy logic. You need both. Your skills should call code or applications or some static structure. You can use skills to distill what the procedure should be or how the code should act.Jake [00:47:02]: I'm coming to a thesis: you need three points. You need a clear spec defining the system, the code, and the tests. When you say it out loud, if you've been in engineering long enough, you're like, “Of course. That's an RFC, tests, and code.” But they all matter. Having them together lets them reinforce each other: the spec and tests match, but the code doesn't, so reconcile it. Or the tests and code match but the spec doesn't, so reconcile that. That's the iteration loop.Jake [00:47:41]: That's why you're seeing people talk about software factories, docs, and reconciliation. Some of that is architectural astronomy if you don't implement it, but that loop is where most things will end up.Swyx [00:48:07]: For listeners, we've been talking about this on the pod for three years: the holy trinity of specs and tests. Itamar Friedman from Qodo is the reference if people want to look it up.Self-Modifying Infrastructure and the End of Push-Pull-RebuildSwyx [00:48:18]: One thing I want to mention on the OpenCloud idea is self-modification. I don't know how Railway would support it, but I have my OpenClaw, and I just tell it it has the Railway CLI and can do whatever. In theory, whatever capabilities or new infra it needs, it can call the Railway CLI, provision it, and add it to itself. The agent can modify its own infra.Jake [00:48:45]: It's nuts. I have a loop set up where you put the Railway CLI on top of something that runs on Railway. You're authenticated as whatever the current box is, and you can make any changes to it. Then you call Railway deploy, and it deploys itself.Jake [00:49:04]: It's like: “I need to spin up this instance of this environment. I already exist in this environment. Excellent, I have access to a Postgres instance now.” That's where we want to go with agentic, self-replicating infrastructure. That's your loop: iterate in production. You continue making changes. If it works, merge it upstream. If it doesn't, throw it away.Jake [00:49:37]: How do you make throwaway copies trivial to spin up and super cheap? The era of “I have an AWS instance with four vCPU and 16 gigs of RAM” is going to get destroyed. If you do that for agents, you need a thousand of those machines. It's prohibitively expensive compared with what we've spent a ton of time figuring out: the atomic unit of deploy, whether you call it isolates, sandboxes, or something else. Only pay for what you use, spin up instantaneously, and close the loop as quickly as possible.Jake [00:50:15]: If the system can self-replicate safely and say, “This is my environment, I'm making these changes,” it can come back with, “Does this look good? This is a new state of infrastructure given this prompt. I think I've solved it.” Then you go back and say, “Actually, it looks different.” It does the loop again. Then you say, “Cool. Apply.”Swyx [00:50:38]: That's retroactively obvious, which is the most useful kind. Any other comments on agent deployment on Railway?Jake [00:50:51]: It's getting better every day. I'm on X or Twitter. You can always yell at me about the parts not working as well as they should, because plenty of things should work way better.The New Serverless: Stateful, Long-Running, Pay-for-What-You-Use LinuxSwyx [00:51:04]: At this stage, when people want massively or embarrassingly parallel compute, they usually talk serverless. I feel like there's a new serverless compared to the previous five years of serverless. You're in that new bucket. Do you have comparisons or philosophical differences you want to call out?Jake [00:51:31]: It's somewhere in between. It's the ability to run stateful, long-running workflows or executions.Swyx [00:51:42]: Vercel has Fluid Compute, Cloudflare has some container thing, Google has App Runner and others.Jake [00:51:55]: That's where everything is roughly going, and it's why we've been working on this for six years. We believe users need access to a computer: a box that speaks Linux. They need to deploy what they want. Other systems change the surface area of what you can build. For us, users need a computer and need to deploy anything they truly want. That's why we've focused on the primitives: network, compute, storage. If we give you those and expose them so you can run things indefinitely, that's where we believe it's going.Jake [00:52:43]: Twitter has no nuance, so everyone says “servers” or “serverless.” It's always somewhere in the middle: I want to run it for a long time, but I don't want to provision the resource statically or pay for things I'm not using. That's been our thesis from day one: pay only for what you use, run it indefinitely, and it is full Linux.Swyx [00:53:12]: That's why I like the naming of Fluid. It's fluid. Flexible.Heroku, Focus, and Carrying the Torch Without Becoming the PastSwyx [00:53:18]: Another milestone is the Heroku official deprecation. You're one of the presumptive new Herokus. “New Heroku” has been a category for as long as I've been in developer tooling. It's finally happening. What was that like? Any behind-the-scenes of, “This is the moment”?Jake [00:53:42]: You have people where you're like, “You were running stuff on here? You, as this company?” It's crazy that names you would know are running on it and now coming to us saying, “We want to move a lot of this off.”Swyx [00:54:00]: Any behind-the-scenes on why Salesforce let Heroku stagnate?Jake [00:54:05]: I can only guess. It's hard when it's not your business. Salesforce's business is to build a great CRM. That's their focus. Then you acquire a compute business as an offshoot. A lot of early Meta people talk about focus. Boz has a write-up about how in the early days of Meta they had no money, so they were forced to focus. Then they turned on the money tree and had no reason not to split their focus.Jake [00:54:52]: But that dilutes your product. You get offshoots where you ask, “Is this the focus of the business?” If it's not core, it languishes. A lot of companies get in trouble when they split focus because they're fighting a multi-front war, not just externally but internally for alignment. Where are we going? What are we doing? What is our purpose?Jake [00:55:24]: If you're Salesforce-built and mission-driven, you want to work on Salesforce. Heroku is off to the side. It's not core to the business. Getting resources, budget, focus, and alignment internally becomes hard. It was a matter of time.Swyx [00:56:06]: Kudos for them to call it out instead of leaving it unknown.Jake [00:56:12]: Their release was a little odd. They called it out, but they didn't say they were shutting it down. Behind the scenes, I think they issued messages to people saying they should close accounts and that they were going to deprecate and remove things over time.Jake [00:56:30]: It's crazy because some of my first deployment experiences were on Heroku. You start with dragging things into an FTP server, then you try to get a deploy working, and then it's Heroku. It was the on-ramp for us. But the wheel turns. New things emerge. We're happy to carry the torch for a lot of that. But we don't want to be the new Heroku. We want to be the way people build and deploy software, and ultimately the way people monetize software over time.Swyx [00:57:19]: It's still a big crown to be the new Heroku. There are 50 companies that fought for that.Jake [00:57:23]: Everybody is holding some portion of it. We're happy to support people and companies. The platform works differently. The game loop is similar, but we've been dogmatic about where these things are going: primitives, agents, fan-out. Some things fit; some workflows need to change. We have an approximation of Heroku pipelines with the environment system. It's exciting. We've got a ton of people we can support, and it's growing a lot.Temporal, Workflow Engines, and State MachinesSwyx [00:58:12]: I have one more technical question about Temporal. I've sold my shares. You're a power user and one of our earliest customers. I met you through Temporal. You built on Temporal. You have complaints. This may be the most neutral and informed conversation anyone will hear about Temporal without someone working at the company.Jake [00:58:39]: That's fair. I've used Temporal for almost 10 years because of Cadence at Uber.Swyx [00:58:52]: Give people a sense of what Cadence was at Uber.Jake [00:58:57]: Cadence was the precursor to Temporal. It powers trip actions, rides, when you rent a Jump bike or scooter or car. You're running workflows for a period of time and saying, “This ride will run indefinitely until it finishes.” You attach information: you paused in this zone, so add this charge to the bill. When you end the trip, the workflow is done. That experience was powered by Cadence at the time.Swyx [00:59:34]: I used to say it's like programming the entire user journey top-down as one function.Jake [00:59:39]: It's a powerful idea and important. It's also important for the next phase of the agentic journey. You want an agent to do a specific task, be complete or incomplete on that task, and move on to the next thing. You need a way to manage workflows dynamically.Jake [00:59:59]: Temporal was always great in theory, and great when you got it working the way you wanted in production. But it required you to model the entire journey in your head. If you didn't, you could cause issues where replaying the state of the workflow causes non-determinism.Swyx [01:00:25]: Because it works on deterministic workflow history.Jake [01:00:28]: Exactly. I describe it as a jet engine. If you know how to operate it and run it, it's great. But you can't hand it to people trying to build complicated things if they don't have the whole state in their head.Jake [01:00:48]: We run our whole deployment pipeline on top of it. That's a reasonably complicated workflow: pre-commit hooks, signaling, queuing, and all the rest. We ran into the same thing at Uber. As you express a large workflow, it gets more complicated, with more states in the state machine that you have to map back to the workflow.Swyx [01:01:15]: It's a lot of ifs.Jake [01:01:16]: Exactly. At Uber, we built a system for doing the state machine and testing it. We've started to build some of those things here because it's grown heavily. It's not quite love-hate. When it works well, it works super well. But if someone who doesn't have full context puts something into the system that invalidates state or causes non-determinism, or spins off a ton of activities, you have to keep track of underlying SRE knobs like activity slots. Those should scale with memory, vCPU, and so on. It becomes a bear to scale.Swyx [01:02:10]: You need a capable sysadmin running things behind the scenes. If you moved off, what would you do?Jake [01:02:19]: We'd build our own workflow engine. We have a few internally that we've worked on.Swyx [01:02:27]: This is one of those classes of things you typically wouldn't vibe code, but I'm wondering if you can.Jake [01:02:33]: I still don't think you should vibe code it. You still want to run decent tests to make sure it works.Swyx [01:02:39]: Timo didn't invent that from scratch either. There are libraries you can run. On top of that, it's just a state machine that you have to map out. Ultimately, you define the instructions you want and run them through a state machine.Jake [01:03:00]: It's very doable. Workflow stuff is interesting. Restate is doing neat stuff here.Swyx [01:03:10]: You're tied into JavaScript. Are you a JavaScript maxi?Jake [01:03:13]: Internally, we have TypeScript, Rust, and Go. We don't add more languages. Actually, we have a little C because we write BPF code and hooks. But those are the languages.Swyx [01:03:28]: Is this for sidecars?Jake [01:03:32]: No. It's for the networking stack, volumes, and things like that. We use TypeScript a lot because it powers the dashboard, but we're moving a lot of workflow stuff off the dashboard stack and into the infrastructure stack.Railpack, Nixpacks, and Content-Addressable FilesystemsSwyx [01:04:00]: Cool. Any other technical infrastructure stuff? Railpacks?Jake [01:04:07]: We built an engine for determining dependencies based on source code. It's called Railpack. We built the first version, Nixpacks, on top of Nix, and then we moved.Swyx [01:04:17]: People have been trying to get me to adopt Nix and NixOS for four years. Is it ever going to be a thing?Jake [01:04:23]: I don't know. We're excited about it, but it has pain points. Think of it as a stack of versioned binaries at specific slices in time. If you want version X and version Y, you bloat the package space, which blows up image size and makes real-world workloads difficult.Swyx [01:04:53]: But you content-address it and cache it. In theory, there are optimizations.Jake [01:05:00]: In theory, yes. But with a large enough user base and disparate enough machines, you run into a problem Meta described in the XFAAS paper, their internal serverless system. It becomes difficult at scale unless you break out specific runtimes.Jake [01:05:24]: We didn't want to do that because we wanted to truly allow you to deploy anything. That was our initial thing with Nix. But we've moved toward interesting work around content-addressable file systems that can lazy-load anything from any point and page it into memory.Swyx [01:05:48]: Amazing.Jake [01:05:49]: The future is very bright. It's crazy, and it's going to be nuts.Coding Agent Spend, Roadmaps, and Token ROISwyx [01:05:54]: Founder journey stuff?Alessio [01:05:56]: Your cloud usage: you tweeted you're going to spend $300K this month?Jake [01:06:01]: I think we got to $200K.Alessio [01:06:02]: Coding agents?Jake [01:06:03]: Yeah.Swyx [01:06:04]: Across the company?Alessio [01:06:05]: You only have 35 people, so I'm sure they're not all spending $10K a month. What's the distribution?Jake [01:06:10]: I think I'm at about $25K. We have power users all the way down. We came back from winter break, and I basically said, “If you're writing code by hand, you're doing this wrong.” The tools are good enough now that you can move extremely quickly. There are issues and pain points, but you should be reviewing the code you are writing instead of writing it by hand.Jake [01:06:40]: Architectural patterns matter more now than ever, but you shouldn't spend your time generating code you would write. If you know how to write it, ask the agent to write it and reconcile it until it looks like you would have written it yourself.Jake [01:06:58]: People misconstrue my propensity to push people toward agents as connected to our growth and some reliability bumps. They're not necessarily related. The tools are good enough to move extremely quickly and build things way larger than you could before.Jake [01:07:19]: To the earlier point about cooling data centers in space: I don't know. But with software, you can ask, “How would I build block storage from scratch? How would I do these things?” I have ideas because I have history and have read papers. Let me work them out and build massive test benches with thousands of tests, because those are now free to author. If you're not using AI systems to speed-run your roadmap and reconcile your existing system onto the future, you're missing a large point of what's happening.Alessio [01:08:12]: What's the path to spending $3 million a month? Is it bound by ideas and things customers can absorb?Jake [01:08:19]: For most companies, it's bound by deployment at this point. That's why we've seen a massive boom in users and companies, from Fortune 50s down, asking how to get developers to move faster. You'll probably hit your CFO before any technical limits because they'll look at the eye-watering amount of money spent on tokens. Inference costs have to come down, but we're inference constrained now. There will be price discovery around what makes sense for an org to adopt.Jake [01:09:06]: I think you'll end up with the F1 driver concept. If someone is really adept at these things, it makes sense to put them in a $3 million car. If they're not, it probably doesn't make sense. You'll take a few people and say, “You can drive the F1 car. We need to go in this direction. Figure out if it works and prototype it.”Jake [01:09:33]: We've done some of that and vastly accelerated our roadmap. We thought we'd ship something in a few years; now we can probably ship it in a few months because we validated it and don't have to build it incrementally. We can skip steps and move toward our vision.Alessio [01:09:58]: A lot of people are realizing the roadmap doesn't always have a business impact, so they say tokens are too expensive. But if your roadmap were built to make more money by the time you built it, you'd have token pricing for it, the same way you do with sales. You'd spend a billion dollars on sales if you knew you would get $2 billion of revenue.Jake [01:10:19]: Exactly. A naive way to measure this is the percentage of tokens that end up in production. If you can measure impact because those tokens end up in production, that's awesome. But the burden of proof will rise. Internally, we have a growing number of pull requests that haven't merged. The question becomes: how do you get this into production? It's about how quickly you can build and deploy software, which is exciting because that's our whole thing.The SDLC Shift: Prompt Requests, Feature Flags, and Safe RolloutsSwyx [01:10:56]: The SDLC is changing. One thesis is that the pull request is dying. It's going to be the prompt request. Beyond that, code review is also kind of dying if you have all the other systems in place. What else is changing about the SDLC?Jake [01:11:19]: The AISRE and the tools to make it happen. AISRE is pie-in-the-sky aspirational. What does it take to get an AISRE? What tools do you need to build?Swyx [01:11:32]: You should expose your tooling to customers at some point. The Central Station command center.Jake [01:11:39]: We have it for template maintainers. Template maintainers can deploy and maintain templates, and they get feedback. We're going to expose those things incrementally.Swyx [01:11:51]: Clustering around incidents. Everyone has a version of that, but I don't think anyone has solved it.Jake [01:11:56]: I won't say we've solved it internally, but it's gotten so good that we can see incidents forming pretty quickly. At some point, those will be things either someone else builds or we build. We've always built things purpose-built for us. If it makes sense to make it useful for users, monetize it, or turn that loop into a profit center instead of a cost center, we want to do that.Jake [01:12:28]: Pull request is definitely dying.Swyx [01:12:29]: Do you do first-party feature flagging and incremental rollout stuff?Jake [01:12:34]: We have a feature-flagging engine we built internally and will eventually roll out.Swyx [01:12:38]: I don't see it as a user. How come you didn't give us what you have?Jake [01:12:43]: We have to beta test it. We care a lot about the quality of the things. There's plenty we've used internally that doesn't make it all the way through the journey because it fails. It works for one service but not multiple services. We'd have to build it for multiple services and know that if we released it, we'd rebuild it again and again. Some things are worth that, but many inform the roadmap.Jake [01:13:18]: We don't want to dilute the experience by saying, “This works, but only for this service,” unless it's a core initiative. Over the next few months, we'll roll out things that work for a single service, then multiple services, then multiple services across the environment. You have to be deliberate. Otherwise you create broken disparate experiences and support load because people ask how to use the feature.Jake [01:13:52]: It's the earlier expansion and compaction pattern. You expand the company to get features, then compact and smooth them out so the experience is stellar. You told me in the hallway, “It's gotten so much better.” Internally we're saying, “This part really sucks. We need to make it significantly better.”Swyx [01:14:11]: I can attest to that over the last three years watching you build Railway. For listeners, feature flagging is a huge part of Uber culture. So much so that they have too many feature flags and another thing to remove feature flags. Facebook has Gatekeeper. Agents are going to need this. It's fundamental to incremental rollouts. OpenAI acquired Statsig. GPT-5 is routing and flagging through different models.Jake [01:14:56]: It's super important. If the software development lifecycle is going to change because we're doing things 1,000 times faster and 1,000 times more concurrently, what becomes important at scale?Jake [01:15:16]: Before I started Railway, I built a feature-flagging product and tried to sell it. It was an easier version of LaunchDarkly. I ran into a problem: anyone small enough to adopt your technology doesn't care about feature flags, and anyone large enough to need feature flags needs so much scale that you have to build out all the infrastructure. I scrapped it.Jake [01:15:42]: But what is old is new again. Companies are trying to move quickly, but you can't YOLO a vibe-coded thing straight into production. You need to say, “Here's my blast radius, my impact, and I want to shadow it for these users.” Feature flags. You're going to need the tools larger companies built to maintain their structures. Everything gets compressed by 1,000x so everybody can build those structures quickly.Jake [01:16:07]: That's exactly where we are: compressing the software development lifecycle, then expanding it and adding more new things.Cattle, Pets, and Clonable InfrastructureSwyx [01:16:15]: Another term that comes to mind for newer developers is “cattle, not pets.” People treat production like a pet. It has a name. You baby it and keep it alive. With cattle, you can mass farm, roll out, portion parts out, and kill them.Jake [01:16:37]: I think that might change. You can move toward having pets as long as you have a cloning machine for your pets.Swyx [01:16:52]: Yeah.Jake [01:16:52]: If you can snapshot every single thing at every frame, it doesn't matter if something gets obliterated because you have a snapshot of it. The things we've built right now are designed to block changes from the hermetically sealed DevOps line. You have to write a Dockerfile because you nee

Milan De Reede is the co-founder of Nano GPT: a service that allows you to access all the premium (as well as free) AI models from the same interface, as you pay with bitcoin (and a bunch of other supported coins) for every query and prompt. It successfully allows users to experiment, save money on otherwise expensive subscriptions, and build without any limits (outside of the tokens budget). In this episode, we talk about the origins of Nano GPT, why it's so useful, and how you can get started with vibe coding your dream project. Essentially, my conversation with Milan De Reede finds itself at the intersection between cryptocurrencies and AI: a concept that may become increasingly popular in the future. Try Nano GPT today with my referral code, get some bonuses: https://nano-gpt.com/r/Qx83bnwz Time stamps: 00:01:07 Introducing Milan & Nano GPT 00:03:03 The Origins of Nano GPT 00:04:31 How Nano Cryptocurrency Works 00:05:25 Nano vs. Sharding 00:08:08 Validator Incentives in Nano 00:10:44 Nano's Proof-of-Stake-like System 00:11:57 Critique of Bitcoin Mining Centralization 00:16:30 The Impact of AI on Bitcoin Mining 00:18:11 Proof-of-Work vs. Proof-of-Stake Security 00:22:18 The Monero 51% Attack Risk 00:23:41 Nano GPT's Most Used Cryptocurrencies (Monero, Zcash, Bitcoin, and Nano) 00:27:16 Upcoming Bitcoin Hard Fork: ECash 00:30:15 The Quantum Computing Threat to Bitcoin 00:33:15 The Blurring Lines at Bitcoin Conferences 00:36:41 Online vs. In-Person Crypto Debates 00:39:47 Sponsor Mentions 00:42:40 How Nano GPT Selects and Hosts AI Models 00:45:06 The "Auto Model" Feature 00:46:33 Privacy and Anonymity on Nano GPT 00:54:14 The Business Model of Nano GPT 00:55:23 User Growth and Community 00:57:15 Incentivizing Crypto Payments on Nano GPT 01:07:02 The Impact of AI on Society 01:09:44 AI's Role in Education and Plagiarism 01:13:16 The Future of AI and Human Creativity 01:18:20 The Vision Behind Nano GPT

At last, the secrets of healthcare technology will be revealed during a fresh new two-part series, especially written for the accomplished healthcare professional who wants a refresher course on the latest developments that are quickly enveloping healthcare.Coders, clinical documentation integrity specialists (CDISs), and Revenue Cycle professionals who comprise the Talk Ten Tuesday (TTT) audience and who live inside the chart and the queue, are expected to benefit from an eye level rather than at the strategy level approach from senior healthcare analyst Frank Cohen, a renowned computer scientist and respected Monitor Monday panelist.Other well-known subject-matter experts will also join the broadcast with more news to report, including the following:• IPPS Proposed Rule: George Kelly, President of KA Consulting Division at Panacea Healthcare Solutions, will provide an overview of the 2027 IPPSS Proposed Rule during a presentation on May 28.• POV: Penny Jefferson, Manager of Coding & Clinical Documentation Integrity Services for the University of California-Davis Medical Center, will share her point of view during the broadcast.• CDI Report: Cheryl Ericson will provide an update on all things CDI.• SDoH Report: Tiffany Ferguson will report on news happening at the intersection of compliance and medical record coding.• The Coding Report: Christine Geiger will report on the latest coding news.

Ian and Aaron discuss finally recording Token Town, Ian finally shipping Outro, the benefits of working in public, what's coming next to Solo, and more.Sponsored by InterNACHI, Honeybadger, Bento, Vask, and NativePHP UltraInterested in sponsoring Mostly Technical?  Head to https://mostlytechnical.com/sponsor to learn more.Going to Laracon?  Sign up for the Mostly Technical Pre-Party!(00:00) - We Finally Recorded Token Town (07:43) - Outro Is Live! (21:43) - Working In Public Is Required (27:54) - What's Next For Outro (30:22) - What's New With Solo (43:59) - Free vs. Paid (55:20) - First Actual Resume (01:04:41) - Starting A Movement (01:12:36) - Everybody Loves Discord Links:Token TownOutroSyntaxSoloFasterAaron's page for TupleDerek Sivers's "Now" page

Got questions? Send Ericka a Text!Download "The Most Underused Codes in Dentistry - And How to Get Them Paid" checklist here:https://docs.google.com/forms/d/e/1FAIpQLSfxnnfSlNd0NPhMoBWq-1D_xU5R8LS4xPhHNKIjfLQwStOUag/viewform?usp=headerRegister for the free one-hour webinar on Wednesday, April 29, 2026, here:https://us06web.zoom.us/meeting/register/ABKZUAxNQEynSqUET8SwAgSchedule a billing chat with Ericka:https://calendly.com/ericka-dentalbillingdoneright/30minDM Ericka on Instagram to join the wait list for Elevate Billing & Coding:@dental_billing_coach Email Ericka:ericka@dentalbillingdoneright.comEmail Jen:jen@dentalbillingdoneright.comGrab the Hygiene Billing and Coding Playbook Here:https://stan.store/hygieneunlockedEmail Ed:ed@dentalbillingdoneright.comSchedule a demo with MaxAssist to unlock scheduleing potential here:https://maxassist.com/book-a-demo-fortune-billing/Perio performance formula: (D4341+D4342+D4346+D4355+D4910)/(D4341+D4342+D...

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Introducing FOCUS (Fraud Oversight through Careful Use of Statistics).  The U.S. Department of Justice (DOJ) has launched a new initiative in response to the surge in False Claims Act qui tam filings by data miners.Today, roughly 45 percent of DOJ cases involve FCA data miners. You and your team will learn the inside story of this new initiative along with news of two significant data miner-initiated cases: a $6.73 million settlement against a California vascular physician who billed Medicare for unnecessary stent procedures at 30 times the national average; and a $300,000 settlement against three Illinois skilled nursing facilities that billed Medicare for unnecessary and inflated rehabilitation services.Reporting this dramatic story will be whistleblower attorney and a partner in the New York office of Whistleblower Partners, Hamsa Mahendranathan. Broadcast segments will also include these instantly recognizable features:·      Monday Rounds: Ronald Hirsch, MD, vice president of R1 RCM, will be making his Monday Rounds. ·      The RAC Report: Healthcare attorney Knicole Emanuel, partner at the law firm of Nelson Mullins, will report the latest news about auditors. ·      Risky Business: Healthcare attorney David Glaser, shareholder in the law offices of Fredrikson & Byron, will join the broadcast with his trademark segment.·      Legislative Update: Adam Brenman, senior legislative affairs liaison for Zelis, will report on current healthcare legislation.

RPCS3 devs are cracking down hard on vibe coding -- the most popular PS3 emulator team just nuked undisclosed AI-generated slop pull requests on GitHub after getting flooded with garbage code that breaks stuff because the submitters don't even understand what they "vibed" out of ChatGPT. Yeah after real human grinders turned the Cell processor nightmare into 70% playable games these AI bros show up expecting merges while the team flat-out warned they'll start banning without disclosure and told everyone to learn how to debug and code instead of peddling junk that doesn't work -- classic open-source reality check hitting the LLM hype train. Watch the podcast episodes on YouTube and all major podcast hosts including Spotify. CLOWNFISH TV is an independent, opinionated news and commentary podcast that covers Entertainment and Tech from a consumer's point of view. We talk about Gaming, Comics, Anime, TV, Movies, Animation and more. Hosted by Kneon and Geeky Sparkles. Get more news, views and reviews on Clownfish TV News - https://more.clownfishtv.com/ On YouTube - https://www.youtube.com/c/ClownfishTV On Spotify - https://open.spotify.com/show/4Tu83D1NcCmh7K1zHIedvg On Apple Podcasts - https://podcasts.apple.com/us/podcast/clownfish-tv-audio-edition/id1726838629 MORE CLOWNFISH TV - Official Merch Store: http://ClownfishMinus.com Facebook - https://facebook.com/ClownfishTV X - https://x.com/ClownfishTVcom Clownfish TV subreddit: https://www.reddit.com/r/ClownfishTVOfficial/ Disclaimer: This series is produced by Clownfish Studios and WebReef Media, and is part of ClownfishTV.com. Opinions expressed by our contributors do not necessarily reflect the views of our guests, affiliates, sponsors, or advertisers. ClownfishTV.com is an unofficial news source and has no connection to any company that we may cover. This channel and website and the content made available through this site are for educational, entertainment and informational purposes only. These so-called “fair uses” are permitted even if the use of the work would otherwise be infringing. #News #Podcast #FYP #Shorts #RPCS3 #VibeCoding #AIslop #PS3Emulator #EmulationDrama #AICode #GamingNews #OpenSourceFail Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Is vibe coding actually good now? This week on The Friday Deploy, Andrew and Ben explore the convergence of vibe coding and agentic engineering, unpack the decline of the traditional technical interview, and discuss why companies like Warp are prioritizing AI prototypes over planning meetings. They also celebrate Andrew's newly published research on "mise en place" context engineering. Finally, they break down the enterprise AI "last mile" crisis and share how they are using personal knowledge graphs to upskill their own agents.Read the guide: The APEX FrameworkFollow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelLeave us a ReviewFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's stories:AOL for agentsVibe coding and agentic engineering are getting closer than I'd likeMise en Place for Agentic Coding: Deliberate Preparation as Context Engineering MethodologyBuild, then alignThink the technical interview is dead? Think againThe last mile is where enterprise AI actually diesOFFERSStart Free Trial: Get started with LinearB's AI productivity platform for free.Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era.LEARN ABOUT LINEARBAI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production.AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance.AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil.MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

As a top cybersecurity expert in the commercial sector, Mark Ryland has spent nearly a decade following the development of AI systems—their possibilities, their risks, and their limits. And he's found reason for measured optimism. At this year's Heights Parents Conference on "AI and Our Sons: Optimism in Uncharted Waters," Mr. Ryland brought a moderating perspective to the podium, sharing his insights into how AI really operates and what kind of impact it may have on the job economy our children will inherit. Chapters: 00:04:41 A recent history of AI 00:10:31 Intelligence: human, animal, and artificial 00:14:15 Brains vs. minds 00:16:55 Incredible possibilities through pattern recognition 00:21:21 AI's dependence, "model collapse" 00:24:49 Expected impact on economy sectors 00:32:47 AI limits: reinforcement learning 00:36:12 AI risks: safety, job loss 00:42:01 Thoughts on the home 00:44:01 Thoughts on the classroom 00:48:07 Catholic chatbots Links: The Mind & The Machine, podcast by Dr. Michael Augros on AI, science, and philosophy Coding after Coders: The End of Computer Programming as We Know It, NYT, March 12, 2026 Why It's Getting Harder to Measure AI Performance by Timothy B. Lee, Understanding AI Substack Attention Is All You Need, seminal paper on generative AI by a Google Team, June 2017 How One Paper Changed Everything, concerning "Attention Is All You Need," Medium, October 10, 2025 Scientists Research Man Missing 90% of His Brain Who Leads a Normal Life, CBC Radio, July 14, 2016 Also on the Forum: The Walled Garden: Critical Considerations for Classroom AI featuring Andrew Cantarutti A Humane Way of Life: The Research Behind Home Tech Decisions featuring Clare Morell

In this episode, Ryan sits down with Denver local and yoga enthusiast Will, creator of the new community tool Denver Yoga Finder. What started as a personal search for the right yoga studio after moving from Philadelphia turned into a lightweight but powerful platform helping Denver residents discover yoga studios by neighborhood, style, and class type. The conversation explores: Why Denver's wellness culture inspired the project How AI and "vibe coding" tools like Perplexity, Claude, and Lovable made building the app fast and accessible The surprising diversity of yoga styles in Denver Ryan's personal yoga transformation journey after years of Brazilian Jiu-Jitsu injuries Favorite Denver taco spots, cheesesteaks, and outdoor lifestyle perks Will walks through the functionality of the tool, including: Interactive neighborhood map Filtering by yoga styles and heated classes Studio Instagram and website integrations Favorites and discovery features for new Denver residents Ryan shares how yoga dramatically improved chronic pain issues, including severe plantar fasciitis, after years of martial arts training. He also celebrates completing his 700th yoga class milestone. The episode closes with classic Denver food talk, including praise for Patzcuaro's, cheesesteak debates, and the unique joy of skiing and golfing in the same weekend. Topics Covered Denver yoga culture Wellness communities Vibe coding and AI-assisted app development Lovable and Perplexity workflows Hot yoga and recovery Denver neighborhoods Restaurant recommendations Startup creativity and lightweight tools Community-focused software projects https://realgooddenver.com/ https://denveryogafinder.info/

A Note from James:Imagine going on Shark Tank in front of Mark Cuban, Mr. Wonderful, Lori Greiner, Robert Herjavec, and the rest of the Sharks. You're offering 10% of your business for $700,000, which values the company at $7 million. They all say no. Then, a few years later, Amazon buys your company for a billion dollars.That's gotta feel really good, and that's the experience of our next guest, Jamie Siminoff.Jamie built the company behind the video doorbell that lets you see who's at your door—Ring—and helped turn a simple household object into a home security platform. He went on Shark Tank in 2013, didn't get a deal, kept building anyway, and eventually sold Ring to Amazon.Jamie has a book coming out right now called Ding Dong: How Ring Went from Shark Tank Reject to Everyone's Front Door. What really impressed me about Jamie was the simplicity of all his business ideas, since this was his fourth business. A doorbell you can answer from your phone. A way to turn voicemail into text. A tool to unsubscribe from unwanted emails. The kind of ideas that make people say, “Someone must have already done that.” But we talk about this very thing and how critical it is for entrepreneurs to get over these feelings of like, "Oh, I can't do that." That's the lesson. Sometimes the obvious problem is still unsolved. And sometimes the person who wins is the one naive enough—or stubborn enough—to fix it anyway. Episode Description:James sits down with Ring founder Jamie Siminoff to talk about one of the great modern startup stories: a rejected Shark Tank pitch, a product investors dismissed as “just a doorbell,” and an eventual billion-dollar acquisition by Amazon. But the episode is not just about the sale. It's about how entrepreneurs see problems before markets know what to call them.Jamie explains why investors misunderstood Ring at first. They looked at it as a doorbell business, not a home security company. That framing made the market look tiny. But customers were already showing something different: they wanted to know who was at the door, feel safer, and use video in a new way around the home.The conversation also moves into Jamie's earlier companies, including PhoneTag and Unsubscribe.com, and what those taught him about declining markets, customer behavior, and the difference between a clever product and a durable business. From there, James and Jamie talk about AI, why software is easier to build than ever, why that does not make startups easy, and why simple pain points still matter.What makes this episode useful is Jamie's clarity: don't start with the technology. Start with the problem. If something is broken, fix it. And don't automatically assume that because an idea sounds obvious, someone has already solved it well.What You'll Learn:Why Ring looked like a tiny doorbell business to investors—but became a massive home security company.What Jamie learned from being rejected on Shark Tank while already showing real sales traction.Why simple ideas are often dismissed precisely because they seem too obvious.The difference between being an “inventor entrepreneur” and a market-first operator.Why declining markets can make even beloved products hard to scale.How AI changes the cost of building software, but not the difficulty of building a valuable business.Why Jamie believes entrepreneurs should focus on problems and solutions, not technology for its own sake.Timestamped Chapters:[02:00] Jamie on why a doorbell sounded like a “steam engine” idea[02:39] A Note from James: from Shark Tank rejection to Amazon acquisition[04:03] What Jamie does now inside Amazon[04:32] Looking back at the Shark Tank pitch[05:51] Why the Sharks misunderstood Ring's market[06:44] Doorbell company or security company?[07:45] Why obvious ideas are hard to see in real time[08:22] The objections investors kept raising[10:10] Simple ideas, doubt, and the fear that “someone already did this”[10:50] The hardest period after Shark Tank[11:43] PhoneTag and the voicemail-to-text opportunity[12:31] Why declining markets are hard businesses[13:16] Building products you personally want to use[14:00] Jamie as an inventor entrepreneur[14:33] Unsubscribe.com and the “gray mail” problem[16:27] The path from earlier startups to Edison Junior[17:05] How Ring came from a garage problem[17:40] Jamie's lifelong habit of fixing what's broken[19:14] Why naivete can be an entrepreneurial advantage[20:19] James and Jamie on Claude Code and AI app-building[21:29] Why AI's “brain” has outrun its scaffolding[22:44] Coding may be easier—but deployment is still clunky[23:37] The future of building apps without seeing the sausage made[26:25] Why Jamie might have sold Ring early for far less[27:52] Hardware is ugly until it gets big[28:47] Why investors are often too early or too late[29:58] OpenAI, Anthropic, and whether AI becomes a commodity[31:48] Why Jamie expects another major AI shift[32:39] What happens when you raise VC money[33:18] Swinging big or dying fast[34:25] Why Amazon bought Ring[35:34] Choosing Amazon instead of an IPO[36:23] How life changed after the sale[37:41] Ring's AI work on lost dogs[39:14] Why people do not always use obvious solutions[40:38] How Ring's lost-dog feature works[41:23] Privacy, consent, and community video[41:45] Fire Watch and using Ring cameras during wildfires[42:57] Why Ring focuses on safer neighborhoods, not cameras[43:48] Building a startup in the AI era[45:03] Why SaaS is not dead[46:10] Where Jamie would look for startup ideas now[47:47] Why people will still pay for useful small software tools[48:23] Ring's app store and the long tail of camera use cases[49:55] Horse monitoring, elder care, and unexpected AI applications[51:41] Shark Tank relationships after the Ring sale[52:29] Jamie's advice for standing out on Shark TankAdditional Resources:Ding Dong: How Ring Went from Shark Tank Reject to Everyone's Front DoorRing official “About” page.Jamie Siminoff's LinkedIn profile.Amazon's article on Ring Search Party for Dogs.Ring Search Party / Fire Watch information page.TechCrunch coverage of Unsubscribe.com. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Anthropic's CEO predicts AI will write almost all code as Wall Street pours billions into automation, threatening white-collar jobs while giving anyone who learns AI and coding a massive edge in the new economy.