Podcasts about Coding

Andrey Breslav (creator of Kotlin and founder of CodeSpeak) discusses why, in the AI era, the hard problem isn't generating code but clearly deciding what software should do. He explains how “specs” range from formal documents to informal prompts, and why CodeSpeak is moving from long docs to a structured, versioned set of interconnected requirements extracted from everyday agent chats. The conversation covers how clearer requirements reduce ambiguity (including using output stability/semantic entropy), help teams communicate, surface inconsistencies early, and ease review bottlenecks as AI increases code volume. They compare prompt-driven work to spec-driven workflows, talk through examples like a modified tic-tac-toe game, and explore testing (regression, conformance, acceptance), constraints and temporary decisions, modularity and discoverability of requirements, and how roles and languages may evolve while humans stay in control.Chapters:00:00 Introduction04:16 What Counts as a Spec07:49 Human vs Machine Specs10:16 Codespeak Structured Requirements12:14 Writing Better Requirements17:51 Right Abstraction Level22:25 Why Prompts Break at Scale29:58 Keeping Requirements in Sync33:30 Testing in Spec Driven Dev39:01 Beyond Tests and Trust41:11 Abstraction Tradeoffs43:49 Trusting Generated Code44:34 Safe Zones and Checks46:58 Shared Spec Artifacts49:24 Modular Specs and Grounding52:28 Why Teams Adopt Specs55:11 Review Bottlenecks and Balance01:00:21 Capturing Constraints as Intent01:02:29 Do Languages Still Matter01:07:48 Roles in the AI Era01:10:42 CodeSpeak Workflow SummaryImportant Links:- https://codespeak.dev/For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!

John Toon is joined by Ian Gregory of Advancetrack and Billie McLoughlin to work through July's accounting tech news, and it turns into a run of arguments about what a general ledger is actually for. Billie opens on the batch newly certified for the Xero App Store. Garfield, the UK's first SRA-regulated AI law firm, reads your Xero data, chases overdue invoices and drafts small claims paperwork for amounts up to £10,000. Autohive is a no-code AI agent that works inside Xero itself. That second one sets up her argument for the episode: firms may be about to stop shopping for tools and start shopping for workers, where you pick the task and the most qualified agent surfaces against it. John is not convinced that reduces the number of apps you end up running, and Ian asks the question nobody has answered yet, which is whether we adapt to Xero's workflows or Xero adapts to ours. Then the leadership news. Xero's CTO Rick Carragher leaves after 16 months, weeks after chief people officer Jeff Ryan went after 15, with Madhuri Dhulipala arriving from BlackRock as SVP of Engineering, Payments and AI Transformation and Maninder Sawhney joining from Adobe as chief business officer. Ian reads the payments hire as a signal about where Xero wants to sit in agentic payments, and makes the point that the future of receipts is the future of bookkeeping. Billie's concern is more practical. If the people who promised you a roadmap leave, does the promise leave with them? Then the ledger layer. FreeAgent now connects directly to Joiin for group consolidation, Acumatica has bought Vertrax to get into fuel and energy distribution, and Crunchafi has launched FRS 102 lease accounting for the UK and Ireland. Ian's line on the Vertrax deal is the sharpest of the episode: this is the operational detail a generic ledger does not understand, and a generic AI agent will not magically invent. Which leads to the argument the episode was always heading for. Someone vibe coded their way off premium accounting software over a weekend and wrote it up on AccountingWEB. Billie is not making her own butter just because butter has gone up, and she puts a number on the Saturday it cost him. Ian reckons it goes the way of open source, a niche for the dabblers and nothing mission critical. John has vibe coded a product himself and is still paying outside experts to check it before anyone touches it. Also covered: the Social Prosperity Network's plan to replace six taxes with a single national contribution, and the progress update on HMRC's Transformation Roadmap, where digital engagement is up and so, awkwardly, is the tax gap. This episode is brought to you by FreeAgent and Suitefiles: freeagent.com suitefiles.com   00:00 Intro 02:17 Xero's July app store intake: an AI law firm and no-code agents 08:38 Xero loses its CTO, and what the BlackRock hire signals 12:33 The chief people officer exits too, and a new chief business officer arrives 17:10 FreeAgent users can now connect straight to Joiin 20:55 Acumatica buys Vertrax and moves deeper into fuel distribution 23:27 Crunchafi brings FRS 102 lease accounting to the UK and Ireland 26:28 Someone vibe coded their way off premium accounting software 34:11 Replacing six taxes with a single national contribution 45:21 Outro

Today I'm sitting down with Raffi Isanians, a former BigLaw M&A lawyer (with experience at Kirkland & Ellis, Gunderson, and Goodwin), Y Combinator alum, and founder of Mage Legal.Mage Legal helps law firms and in-house legal teams automate legal due diligence by scanning data rooms, flagging risky clauses, generating reports, and producing follow-up questionnaires. Alongside building Mage, Rafi runs Coding for Lawyers, an initiative that helps legal professionals develop technical skills to build with AI better.We talk about why AI won't replace human lawyers, but will radically alter how law firms operate, price, and compete.You'll hear fresh perspectives on why today's top law firms are becoming technology companies, why treating AI like a "smart associate" changes how you prompt, and what attorneys need to do now if they want to stay relevant in an AI-first world.Timestamps:00:00 — Intro02:08 — Why AI Won't Replace Lawyers (But Will Reshape Everything)03:40 — Why Lawyers Should Be Worried About Being Left Behind05:38 — The Newsletter06:10 — Overcoming Risk-Aversion & Investing in Tools09:29 — "Vibe Coding" & The Shift in Legal Mindset11:01 — AI-Native Law Firms: Recipe for Disaster or Future Necessity?13:24 — Case Study: Cleary Gottlieb & Cleary X14:08 — Debunking the Myth: Are Lawyers Training AI to Replace Them?18:16 — Prompting Best Practices: Treat AI Like a Smart Associate20:56 — Skills for the Future: Do Lawyers Need Technical Depth?22:52 — Over-Reliance on Legal Tech & Vendor Dynamics26:33 — How Legal Tech Companies Survive: Bridging Tech & Legal Expertise31:02 — What AI Can't Replace: Human Connection & Emotional Labor34:33 — Building the Law Firm of the Future39:09 — Outro---------Each week I take what I'm hearing in conversations with legal leaders.I analyze the market and track emerging trends in this AI era.In my newsletter called The Future Lawyer Market Intel for the AI eraI'm focused on:What AI is exposingThe opportunitiesThe blind spotsAnd the shifts shaping the next five years.This is how you see the chessboard before everyone else does:https://hollycope.my.canva.site/thefuturelawyer Hosted on Acast. See acast.com/privacy for more information.

Mike McQuaid on LinkedIn Mike on LinkedIn Alderon Games [Warp](warp.dev) [The Mad Botter](themadbotter.com) Coder Conduit

Long before Google, Amazon, or Microsoft, computer technology shaped how people worked, how markets operated, and how businesses became big. After World War II, military officials and their partners in industry looked to the newly invented electronic computer as they sought to cut costs, speed up labor, manage supply chains, and—they hoped—bring stability to the postwar economy. Their efforts would shape early computer science and the first applications of computer technology in manufacturing and business, with profound consequences for workers and managers alike. By the 1960s, practices originally developed to improve industrial efficiency were being used by Wall Street, influencing how markets worked and even how traders thought. Digital technology became central to finance, tying together far-flung trading floors and automating decision making—with alarming consequences, including the 1987 Black Monday crash.  In Coding Capitalism: Computers and the Remaking of the Postwar US Economy (Columbia University Press, 2026), Dr. Devin Kennedy offers a new history of the digital economy, showing how the computer emerged from—and transformed—capitalism in the United States. He traces how computer science and technology were made by industry, which molded computation to manage factories, financial markets, and entire firms. Drawing on the archives of businesses, computer researchers, regulators, and financial institutions, Coding Capitalism retells the story of the postwar economy and the computer, revealing how mid-century business laid the foundations of the digital world. Bridging business and economic history with the history of science and technology, this book uncovers the prehistory of big tech and demonstrates how capitalism has shaped computing since its invention. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts.  Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

Long before Google, Amazon, or Microsoft, computer technology shaped how people worked, how markets operated, and how businesses became big. After World War II, military officials and their partners in industry looked to the newly invented electronic computer as they sought to cut costs, speed up labor, manage supply chains, and—they hoped—bring stability to the postwar economy. Their efforts would shape early computer science and the first applications of computer technology in manufacturing and business, with profound consequences for workers and managers alike. By the 1960s, practices originally developed to improve industrial efficiency were being used by Wall Street, influencing how markets worked and even how traders thought. Digital technology became central to finance, tying together far-flung trading floors and automating decision making—with alarming consequences, including the 1987 Black Monday crash.  In Coding Capitalism: Computers and the Remaking of the Postwar US Economy (Columbia University Press, 2026), Dr. Devin Kennedy offers a new history of the digital economy, showing how the computer emerged from—and transformed—capitalism in the United States. He traces how computer science and technology were made by industry, which molded computation to manage factories, financial markets, and entire firms. Drawing on the archives of businesses, computer researchers, regulators, and financial institutions, Coding Capitalism retells the story of the postwar economy and the computer, revealing how mid-century business laid the foundations of the digital world. Bridging business and economic history with the history of science and technology, this book uncovers the prehistory of big tech and demonstrates how capitalism has shaped computing since its invention. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts.  Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/american-studies

Long before Google, Amazon, or Microsoft, computer technology shaped how people worked, how markets operated, and how businesses became big. After World War II, military officials and their partners in industry looked to the newly invented electronic computer as they sought to cut costs, speed up labor, manage supply chains, and—they hoped—bring stability to the postwar economy. Their efforts would shape early computer science and the first applications of computer technology in manufacturing and business, with profound consequences for workers and managers alike. By the 1960s, practices originally developed to improve industrial efficiency were being used by Wall Street, influencing how markets worked and even how traders thought. Digital technology became central to finance, tying together far-flung trading floors and automating decision making—with alarming consequences, including the 1987 Black Monday crash.  In Coding Capitalism: Computers and the Remaking of the Postwar US Economy (Columbia University Press, 2026), Dr. Devin Kennedy offers a new history of the digital economy, showing how the computer emerged from—and transformed—capitalism in the United States. He traces how computer science and technology were made by industry, which molded computation to manage factories, financial markets, and entire firms. Drawing on the archives of businesses, computer researchers, regulators, and financial institutions, Coding Capitalism retells the story of the postwar economy and the computer, revealing how mid-century business laid the foundations of the digital world. Bridging business and economic history with the history of science and technology, this book uncovers the prehistory of big tech and demonstrates how capitalism has shaped computing since its invention. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts.  Learn more about your ad choices. Visit megaphone.fm/adchoices

The government has announced plans to introduce new technical education pathways for Key Stage 4 from 2028, allowing pupils to study subjects such as AI, engineering, coding, manufacturing and mechanics alongside their core GCSEs. The reforms aim to give young people more choice, strengthen links with local employers and equip students with the skills needed for a rapidly changing economy. But could this represent one of the biggest shifts in secondary education for a generation? Is 14 the right age for pupils to begin specialising, or does it risk narrowing opportunities too early? How will schools find the funding, facilities and specialist teachers needed to make these pathways a success? And what impact could this have on traditional GCSE subjects, teacher recruitment and educational inequality? Join us for a live debate as we explore the opportunities, challenges and unintended consequences of a radical new vision for Key Stage 4! On the panel: Andrew Old, Josh Taylor and Carly Holness.

Long before Google, Amazon, or Microsoft, computer technology shaped how people worked, how markets operated, and how businesses became big. After World War II, military officials and their partners in industry looked to the newly invented electronic computer as they sought to cut costs, speed up labor, manage supply chains, and—they hoped—bring stability to the postwar economy. Their efforts would shape early computer science and the first applications of computer technology in manufacturing and business, with profound consequences for workers and managers alike. By the 1960s, practices originally developed to improve industrial efficiency were being used by Wall Street, influencing how markets worked and even how traders thought. Digital technology became central to finance, tying together far-flung trading floors and automating decision making—with alarming consequences, including the 1987 Black Monday crash.  In Coding Capitalism: Computers and the Remaking of the Postwar US Economy (Columbia University Press, 2026), Dr. Devin Kennedy offers a new history of the digital economy, showing how the computer emerged from—and transformed—capitalism in the United States. He traces how computer science and technology were made by industry, which molded computation to manage factories, financial markets, and entire firms. Drawing on the archives of businesses, computer researchers, regulators, and financial institutions, Coding Capitalism retells the story of the postwar economy and the computer, revealing how mid-century business laid the foundations of the digital world. Bridging business and economic history with the history of science and technology, this book uncovers the prehistory of big tech and demonstrates how capitalism has shaped computing since its invention. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts.  Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/science-technology-and-society

Long before Google, Amazon, or Microsoft, computer technology shaped how people worked, how markets operated, and how businesses became big. After World War II, military officials and their partners in industry looked to the newly invented electronic computer as they sought to cut costs, speed up labor, manage supply chains, and—they hoped—bring stability to the postwar economy. Their efforts would shape early computer science and the first applications of computer technology in manufacturing and business, with profound consequences for workers and managers alike. By the 1960s, practices originally developed to improve industrial efficiency were being used by Wall Street, influencing how markets worked and even how traders thought. Digital technology became central to finance, tying together far-flung trading floors and automating decision making—with alarming consequences, including the 1987 Black Monday crash.  In Coding Capitalism: Computers and the Remaking of the Postwar US Economy (Columbia University Press, 2026), Dr. Devin Kennedy offers a new history of the digital economy, showing how the computer emerged from—and transformed—capitalism in the United States. He traces how computer science and technology were made by industry, which molded computation to manage factories, financial markets, and entire firms. Drawing on the archives of businesses, computer researchers, regulators, and financial institutions, Coding Capitalism retells the story of the postwar economy and the computer, revealing how mid-century business laid the foundations of the digital world. Bridging business and economic history with the history of science and technology, this book uncovers the prehistory of big tech and demonstrates how capitalism has shaped computing since its invention. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts.  Learn more about your ad choices. Visit megaphone.fm/adchoices

Long before Google, Amazon, or Microsoft, computer technology shaped how people worked, how markets operated, and how businesses became big. After World War II, military officials and their partners in industry looked to the newly invented electronic computer as they sought to cut costs, speed up labor, manage supply chains, and—they hoped—bring stability to the postwar economy. Their efforts would shape early computer science and the first applications of computer technology in manufacturing and business, with profound consequences for workers and managers alike. By the 1960s, practices originally developed to improve industrial efficiency were being used by Wall Street, influencing how markets worked and even how traders thought. Digital technology became central to finance, tying together far-flung trading floors and automating decision making—with alarming consequences, including the 1987 Black Monday crash.  In Coding Capitalism: Computers and the Remaking of the Postwar US Economy (Columbia University Press, 2026), Dr. Devin Kennedy offers a new history of the digital economy, showing how the computer emerged from—and transformed—capitalism in the United States. He traces how computer science and technology were made by industry, which molded computation to manage factories, financial markets, and entire firms. Drawing on the archives of businesses, computer researchers, regulators, and financial institutions, Coding Capitalism retells the story of the postwar economy and the computer, revealing how mid-century business laid the foundations of the digital world. Bridging business and economic history with the history of science and technology, this book uncovers the prehistory of big tech and demonstrates how capitalism has shaped computing since its invention. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts.  Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/finance

A rare mammoth emerges beneath a garage, George Peck examines vibe coding, and free coding clubs help North Dakota students build valuable skills.

Long before Google, Amazon, or Microsoft, computer technology shaped how people worked, how markets operated, and how businesses became big. After World War II, military officials and their partners in industry looked to the newly invented electronic computer as they sought to cut costs, speed up labor, manage supply chains, and—they hoped—bring stability to the postwar economy. Their efforts would shape early computer science and the first applications of computer technology in manufacturing and business, with profound consequences for workers and managers alike. By the 1960s, practices originally developed to improve industrial efficiency were being used by Wall Street, influencing how markets worked and even how traders thought. Digital technology became central to finance, tying together far-flung trading floors and automating decision making—with alarming consequences, including the 1987 Black Monday crash.  In Coding Capitalism: Computers and the Remaking of the Postwar US Economy (Columbia University Press, 2026), Dr. Devin Kennedy offers a new history of the digital economy, showing how the computer emerged from—and transformed—capitalism in the United States. He traces how computer science and technology were made by industry, which molded computation to manage factories, financial markets, and entire firms. Drawing on the archives of businesses, computer researchers, regulators, and financial institutions, Coding Capitalism retells the story of the postwar economy and the computer, revealing how mid-century business laid the foundations of the digital world. Bridging business and economic history with the history of science and technology, this book uncovers the prehistory of big tech and demonstrates how capitalism has shaped computing since its invention. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts. 

Long before Google, Amazon, or Microsoft, computer technology shaped how people worked, how markets operated, and how businesses became big. After World War II, military officials and their partners in industry looked to the newly invented electronic computer as they sought to cut costs, speed up labor, manage supply chains, and—they hoped—bring stability to the postwar economy. Their efforts would shape early computer science and the first applications of computer technology in manufacturing and business, with profound consequences for workers and managers alike. By the 1960s, practices originally developed to improve industrial efficiency were being used by Wall Street, influencing how markets worked and even how traders thought. Digital technology became central to finance, tying together far-flung trading floors and automating decision making—with alarming consequences, including the 1987 Black Monday crash.  In Coding Capitalism: Computers and the Remaking of the Postwar US Economy (Columbia University Press, 2026), Dr. Devin Kennedy offers a new history of the digital economy, showing how the computer emerged from—and transformed—capitalism in the United States. He traces how computer science and technology were made by industry, which molded computation to manage factories, financial markets, and entire firms. Drawing on the archives of businesses, computer researchers, regulators, and financial institutions, Coding Capitalism retells the story of the postwar economy and the computer, revealing how mid-century business laid the foundations of the digital world. Bridging business and economic history with the history of science and technology, this book uncovers the prehistory of big tech and demonstrates how capitalism has shaped computing since its invention. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts.  Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/technology

Long before Google, Amazon, or Microsoft, computer technology shaped how people worked, how markets operated, and how businesses became big. After World War II, military officials and their partners in industry looked to the newly invented electronic computer as they sought to cut costs, speed up labor, manage supply chains, and—they hoped—bring stability to the postwar economy. Their efforts would shape early computer science and the first applications of computer technology in manufacturing and business, with profound consequences for workers and managers alike. By the 1960s, practices originally developed to improve industrial efficiency were being used by Wall Street, influencing how markets worked and even how traders thought. Digital technology became central to finance, tying together far-flung trading floors and automating decision making—with alarming consequences, including the 1987 Black Monday crash.  In Coding Capitalism: Computers and the Remaking of the Postwar US Economy (Columbia University Press, 2026), Dr. Devin Kennedy offers a new history of the digital economy, showing how the computer emerged from—and transformed—capitalism in the United States. He traces how computer science and technology were made by industry, which molded computation to manage factories, financial markets, and entire firms. Drawing on the archives of businesses, computer researchers, regulators, and financial institutions, Coding Capitalism retells the story of the postwar economy and the computer, revealing how mid-century business laid the foundations of the digital world. Bridging business and economic history with the history of science and technology, this book uncovers the prehistory of big tech and demonstrates how capitalism has shaped computing since its invention. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts.  Learn more about your ad choices. Visit megaphone.fm/adchoices

Hope Weatherford, VP of Talent Acquisition at Camunda, is doing what most TA leaders are still evaluating: building. In this episode, she sits down with Matt Staney to unpack how she vibe-coded her own interview question generator and JD tool with zero engineering background, why "change leadership is the job now," and how Camunda's process orchestration mindset is reshaping how her team hires. We get into over-engineering vs. first principles, the deepfake and fake-candidate problem, and the near-future where AI agents are extensions of us; applying for jobs, splitting revenue, and forcing a rethink of what "capability" even means. If you're a TA leader wondering where to start, Hope's answer is simple: just do it.

AI adoption feels chaotic right now. Saumyo Mukherjee calls it "good chaos."He's VP of Business Systems at Braze. 24-person team. Supports 2,000 employees. Sits between every GTM function using AI.This episode covers vibe coding sprawl. Duplicate automations. And a handoff automation that replaced 1.5 days of manual work.Plus his take on in-app intelligence, token budget and why tool-switching kills CSM productivity.Real tactics from inside a fast-scaling SaaS company.---Want the playbook, not just the conversation? Subscribe for deep-dive, actionable breakdowns from every episode at unchurned.substack.com.---What you'll learn- Why "good chaos" beats no chaos- How Braze runs quarterly AI hackathons- The sales-to-services handoff fix that saved days- Why token costs are the new budget battle- Why in-app intelligence beats tool-switching--- Timestamps0:00 - Preview & Introduction1:30 - Meet Saumyo Mukherjee, VP of Business Systems at Braze3:52 - Vibe coding sprawl problem7:53 - The AI idea box & Sales-to-services handoff fix11:52 - Balancing freedom and guardrails (Quarterly AI hackathons)14:32 - Anthropic, token costs, and budget strategy18:30 - Why in-app intelligence wins19:45 - Braze's return to Gainsight---Josh is writing a book on building customer relationships. Follow his journey and insights at www.joshschachter.com---Where to Find the GuestSaumyo Mukherjee: https://www.linkedin.com/in/saumyo/---Where to Find Josh:LinkedIn: https://www.linkedin.com/in/jschachter/Unchurned Substack: https://unchurned.substack.com/

What does it take to build a frontier AI model lab in a world dominated by OpenAI, Anthropic and Google?In this episode of Riding Unicorns, James and Hector sit down with Yang Li, Co-Founder & CEO of Cosine, one of the UK's leading frontier AI companies building sovereign AI models for enterprise and government.Cosine began by fine-tuning foundation models for software engineering before evolving into a full frontier AI lab, training its own models from scratch. Today, the company works with organisations across defence, financial services and critical national infrastructure, deploying AI securely inside highly regulated environments.The conversation explores why sovereign AI has become a strategic priority, how smaller model labs can compete with the biggest players, and what the future of enterprise AI will look like.Yang also shares how AI is changing software engineering, why synthetic data has become a competitive advantage, and the lessons he's learned building one of Europe's most ambitious AI companies.Topics Covered• How Cosine evolved from AI coding tools into a frontier AI model lab • Why sovereign AI is becoming critical for governments and enterprises • Competing with OpenAI and Anthropic without billions in funding • Building AI models using synthetic data and post-training techniques • Why defence, banking and healthcare are driving enterprise AI adoption • The future of software engineering in the age of AI coding agents • Air-gapped AI, on-premise deployment and enterprise security • The geopolitical race for AI infrastructure and compute sovereignty • Hiring world-class AI talent and building high-performance teams • Founder lessons on conviction, resilience and staying alive long enough to winThis is a conversation about the future of frontier AI, the growing importance of sovereign technology, and what it takes to build an AI company that competes on the global stage.

Topics covered in this episode: Some more things about Django I've been enjoying Who cleans up after the vibe-coding party? Where Did All Your AI Tokens Go? AgentsView to the rescue! Careful with phishing all Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: Some more things about Django I've been enjoying Julia Evans is learning "2010-style" web dev (Django + SQL + server-rendered HTML) after years of Go backends and JS-heavy frontends Query builders: likes defining custom QuerySet classes with chainable filter methods (.approved().future().with_tags()) — more readable than raw SQL Template filters: highlights urlize, linebreaksbr, json_script, and especially querystring for building/modifying query-string links in templates Migrations: still loves Django's auto-generated migrations — 19 and counting on her project Skips inheritance for class-based views; prefers function-based views for sharing code, though fine using Django's own mixins/interfaces Performance surprise: CPU profiling (via py-spy) — not slow DB queries — revealed the culprit; she'd accidentally disabled the cached template loader, and re-enabling it took throughput from ~2-3 req/s to ~12 req/s on a $10/mo VM Michael #2: Who cleans up after the vibe-coding party? FT Magazine piece by Sam Learner (July 11) on AI coding tools overwhelming open source maintainers - sent in by listener Dylan McConnell, whose main point was that this ran in the Financial Times, not a dev blog. cURL as the case study - Daniel Stenberg has been the only full-time person on it for years; libcurl has been installed an estimated 20+ billion times with 3,000+ listed contributors. Bug bounty killed - cURL ended its paid security bounty program in January, citing an "explosion of AI slop reports" that take real time to debunk and drain morale. Extractive contributions - authoring a PR is now nearly free, reviewing one still costs a human; tldraw's Steve Ruiz closed outside contributions entirely, asking why he'd want someone else writing the easy part. Guido weighs in - van Rossum says projects are holding emergency meetings over the slop flow, and notes LLM patches tend to touch unrelated parts of a file, making review more tedious. "Vibe Coding Kills Open Source" - paper from Miklós Koren's group: packages frequently recommended by coding models saw big download jumps with no matching engagement, breaking the reputation loop that sustains maintainers. Stack Overflow flatlined - over 100,000 questions a month before ChatGPT, under 1,500 last month, with the response rate cut roughly in half; the public archive is now stale training data. The course-creator angle - Josh Comeau's newest web dev course launched at about a third of prior enrollment, and he worries about devs who never learn which questions to ask. But the most interesting portion is what was omitted. Focused on: The end of the curl bug-bounty Omitted: High-Quality Chaos Why the omission is interesting It fits a narrative. The FT piece is a maintenance-and-decline story, and January-Stenberg is a perfect witness for it. April-Stenberg complicates it - same person, same project, better data, opposite direction on the specific claim being used. The tell is already in the article. Learner quotes Stenberg saying AI tools are much better at finding problems than fixing them. That's the April thesis in one line, and it goes undeveloped. Reason for the shift is process, not vibes. Killing the bounty removed the cash incentive and the venue change filtered the rest. Worth saying out loud, because "AI reports got better" isn't quite it - "no bounty plus a real triage platform" is closer. Joke too: Sarah O'Connor wrote a related piece (is this just before skynet launches?) Calvin #3: Where Did All Your AI Tokens Go? AgentsView to the rescue! Local-first desktop/web app for browsing, searching, and analyzing your past AI coding agent sessions (Claude Code, Codex, Copilot, Cursor, Gemini, Aider, and dozens more) Auto-discovers session files on your machine — no config needed; everything stored locally in SQLite, no cloud/accounts agentsview usage is a drop-in ccusage alternative — reads from pre-indexed SQLite, reports run 80–220× faster on large histories New Activity dashboard shows peak concurrency, active vs. idle time, agent-minutes, and cost — filterable by project/agent/machine, with a -json CLI report too Full-text + optional semantic search across every session; also imports Claude.ai/ChatGPT chat exports Install via pip install agentsview, uvx agentsview, brew install --cask agentsview, or download desktop binaries from GitHub Releases Michael #4: Careful with phishing all The situation I pass this along because it was a pretty sneaky bit of targeted phishing, and happened to play off an old interaction in bandit's repo. As usual with phishing scams there are a bunch of tells that this isn't legitimate, but just enough plausibility that I could see falling for it in a weak moment. Relative nobodies like me haven't historically been worth the effort to hit with scams this specific. Agents change the game though :-/. Be careful out there folks! Original message From: "Patrick (Blacktrace)" [HTML_REMOVED] To: LISTENER EMAIL Subject: Your Bandit #1350 (B105 NextToken false positive) -- just fixed that exact case Date: Wednesday, July 15, 2026 12:02 AM Hi AJ, Saw your Bandit issue #1350 -- the B105 hardcoded-password false positive on the string NextToken. I build a deterministic gate that filters that class of Bandit noise, and #1350 was literally the case I just fixed: NextToken / next_token / page_token / nextPageToken now stay quiet, while a genuine hardcoded token like api_token="sk-live-..." still fires. Verified against your exact case. 30-second paste: https://blacktrace.co/noise-eraser Where it still trips, published: https://blacktrace.co/kruc Curious whether it clears what you hit -- and if it trips on something of yours, that's the more useful reply. Patrick, Blacktrace I asked Claude for some analysis too. It was pretty good at finding them. The message name-drops enough real detail to feel legit, but the structure is pure phishing - everything in it exists to get AJ onto blacktrace.co. The strongest ones: Freemail sender, corporate signoff. Signs as "Patrick, Blacktrace" but sends from emailpjv@gmail.com. Real company outreach comes from the company domain, not a personal Gmail - and there's no last name. Over-specific targeting. It mirrors AJ's exact public activity - issue #1350, the B105 rule, the NextToken false positive, even the token variants. That's the "just enough plausibility" AJ flagged, and it's exactly what agents make cheap: scrape a GitHub issue, auto-generate tailored bait. Legit cold outreach rarely reads your history back to you this precisely. The entire payload is two links. Strip the technical flattery and the message is just "paste here" plus "see results here." When the whole point of an email is the click, that's the tell. "30-second paste." Low-friction urgency, and "paste" most likely means paste your source into their tool - handing your code to a stranger's site. Exfiltration dressed as convenience. Brand-new, no-reputation domain. blacktrace.co has no track record, and the name is doing some ominous work. The /kruc slug is random noise, not how real product pages get named. Precise-sounding jargon that's actually vague. "Deterministic gate," "noise-eraser" - impressive, empty. Bolted onto correct real details (B105 is the Bandit hardcoded-password test, sk-live- is a Stripe live-key prefix) to borrow credibility. The disarming close. "if it trips on something of yours, that's the more useful reply" - engineered humility that flatters your expertise and baits a response. Makes engaging feel like you're doing them a favor, which drops your guard. Extras Calvin: DjangoCon US 2026 is rapidly approaching, August 24-28, Chicago Ruff v0.16.0 massively expands its default rule set Ruff now enables 413 rules by default, up from 59 https://astral.sh/blog/ruff-v0.16.0 Michael: Completely redesigned the home page. Try /insights in Claude Code (terminal) Joke: We're Safe

We're back with Consumer Confidential to highlight all the things you can get for free in August. Also, Karlie Kloss shares her new children's book "Spaghetti Code" which follows the journey of a young computer programmer. Plus, catching up with actor and soon-to-be director Jason Biggs. And, chef Anna Francese Gass shows us two dishes that celebrate the best of summer's fresh produce. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Machine learning teams are moving faster, but the hard part has not disappeared. The work is shifting from writing and debugging every line of code toward defining the right problem, setting requirements, reviewing outputs, and deciding what belongs in a durable platform.Niels Bantilan, Chief Machine Learning Engineer at Union AI, explains how machine learning work has changed, why coding agents are accelerating prototyping, and what engineers must consider when building infrastructure that supports many teams instead of optimizing one model. He also shares how customer needs become product decisions, why machine learning roles are becoming more specialized, and why measuring AI productivity remains difficult.Key Takeaways• Coding agents reduce time spent on implementation, debugging, and exploration, but engineers still need judgment around architecture, quality, and business value.• Platform teams must balance experimentation with stability by giving users freedom at the edges while protecting a reliable foundation.• Machine learning engineering now spans a wider range of skills, from low level performance work to customer empathy, education, documentation, and developer advocacy.• The best model for a task may depend on complexity. Smaller self hosted models can handle tightly scoped changes, while longer and more complex work may still require stronger hosted tools.Episode Highlights00:50 What Union AI means by an AI runtime for production02:10 How machine learning work has changed over the past five years10:40 The mindset shift from model building to platform engineering15:00 Turning customer problems into reusable product capabilities19:00 Why machine learning roles are becoming more specialized21:50 Using coding agents through specifications, tickets, and code review26:50 Token costs, productivity measurement, and choosing the right modelOne Line That Stuck“I'm still solving problems. It's just the level at which I'm doing it doesn't require me to necessarily get into the weeds of the implementation.”Follow The Tech Trek for more conversations on AI, data, engineering, product, and technical leadership.

AI makes it easier than ever to find and act on information—especially now that teams can connect to and search across all the apps they use for work. So how do you ensure that only the right people and the right tools can access your team's most sensitive content? In this episode, we talk with Jess Jimenez, the head of security at Dropbox, about what security looks like in the age of AI at Dropbox-scale—from building AI products securely to building trust with the people who use them. Jess talks about the importance of access control lists, defending against the latest AI threats, and how Dropbox Protect helps teams securely share content with both humans and AI so they can collaborate more safely. ~ ~ ~  Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck.  Our theme song was composed by Doug Stuart.  Working Smarter is hosted by Matthew Braga. Thanks for listening!

Tuesday, July 28 marks the 700th live edition of the commemorative Talk Ten Tuesdays broadcast. And in honor of this celebration, the ICD10monitor producers of the popular news and information service have invited Dennis Jones, the assistant vice president of revenue cycle for Jefferson Health, to be the guest host.During the first half of the broadcast, our other well-known subject-matter experts will also join the broadcast with more news to report, including the following:• Tech Report: Senior healthcare analyst Frank Cohen to reveal how when claims are viewed in isolation, there is a great likelihood of audits.• POV: Penny Jefferson, cohost of Talk Ten Tuesdays, will share her point of view (POV) during the broadcast.• The Coding Report: Chris Geiger, will report on the latest coding news.• CDI Report: Cheryl Ericson will provide an update on clinical documentation integrity (CDI).• SDoH Report: Tiffany Ferguson, the CEO for Phoenix Medical Management, will report on news that's happening at the intersection of patient care and medical record coding.Then, during the second half of the broadcast, we have invited a host of renowned guest panelists, including the following:1. Edward Roche2. Ronald Hirsch, MD3. Matthew Albright4. Maureen Testoni. Esq.5. Denise Buenning6. Fred Stodolak

---------------------- 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  https://www.practiceperformancepartners.com/ Show Sponsors: CooperVision MacuHealth

不是理科出身，这辈子跟科技也没什么关系，作为普通人莎莎和Eric，AI是如何在我们的生活中无孔不入的？01:00 帮莎莎当语文老师11:00 未来文科可能要超越理科生了？沟通能力是利用AI的核心技能吧16:00 AI教练还是需要有辨别力，否则可能越聊越烂22:00 码农警报️前锋莎莎vibe coding29:00 智能驾驶的幻想：再也不用坐臭臭出租车了！37:00 AI短剧，害人不浅啊？50:00 提升情商、平复情绪，总之就是哪里不足补哪里吧！关于Passion Fruits欢迎来到一对普通夫妻Eric和莎莎的Marriage Therapy从恋爱、结婚、生娃，万事都在改变，但我们希望不变的是我们内心对于喜爱事物的热情。所以Passion Fruits不是百香果，是我们用对生命的热情去探讨健康的生活方式、自我追寻的旅途，还有日常的那些碎碎念。主理人Eric：15年篮球玩家退役 10年康复撸铁 4年赛车手 3年Crossfiter 2年Hyrox运动员莎莎：藤校毕业创业9年MomentZ迷之创始人微博小红书抖音如果有就都是 @莎莎Pluss @楼长Eric听友群vx+：momentz0518

Can a $35 padlock from a hardware store really keep anyone out? In this episode of The Audit, Joshua Schmidt, Eric Brown, and Nick Mellem sit down with Eric Osterberg of Grey Duck Locks for a live lock picking demo, a deep dive into how modern car keys get cloned, and a wide-ranging conversation about vibe coding, AI agents, and 3D printing. Eric brings decades of hands-on locksmithing and automotive security experience, along with a builder's instinct that has him constantly shipping his own tools, from a computer-aided dispatch app for emergency management volunteers to a searchable database for ham radio operators. The crew gets into how pin tumbler locks are actually picked, why European vehicles like Audi and Volvo are harder to clone than most domestic trucks, and how devices like the Softdrill can manipulate a safe's dial by listening to its own internal mechanics. From there the conversation turns to AI, covering Eric's use of large language models to streamline his business, the rise of vibe coding for non-programmers, and a heated but even-handed debate over new federal rules pushing automakers toward built-in driver monitoring systems. In this episode: Live lock picking demo and how pin tumbler locks actually work How car key cloning really works, and why some brands resist it better  Safe manipulation tools like the Softdrill and TL2000 Vibe coding, AI agents, and building your own tools without a dev team The driver monitoring debate — A new federal mandate pushes automakers toward built-in impairment detection Whether you're curious about lock picking as a hobby or trying to understand how exposed your car key really is, this episode has something for you. Like, share, and subscribe for more conversations at the intersection of security, hardware, and AI. #LockPicking #Locksmith #Cybersecurity #CarKeySecurity #VibeOps #AIAgents #ITAudit #Podcast #3DPrinting #InfoSec 

Maybe it's the summer doldrums that keep otherwise rational people from taking a chance—especially when better alternatives may be available. But according to physician and attorney Dr. John K. Hall, the special guest on the next live edition of Monitor Mondays, some individuals prefer to maintain the status quo rather than pursue a potentially better approach. Case in point: Aetna's recent discharge policy. Beyond the policy itself, which seems to be causing consternation among some hospitals, are there other examples? Listen to the special broadcast Monday, July 27, at 10:00 a.m. EST.The broadcast will also include these instantly recognizable segments:• 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, legislative affairs liaison for Zelis, will report on current healthcare legislation.

Why do so many enterprise AI initiatives begin with impressive demonstrations but struggle to produce measurable business value? In this episode of Tech Talks Daily, I speak with Dom Selvon, CTO and value partner at Valiance, about enterprise AI ROI, outcome-based consulting, build versus buy decisions, proprietary data, ontologies, and governance. Valiance is an AI-native consultancy that charges against client outcomes rather than hours worked. Dom explains why his "value partner" title is deliberate. The company begins by identifying the financial or operational result a client wants and connects its own compensation with achieving that result. Dom argues that many AI initiatives begin without a clear definition of success. The pressure to adopt AI is real, but companies frequently select technology before agreeing on the business problem, desired outcome, or measurement. He identifies three recurring mistakes. The first is framing the project around AI rather than the business need. The second is failing to establish a metric and baseline before work begins. The third is using a consulting model that rewards billable time without connecting payment to the client's result. We also discuss how generative AI is changing traditional build versus buy decisions. Companies historically bought software because custom development was slow, expensive, and difficult to maintain. Coding agents can now reduce the time and cost required to create software for specific internal needs. Dom does not believe SaaS will simply disappear. However, vendors selling convenience, workflow wrappers, or integration glue face new competition from customers who can create similar capabilities themselves. He argues that stronger SaaS positions will depend on assets a model cannot easily regenerate, including proprietary data, networks, regulatory standing, and deep workflow adoption. This leads to a wider discussion about competitive advantage. When companies have access to similar models, generated code begins to converge. Dom believes lasting differentiation comes from company data, institutional knowledge, connected systems, employee experience, and the semantic context surrounding that information. Dom explains why ontologies matter to enterprise AI. Raw data tells an agent what is stored in a particular field. An ontology describes the customers, orders, contracts, payments, relationships, and business rules represented by that data. This context allows people and agents to reason about information in a way that reflects how the company actually works. Governance also needs to be designed from the beginning. Dom argues that security, permissions, accountability, and compliance allow successful pilots to expand without forcing the business to rebuild everything later. How can leaders tell when AI is genuinely being adopted? Dom offers a surprisingly simple signal: people stop talking about AI. The technology becomes part of ordinary Monday morning work, and employees focus on completing the task rather than explaining the tool. Has your company defined the business result, measurement, proprietary context, and governance required to turn AI enthusiasm into operational value? Listen to the episode and share your thoughts with me.

https://www.practiceperformancepartners.com/   ---------------------- 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/... Go to MacuHealth.com and use the coupon code PODCAST2024 at checkout for special discounts  Show Sponsors: CooperVision MacuHealth

Chris and Mark break down some headline stories and dig into a longer conversation about vibe coding in K12. They discuss Common Sense Media's poor safety rating of Google Search's AI mode, continued reporting from databreaches.net about the unresolved Navigate360 anonymous tip database breach, and ClassLink data analyzing device screen time across 5.5 million students. The main conversation is vibe coding - using large language models (Gemini, ChatGPT, Claude, etc.) to generate and deploy code. They talk practical use cases and big concerns. Should vibe coding be used in schools for small utilities? Classroom tools? IT automations? They talk about security, data exposure, maintainability, single‑person liabilities, and more. ———— Sponsored by: Meter NTP VIZOR Fortinet Managed Methods Howard Incident IQ  ———— Join the K12TechPro Community (exclusively for K12 Tech professionals) Buy some swag (tech dept gift boxes, shirts, hoodies...)!!! Email us at k12techtalk@gmail.com OR our "professional" email addy is info@k12techtalkpodcast.com X @k12techtalkpod Facebook Visit our LinkedIn Music by Colt Ball Disclaimer: The views and work done by Josh, Chris, and Mark are solely their own and do not reflect the opinions or positions of sponsors or any respective employers or organizations associated with the guys. K12 Tech Talk itself does not endorse or validate the ideas, views, or statements expressed by Josh, Chris, and Mark's individual views and opinions are not representative of K12 Tech Talk. Furthermore, any references or mention of products, services, organizations, or individuals on K12 Tech Talk should not be considered as endorsements related to any employer or organization associated with the guys.

Almost no one writes an application from scratch anymore, and that's exactly the problem. In Part 3 of our OWASP Top 10 series, Brad Causey and Jordan Natter break down A03: Software Supply Chain Failures, the category that climbed to #3 and topped OWASP's own community survey as the vulnerability organizations worry about most. If your team pulls in third-party libraries, buys SaaS, or lets anyone "vibe code" a project, this episode is for you.Brad and Jordan cover both sides of supply chain risk: the trusted third-party applications you deploy (SolarWinds being the case that put this category on the map) and the open-source components you pull into your own code without always knowing what's inside. They explain why AI and vibe coding are accelerating the problem, why jQuery is the modern-day Flash, and why "just upgrade the package" is rarely that simple.From there it gets practical:What a Software Bill of Materials (SBOM) is and why you need oneTransitive dependencies — the packages hiding beneath your packagesBuilding security checks into your CI/CD pipeline and shifting leftWhy a flaw caught in static analysis can cost ~$200, while the same flaw found in a pen test can cost $20,000+Why a pen test should validate your controls, not be your first line of defenseHow SecurIT360's Project Lantern and ChainGarde automate SBOM analysis against known and actively-exploited vulnerabilitiesA playbook for vetting vendors, writing accountability into contracts, and holding third parties responsible for actually fixing findingsThe takeaway: whether you're writing software or buying it, you need a way to inventory your components, check them against known vulnerabilities, and hold your vendors accountable — and most of it you can do with tools and teams you already have.Part 1 — Broken Access Control, IDOR & CORS: https://youtu.be/BwYJ-kZ3XaYPart 2 — Security Misconfigurations: https://youtu.be/Po8H140BijENeed a web app pen test? SecurIT360 | Cybersecurity From Every Angle More content: https://offsec.blogBlog: https://offsec.blog/Youtube: https://www.youtube.com/@cyberthreatpovTwitter: https://x.com/cyberthreatpovFollow Spencer on social ⬇Spencer's Links: https://spenceralessi.comWork with Us: https://securit360.com | Find vulnerabilities that matter, learn about how we do internal pentesting here.

July 24, 2026In this episode, Scott, Mark, and Dr. Ray Painter examine a major shift in Medicare's approach to coding UTI PCR testing. The discussion explains why reimbursement may now depend on the number of testing kits, methodologies, or procedures used—not simply the number of organisms reported—and what practices should review with their laboratory partners. The team also addresses denials, RAC and UPIC audits, corrected claims, potential commercial payer takebacks, and the continuing uncertainty in MolDX states. They then review the proposed 2027 RVU changes affecting urology, including a significant increase for in-office bulking agent injections, potential reductions for urodynamics, and additional changes to prostate biopsy codes. PRS Coding and Reimbursement HubAccess the HubBotox LCD AlertDownload the AlertFree In-Office Prostate Biopsy Calculator (Suppoted by UC-Care)Download NowPRS Coding CoursesFor UrologistFor APPsFor Coders, Billers, and Admins Join the Urology Pharma and Tech Pioneer GroupEmpowering urology practices to adopt new technology faster by providing clear reimbursement strategies—ensuring the practice gets paid and patients benefit sooner.         https://www.prsnetwork.com/joinuptpClick Here to Start Your Free Trial of AUACodingToday.com   The Thriving Urology Practice Facebook group.The Thriving Urology Practice Facebook Group link to join:https://www.facebook.com/groups/ThrivingPractice/ 

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

In the final episode of Season 15, Charles Suggs and Emma Whamond are joined by James Gray, co-author of Designing Elixir Systems with OTP, to talk about what has changed in software development and what still holds true. Years after the book's release, many of its core ideas around architecture, boundaries, supervision, and system design remain deeply relevant, even as AI tools reshape how developers learn, build, and collaborate. James brings a unique perspective to the conversation: he is an experienced Elixir and OTP educator who only recently began using LLMs in his own workflow. He shares what surprised him, where the tools have been useful, where they have created risk, and what happened when Claude accidentally wiped his local development database. The conversation explores how AI can speed up parts of the work while making foundational knowledge, code review, testing, and clear system boundaries even more important. To close the season, James and the hosts reflect on what developers should hold onto as the stack continues to shift. They discuss responsible LLM usage, the changing role of pair programming, who owns the mental model when AI helps write code, and why Elixir's long-standing strengths may matter even more in an AI-assisted development world. James is scheduled to speak at ElixirConf 2026, September 10–11 in Chicago, and the Elixir Wizards will be there too! Join us and the broader Elixir community, and use promo code Elixirwizards for 10% off in-person or virtual tickets at https://elixirconf.com/ Key topics discussed in this episode: James Gray's work on Designing Elixir Systems with OTP What still holds true in Elixir system design Layered architecture and durable software fundamentals Functional core, imperative shell in modern applications Supervision trees as application lifecycle maps Why boundaries still matter in AI-assisted development What AI changes about learning and building software What AI does not change about software design James' first month using LLMs When Claude erased a local development database Trust, verification, and responsible LLM usage The risk of AI-generated boundary violations Pair programming, mental models, and developer judgment Ethical and legal questions around AI tools Why OTP concepts still matter in distributed systems How Elixir thinking applies to AI agent workflows What developers should preserve as the stack shifts James' upcoming ElixirConf talk Links mentioned: Java Programming Language https://www.java.com/en/ Perl Programming Language https://www.perl.org/ Ruby Programming Language https://www.ruby-lang.org/en/ Best of Ruby Quiz by James Gray https://www.google.com/books/edition/Best_of_Ruby_Quiz/bMggAQAAIAAJ Designing Elixir Systems with OTP https://pragprog.com/titles/jgotp/designing-elixir-systems-with-otp/ RubyConf talk: Boundaries by Gary Bernhardt https://youtu.be/yTkzNHF6rMs Book: Real-World Event Sourcing by Kevin Hoffman https://pragprog.com/titles/khpes/real-world-event-sourcing/ Broadway Library https://elixir-broadway.org/ Supervision Trees https://elixir.hexdocs.pm/supervisor-and-application.html PostgreSQL https://www.postgresql.org/ ClickHouse https://clickhouse.com/ GenServer https://elixir.hexdocs.pm/GenServer.html Programming as Theory Building by Peter Naur https://pages.cs.wisc.edu/~remzi/Naur.pdf “A computer can never be held accountable, therefore a computer must never make a management decision.” – IBM Training Manual, 1979 Oban https://oban.pro/ Anthropic Claude Fable https://www.anthropic.com/claude/fable Claude Design https://claude.com/product/design Tidewave Agentic Dev Environment for Phoenix and Rails https://tidewave.ai/ 2x – nine months later: We did it https://ideas.fin.ai/p/2x-nine-months-later James Gray's Blog https://programmersstone.blog/about/ ElixirConf https://elixirconf.com/ ExMex https://exmexconf.com/Special Guest: James Gray.

As agentic AI becomes more commonplace, the way in which we interact with our technology is fundamentally changing. Engineers are giving way to domain experts, as the role of IT in the workplace evolves faster than ever before. This week, Technology Now is joined by Bob Friday, Chief AI Officer HPE Networking to discuss: • What AI looks like in 2026, traditional models, agentic AI, and more• The surprising role of “bad” data in training models• How businesses and organisations must respond to the new AI-first world 

Most “AI is changing engineering” content stays at the level of anecdote. This piece tries to go one layer deeper into four specific claims made in the conversation, each of which has concrete operational implications for engineering teams: Cloud native's definition is shifting under AI-assisted development, Code review is becoming a two-stage, agent-then-human pipeline, Model selection is a benchmarking problem, not a leaderboard-reading problem, and, Token spend is emerging as a per-developer budget line that companies don't yet know how to reason about. A closing section covers the SaaS market debate. Krish Palaniappan sits down with Srinivas Chippagiri, a senior technical staff member with 15 years in software engineering, to unpack how AI is reshaping coding, code review, model selection, and the SaaS industry.

Thanos Diacakis and Thomas Erl discuss the increasing popularity of AI-assisted coding and its effects on development teams and the software industry. Hosted on Acast. See acast.com/privacy for more information.

Should you use Vibe coding?Can you trust AI to build your website or automate your workflows, or are you trading speed for reliability and security? What if the code breaks just when you need it most? In this episode, I dig into why Vibe coding sounds easy, but the real experts know the dangers of letting AI take the wheel for critical parts of your business.Listen to this new 9-minute episode for insights on when to trust AI-powered solutions, where you should use a pro, and how to protect your business from costly technology surprises.If you have any questions about anything in this, or any of my podcasts, or have a suggestion for a topic or guest, please reach out directly to me at Alan@WeddingBusinessSolutions.com or visit my website Podcast.AlanBerg.com Please be sure to subscribe to this podcast and leave a review (thanks, it really does make a difference). If you want to get notifications of new episodes and upcoming workshops and webinars, you can sign up at www.ConnectWithAlanBerg.com  View the full transcript on Alan's site: https://alanberg.com/blog/Are you going to Wedding MBA? Use the promo code - Alan - to save $20 off your tickets, at www.WeddingMBA.com And don't worry, if you can't use your tickets this year, they're transferrable or you can hold them to use next year. I'm Alan Berg. Thanks for listening. If you have any questions about this or if you'd like to suggest other topics for "The Wedding Business Solutions Podcast" please let me know. My email is Alan@WeddingBusinessSolutions.com. Look forward to seeing you on the next episode. Thanks. Listen to this and all episodes on Apple Podcast, YouTube or your favorite app/site: Apple Podcast: http://bit.ly/weddingbusinesssolutions YouTube: www.WeddingBusinessSolutionsPodcast.tv Spotify: https://spoti.fi/3sGsuB8 Stitcher:  http://bit.ly/wbsstitcher Google Podcast: http://bit.ly/wbsgoogle iHeart Radio: https://ihr.fm/31C9Mic Pandora: http://bit.ly/wbspandora ©2025 Wedding Business Solutions LLC & AlanBerg.com 

In this episode, I share a quick introduction to vibe coding and five websites I recently built with the help of AI. You'll also hear how I used Claude, Gemini, and Google Sites to turn simple ideas into working tools without traditional coding skills. If you want to try vibe coding and create a useful website, app, or classroom resource of your own, this episode has you covered! Show notes: https://classtechtips.com/2026/07/21/vibe-coding-380/ Sponsored by Pollzy:  https://pollzy.co/ Follow Monica on Instagram: https://www.instagram.com/classtechtips/  Take your pick of free EdTech resources: https://classtechtips.com/free-stuff-favorites/   

Are AI agents ready for banking and financial services? Former UBS Group CIO Oliver Bussmann explains the risks to trust and reputation in banking.This session examines the current adoption landscape of AI agents within the financial sector. With roughly 50% of financial institutions now integrating these tools into their workflows, understanding the operational implications is critical for industry leaders and tech professionals alike.Bussmann breaks down why financial AI requires a balanced approach. You will learn how firms are navigating the tension between rapid innovation and the need to maintain client trust as they deploy AI agents at scale.Whether you are managing banking technology or assessing the impact of financial AI, this overview provides context on the real-world challenges facing major institutions today. The discussion highlights the specific reputational hazards that arise when automating sensitive financial processes.YOU'LL DISCOVER✅ How copilot use is shifting toward autopilot across back office, IT, and marketing functions✅ What has to be in place before an agent gets write access to a core ledger: testing, traceability, audit logs, and rollback✅ Why Bussmann expects audit agents to move into the second and third lines of defense, with PwC and Deloitte already bringing their own✅ How the risk classification of a use case drives the level of cross-model validation, human verification, and cross-checks✅ Trust is the asset a bank cannot lose, and Bussmann is waiting for an industry incident driven by hallucination✅ Why junior software engineer job advertisements are down about 40%, and why you cannot stop hiring juniors you will need as seniors in three to five years✅ Coding is not the bottleneck; the organizational change required for process redesign is the real constraint✅ Bussmann is optimistic that agents will run across bank functions within a year, with gains of one, two, or three times in certain use cases against the copilot era's 10 to 30%⏱️ TIMESTAMPS0:00 Introduction0:22 The technology works, the controls lag6:22 Guardrails first, then measure the gains9:38 How regulation shapes what agents may do17:15 Machine learning and high-risk decisions20:14 Trust is what a bank cannot lose25:19 Agents are reshaping technology careers34:26 Risk classification sets the autonomy line39:29 Customer agents need verified digital identity45:12 Proving control to regulators and boards48:23 AI native banks still need people51:41 Agents in production within a yearSubscribe for weekly conversations with leading business and technology leaders.Get the CXOTalk newsletter: https://newsletter.cxotalk.comShow notes, transcript, and summary: https://www.cxotalk.com/episode/agentic-ai-in-financial-services-former-ubs-and-sap-group-cioEpisode 925 | Recorded July 17, 2026#CXOTalk #AgenticAI #AIinBanking #FinancialServices #AIGovernance #EnterpriseAI #AIAgents #RiskManagement

AI coding tools can make developers faster, remove tedious work, and help us build things that once felt out of reach. So why can using them still leave us feeling drained? In this episode, Matt and Mike explore the complicated relationship between AI coding and developer burnout. They discuss losing the satisfaction of solving difficult problems, the mental cost of managing multiple AI workflows, whether AI-generated work still feels like your own, and why reviewing code can be more exhausting than writing it. They also share ways developers can build a healthier relationship with AI - from finding meaningful personal projects to testing strategically and creating a workflow that doesn't require reviewing every generated line. ‍Show Notes: https://www.htmlallthethings.com/podcast/ai-coding-makes-development-easier-so-why-are-we-burning-out Use our Scrimba affiliate link (https://scrimba.com/?via=htmlallthethings) for a 20% discount!! Full details in show notes.

The squeeze is on.Federal enforcement is intensifying, with behavioral health agencies facing heightened scrutiny following revealing reports from the U.S. Department of Health and Human Services (HHS) Office of Inspector General (OIG). According to the OIG, there have been numerous fraud cases involving false claims and falsified documentation.But among this emerging sturm und drang, there is a critical role that health information management (HIM) can play – as you and your team will learn during the next live edition of Talk Ten Tuesdays. That's when special guest Veronica Richardson, senior compliance consultant at First Class Solutions, will discuss how HIM can play a significant role in behavioral health compliance.The popular weekly Internet broadcast will also feature these additional instantly recognizable panelists, who will report more news during their segments:• 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 clinical documentation integrity (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.

Send a message to InsidetheSquareEveryone thinks vibe coding means handing AI the wheel and walking away. I've got a countdown timer that proves exactly why that's wrong, and what happened when I wasn't specific enough while vibe coding it live on YouTube.In this episode, I get into what separates a designer who vibe codes well from someone who just generates AI slop, why "diagnose more, prompt less" is becoming my new rule, and how knowing Squarespace inside and out is the actual unlock, not the AI itself.Chapter Markers00:00 What vibe coding really is (and isn't)01:04 Where AI fits alongside Squarespace's native features01:48 The real reason AI slop happens02:32 My countdown timer experiment, live and unscripted03:29 The one skill that makes or breaks vibe coding04:26 Diagnose more, prompt less05:19 Borrowing a framework from Will Myers on where AI's job ends and ours begins06:19 Where to go next if you want to learn this for realLinks mentioned:insidethesquare.co/beyondWill Myers (Squarespace coder, referenced for his take on AI's role in the design process)Support the showThe term "Squarespace" is a trademark of Squarespace, Inc. This content is not affiliated with Squarespace, Inc.  For a transcript of this episode, along with the links to any resources mentioned, visit insidethesquare.co/podcast

Ian and Aaron discuss letting Sol Ultra rip for over 2 days straight, Aaron's new side project, problems with macOS, trying to price AI usage in apps, and more.Sponsored by Bento & DropInBlog.Interested 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) - Tim Cook's Office (06:00) - Introducing cfmpeg (12:27) - Ian & ElevenLabs (15:04) - Subscriptions In Outro (18:45) - World Cup (24:06) - Sol Ultra (28:07) - An Update on HelpSpot AI Pricing (52:04) - Aaron's Custom Model (01:07:40) - Next Week: Laracon US! (01:10:23) - Great Illustrated Classics Links:cfmpegElevenLabsGPT-5.6 SolIan on Notes on WorkHelpSpot AIToken Town Episode 008BigSkyDevConfLaracon USGreat Illustrated Classics

---------------------- 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  https://www.practiceperformancepartners.com/ Show Sponsors: CooperVision MacuHealth

A small but rising number of firms are turning to AI-guided vibe coding for their firm's solutions versus turning to one from a vendor. We talk to Ellen Choi, CEO of Edgefield Group, about the benefits and risks of this approach.

Prof Andrew on LinkedIn Mike on LinkedIn Coder Radio Discord Alderon Games Alice Pilot - Use Code Coder

News sources: https://lmg.gg/L8hqh Timestamps: 0:00 Linus Torvalds welcomes AI coding 1:14 Lenovo's inkjet-printed OLED laptop 2:32 EU forces Google to open Android 4:04 QUICK BITS INTRO 4:13 Ransomware halts Fairlife production 4:44 Samsung foldable specs leak 5:20 Moonshot unveils Kimi K3 5:56 23andMe settles its data breach 6:30 OpenAI sells a $70 basketball 7:04 Credits Learn more about your ad choices. Visit megaphone.fm/adchoices

(0:00) Former Intel CEO Pat Gelsinger joins Jason! (1:41) What Went Wrong at Intel (15:19) Why a Taiwan Blockade Would Cripple the US Economy (25:00) Lovable's Anton Osika: One Million New Apps a Week (33:38) How Lovable is Bringing Down Builder Costs Thanks to our partners for making this possible! Airwallex is a leading global payments and financial platform for modern businesses, offering trusted solutions to manage everything from business accounts, payments, treasury, and spend management to embedded finance. https://airwallex.com/allin Plaud - If your work depends on conversations — meetings, deal flow, interviews, customer calls — Plaud helps you capture and organize everything with highly accurate AI-generated notes that are not just simple summaries, but also highlight pain points, key decisions, next steps, and customizable summary templates. Check out Plaud at https://plaud.ai/allin and use code ALLIN for up to 20% off! Which is also available on Amazon: https://amzn.to/43URLff (Code: ALLIN20X) Follow Pat: https://x.com/PGelsinger Follow Anton: https://x.com/antonosika Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@allin Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg And a special thank you to Raise Summit for hosting us in Paris