Podcasts about openai codex

Everyone's chasing the next big model drop.

Get the AI Agents Playbook: https://clickhubspot.com/fno Ep. 401 There's a huge social network for agents—where humans aren't allowed to post. Kieran dives into how a weekend project became an autonomous AI ecosystem, changing how we interact with the web. Learn more about the meteoric growth and viral community of OpenClaw, the wild agent-only social network Moltbook, the real-world security challenges that come with autonomous agents, and how you can safely get started experimenting with your own AI agent today. Mentions OpenClaw https://openclaw.ai/ Moltbook https://www.moltbook.com/ OpenAI Codex https://openai.com/codex/ Claude https://claude.ai/ Gemini https://gemini.google.com/ Get our guide to build your own Custom GPT: https://clickhubspot.com/customgpt We're creating our next round of content and want to ensure it tackles the challenges you're facing at work or in your business. To understand your biggest challenges we've put together a survey and we'd love to hear from you! https://bit.ly/matg-research Resource [Free] Steal our favorite AI Prompts featured on the show! Grab them here: https://clickhubspot.com/aip We're on Social Media! Follow us for everyday marketing wisdom straight to your feed YouTube: ​​https://www.youtube.com/channel/UCGtXqPiNV8YC0GMUzY-EUFg  Twitter: https://twitter.com/matgpod  TikTok: https://www.tiktok.com/@matgpod  Join our community https://landing.connect.com/matg Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934   If you love this show, please leave us a 5-Star Review https://link.chtbl.com/h9_sjBKH and share your favorite episodes with friends. We really appreciate your support. Host Links: Kipp Bodnar, https://twitter.com/kippbodnar   Kieran Flanagan, https://twitter.com/searchbrat  ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by Hubspot Media // Produced by Darren Clarke.

In this episode of Project Synapse, the hosts discuss how "agentic" AI has rapidly accelerated and become widely distributed, using the explosion of OpenClaw (with claims of ~160,000 instances) as a sign that autonomous agent tools are now in anyone's hands.  Hashtag Trending  would like to thank Meter for their support in bringing you this podcast. Meter delivers a complete networking stack, wired, wireless and cellular in one integrated solution that's built for performance and scale. You can find them at Meter.com/htt They compare the speed and societal impact of current AI progress to COVID-19's early days, arguing the pace may be even more destabilizing. They cover Anthropic's Claude 4.6 and OpenAI's Codex 5.3, including claims that Claude 4.6 helped produce a functional C compiler for about $20,000, and that a Cowork-like tool could be replicated in a day with Codex 5.3 after Claude reportedly took two weeks to build Cowork.  The conversation highlights improved long-context memory performance (needle-in-haystack-style metrics reportedly in the 90% range) and increasingly autonomous behavior such as self-testing, self-correction, and coordinating teams of agents. The hosts then focus on security: MCP (Model Context Protocol) as a widely adopted but "fundamentally insecure" connector requiring broad permissions; the risk of malicious tools/skills and malware in agent ecosystems; and the rise of "shadow AI," where employees or individuals deploy agents without organizational vetting—potentially leaking sensitive data or running up massive token bills.  They discuss incentives that push both humans and models toward fast answers and risky deployment, referencing burnout and an HBR study on rising expectations without proportional hiring. The episode also touches on realism and deepfakes, citing impressive new AI video generation (including a Chinese model "SEEDANCE 2.0" example) and how this erodes trust in what's real.  They conclude with practical advice for organizations—don't just say "no," create safe outlets and governance ("say how")—and briefly discuss wearables/AR, Meta's continued AI efforts (including the Meta AI app and "Vibes"), and the coming integration of AI into always-on devices. Sponsor: Meter, an integrated wired/wireless/cellular networking stack (meter.com/htt). 00:00 Cold Open + Sponsor: Meter Networking Stack 00:18 Welcome to Project Synapse (and immediate chaos) 00:57 'Something Big Is Happening': AI feels like COVID-speed disruption 02:57 OpenClaw goes viral: 160k instances and easy DIY clones 04:03 Claude Code 'Cowork' on Windows… and why it's broken 06:47 Rebuilding Cowork in a day with OpenAI Codex 5.3 08:18 Why Opus 4.6 feels like a step-change: memory, autonomy, agent teams 11:24 Model leapfrogging + the end of 'can AI write code?' debates 14:45 Hallucinations, 'I don't know,' and self-correction in modern models 18:42 Autonomous agents in practice: cron-like loops, tool use, and fallout 21:00 MCP security: powerful connectors, scary permissions, and 500 zero-days 24:33 Shadow AI & skill marketplaces: the app-store malware analogy 32:02 Incentives drive risk: move fast culture, confident wrong answers, burnout 34:16 AI Agents Boost Productivity… and Raise the Bar at Work 35:14 Warnings of a Coming AI-Driven Crash (and Why We're Not Steering Away) 36:28 "I Quit to Write Poetry": Existential Dread & On the Beach Vibes 37:21 Tech Safety Is Reactive: Seatbelts, Crashes, and the AI Double-Edged Sword 39:42 Fast-Moving Threats: Agents Hacking Infrastructure & Security Debt 40:54 From Doom to Adaptation: Using the Same Tools to Survive the Disruption 42:21 Why We're Numb to AI Warnings + The 'Free Energy' Thought Experiment 46:43 AGI Is Already Here? Prompts, Ego, and the 'If It Quacks Like a Duck' Test 48:56 Deepfake Video Leap: Seedance, Perfect Voices, and What's Real Anymore 52:39 Contain the Damage: 'Don't Say No—Say How' and Shadow AI in Companies 54:58 Holodeck on the Horizon: VR + GenAI + Wearables (Meta, Apple, OpenAI/Ive) 59:53 Meta's AI Reality Check: Bots, the Meta AI App, 'Vibes,' and Who's Making Money 01:04:41 Final Wrap + Sponsor Thanks

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In this episode of Project Synapse, the hosts discuss how "agentic" AI has rapidly accelerated and become widely distributed, using the explosion of OpenClaw (with claims of ~160,000 instances) as a sign that autonomous agent tools are now in anyone's hands.  Hashtag Trending  would like to thank Meter for their support in bringing you this podcast. Meter delivers a complete networking stack, wired, wireless and cellular in one integrated solution that's built for performance and scale. You can find them at Meter.com/htt They compare the speed and societal impact of current AI progress to COVID-19's early days, arguing the pace may be even more destabilizing. They cover Anthropic's Claude 4.6 and OpenAI's Codex 5.3, including claims that Claude 4.6 helped produce a functional C compiler for about $20,000, and that a Cowork-like tool could be replicated in a day with Codex 5.3 after Claude reportedly took two weeks to build Cowork.  The conversation highlights improved long-context memory performance (needle-in-haystack-style metrics reportedly in the 90% range) and increasingly autonomous behavior such as self-testing, self-correction, and coordinating teams of agents. The hosts then focus on security: MCP (Model Context Protocol) as a widely adopted but "fundamentally insecure" connector requiring broad permissions; the risk of malicious tools/skills and malware in agent ecosystems; and the rise of "shadow AI," where employees or individuals deploy agents without organizational vetting—potentially leaking sensitive data or running up massive token bills.  They discuss incentives that push both humans and models toward fast answers and risky deployment, referencing burnout and an HBR study on rising expectations without proportional hiring. The episode also touches on realism and deepfakes, citing impressive new AI video generation (including a Chinese model "SEEDANCE 2.0" example) and how this erodes trust in what's real.  They conclude with practical advice for organizations—don't just say "no," create safe outlets and governance ("say how")—and briefly discuss wearables/AR, Meta's continued AI efforts (including the Meta AI app and "Vibes"), and the coming integration of AI into always-on devices. Sponsor: Meter, an integrated wired/wireless/cellular networking stack (meter.com/htt). 00:00 Cold Open + Sponsor: Meter Networking Stack 00:18 Welcome to Project Synapse (and immediate chaos) 00:57 'Something Big Is Happening': AI feels like COVID-speed disruption 02:57 OpenClaw goes viral: 160k instances and easy DIY clones 04:03 Claude Code 'Cowork' on Windows… and why it's broken 06:47 Rebuilding Cowork in a day with OpenAI Codex 5.3 08:18 Why Opus 4.6 feels like a step-change: memory, autonomy, agent teams 11:24 Model leapfrogging + the end of 'can AI write code?' debates 14:45 Hallucinations, 'I don't know,' and self-correction in modern models 18:42 Autonomous agents in practice: cron-like loops, tool use, and fallout 21:00 MCP security: powerful connectors, scary permissions, and 500 zero-days 24:33 Shadow AI & skill marketplaces: the app-store malware analogy 32:02 Incentives drive risk: move fast culture, confident wrong answers, burnout 34:16 AI Agents Boost Productivity… and Raise the Bar at Work 35:14 Warnings of a Coming AI-Driven Crash (and Why We're Not Steering Away) 36:28 "I Quit to Write Poetry": Existential Dread & On the Beach Vibes 37:21 Tech Safety Is Reactive: Seatbelts, Crashes, and the AI Double-Edged Sword 39:42 Fast-Moving Threats: Agents Hacking Infrastructure & Security Debt 40:54 From Doom to Adaptation: Using the Same Tools to Survive the Disruption 42:21 Why We're Numb to AI Warnings + The 'Free Energy' Thought Experiment 46:43 AGI Is Already Here? Prompts, Ego, and the 'If It Quacks Like a Duck' Test 48:56 Deepfake Video Leap: Seedance, Perfect Voices, and What's Real Anymore 52:39 Contain the Damage: 'Don't Say No—Say How' and Shadow AI in Companies 54:58 Holodeck on the Horizon: VR + GenAI + Wearables (Meta, Apple, OpenAI/Ive) 59:53 Meta's AI Reality Check: Bots, the Meta AI App, 'Vibes,' and Who's Making Money 01:04:41 Final Wrap + Sponsor Thanks

In this episode of Project Synapse, the hosts discuss how "agentic" AI has rapidly accelerated and become widely distributed, using the explosion of OpenClaw (with claims of ~160,000 instances) as a sign that autonomous agent tools are now in anyone's hands.  Hashtag Trending  would like to thank Meter for their support in bringing you this podcast. Meter delivers a complete networking stack, wired, wireless and cellular in one integrated solution that's built for performance and scale. You can find them at Meter.com/htt They compare the speed and societal impact of current AI progress to COVID-19's early days, arguing the pace may be even more destabilizing. They cover Anthropic's Claude 4.6 and OpenAI's Codex 5.3, including claims that Claude 4.6 helped produce a functional C compiler for about $20,000, and that a Cowork-like tool could be replicated in a day with Codex 5.3 after Claude reportedly took two weeks to build Cowork.  The conversation highlights improved long-context memory performance (needle-in-haystack-style metrics reportedly in the 90% range) and increasingly autonomous behavior such as self-testing, self-correction, and coordinating teams of agents. The hosts then focus on security: MCP (Model Context Protocol) as a widely adopted but "fundamentally insecure" connector requiring broad permissions; the risk of malicious tools/skills and malware in agent ecosystems; and the rise of "shadow AI," where employees or individuals deploy agents without organizational vetting—potentially leaking sensitive data or running up massive token bills.  They discuss incentives that push both humans and models toward fast answers and risky deployment, referencing burnout and an HBR study on rising expectations without proportional hiring. The episode also touches on realism and deepfakes, citing impressive new AI video generation (including a Chinese model "SEEDANCE 2.0" example) and how this erodes trust in what's real.  They conclude with practical advice for organizations—don't just say "no," create safe outlets and governance ("say how")—and briefly discuss wearables/AR, Meta's continued AI efforts (including the Meta AI app and "Vibes"), and the coming integration of AI into always-on devices. Sponsor: Meter, an integrated wired/wireless/cellular networking stack (meter.com/htt). 00:00 Cold Open + Sponsor: Meter Networking Stack 00:18 Welcome to Project Synapse (and immediate chaos) 00:57 'Something Big Is Happening': AI feels like COVID-speed disruption 02:57 OpenClaw goes viral: 160k instances and easy DIY clones 04:03 Claude Code 'Cowork' on Windows… and why it's broken 06:47 Rebuilding Cowork in a day with OpenAI Codex 5.3 08:18 Why Opus 4.6 feels like a step-change: memory, autonomy, agent teams 11:24 Model leapfrogging + the end of 'can AI write code?' debates 14:45 Hallucinations, 'I don't know,' and self-correction in modern models 18:42 Autonomous agents in practice: cron-like loops, tool use, and fallout 21:00 MCP security: powerful connectors, scary permissions, and 500 zero-days 24:33 Shadow AI & skill marketplaces: the app-store malware analogy 32:02 Incentives drive risk: move fast culture, confident wrong answers, burnout 34:16 AI Agents Boost Productivity… and Raise the Bar at Work 35:14 Warnings of a Coming AI-Driven Crash (and Why We're Not Steering Away) 36:28 "I Quit to Write Poetry": Existential Dread & On the Beach Vibes 37:21 Tech Safety Is Reactive: Seatbelts, Crashes, and the AI Double-Edged Sword 39:42 Fast-Moving Threats: Agents Hacking Infrastructure & Security Debt 40:54 From Doom to Adaptation: Using the Same Tools to Survive the Disruption 42:21 Why We're Numb to AI Warnings + The 'Free Energy' Thought Experiment 46:43 AGI Is Already Here? Prompts, Ego, and the 'If It Quacks Like a Duck' Test 48:56 Deepfake Video Leap: Seedance, Perfect Voices, and What's Real Anymore 52:39 Contain the Damage: 'Don't Say No—Say How' and Shadow AI in Companies 54:58 Holodeck on the Horizon: VR + GenAI + Wearables (Meta, Apple, OpenAI/Ive) 59:53 Meta's AI Reality Check: Bots, the Meta AI App, 'Vibes,' and Who's Making Money 01:04:41 Final Wrap + Sponsor Thanks

In this week's live-stream replay, we go live for a 2-hour, hands-on deep dive into GPT-5.1 Codex Max with Alexander Embiricos, product lead for OpenAI Codex. You'll walk out feeling like an agentic-coding wizard, even if you're starting from zero. GPT-5.1 Codex Max is OpenAI's latest frontier agentic coding model. It's built on an upgraded reasoning backbone and trained to handle real-world software engineering tasks end to end: PRs, refactors, frontend builds, and deep debugging. It can work independently for hours, compacting its own history so it can refactor entire projects and run multi-hour agent loops without losing context. In this live session, we'll set it up together, build real agents, and push Codex Max to its limits.

New podcast series within Tacos and Tech: AI Builders Roundtable!Neal sits down with Craig Lauer and Ross Young on a day both Anthropic and OpenAI dropped major releases to talk about what it actually looks like to build with AI right now. Ross walks through how his team at Clinically AI built an internal AI operating system using Claude Co-work - from voice-interviewing department heads to capture tribal knowledge, to running full pipeline reviews from HubSpot in natural language. Craig shares how LaunchMate, the AI co-pilot he's building for student founders at SDSU's Zip Launchpad, uses persistent memory and multi-agent communication to keep founders moving. The conversation moves from tools to workflows to a surprisingly honest riff on identity - and what it means when intelligence is no longer your competitive advantage.Key Topics Covered:* The Anthropic 4-6 / OpenAI Codex same-day release and what it signals* LaunchMate: AI agents with persistent memory for founders, mentors, and cohort management at SDSU Zip Launchpad* “Tidbits” — auto-generated founder progress updates (”share without sharing”)* Ross's AI operating system at Clinically AI: markdown knowledge bases, Claude Co-work projects, HubSpot integration, voice-mode interviews for tribal knowledge capture* The AI capability spectrum: chatbots → cloud agents with tool access → local agents with full computer access* OpenClaw vs. Co-work: excitement vs. enterprise readiness and security* Craig's LettaBot/WhatsApp cautionary tale* Natural language as the new programming language - and why social workers may outperform engineers at agent programming* Processes they'll never go back to: manual contract redlines, email triage* Identity in the age of AI - detaching professional worth from intelligenceLinks & Resources:* Clinically AI* SDSU Zip Launchpad* Claude Co-work by Anthropic* LaunchMate (in development)Connect on LinkedIn:* Craig Lauer* Ross Young* Neal Bloom This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit risingtidepartners.substack.com/subscribe

In dieser Episode sprechen Tom und André über die neuesten Entwicklungen in der KI- und Entwickler-Welt: Updates & Erfahrungsbericht zu JetBrains AI, Anthropics neues Tool Claude Cowork und das Release von Opus 4.6, OpenAI Codex, Googles Antigravity Projekt und viele weitere Themen rundum (agentic) AI. Natürlich darf dabei auch openclaw nicht fehlen ;-)

This week at the Desk of Ladyada, we're comin' back from maternity leave and getting spun up with a bunch of projects. First, a fun mailbag item: the XTEInk ‘pocket' reader running crosspoint open firmware on an ESP32-C3. Since it's running an Espressif chip, we could also install CircuitPython or WipperSnapper on it…a great side-effect of more off-the-shelf goods coming with ESP32 chips! See also our Yoto-hacking guide on learn. Next, we've been really enjoying running OpenClaw on a Pi 5 and connecting it up to Adafruit hardware to do ‘full circle' test-driven development. We have Anthropic Opus do the datasheet parsing and design document, then ‘farm' out the coding work to OpenAI Codex. After the driver is written, we also have it design tests to verify hardware functionality, using other GPIO pins, NeoPixels, servos, etc., to exercise the chip capability. It's able to run tests and then fix bugs all on its own, then text me when it needs help or to alert that it's done. It's very slick and fun! Finally, using that same system of coding, we had it ‘one-shot' a miniature oscilloscope demo for the unreleased Stemma Friend from way back (well, it took a few back-and-forths to get it just how we like). Could we have coded it by hand? Probably! But we were able to guide the development and get the look and performance we wanted in about 30 minutes of prompting while also laying in bed and chillin' with a newborn.

"I didn't use my own software this week because the OpenAI agents were better. And that's me retiring my own software." — Keith TeareSomething broke this week. Both Anthropic and OpenAI launched multi-agent systems—"agent swarms"—that don't just assist with tasks but replace custom-built software entirely. The market noticed: Adobe, Salesforce, Workday, and other legacy SaaS companies saw their stocks collapse in what some are calling a trillion-dollar selloff. Keith Teare joins Andrew Keen on Super Bowl weekend to unpack what may be the most consequential week in AI since ChatGPT launched.The conversation ranges from the Anthropic-OpenAI advertising spat (Dario Amodei's Super Bowl ad vs. Sam Altman's "online tantrum") to the deeper structural shifts: Microsoft and Amazon becoming utilities, Google betting $185 billion on an AI-first pivot, and Elon Musk merging SpaceX with xAI to put data centers in space. Along the way, Teare and Keen debate whether the AI race is a myth or a wacky race, whether venture capital is in crisis, and what happens to human labor when agents do the work.About the GuestKeith Teare is a British-American entrepreneur, investor, and technology analyst. He co-founded RealNames Corporation, a pioneering internet company, and later served as Executive Chairman of TechCrunch. He is the founder of That Was The Week and SignalRank, and publishes a widely-read weekly newsletter on technology, venture capital, and the business of innovation. He brings four decades of experience in Silicon Valley to his analysis of the AI revolution.Chapters:00:00 Super Bowl and the Anthropic ad The spat between Dario Amodei and Sam Altman01:09 "Fundamentally dishonest" Keith's take on the ad war and who's really Dick Dastardly05:47 Anthropic's breakout week Claude Opus 4.6 and the agent swarm launch06:48 OpenAI Codex Multiple agents collaborating on tasks in 10-15 minutes07:42 "It replaces software" Keith retires his own custom-built tools08:16 The trillion-dollar selloff Adobe, Salesforce, Workday, PayPal collapse11:02 Infrastructure vs. innovation Microsoft and Amazon become "utilities"11:45 Google's $185 billion bet Pivoting from hybrid to AI-first13:15 The SpaceX/xAI merger Musk's plan for space-based data centers15:18 The AI wacky race Kimi, OpenAI, Anthropic leapfrog Google17:03 Does AI make us smarter? Leverage tools, not intelligence18:53 AI growing up, CEOs not The adolescence of the industry21:06 US job openings hit five-year low The coming labor crisis22:44 The VC crisis Five funds sucking the air out of the room25:04 Palantir and Anduril The winners in defense AI25:42 Facebook as laggard Huge revenues, no AI momentum26:41 The Washington Post crisis "Boogeyman journalism" and partisan media29:23 Ads in AI Paid links vs. enshittification31:26 Spotify's innovation Physical book + audiobook bundle32:32 Startup of the week Cursor for CRM, $20M from Sequoia33:45 Om Malik on the end of software distribution From CDs to app stores to self-made35:41 Super Bowl prediction Seattle vs. New England36:02 Closing "That really was the week in tech"Links & ReferencesMentioned in this episode:That Was The Week newsletter by Keith TeareAnthropic's Super Bowl ad and ad-free pledge (CNBC)Sam Altman's response to Anthropic ads (TechCrunch)SpaceX acquires xAI in $1.25 trillion merger (CNBC)The Washington Post layoffs and crisis (Poynter)Om Malik on the evolution of software distributionOpenAI Codex app launch (OpenAI)About Keen On America Nobody asks more impertinent questions than the Anglo-American writer, filmmaker and SiliconValley entrepreneur Andrew Keen. In Keen On America , Andrew brings his sharp Transatlanticwit to the forces reshaping the United States — hosting daily interviews with leading thinkersand writers about American history, politics, technology, culture, and business. With nearly2,800 episodes since the show launched on TechCrunch in 2010, Keen On America is the mostprolific intellectual interview show in the history of podcasting.Website | Substack | YouTube

O Claude Code e OpenAI Codex chegaram ao Xcode, e um app nasceu durante o episódio.

Visit Mixture of Experts podcast page to get more AI content → https://www.ibm.com/think/podcasts/mixture-of-experts Is OpenAI Codex a game-changer or just catching up? This week on Mixture of Experts, we analyze OpenAI's first-party coding agent app, Codex. Host Tim Hwang and panelists Abraham Daniels, Ambhi Ganesan and first-time guest Sandhya Iyer debate whether Codex gives OpenAI an edge in the crowded AI coding space—or if it's simply table stakes in the agent orchestration race. Next, we revisit Moltbot (now OpenClaw), which spun off Moltbook, the Reddit-style social network for AI agents. Are these agent simulations revealing insights or just fun experiments? Our experts weigh in on the security risks, hallucinations and more. Join us for a packed episode covering coding agents, agent economies and the evolving code assistant landscape. 00:00 – Introduction 01:09 – OpenAI Codex app launch 10:18 – MoltBot/OpenClaw: AI agent social networks The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity. Subscribe for AI updates → https://www.ibm.com/account/reg/us-en/signup?formid=news-urx-52120 #OpenAICodex, #AICodingAgents, #MoltBot, #NVIDIAOpenAI, #AIAgentOrchestration

Praėjusią savaitę aptarti AI agentai „Moltbot“ dabar turi savo socialinį tinklą „Moltbook“. „OpenAI Codex“ programėlė gali ilgai dirbti prie kelių skirtingų programavimo užduočių vienu metu. Apskritai: su kuo dabar geriausia „vibe-code'inti“? „Google“ projektas „Genie“ leidžia generuoti į kompiuterinius žaidimus panašius virtualius pasaulius. „SpaceX“ nusipirko „xAI“ – ar čia tam, kad darytų duomenų centrus orbitoje aplink Žemę, ar dėl žemiškesnių priežasčių? Elonas Muskas gauna karmos taškų: su Ukraina sutarė blokuoti rusų atakoms naudojamas „Starlink“ stotis. O europietiškuoju „Starlink“ vadinama 290 palydovų daugiaplanė sistema „IRIS²“ turėtų pradėti veikti metais anksčiau nei planuota. „Apple“ išleido nemažai pinigų, įsigydami „[Q.ai](http://q.ai/)“ – ar sulauksime „tylios kalbos“ atpažinimo įrenginiuose?

Full Audio at https://podcasts.apple.com/us/podcast/ai-business-and-development-daily-news-rundown/id1684415169?i=1000748010478

This week, AI showed up where decisions cannot slow down.The Seattle Seahawks will use Microsoft AI tools during the Super Bowl to analyze plays and adjust strategy in real time. At the same moment, OpenAI released Codex, embedding AI directly into live software development environments.In this episode of AI for Everyone, Harrison Painter connects those two stories and explains why they point to the same shift.AI is no longer something teams review after decisions are made.It is being used while work is happening, under pressure, with real consequences.We compare OpenAI Codex and Claude Code, unpack what this means for leaders, and outline practical steps organizations should take as AI becomes part of everyday workflows.~ Harrison Painter

話したこと シールブーム（ボンボンドロップ／偽物・プレ値・シール帳） 公式（X） ボンボンドロップ - しずくちゃん公式オンラインストア ボンボンドロップ ディズニー（商品ページ） しずくちゃん（Wikipedia） しずくちゃん【公式】（X） ぷるるんっ!しずくちゃん（Wikipedia） しまむらパーク しまむら公式オンラインストア メルカリ イオン（公式） AliExpress（日本語サイト） Temu（日本向け） 小倉競馬場（JRA 施設案内） AIと「知能は確率なのか」／Transformer・Attention・ハルシネーション Attention Is All You Need（Transformer原論文 / arXiv） World Models（世界モデルの代表的論文 / arXiv） OpenAI Codex（公式ドキュメント） Anthropic Claude Code（公式ドキュメント） Google Gemini（Gemini API モデル一覧 / 日本語） MLCommons Chakra（実行トレース/ワークロード表現プロジェクト） CWM: Code World Model（論文 / arXiv） Meta Research：CWM紹介ページ エリック・エヴァンスのドメイン駆動設計（書籍情報） ドメイン駆動設計をはじめよう（書籍情報） つくりながら学ぶ！ ドメイン駆動設計 実践入門（書籍情報） ファインディ：山下さん、増田さんと学ぶ！ドメイン駆動設計 実践入門（connpass） パタン・ランゲージ（書籍情報） 妻を帽子とまちがえた男（書籍情報） オリヴァー・サックス（Wikipedia） JASRAC：録音物・映像ソフト・出版物などの製作 話してる人 tetuo41 sugaishun snowlong Yarukinai.fmについて Yarukinai.fmをサポートする

Jason William Johnson, PhD, Founder of SoundStrategist, is driven by two lifelong passions: creating and teaching. Through SoundStrategist, Jason designs AI-powered learning experiences and intelligent coaching systems that blend music, gamification, and experiential learning to drive real skill development and engagement for enterprises and entrepreneur support organizations. We explore Jason's journey as a musician, educator, and business coach, and how he fused those disciplines into an AI-first company. Jason shares his AI for Deep Experts Framework, showing how subject-matter experts can identify an industry pain point, envision a solution, brainstorm with AI, leverage AI tools to build it, and go after high-value impact—turning deep expertise into scalable products and platforms without needing to be technical. He also explains how AI accelerates research and product design, how “vibe coding” enables rapid MVP development, and why focusing on high-value B2B impact creates faster traction with less complexity. — Turn Your Expertise Into Software with Jason W. Johnson Good day, dear listeners. Steve Preda here, the Founder of the Summit OS Group, developing the Summit OS Business Operating System. And my guest today is Jason William Johnson, PhD, the Founder of SoundStrategist. His team designs AI-powered learning experiences and deploys intelligent coaching systems for enterprises and entrepreneur support organizations blending music, gamification, and experiential learning to drive real skill development and engagement. Jason, welcome to the show.  Thanks for having me, Steve.  I’m excited to have you and to learn about how you blend music and learning and all that together. But to start with, I’d like to ask you my favorite question. What is your personal ‘Why’ and how are you manifesting it in your business?  I would say my personal ‘Why’ is creating and teaching. Those are my two passions. So when I was younger, I was always a creative. I did music, writing, and a variety of other things. So I was always been passionate about creating, but I’ve also been passionate about teaching. I've been informally a teacher for my entire adult life—coaching, training. I've also been an actual professor. So through  SoundStrategist, I’m kind of combining those two passions: the passion for teaching and imparting wisdom, along with the passion for creating through music, AI-powered experiences, gamification, and all of those different things. So I'm really in my happy place.Share on X  Yeah, sounds like it. It sounds like you're very excited talking about this. So this is quite an unusual type of business, and I wonder how do you stumbled upon this kind of combination, this portfolio of activities and put them all into a business. How did that come about? So Liam Neeson says, “I have a unique combination of skills,” like in Taken. I guess that's kind of how I came up with SoundStrategist. I've pretty much been in music forever. I've been a musician, songwriter, producer, and rapper since I was a child. My father was a musician, so it was kind of like a genetic skill that I kind of adopted and was cultivated at an early age. So I was always passionate about music. Then got older, grew up, got into business, and really became passionate about training and educating. So I pretty much started off running entrepreneurship centers. My whole career has been in small business and economic development. SoundStrategist was a happy marriage of the two when I realized, oh, I can actually use rap to teach entrepreneurship, to teach leadership skills, and now to teach AI and a variety of other things.Share on X So pretty much it was just that fusion of things. And then when we launched the company, it was around the time ChatGPT came out. So we really wanted to make sure we were building it to be AI-first. At first, we were just using AI in our business operations, but then we started experimenting  with it for client work—like integrating AI-powered coaches in some of the training programs we were running and things like that. And that really proved to be really valuable, because one of the things I learned when I was running programs throughout my career was you always wanted to have the learning side and the coaching side. Because the learning side generalizes the knowledge for everybody and kind of level-sets everybody.Share on X But everybody’s business, or everybody’s situation, is extremely unique, so you need to have that personalized support and assistance. And when we were running programs in the entrepreneurship centers I were running and things like that, we would always have human coaches. AI enabled us to kind of scale coaching for some of the programs we’re building at SoundStrategist through AI. So with me having been a business coach for over 15 years, I knew how to train the AI chatbots. It started off as simple chatbots, and now it's evolved into full agents that use voice and all those other capabilities. But it really started as, let's put some chatbots into some of our courses and some of our programs to kind of reinforce the learning, personalize it, and then it just developed from there. Okay, so there's a lot in there, and I'd like to unpack some of it. When you say use rap to teach, I’m thinking about rap is kind of a form of poetry. So how do you use poetry, or how do you use rap to teach people? Is it more catchy if it is delivered in the form of a rap song? How does it work? So you kind of want to make it catchy. Our philosophy is this: when you listen to it, it should sound like a good song.Share on X Because there’s this real risk of it sounding corny if it's done wrong, right? So we always focus on creating good music first and foremost when we’re creating a music-based lesson. So it should be a good song. It should be something you hear and think, oh, between the chorus and the music, this actually sounds good. But then, the value of music is that once you learn the song, you learn the concept, right? Because once you memorize the song, you memorize the lyrics, which means you memorize the concept. One of the things we also make sure to do is introduce concepts. The best way I could describe this is this, and this might be funny, but I grew up in the nineties, and a lot of rappers talked about selling dr*gs and things like that. I never sold dr*gs in my life. But just by listening to rap music and hearing them introduce those concepts, if I ever decided to go bad, I would have a working theory, right? So the same thing with entrepreneurship, and the same thing with business principles. You can create songs that introduce the concepts in a way where if a person's never done it, they're introduced to the vocabulary.Share on X They’re introduced to the lived experiences. They’re introduced to the core principles. And then they can take that, and then they can go apply it and have a working theory on how to execute in their business. So that’s kind of the philosophy that we took, let’s make it memorable music, but also introduce key vocabulary. Let’s introduce lived experiences. Let’s introduce key concepts so that when people are done listening to the song, they memorize it, they embody it, and they connect with it. Now they have a working theory for whatever the song is about.  And are you using AI to actually write the song?  No, we're not. That’s one of the things we haven’t really integrated on the AI front, because the AI is not good enough to take what’s exactly in my head and turn it into a song. It’s good for somebody who doesn’t have any songwriting capability or musical capability to create something that’s cool. But as a musician, as somebody who writes, you have a vision in your head on how something should sound sonically, and the AI is not good enough to take what’s in my head and put it into a song. Now, what we are using are some of the AI tools like Suno for background music. So at first, we used that to create all our background music for our courses from scratch. We are using some of the AI to help with some of the background music and everything and all of that so that we can have original stuffShare on X as opposed to having to use licensed music from places like Epidemic Sound. So we are using it for like the background music. But for the actual music-based lessons, we're still doing those old school.  Okay, that's pretty good. We are going to dive in a little bit deeper here, but before we go there, I’d like to talk about the framework that you’re bringing to the show. I think we called it the AI for Deep Experts Framework. That's the working title right now, but yeah, we're still finalizing it. But that’s the working title. Yeah.  But the idea—at least the way I'm understanding it—is that if someone has deep domain expertise, AI can be a real accelerator and amplifier of that expertise. Yep.  So people who are listening to this and they have domain expertise and they want to do AI so that they can deliver it to more people, reach more people, create more value, what is the framework? What is the five-step framework to get them there?  Number one: provided that you have deep expertise, you should be able to identify a core pain point in your respective industry that needs solving.Share on X Maybe it’s something that, throughout your career, you wanted to solve, but you weren’t able to get the resources allocated to get it done in your job. Or maybe it required some technical talent and you weren’t a developer, or whatever, right? But you should be able to identify what’s the pain point, a sticking pain point that needs to be solved—and if it's solved, it could really create value for customers. That's just old-school opportunity recognition. Number two: now, the great thing about AI is that you can leverage AI to do a lot of deep research on the problem. So obviously, you're still going to have conversations to better understand the pain point further. You're going to look at your own lived experiences and things like that. But now you can also leverage AI tools—using Perplexity or Claude—to do deep research on a market opportunity. So whether or not you have experience in market research, you can use an AI tool to help identify the total addressable market. You can brainstorm with it to uncover additional pain points, and it help you flesh out your value proposition, your concept statement, and all of those things that are critical to communicating the offering. Because before we transact in money, we always transact in language, right? So pretty much, AI can help you articulate the value proposition, understand the pain point, all of those different things. And then also if you have like deep expertise and you haven't really turned it into a framework, the AI can help you framework it and then develop a workflow to deliver value.Share on X So now you have the framework, you have the market understanding, and all of those different things. AI can even help you think through what the product would look like—the user experience, the workflow, things like that. Now you can use the AI-powered tool to help you build that. You can use something like Lovable. You can use something like Bolt. You could use something like Cursor, all different AI-powered tools. For people who are newer to development and have never done development before, I would recommend something like Lovable or maybe Bolt. But once you get more comfortable and want to make sure you're building production-ready software, then you move to something like Cursor.  Cursor has a large enough context window—the context window is basically the memory of an AI tool. It has a large enough context window to deal with complex codebases. A lot of engineers are using it to build real, production-ready platforms. But for an MVP, Bolt and Lovable are more than good enough. So one of the things I recommend when building with one of these tools is to do what's called a PRD prompt. PRD stands for Product Requirements Document.Share on X For those who aren’t familiar with software development, typically, and this is not even really happening anymore, but traditionally with software development, you would have the product manager create a Product Requirements Document. So this basically outlines the goals of the platform, target audience, core features, database, architecture, technology stack, all of the different things that engineers would need to do in order to build the platform. So you can go to something like Claude, or ChatGPT, and you can say: “Create a PRD prompt for this app idea,” and then give as much detail as possible—the features, how it works, brand colors, all of those different things. Then the AI tool—whether you're using ChatGPT, Claude, or Gemini—will generate your PRD prompt. So it’s going to be like this really, really long prompt. But it’s going to have all of the things that the AI tool, web-building or app-building tool needs to know in order to build the platform. It’s going to have all the specifications. So you copy and paste.  Is this what people call vibe coding?  Yeah, this is vibe coding. But the PRD prompt helps you become more effective at vibe coding because it gives the AI the specifications it needs and the language that it understands to increase the likelihood that you build your platform correctly. Because once you build the PRD prompt, the AI is going to know, okay, this is the database structure. It's going to know whether this is a React app versus a Next.js app. It's going to know, okay, we're building a frontend with Netlify. The stuff that you may not know, the AI will know, and it will build the platform for that. So then you take that prompt, you paste it into Lovable, paste it into Cursor, and then you can kind of get into your vibe coding flow. Don't let the hype fool you, though, because a lot of people will say, “Oh, I built this app in 15 minutes using Lovable.” No—it still requires time. But if you can build a full-stack application in two weeks when it typically takes several months, that’s still like super fast. So pretty much, on average, you can build something in a couple of weeks—especially once you get familiar with the process, you can build something in a couple of weeks. But if this is your first time ever doing this, pay attention to things like when the app debugs and some of the other issues that come up.  Start paying attention because you’re going to learn certain things by doing. As you go through the process, you'll begin to understand things like, okay, this is what an edge function is, this is what a backend is. You’ll start learning these different things as you’re going through the process, right? So you get the platform built. Now the next step is you want to distribute the platform. So obviously, if you’ve been in your industry for a while and you have some expertise, you should have some distribution. You should have some folks in your space who are your ICP that you can kind of start having some customer conversations with and start trying to sell the platform. One of the things that I always recommend is going B2B and selling something for significant valueShare on X as opposed to going B2C and selling a bunch of $19.99 subscriptions. And the reason for that is a couple of different things. Number one, when you have to do a lot of volume, your business model becomes more complicated. And then you have to introduce things to manage that volume. Whereas if you’re selling a solution that’s a five-figure to six-figure offering, like 10 clients, 15 clients, the amount of money that you can get to with less complexity in your business model. So I always say go B2B, at least a five-figure annual offering, because I know most of the offerings that we offer are at least high five figures, low six figures—subscriptions, SaaS licensing, or whatever. And that way it just introduces less complexity to your business model, and it allows you to get as much revenue as possible. And then as you go to market, you’re going to learn. So the learning aspect, you’re going to learn maybe customers want this or this feature. We thought the people were going to use the platform this way, but they’re actually using it this way. So you’re always learning, always evolving, and adjusting the offering. Okay, so let's say I have deep expertise in some area—maybe investment banking or whatever. I want to use AI. I identify an industry pain point that I've addressed or maybe I personally experienced. I visualize a solution, then I brainstorm with ChatGPT or Claude or whatever, figure out what to do, and then I leverage AI tools like Cursor, Lovable, or Bolt. I set the price point. I go B2B. Is this something that, as a subject-matter expert, is efficient for me to do myself because I have the expertise and the vision? Or is it better for me to hire someone to do this?  It depends on what your bandwidth is. I mean, pretty much I’m of the firm belief that like these are skills that you probably want to unlock anyway. So it might be worth going through the process of learning the tools, leveraging them, and everything, and all of that. And that’s kind of how you future-proof yourself. Now, obviously, if you have bandwidth limitations, there are firms and organizations that you could hire, et cetera, et cetera, that can do it for you. Obviously, developers and things like that. But the funny thing about a lot of developers is, even though they're using AI, they're still charging the prices they charged before AI, right? They’re just getting it done faster, and their margins are a lot lower. So you're still going to pay, in a lot of instances, developer pricing for a platform. Those are the things that you have to consider as far as your own personal situation. But me personally, I believe these are skills worth unlocking.Share on X Because one of the things is, if you get very senior in your career—let's say you've been there 15, 16 years, 20 years—we all know there's this point where you either move up to the C-suite or you get caught in upper-middle-management purgatory, where you're kind of in that VP, senior director space, et cetera, et cetera, and you just kind of hover there. At that point, your career moves tend to be lateral—going from one VP role to another VP role, one senior director role to another senior director role, right? At that point, your income potential starts to get limited. So unlocking one of these skills and becoming more entrepreneurial is something I genuinely believe is worth developing personally. And what would you say is the time requirement for someone to get competent in vibe coding?  Three months minimal. You could be pretty solid in three months.  But three months full-time or three months part-time?  Three months part-time.  So three months. That's about 143 working hours in a regular month. So that's basically around 420–430 hours if you were full-time.  If you spend weekends working on your project, learning how to build it, taking notes, and actually going through the process, you can get pretty decent in a couple of months. Now, obviously, there are still levels as you continue and to progress and things like that, but you can get pretty solid in a couple months. Another thing you want to consider is who you're selling to. You obviously wanna make sure that your platform security is really well, is really done. So even if you build it yourself and then you have an engineer do code review, that’s cheaper than having them build it. I think if you spend three months, you can get really good at building solutions for what you need to get done. And then from there, you just get better and better and better and better.  How do I know that, let's say I hire someone in Serbia to do a code review for me? Let's say I learn the vibe coding thing and create the prototype, then I have someone to clean the code. How do I know that they did a good job or not?  You really don’t. You really don’t know until the platform’s in the wild, and it’s like, okay, it’s secure. So there are some things that you can do to check behind people. Let's say you don't have the money to do a full security audit or hire someone specifically for a security review, a developer for security review. One of the things that you can do is you can do multi-agent review. Like you take your codebase, have Claude review it, have OpenAI Codex review it, have a Cursor agent review it. You have multiple agents do a review. Then they kind of check each other’s work, if you will.  They kinda identify things that others may not have identified, so you can get the collective wisdom of those three to be able to be like, “Okay, I need to shore this up. I need to fix this. I need to address that.” That gives you more confidence. It still doesn’t replace a person who has deep expertise and making sure they build secure code, but it will catch common issues, like hard-coding API keys, which is a risk, right? It’ll catch those type of things that typically happen. But let’s say you do have a security, a code review, you could just kind of take that same approach also to check their work. Because they shouldn’t find any major vulnerabilities. The AI agents that come in after it shouldn’t really find any major vulnerabilities if it was like done securely securely. Another thing to consider is that a lot of these tools use Supabase for the backend and database. Supabase also has a built-in security advisor, including an AI security advisor, that points out security issues, performance problems, and configuration errors. So like you do have some AI-powered check and balances to check behind people.Share on X  Interesting. So basically, I can audit their applications, and the AI will check the code and tell me what needs to be improved?  Yeah. And they can make the fixes for you.  Yeah. Wow, that’s amazing. It still sounds a little bit overwhelming. It’s basically a language, a new language to learn, isn’t it?  It’s not really — it’s English. That’s the amazing thing about it—it’s English. I mean, you literally talk to AI in natural language, and it builds stuff for you, which is, if somebody is like, had a idea for a minute, because I mean, pretty much running entrepreneurship centers, I’ve known so many people who’ve had ideas that they were never able to launch or build, and then they see somebody build it later. If you learn these skills, you get to the point where anything that's in your head, you can kind of start bringing it to life in reality.Share on X And even if you've got to bring somebody in to make sure it's secure and production-ready, it's way cheaper than having them build it from scratch. And then another thing that you’ll find also is if you’re able to build something, let’s say you want to turn it into a startup or something, right? It’s a lot easier to bring in a technical co-founder when they don’t got to build the thing from scratch, and then they also see that you were able to build something, they’re able to see your product vision, et cetera, et cetera. It becomes a lot more easier to recruit people who actually have that expertise into the company because you’ve already handled the hard part. You got something and it works. And all they got to do is just come in, make it safe, and make it work better.  Yeah, that is very interesting. It feels analogous to writing a book yourself or having a ghostwriter. Because essentially, you are vibe coding with a ghostwriter, right? You tell the stories, and then the ghostwriter writes the book for you. Probably now you can use  AI to do that. Yep.  But that's a skill. Not everyone has the skill to write it themselves, and then they need to go to the ghostwriter, but still is their book, right?  Yep.  So it sounds a little bit similar. That’s fascinating. So what’s the path to launching an MVP? So let’s say I’m a subject matter expert, and I want to launch an MVP within a few weeks. Is there a path for me to go there?  Once you get good with the platform, once you get comfortable with the tools, yeah. So for example, we're launching an AI platform. It's an AI coaching platform, but it's also a data analytics platform. Basically, it's targeted to entrepreneur support organizations and municipalities supporting small businesses. So on the front end, it's an AI-powered advisor — it's a hotline that people can call 24/7. But on the back end, the municipalities and entrepreneur support organizations get access to analytics from each of those calls. We built this in two weeks. We’re already talking to customers, we’re already having conversations, and all of those things. We literally brought it to market in two weeks. So the thing is, once you kind of get caught up with the tools—and I'm not a developer, I'm not a developer by trade at all. I had a tech startup before, but I was a non-technical founder. I just know how to put together a product. But once you get good with the tools, that's very conceivable. And then you just go out there, and you go in the market, you start having conversations with your ideal customer profile.Share on X As you’re going through that process, you’re learning, okay, maybe this isn’t my ideal customer profile, this is their pain point. Or maybe instead of this being the feature they want, this is the feature they want. And the crazy thing about it is in the past you had to really get that ICP real tight and the feature set real tight because it cost so much money to go back and have to make tweaks and changes and to get it to market in the first place. Now, you can get a new feature added in the afternoon. It allows you to go to market a little bit faster. You don’t have to have the ideal feature set. You don’t have to have the ICP figured out. You get out there, you learn, and then you’re able to iterate a lot faster because the cost of development is super cheap now, and the speed in which like new features can be added or deprecated is a lot faster. So it allows you to go to market a lot faster than in the past.  Okay, I got it. You can do this, you can code. What do you recommend for someone who’s starting out? You mentioned Lovable, Bolt, and then Cursor. Is Cursor like an advanced product?  Cursor’s a little bit more advanced, but if you want to build production-ready software, it's something you're going to eventually have to use. But can you convert from Lovable to Cursor?  Yes, you can. Yep. So what you typically do — and I still do this to this day — is every time I launch a product, I build it in Bolt first. You could use Bolt or Lovable, either one's fine. I use Bolt because Bolt came out first, and that's what I started using. Then Lovable came out like a month later. But I use Bolt. I’ll spin up the idea in Bolt. And the reason I like doing it in Bolt or Lovable is that it's really good at doing two things. It's really good at quickly launching your initial feature set, and then spinning up your backend. Your database — it's really good at that. So I start off in Bolt, then I connect it to a repository.  For those who aren't familiar with GitHub, there's a button in Bolt or Lovable where you can easily connect it to a GitHub repository. So then once I kind of get the app to a point where the basic skeleton is set, then I go into Cursor. Then I pull the repository into Cursor and do the heavy work. The reason Cursor has a learning curve is because there are still some traditional developer things you need to know to spin up a project. Your initial database — it's a lot harder to spin up your initial database and backend in Cursor. It's also harder to identify your initial libraries and all of those things. If you're a developer, it's not difficult. But if you're new, it is. Bolt and Lovable abstract those things out for you. So you start it off in Bolt or Lovable. Basically, since they're limited in their context windows, when you're trying to build something complex, eventually they start making a whole bunch of errors. They basically start getting stup*d. That's when you know it's time to move to Cursor, because Cursor can handle the heavy lifting. So if you build in Bolt or Lovable until it gets stup*d, then you move to Cursor for the heavy lifting.  And then is there a point where Cursor gets stup*d as well? No. Cursor has a couple of different things that allow it to extend its context window, which is his memory. You can put documentation into Cursor. For example, whatever your PRD prompt was, you can save that as a document in Cursor. You can also set rules. One of my rules in Cursor is: I'm not technical, so explain everything in layman's terms. And then as you’re starting to build code, you can save that code or you can point it to that repository. So there's some more flexibility with Cursor as far as managing your context window.Share on X But with Bolt and Lovable, the context window is more limited right now. So I start off in those, and then once I kind of get the skeleton up, then I move to Cursor. And at that point, a lot of the complicated things like spinning up your dev environment and all those things are kind of abstracted out. Then you can just jump in and use it the same way you use Bolt and Lovable. Fantastic. Fantastic. So, Jason, super helpful information for domain experts who want to build an application that will help them promote their product or manifest their ideas in product form. I think that’s super powerful. So if someone would like to learn about SoundStrategist and what SoundStrategist can do for them in terms of learning and experiential products, incorporating music, or building curriculum, or they would just like to connect with you to learn more about what you can do for them, where should they go?  Jason William Johnson, PhD, on LinkedIn, or www.getsoundstrategies.com.  Okay. Well, Jason William Johnson, you are really ahead of the curve, especially connecting this whole idea of vibe coding to people who are subject matter experts and not technical. And you know it because you don't come from a technical background, yet you've mastered it. I’m living it. Everything I’m sharing—this is not like a theoretical framework. I'm living all of this. So everything I’m saying. Super authentic. And especially coming from you—you understand what it's like to not be technical person, learning this, applying this.  So if you'd like to do this, learn more, or maybe have Jason guide you, reach out to him. You can find him on LinkedIn at Jason William Johnson, PhD, or visit www.getsoundstrategies.com. And if you enjoyed this episode, make sure you follow us and subscribe on YouTube, follow us on LinkedIn, and on Apple Podcasts. Because every week I bring a super interesting entrepreneur, subject matter expert, or a combination of the two—like Jason—to the show, who will help you accelerate your journey with frameworks and AI frameworks in that gear. So thank you for coming, Jason, and thank you for listening. Important Links: Jason's LinkedIn Jason's website

From the frontlines of OpenAI's Codex and GPT-5 training teams, Bryan and Bill are building the future of AI-powered coding—where agents don't just autocomplete, they architect, refactor, and ship entire features while you sleep. We caught up with them at AI Engineer Conference right after the launch of Codex Max, OpenAI's newest long-running coding agent designed to work for 24+ hours straight, manage its own context, and spawn sub-agents to parallelize work across your entire codebase.We sat down with Bryan and Bill to dig into what it actually takes to train a model that developers trust—why personality, communication, and planning matter as much as raw capability, how Codex is trained with strong opinions about tools (it loves rg over grep, seriously), why the abstraction layer is moving from models to full-stack agents you can plug into VS Code or Zed, how OpenAI partners co-develop tool integrations and discover unexpected model habits (like renaming tools to match Codex's internal training), the rise of applied evals that measure real-world impact instead of academic benchmarks, why multi-turn evals are the next frontier (and Bryan's “job interview eval” idea), how coding agents are breaking out of code into personal automation, terminal workflows, and computer use, and their 2026 vision: coding agents trusted enough to handle the hardest refactors at any company, not just top-tier firms, and general enough to build integrations, organize your desktop, and unlock capabilities you'd never get access to otherwise.We discuss:* What Codex Max is: a long-running coding agent that can work 24+ hours, manage its own context window, and spawn sub-agents for parallel work* Why the name “Max”: maximalist, maximization, speed and endurance—it's simply better and faster for the same problems* Training for personality: communication, planning, context gathering, and checking your work as behavioral characteristics, not just capabilities* How Codex develops habits like preferring rg over grep, and why renaming tools to match its training (e.g., terminal-style naming) dramatically improves tool-call performance* The split between Codex (opinionated, agent-focused, optimized for the Codex harness) and GPT-5 (general, more durable across different tools and modalities)* Why the abstraction layer is moving up: from prompting models to plugging in full agents (Codex, GitHub Copilot, Zed) that package the entire stack* The rise of sub-agents and agents-using-agents: Codex Max spawning its own instances, handing off context, and parallelizing work across a codebase* How OpenAI works with coding partners on the bleeding edge to co-develop tool integrations and discover what the model is actually good at* The shift to applied evals: capturing real-world use cases instead of academic benchmarks, and why ~50% of OpenAI employees now use Codex daily* Why multi-turn evals are the next frontier: LM-as-a-judge for entire trajectories, Bryan's “job interview eval” concept, and the need for a batch multi-turn eval API* How coding agents are breaking out of code: personal automation, organizing desktops, terminal workflows, and “Devin for non-coding” use cases* Why Slack is the ultimate UI for work, and how coding agents can become your personal automation layer for email, files, and everything in between* The 2026 vision: more computer use, more trust, and coding agents capable enough that any company can access top-tier developer capabilities, not just elite firms—Bryan & Bill (OpenAI Codex Team)* http://x.com/bfioca* https://x.com/realchillben* OpenAI Codex: https://openai.com/index/openai-codex/Where to find Latent Space* X: https://x.com/latentspacepodFull Video EpisodeTimestamps00:00:00 Introduction: Latent Space Listeners at AI Engineer Code00:01:27 Codex Max Launch: Training for Long-Running Coding Agents00:03:01 Model Personality and Trust: Communication, Planning, and Self-Checking00:05:20 Codex vs GPT-5: Opinionated Agents vs General Models00:07:47 Tool Use and Model Habits: The Ripgrep Discovery00:09:16 Personality Design: Verbosity vs Efficiency in Coding Agents00:11:56 The Agent Abstraction Layer: Building on Top of Codex00:14:08 Sub-Agents and Multi-Agent Patterns: The Future of Composition00:16:11 Trust and Adoption: OpenAI Developers Using Codex Daily00:17:21 Applied Evals: Real-World Testing vs Academic Benchmarks00:19:15 Multi-Turn Evals and the Job Interview Pattern00:21:35 Feature Request: Batch Multi-Turn Eval API00:22:28 Beyond Code: Personal Automation and Computer Use00:24:51 Vision-Native Agents and the UI Integration Challenge00:25:02 2026 Predictions: Trust, Computer Use, and Democratized Excellence Get full access to Latent.Space at www.latent.space/subscribe

From the frontlines of OpenAI's Codex and GPT-5 training teams, Bryan and Bill are building the future of AI-powered coding—where agents don't just autocomplete, they architect, refactor, and ship entire features while you sleep. We caught up with them at AI Engineer Conference right after the launch of Codex Max, OpenAI's newest long-running coding agent designed to work for 24+ hours straight, manage its own context, and spawn sub-agents to parallelize work across your entire codebase. We sat down with Bryan and Bill to dig into what it actually takes to train a model that developers trust—why personality, communication, and planning matter as much as raw capability, how Codex is trained with strong opinions about tools (it loves rg over grep, seriously), why the abstraction layer is moving from models to full-stack agents you can plug into VS Code or Zed, how OpenAI partners co-develop tool integrations and discover unexpected model habits (like renaming tools to match Codex's internal training), the rise of applied evals that measure real-world impact instead of academic benchmarks, why multi-turn evals are the next frontier (and Bryan's "job interview eval" idea), how coding agents are breaking out of code into personal automation, terminal workflows, and computer use, and their 2026 vision: coding agents trusted enough to handle the hardest refactors at any company, not just top-tier firms, and general enough to build integrations, organize your desktop, and unlock capabilities you'd never get access to otherwise. We discuss: What Codex Max is: a long-running coding agent that can work 24+ hours, manage its own context window, and spawn sub-agents for parallel work Why the name "Max": maximalist, maximization, speed and endurance—it's simply better and faster for the same problems Training for personality: communication, planning, context gathering, and checking your work as behavioral characteristics, not just capabilities How Codex develops habits like preferring rg over grep, and why renaming tools to match its training (e.g., terminal-style naming) dramatically improves tool-call performance The split between Codex (opinionated, agent-focused, optimized for the Codex harness) and GPT-5 (general, more durable across different tools and modalities) Why the abstraction layer is moving up: from prompting models to plugging in full agents (Codex, GitHub Copilot, Zed) that package the entire stack The rise of sub-agents and agents-using-agents: Codex Max spawning its own instances, handing off context, and parallelizing work across a codebase How OpenAI works with coding partners on the bleeding edge to co-develop tool integrations and discover what the model is actually good at The shift to applied evals: capturing real-world use cases instead of academic benchmarks, and why ~50% of OpenAI employees now use Codex daily Why multi-turn evals are the next frontier: LM-as-a-judge for entire trajectories, Bryan's "job interview eval" concept, and the need for a batch multi-turn eval API How coding agents are breaking out of code: personal automation, organizing desktops, terminal workflows, and "Devin for non-coding" use cases Why Slack is the ultimate UI for work, and how coding agents can become your personal automation layer for email, files, and everything in between The 2026 vision: more computer use, more trust, and coding agents capable enough that any company can access top-tier developer capabilities, not just elite firms — Bryan & Bill (OpenAI Codex Team) http://x.com/bfioca https://x.com/realchillben OpenAI Codex: https://openai.com/index/openai-codex/ Where to find Latent Space X: https://x.com/latentspacepod Substack: https://www.latent.space/ Chapters 00:00:00 Introduction: Latent Space Listeners at AI Engineer Code 00:01:27 Codex Max Launch: Training for Long-Running Coding Agents 00:03:01 Model Personality and Trust: Communication, Planning, and Self-Checking 00:05:20 Codex vs GPT-5: Opinionated Agents vs General Models 00:07:47 Tool Use and Model Habits: The Ripgrep Discovery 00:09:16 Personality Design: Verbosity vs Efficiency in Coding Agents 00:11:56 The Agent Abstraction Layer: Building on Top of Codex 00:14:08 Sub-Agents and Multi-Agent Patterns: The Future of Composition 00:16:11 Trust and Adoption: OpenAI Developers Using Codex Daily 00:17:21 Applied Evals: Real-World Testing vs Academic Benchmarks 00:19:15 Multi-Turn Evals and the Job Interview Pattern 00:21:35 Feature Request: Batch Multi-Turn Eval API 00:22:28 Beyond Code: Personal Automation and Computer Use 00:24:51 Vision-Native Agents and the UI Integration Challenge 00:25:02 2026 Predictions: Trust, Computer Use, and Democratized Excellence

Alexander Embiricos leads product on Codex, OpenAI's powerful coding agent, which has grown 20x since August and now serves trillions of tokens weekly. Before joining OpenAI, Alexander spent five years building a pair programming product for engineers. He now works at the frontier of AI-led software development, building what he describes as a software engineering teammate—an AI agent designed to participate across the entire development lifecycle.We discuss:1. Why Codex has grown 20x since launch and what product decisions unlocked this growth2. How OpenAI built the Sora Android app in just 18 days using Codex3. Why the real bottleneck to AGI-level productivity isn't model capability—it's human typing speed4. The vision of AI as a proactive teammate, not just a tool you prompt5. The bottleneck shifting from building to reviewing AI-generated work6. Why coding will be a core competency for every AI agent—because writing code is how agents use computers best—Brought to you by:WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs: https://workos.com/lennyFin—The #1 AI agent for customer service: https://fin.ai/lennyJira Product Discovery—Confidence to build the right thing: https://atlassian.com/lenny/?utm_source=lennypodcast&utm_medium=paid-audio&utm_campaign=fy24q1-jpd-imc—Transcript: https://www.lennysnewsletter.com/p/why-humans-are-ais-biggest-bottleneck—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/180365355/my-biggest-takeaways-from-this-conversation—Where to find Alexander Embiricos:• X: https://x.com/embirico• LinkedIn: https://www.linkedin.com/in/embirico—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Alexander Embiricos (05:13) The speed and ambition at OpenAI(11:34) Codex: OpenAI's coding agent(15:43) Codex's explosive growth(24:59) The future of AI and coding agents(33:11) The impact of AI on engineering(44:08) How Codex has impacted the way PMs operate(45:40) Throwaway code and ubiquitous coding(47:10) Shipping the Sora Android app(49:01) Building the Atlas browser(53:34) Codex's impact on productivity(55:35) Measuring progress on Codex(58:09) Why they are building a web browser(01:01:58) Non-engineering use cases for Codex(01:02:53) Codex's capabilities(01:04:49) Tips for getting started with Codex(01:05:37) Skills to lean into in the AI age(01:10:36) How far are we from a human version of AI?(01:13:31) Hiring and team growth at Codex(01:15:47) Lightning round and final thoughts—Referenced:• OpenAI: https://openai.com• Codex: https://openai.com/codex• Inside ChatGPT: The fastest-growing product in history | Nick Turley (Head of ChatGPT at OpenAI): https://www.lennysnewsletter.com/p/inside-chatgpt-nick-turley• Dropbox: http://dropbox.com• Datadog: https://www.datadoghq.com• Andrej Karpathy on X: https://x.com/karpathy• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• Atlas: https://openai.com/index/introducing-chatgpt-atlas• How Block is becoming the most AI-native enterprise in the world | Dhanji R. Prasanna: https://www.lennysnewsletter.com/p/how-block-is-becoming-the-most-ai-native• Goose: https://block.xyz/inside/block-open-source-introduces-codename-goose• Lessons on building product sense, navigating AI, optimizing the first mile, and making it through the messy middle | Scott Belsky (Adobe, Behance): https://www.lennysnewsletter.com/p/lessons-on-building-product-sense• Sora Android app: https://play.google.com/store/apps/details?id=com.openai.sora&hl=en_US&pli=1• The OpenAI Podcast—ChatGPT Atlas and the next era of web browsing: https://www.youtube.com/watch?v=WdbgNC80PMw&list=PLOXw6I10VTv9GAOCZjUAAkSVyW2cDXs4u&index=2• How to measure AI developer productivity in 2025 | Nicole Forsgren: https://www.lennysnewsletter.com/p/how-to-measure-ai-developer-productivity• Compiling: https://3d.xkcd.com/303• Jujutsu Kaisen on Netflix: https://www.netflix.com/title/81278456• Tesla: https://www.tesla.com• Radical Candor: From theory to practice with author Kim Scott: https://www.lennysnewsletter.com/p/radical-candor-from-theory-to-practice• Andreas Embirikos: https://en.wikipedia.org/wiki/Andreas_Embirikos• George Embiricos: https://en.wikipedia.org/wiki/George_Embiricos: https://en.wikipedia.org/wiki/George_Embiricos—Recommended books:• Culture series: https://www.amazon.com/dp/B07WLZZ9WV• The Lord of the Rings: https://www.amazon.com/Lord-Rings-J-R-R-Tolkien/dp/0544003411• A Fire Upon the Deep (Zones of Thought series Book 1): https://www.amazon.com/Fire-Upon-Deep-Zones-Thought/dp/1250237750• Radical Candor: Be a Kick-Ass Boss Without Losing Your Humanity: https://www.amazon.com/Radical-Candor-Kick-Ass-Without-Humanity/dp/1250103509—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

AI agents can reason, but they don't actually understand your systems. MCP servers fix that by giving your copilots and assistants structured access to your tools, APIs, and CI/CD data. And the best part is that building one is much simpler than most people expect.In the latest episode of Technical Tips, Tommy walks through how to create a functional MCP server in just a few minutes. He connects it to Semaphore's API, pulls real project data, and shows how to expose those insights to tools like OpenAI Codex. It's a straightforward, hands-on demo that finally makes MCP feel practical instead of theoretical.If you've been curious about MCP or you're trying to make your AI tools genuinely useful in day-to-day engineering work, this is the perfect place to start. You'll see how an MCP server communicates with AI clients, how to wrap a real API into it, how to test everything using the MCP Inspector, and how easily you can turn your CI/CD workflows into a conversational experience.By the end, you'll understand how surprisingly little code it takes to give your AI agents real operational awareness.Access the full guide on our blog. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit semaphoreio.substack.com

Google has launched its latest AI model, Gemini 3, which is designed to enhance multimodal processing capabilities by simultaneously handling text, images, and audio. This model, particularly the Gemini 3 Pro version, aims to improve the accuracy and reasoning capabilities of AI systems, positioning Google to compete more effectively with OpenAI in the consumer AI market. The introduction of Gemini 3 Pro is part of a broader trend where companies are increasingly integrating AI into their existing workflows, as seen with Intuit's incorporation of ChatGPT into its financial services, which seeks to streamline tax and accounting processes for users.OpenAI has also made strides with the release of Codex MAX, an upgraded version of its programming-focused AI model that reportedly executes coding tasks 27-42% faster than its predecessor while using 30% fewer tokens. This enhancement is expected to improve coding efficiency and cybersecurity by enabling long-horizon reasoning, which is essential for identifying vulnerabilities in code. Additionally, PIA has launched an Automation Hub, a marketplace for managed service providers (MSPs) to access pre-built automations, signaling a shift towards purchasing rather than developing custom solutions.The episode also discusses the evolving regulatory landscape in Europe, where proposed changes to the General Data Protection Regulation (GDPR) and AI laws aim to simplify compliance requirements. These changes could create ambiguity regarding the use of personal data for AI training, raising concerns about potential liabilities for businesses. The simplification of cookie consent policies is another significant development, which may shift responsibility to businesses for compliance with user preferences.For MSPs and IT service leaders, these developments underscore the importance of staying informed about AI advancements and regulatory changes. As AI becomes more integrated into business operations, the ability to evaluate and govern these technologies will be crucial. MSPs must navigate the complexities of compliance and operational efficiency while ensuring that clients are prepared for the implications of AI adoption, particularly in light of the ongoing challenges related to AI's performance in tasks such as mathematical calculations. Four things to know today 00:00 New AI Models, Embedded Integrations, and Automation Marketplaces Signal the Next Shift in How MSPs Evaluate and Govern AI Tools05:42 Europe Softens Privacy and AI Regulations as Cookie Rules Shift and High-Risk AI Deadlines Are Delayed08:51 AI Adoption Rises but Value Lags: Workforce Gaps, Model Failures, and Overhyped Expectations Confront IT Teams14:05 Huntress Expands Into Identity Security as N-able Adds CMMC Controls, Signaling New Expectations for MSP Discipline This is the Business of Tech.     Supported by:  https://try.auvik.com/dave-switchhttps://scalepad.com/dave/

Jupyter AI v3 marks a major step forward in integrating intelligent coding assistance directly into JupyterLab. Discussed by AWS engineers David Qiu and Piyush Jain at JupyterCon, the new release introduces AI personas— customizable, specialized assistants that users can configure to perform tasks such as coding help, debugging, or analysis. Unlike other AI tools, Jupyter AI allows multiple named agents, such as “Claude Code” or “OpenAI Codex,” to coexist in one chat. Developers can even build and share their own personas as local or pip-installable packages. This flexibility was enabled by splitting Jupyter AI's previously large, complex codebase into smaller, modular packages, allowing users to install or replace components as needed. Looking ahead, Qiu envisions Jupyter AI as an “ecosystem of AI personas,” enabling multi-agent collaboration where different personas handle roles like data science, engineering, and testing. With contributors from AWS, Apple, Quansight, and others, the project is poised to expand into a diverse, community-driven AI ecosystem.Learn more from The New Stack about the latest in Jupyter AI development: Introduction to Jupyter Notebooks for DevelopersDisplay AI-Generated Images in a Jupyter NotebookJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Our 221st episode with a summary and discussion of last week's big AI news!Recorded on 09/19/2025Note: we transitioned to a new RSS feed and it seems this did not make it to there, so this may be posted about 2 weeks past the release date.Hosted by Andrey Kurenkov and co-hosted by Michelle LeeFeel free to email us your questions and feedback at contact@lastweekinai.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:OpenAI releases a new version of Codex integrated with GPT-5, enhancing coding capabilities and aiming to compete with other AI coding tools like Cloud Code.Significant updates in the robotics sector include new ventures in humanoid robots from companies like Figure AI and China's Unitree, as well as expansions in robotaxi services from Tesla and Amazon's Zoox.New open-source models and research advancements were discussed, including Google's DeepMind's self-improving foundation model for robotics and a physics foundation model aimed at generalizing across various physical systems.Legal battles continue to surface in the AI landscape with Warner Bros. suing MidJourney for copyright violations and Rolling Stone suing Google over AI-generated content summaries, highlighting challenges in AI governance and ethics.Timestamps:(00:00:10) Intro / BanterTools & Apps(00:02:33) OpenAI upgrades Codex with a new version of GPT-5(00:04:02) Google Injects Gemini Into Chrome as AI Browsers Go Mainstream | WIRED(00:06:14) Anthropic's Claude can now make you a spreadsheet or slide deck. | The Verge(00:07:12) Luma AI's New Ray3 Video Generator Can 'Think' Before Creating - CNETApplications & Business(00:08:32) OpenAI secures Microsoft's blessing to transition its for-profit arm | TechCrunch(00:10:31) Microsoft to lessen reliance on OpenAI by buying AI from rival Anthropic | TechCrunch(00:12:00) Figure AI passes $1B with Series C funding toward humanoid robot development - The Robot Report(00:13:52) China's Unitree plans $7 billion IPO valuation as humanoid robot race heats up(00:15:45) Tesla's robotaxi plans for Nevada move forward with testing permit | TechCrunch(00:17:48) Amazon's Zoox jumps into U.S. robotaxi race with Las Vegas launch(00:19:27) Replit hits $3B valuation on $150M annualized revenue | TechCrunch(00:21:14) Perplexity reportedly raised $200M at $20B valuation | TechCrunchProjects & Open Source(00:22:08) [2509.07604] K2-Think: A Parameter-Efficient Reasoning System(00:24:31) [2509.09614] LoCoBench: A Benchmark for Long-Context Large Language Models in Complex Software EngineeringResearch & Advancements(00:28:17) [2509.15155] Self-Improving Embodied Foundation Models(00:31:47) [2509.13805] Towards a Physics Foundation Model(00:34:26) [2509.12129] Embodied Navigation Foundation ModelPolicy & Safety(00:37:49) Anthropic endorses California's AI safety bill, SB 53 | TechCrunch(00:40:12) Warner Bros. Sues Midjourney, Joins Studios' AI Copyright Battle(00:42:02) Rolling Stone Publisher Sues Google Over AI Overview SummariesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Episode 142: In this episode of Critical Thinking - Bug Bounty Podcast Rez0 and Gr3pme join forces to discuss Websocket research, Meta's $111750 Bug, PROMISQROUTE, and the opportunities afforded by going full time in Bug Bounty.Follow us on twitter at: https://x.com/ctbbpodcastGot any ideas and suggestions? Feel free to send us any feedback here: info@criticalthinkingpodcast.ioShoutout to YTCracker for the awesome intro music!====== Links ======Follow your hosts Rhynorater and Rez0 on Twitter: ====== Ways to Support CTBBPodcast ======Hop on the CTBB Discord at https://ctbb.show/discord!We also do Discord subs at $25, $10, and $5 - premium subscribers get access to private masterclasses, exploits, tools, scripts, un-redacted bug reports, etc.You can also find some hacker swag at https://ctbb.show/merch!Today's Sponsor: ThreatLocker. Check out ThreatLocker DACToday's Guest: https://x.com/gr3pme====== This Week in Bug Bounty ======New Monthly Dojo challenge and Dojo UI designThe ultimate Bug Bounty guide to exploiting race condition vulnerabilities in web applicationsWatch Our boy Brandyn on the TV====== Resources ======murtasecWebSocket Turbo Intruder: Unearthing the WebSocket GoldmineChaining Path Traversal Vulnerability to RCE — Meta's 111,750$ BugFinding vulnerabilities in modern web apps using Claude Code and OpenAI CodexMind the GapPROMISQROUTE====== Timestamps ======(00:00:00) Introduction(00:05:16) Full Time Bug Bounty and Business Startups(00:15:50) Websockets(00:22:17) Meta's $111750 Bug(00:28:38) Finding vulns using Claude Code and OpenAI Codex(00:39:32) Time-of-Check to Time-of-Use Vulns in LLM-Enabled Agents(00:45:22) PROMISQROUTE

Our 221st episode with a summary and discussion of last week's big AI news! Recorded on 09/19/2025 Hosted by Andrey Kurenkov and co-hosted by Michelle Lee Feel free to email us your questions and feedback at contact@lastweekinai.com and/or hello@gladstone.ai Read out our text newsletter and comment on the podcast at https://lastweekin.ai/ In this episode: OpenAI releases a new version of Codex integrated with GPT-5, enhancing coding capabilities and aiming to compete with other AI coding tools like Cloud Code. Significant updates in the robotics sector include new ventures in humanoid robots from companies like Figure AI and China's Unitree, as well as expansions in robotaxi services from Tesla and Amazon's Zoox. New open-source models and research advancements were discussed, including Google's DeepMind's self-improving foundation model for robotics and a physics foundation model aimed at generalizing across various physical systems. Legal battles continue to surface in the AI landscape with Warner Bros. suing MidJourney for copyright violations and Rolling Stone suing Google over AI-generated content summaries, highlighting challenges in AI governance and ethics. Timestamps: (00:00:10) Intro / Banter Tools & Apps (00:02:33) OpenAI upgrades Codex with a new version of GPT-5 (00:04:02) Google Injects Gemini Into Chrome as AI Browsers Go Mainstream | WIRED (00:06:14) Anthropic's Claude can now make you a spreadsheet or slide deck. | The Verge (00:07:12) Luma AI's New Ray3 Video Generator Can 'Think' Before Creating - CNET Applications & Business (00:08:32) OpenAI secures Microsoft's blessing to transition its for-profit arm | TechCrunch (00:10:31) Microsoft to lessen reliance on OpenAI by buying AI from rival Anthropic | TechCrunch (00:12:00) Figure AI passes $1B with Series C funding toward humanoid robot development - The Robot Report (00:13:52) China's Unitree plans $7 billion IPO valuation as humanoid robot race heats up (00:15:45) Tesla's robotaxi plans for Nevada move forward with testing permit | TechCrunch (00:17:48) Amazon's Zoox jumps into U.S. robotaxi race with Las Vegas launch (00:19:27) Replit hits $3B valuation on $150M annualized revenue | TechCrunch (00:21:14) Perplexity reportedly raised $200M at $20B valuation | TechCrunch Projects & Open Source (00:22:08) [2509.07604] K2-Think: A Parameter-Efficient Reasoning System (00:24:31) [2509.09614] LoCoBench: A Benchmark for Long-Context Large Language Models in Complex Software Engineering Research & Advancements (00:28:17) [2509.15155] Self-Improving Embodied Foundation Models (00:31:47) [2509.13805] Towards a Physics Foundation Model (00:34:26) [2509.12129] Embodied Navigation Foundation Model Policy & Safety (00:37:49) Anthropic endorses California's AI safety bill, SB 53 | TechCrunch (00:40:12) Warner Bros. Sues Midjourney, Joins Studios' AI Copyright Battle (00:42:02) Rolling Stone Publisher Sues Google Over AI Overview Summaries

Das ist das KI-Update vom 17.09.2025 unter anderem mit diesen Themen: OpenAI startet Codex für Softwareentwicklung KI soll Werbeeinnahmen auf Youtube steigern Europäische Alternativen zu Palantir rücken in den Fokus und KI lokalisiert feindliche U-Boote Links zu allen Themen der heutigen Folge findet Ihr hier: https://heise.de/-10653860 https://www.heise.de/thema/KI-Update https://pro.heise.de/ki/ https://www.heise.de/newsletter/anmeldung.html?id=ki-update https://www.heise.de/thema/Kuenstliche-Intelligenz https://the-decoder.de/ https://www.heiseplus.de/podcast https://www.ct.de/ki Eine neue Folge gibt es montags, mittwochs und freitags ab 15 Uhr.

Hey everyone, Alex here

Todd Ogasawara and Jon Westfall covered a range of interesting topics, from real-world natural disasters to the cutting edge of AI development and personal tech. Todd shared his recent experience during a statewide tsunami alert in Hawaii, triggered by an 8.8 magnitude earthquake off Russia. While initial information was well-managed, he highlighted significant issues with traffic chaos during evacuation and a concerning lack of information post-wave impact. On the technology front, Todd discussed Google Notebook LM, praising its ability to create succinct summaries and slideshows with voiceovers from source material. He also introduced Google Opal, a new experimental tool from Google Labs that allows users to build and share powerful AI mini-apps using natural language and visual editing, describing it as a "step beyond Visual Basic" for accelerating AI prototyping and workflows. Jon Westfall also shared his recent tech purchases and an exciting new project. He acquired an 8Bitdo Micro Bluetooth Gamepad, a pocket-sized mini-controller weighing just 24.8 grams with 16 buttons. Its versatility allows it to function as a game controller for Switch, Android, and Raspberry Pi, or as a keyboard mode device for various applications, including as a remote for his new Kobo Libra Colour eReader. The Kobo Libra Colour features a 7" E Ink Kaleido™ 3 color display and Kobo Stylus 2 compatibility for colorful mark-ups and note-taking, with notebooks backed up to Kobo Cloud, Dropbox, or Google Drive. Jon also unveiled his open-source project, Uncle John's Bank, a self-hostable banking system for parents and kids designed to teach financial literacy, notably incorporating daily compounding interest and Certificates of Deposit (CDs). This sophisticated project was developed remarkably fast (75 hours) thanks to extensive use of OpenAI Codex, which integrated directly with his GitHub repository, even writing developer documentation. However, Jon noted a peculiar issue where GitHub Copilot (AI) reviewing Codex (AI)-generated code sometimes caused new problems, suggesting limitations in AI-to-AI code interaction. Finally, Jon shared intriguing results from asking various AIs (Google Gemini, ChatGPT, Microsoft Copilot, Anthropic Claude) for investment advice, observing their diverse recommendations and risk appetites.

# SummaryIn this conversation, Tim Abell and David Sheardown explore the challenges and innovations in productivity tools and AI coding assistants and the overwhelming landscape of AI tools available for software development.The dialogue delves into the nuances of using AI in coding, the potential of multi-agent systems, and the importance of context in achieving optimal results.They also touch on the future of AI in automation and the implications of emerging technologies.# TakeawaysAI is reshaping the workplace, requiring adaptation from professionals.Understanding engineering problems requires a structured approach.AI coding tools are rapidly evolving and can enhance productivity.Providing clear context improves AI coding results.Multi-agent systems can coordinate tasks effectively.The landscape of AI tools is overwhelming but offers opportunities.Understanding the limitations of AI tools is crucial for effective use.Innovations in AI are making automation more accessible.It's important to balance AI use with traditional coding skills.The future of AI in software development is promising but requires careful navigation.# Full detailsIn this episode of Software Should Be Free, Tim Abell and David Sheardown delve into the rapidly evolving landscape of AI-powered coding assistants. They share hands-on experiences with various AI coding tools and models, discuss best practices (like providing clear project context vs. “vibe coding”), and outline a mental model to categorize these tools. Below are key highlights with timestamps, followed by a comprehensive list of resources mentioned.Episode Highlights00:05 – Introduction: Tim expresses feeling overwhelmed by the proliferation of AI coding tools. As a tech lead and coder, he's been trying to keep up with the hype versus reality. The discussion is set to compare notes on different tools they've each tried and to map out the current AI coding assistant landscape.01:50 – Tools Tried and Initial Impressions: David shares his journey starting with Microsoft-centric tools. His go-to has been GitHub Copilot (integrated in VS Code/Visual Studio), which now leverages various models (including OpenAI and Anthropic). He has also experimented with several alternatives: Claude Code (Anthropic's CLI agentic coder), OpenAI's Codex CLI (an official terminal-based coding agent by OpenAI), Google's Gemini CLI (an open-source command-line AI agent giving access to Google's Gemini model), and Manus (a recently introduced autonomous AI coding agent). These tools all aim to boost developer productivity, but results have been mixed – for example, Tim tried the Windsurf editor (an AI-powered IDE) using an Anthropic Claude model (“Claude 3.5 Sonnet”) and found it useful but “nowhere near 10×” productivity improvement as some LinkedIn influencers claimed. The community's take on these tools is highly polarized, with skeptics calling it hype and enthusiasts claiming dramatic gains.04:39 – Importance of Context (Prompt Engineering vs “Vibe Coding”): A major theme is providing clear requirements and context to the AI. David found that all these coding platforms (whether GUI IDE like Windsurf or Cursor, or CLI tools like Claude Code and Codex) allow you to supply custom instructions and project docs (often via Markdown) – essentially like giving the AI a spec. When he attempted building new apps, he had much more success by writing a detailed PRD (Product Requirements Document) and feeding it to the AI assistant. For instance, he gave the same spec (tech stack, features, and constraints) to Claude Code, OpenAI's Codex CLI, and Gemini CLI, and each generated a reasonable project scaffold in minutes. All stuck to the specified frameworks and even obeyed instructions like “don't add extra packages unless approved.” This underscores that if you prompt these tools with structured context (analogous to good old-fashioned requirements documents), they perform markedly better. David mentions that Amazon's new AI IDE, Kiro (introduced recently as a spec-driven development tool) embraces this “context-first” approach – aiming to eliminate one-shot “vibe coding” chaos by having the AI plan from a spec before writing code. He notes that using top-tier models (Anthropic's Claude “Opus 4” was referenced as an example, available only in an expensive plan) can further improve adherence to instructions, but even smaller models do decently if guided well.07:03 – Community Reactions: The conversation touches on the culture around these tools. There's acknowledgment of toxicity in some online discussions – e.g. seasoned engineers scoffing at newcomers using AI (“non-engineers” doing vibe coding). Tim and David distance themselves from gatekeeping attitudes; their stance is that anyone interested in the tech should be encouraged, while just being mindful of pitfalls (like code quality, security, or privacy issues when using AI). They see value in exploring all levels of AI assistance, provided one remains pragmatic about what works and stays cautious about sensitive data.29:57 – Models + 4 Levels of AI Coding Tool: Tim introduces a mental model to frame the AI coding assistant ecosystem (around 29:57). The idea is to separate the foundational models from the tools built on top, and to classify those tools into four levels of increasing capability:Underlying Models: First, there are the core large language models themselves – e.g. OpenAI's GPT-4, Anthropic's Claude (various versions like Claude 1.* and 2, including fast “Sonnet” models and the heavier “Opus” models), Google's Gemini model, as well as open-source local models. These are the engines that power everything else, but interacting with raw models isn't the whole story.Level 1 – Basic Chat Interface: Tools where you interact via a simple chat UI (text in/out) with no direct integration into your coding environment. ChatGPT in the browser, or voice assistants that can produce code snippets on request, fall here. They can write code based on prompts, but you have to copy-paste results – the AI isn't tied into your files or IDE.Level 2 – Agentic IDE/CLI Assistants: Tools that deeply integrate with your development environment, able to edit files and execute commands. This includes AI-augmented IDEs and editors like Windsurf Editor (a standalone AI-native IDE) and Cursor (AI-assisted code editor), as well as command-line agents that can manipulate your project (like the CLI versions of Claude Code, OpenAI Codex, or Gemini CLI). At this level, the AI can read your project files, make changes, create new files, run build/test commands, etc., acting almost like a pair programmer who can use the keyboard and terminal. (For example, Windsurf's “Cascade” agent mode and Cursor's agent mode allow multi-file edits and running shell commands automatically.)Level 3 – Enhanced Context and Memory: Tools or techniques focused on feeding the model more project knowledge and context (sometimes dubbed “context en...

News includes the public launch of Phoenix.new - Chris McCord's revolutionary AI-powered Phoenix development service with full browser IDE and remote runtime capabilities, Ecto v3.13 release featuring the new transact/1 function and built-in JSON support, Nx v0.10 with improved documentation and NumPy comparisons, Phoenix 1.8 getting official security documentation covering OWASP Top 10 vulnerabilities, Zach Daniel's new "evals" package for testing AI language model performance, and ElixirConf US speaker announcements with keynotes from José Valim and Chris McCord. Saša Jurić shares his comprehensive thoughts on Elixir project organization and structure, Sentry's Elixir SDK v11.x adding OpenTelemetry-based tracing support, and more! Then we dive deep with Chris McCord himself for an exclusive interview about his newly launched phoenix.new service, exploring how AI-powered code generation is bringing Phoenix applications to people from outside the community. We dig into the technology behind the remote runtime and what it means for the future of rapid prototyping in Elixir. Show Notes online - http://podcast.thinkingelixir.com/259 (http://podcast.thinkingelixir.com/259) Elixir Community News https://www.honeybadger.io/ (https://www.honeybadger.io/utm_source=thinkingelixir&utm_medium=podcast) – Honeybadger.io is sponsoring today's show! Keep your apps healthy and your customers happy with Honeybadger! It's free to get started, and setup takes less than five minutes. https://phoenix.new/ (https://phoenix.new/?utm_source=thinkingelixir&utm_medium=shownotes) – Chris McCord's phoenix.new project is open to the public https://x.com/chris_mccord/status/1936068482065666083 (https://x.com/chris_mccord/status/1936068482065666083?utm_source=thinkingelixir&utm_medium=shownotes) – Phoenix.new was opened to the public - a service for building Phoenix apps with AI runtime, full browser IDE, and remote development capabilities https://github.com/elixir-ecto/ecto (https://github.com/elixir-ecto/ecto?utm_source=thinkingelixir&utm_medium=shownotes) – Ecto v3.13 was released with new features including transact/1, schema redaction, and built-in JSON support https://github.com/elixir-ecto/ecto/blob/v3.13.2/CHANGELOG.md#v3132-2025-06-24 (https://github.com/elixir-ecto/ecto/blob/v3.13.2/CHANGELOG.md#v3132-2025-06-24?utm_source=thinkingelixir&utm_medium=shownotes) – Ecto v3.13 changelog with detailed list of new features and improvements https://github.com/elixir-nx/nx (https://github.com/elixir-nx/nx?utm_source=thinkingelixir&utm_medium=shownotes) – Nx v0.10 was released with documentation improvements and floating-point precision enhancements https://github.com/elixir-nx/nx/blob/main/nx/CHANGELOG.md (https://github.com/elixir-nx/nx/blob/main/nx/CHANGELOG.md?utm_source=thinkingelixir&utm_medium=shownotes) – Nx v0.10 changelog including new advanced guides and NumPy comparison cheatsheets https://paraxial.io/blog/phoenix-security-docs (https://paraxial.io/blog/phoenix-security-docs?utm_source=thinkingelixir&utm_medium=shownotes) – Phoenix 1.8 gets official security documentation covering OWASP Top 10 vulnerabilities https://github.com/phoenixframework/phoenix/pull/6295 (https://github.com/phoenixframework/phoenix/pull/6295?utm_source=thinkingelixir&utm_medium=shownotes) – Pull request adding comprehensive security guide to Phoenix documentation https://bsky.app/profile/zachdaniel.dev/post/3lscszxpakc2o (https://bsky.app/profile/zachdaniel.dev/post/3lscszxpakc2o?utm_source=thinkingelixir&utm_medium=shownotes) – Zach Daniel announces new "evals" package for testing and comparing AI language models https://github.com/ash-project/evals (https://github.com/ash-project/evals?utm_source=thinkingelixir&utm_medium=shownotes) – Evals project for evaluating AI model performance on coding tasks with structured testing https://bsky.app/profile/elixirconf.bsky.social/post/3lsbt7anbda2o (https://bsky.app/profile/elixirconf.bsky.social/post/3lsbt7anbda2o?utm_source=thinkingelixir&utm_medium=shownotes) – ElixirConf US speakers beginning to be announced including keynotes from José Valim and Chris McCord https://elixirconf.com/#keynotes (https://elixirconf.com/#keynotes?utm_source=thinkingelixir&utm_medium=shownotes) – ElixirConf website showing keynote speakers and initial speaker lineup https://x.com/sasajuric/status/1937149387299316144 (https://x.com/sasajuric/status/1937149387299316144?utm_source=thinkingelixir&utm_medium=shownotes) – Saša Jurić shares collection of writings on Elixir project organization and structure recommendations https://medium.com/very-big-things/towards-maintainable-elixir-the-core-and-the-interface-c267f0da43 (https://medium.com/very-big-things/towards-maintainable-elixir-the-core-and-the-interface-c267f0da43?utm_source=thinkingelixir&utm_medium=shownotes) – Saša Jurić's article on organizing Elixir projects with core and interface separation https://medium.com/very-big-things/towards-maintainable-elixir-boundaries-ba013c731c0a (https://medium.com/very-big-things/towards-maintainable-elixir-boundaries-ba013c731c0a?utm_source=thinkingelixir&utm_medium=shownotes) – Article on using boundaries in Elixir applications for better structure https://medium.com/very-big-things/towards-maintainable-elixir-the-anatomy-of-a-core-module-b7372009ca6d (https://medium.com/very-big-things/towards-maintainable-elixir-the-anatomy-of-a-core-module-b7372009ca6d?utm_source=thinkingelixir&utm_medium=shownotes) – Deep dive into structuring core modules in Elixir applications https://github.com/sasa1977/mixphxalt (https://github.com/sasa1977/mix_phx_alt?utm_source=thinkingelixir&utm_medium=shownotes) – Demo project showing alternative Phoenix project structure with core/interface organization https://github.com/getsentry/sentry-elixir/blob/master/CHANGELOG.md#1100 (https://github.com/getsentry/sentry-elixir/blob/master/CHANGELOG.md#1100?utm_source=thinkingelixir&utm_medium=shownotes) – Sentry updates Elixir SDK to v11.x with tracing support using OpenTelemetry Do you have some Elixir news to share? Tell us at @ThinkingElixir (https://twitter.com/ThinkingElixir) or email at show@thinkingelixir.com (mailto:show@thinkingelixir.com) Discussion Resources https://phoenix.new/ (https://phoenix.new/?utm_source=thinkingelixir&utm_medium=shownotes) – The Remote AI Runtime for Phoenix. Describe your app, and watch it take shape. Prototype quickly, experiment freely, and share instantly. https://x.com/chris_mccord/status/1936074795843551667 (https://x.com/chris_mccord/status/1936074795843551667?utm_source=thinkingelixir&utm_medium=shownotes) – You can vibe code on your phone https://x.com/sukinoverse/status/1936163792720949601 (https://x.com/sukinoverse/status/1936163792720949601?utm_source=thinkingelixir&utm_medium=shownotes) – Another success example - Stripe integrations https://openai.com/index/openai-codex/ (https://openai.com/index/openai-codex/?utm_source=thinkingelixir&utm_medium=shownotes) – OpenAI Codex, Open AI's AI system that translates natural language to code https://devin.ai/ (https://devin.ai/?utm_source=thinkingelixir&utm_medium=shownotes) – Devin is an AI coding agent and software engineer that helps developers build better software faster. Parallel cloud agents for serious engineering teams. https://www.youtube.com/watch?v=ojL_VHc4gLk (https://www.youtube.com/watch?v=ojL_VHc4gLk?utm_source=thinkingelixir&utm_medium=shownotes) – Chris McCord's ElixirConf EU Keynote talk titled "Code Generators are Dead. Long Live Code Generators" Guest Information - https://x.com/chris_mccord (https://x.com/chris_mccord?utm_source=thinkingelixir&utm_medium=shownotes) – on X/Twitter - https://github.com/chrismccord (https://github.com/chrismccord?utm_source=thinkingelixir&utm_medium=shownotes) – on Github - http://chrismccord.com/ (http://chrismccord.com/?utm_source=thinkingelixir&utm_medium=shownotes) – Blog Find us online - Message the show - Bluesky (https://bsky.app/profile/thinkingelixir.com) - Message the show - X (https://x.com/ThinkingElixir) - Message the show on Fediverse - @ThinkingElixir@genserver.social (https://genserver.social/ThinkingElixir) - Email the show - show@thinkingelixir.com (mailto:show@thinkingelixir.com) - Mark Ericksen on X - @brainlid (https://x.com/brainlid) - Mark Ericksen on Bluesky - @brainlid.bsky.social (https://bsky.app/profile/brainlid.bsky.social) - Mark Ericksen on Fediverse - @brainlid@genserver.social (https://genserver.social/brainlid) - David Bernheisel on Bluesky - @david.bernheisel.com (https://bsky.app/profile/david.bernheisel.com) - David Bernheisel on Fediverse - @dbern@genserver.social (https://genserver.social/dbern)

Join Dan Vega for the latest updates from the Spring Ecosystem. In this special episode, Dan is joined by Spring expert and author Craig Walls for an exciting AI show and tell segment, where they demonstrate and discuss their favorite AI tools currently transforming their development workflows.Following the show and tell, Craig shares insights from his upcoming Manning book "Spring AI in Action," exploring how developers can build intelligent Java applications using Spring's powerful AI abstractions. The episode wraps up with a preview of their collaborative workshop "Practical AI Integration with Java: A Hands-On Workshop" at dev2next 2025, where they'll teach hands-on AI implementation techniques for Java developers.Whether you're looking to discover new AI tools to boost your productivity or interested in integrating AI capabilities into your Spring applications, this episode offers practical insights and real-world examples from two experts actively working in the AI space.You can participate in our live stream to ask questions or catch the replay on your preferred podcast platform.Show NotesMain Topics Discussed1. Craig's Upcoming Book - "Spring AI in Action"Currently available in early access through Manning PublicationsExpected print release: Fall 2025Covers Spring AI development from basics to advanced topicsIncludes chapter on "Evaluating Generated Responses" - testing AI applications2. Dan's New Course Launch"AI for Java Developers" - Introduction to Spring AINearly 6 hours of contentCovers 12-18 months of Spring AI learningJust launched last week3. AI Development Tool Categories DiscussionStandalone Chatbots: ChatGPT, Google Gemini, Anthropic ClaudeInline IDE Assistants: GitHub Copilot, JetBrains AI, Amazon CodeWhispererAgentic AI IDE Environments: Cursor, Windsurf, JuniTerminal-based Agentic CLI Tools: Claude Code, OpenAI Codex, Gemini CLI4. Live DemonstrationsDan: Demonstrated Claude Code CLI tool for project planning and development workflowsCraig: Showcased Embable framework for building goal-oriented AI agents5. Testing AI ApplicationsDeterministic vs non-deterministic testing approachesUsing evaluators for response validationFact-checking and relevance evaluation techniques6. Future of Spring AIAgent framework capabilitiesAgentic workflows vs autonomous planningIntegration with tools like EmbableLinks and ResourcesBooks and CoursesSpring AI in Action (Early Access) - Craig WallsAI for Java Developers Course - Dan Vega (link to be added to show notes)Tools MentionedIDE Assistants:GitHub CopilotJetBrains AI AssistantAmazon CodeWhispererAgentic IDE Environments:CursorWindsurfJetBrains JunieCLI Tools:Claude CodeGemini CLIOpenAI CodexFrameworks and LibrariesSpring AIEmbable - Rod Johnson's agent frameworkSpring BootSpring ShellContact InformationCraig Walls: Habuma.com - Links to all social mediaDan Vega:Spring Developer Advocate at BroadcomLearn more at https://www.danvega.devUpcoming Eventsdev2Next Workshop: 8-hour Spring AI workshop with Dan Vega and Craig Walls (Colorado Springs)Key Takeaways"You are the pilot, not the passenger" - Stay in control when using AI development toolsStart with simpler tools like Copilot before moving to full agentic environmentsProper testing strategies are crucial for AI applicationsCode reviews and CI/CD pipelines are more important than ever with AI-generated codeThe AI development tool landscape is rapidly evolving with new categories emergingThis episode was recorded live on Monday, June 30, 2025. Watch the replay on the Spring Developer YouTube channel or listen wherever you get your podcasts.

Hanson Wang and Alexander Embiricos from OpenAI's Codex team discuss their latest AI coding agent that works independently in its own environment for up to 30 minutes, generating full pull requests from simple task descriptions. They explain how they trained the model beyond competitive programming to match real-world software engineering needs, the shift from pairing with AI to delegating to autonomous agents, and their vision for a future where the majority of code is written by agents working on their own computers. The conversation covers the technical challenges of long-running inference, the importance of creating realistic training environments, and how developers are already using Codex to fix bugs and implement features at OpenAI. Hosted by Sonya Huang and Lauren Reeder, Sequoia Capital  Mentioned in this episode:  The Culture: Sci-Fi series by Iain Banks portraying an optimistic view of AI The Bitter Lesson: Influential paper by Rich Sutton on the importance of scale as a strategic unlock for AI.

In this podcast, Jon Westfall and Todd Ogasawara discuss a range of tech topics, starting with Todd's ongoing struggles to update his 2019 HP Envy 360 laptop to Windows 11 or Google FlexOS due to processor incompatibility, leading him back to Linux. He notes the quirks of Linux, like his Bluetooth mouse not working with Linux Mint but functioning fine with Ubuntu. The conversation then shifts to the recent ability to use Apple Find My in South Korea as of June 1st, 2025, dispelling previous assumptions about privacy laws preventing its use there. Jon shares amusing anecdotes about using AirTags for unexpected insights, such as detecting activity near his office over the weekend. The duo also delves into rumors about upcoming AirPods Pro 2 and AirPods 4 features, including camera control, sleep detection, and new head gestures for answering calls and dismissing notifications. Jon observes that many people, especially younger generations, wear AirPods constantly, even when not listening to anything, which could drive the development of these features. They express both excitement and skepticism about the rumored iPadOS 26 menu bar and hope for significant improvements to Stage Manager, citing issues with external monitor usage. Finally, they touch upon the evolving landscape of AI in coding, with Jon sharing his experiences using OpenAI's Codex for debugging and code explanation, likening the AI's persistent "help" to a "code therapist". They ponder the increasing integration of AI into everyday tech and humorously speculate about a future where AI becomes so prevalent it might "leave" humanity behind.

Agenci AI zmieniają sposób pracy programistów i mogą całkowicie przedefiniować rolę pracowników umysłowych. W tym odcinku analizujemy, jak narzędzia takie jak OpenAI Codex i Google Jules przyspieszają rozwój oprogramowania i co to oznacza dla rynku pracy w 2025 roku. Pokazujemy też jak model o3 odkrył nową podatność w jądrze Linuxa, rzucając nowe światło na przyszłość zawodów eksperckich. Sprawdzamy też, czy język polski naprawdę działa gorzej niż angielski w komunikacji z LLM-ami – najnowsze badania sugerują coś odwrotnego. Podsumowujemy kluczowe premiery AI z Google I/O oraz analizujemy, czy Claude 4 może zagrozić dominacji OpenAI i Google.

Hey everyone, Alex here

Happy Friday, everyone! You've made it through the week just in time for another Weekly Update where I'm helping you stay ahead of the curve while keeping both feet grounded in reality. This week, we've got a wild mix covering everything from the truth about LIDAR and camera damage to a sobering look at job automation, the looming shift in software engineering, and some high-profile examples of AI-first backfiring in real time.Fair warning: this one pulls no punches, but it might just help you avoid some major missteps.With that, let's get to it.⸻If LIDAR is Frying Phones, What About Your Eyes?There's a lot of buzz lately about LIDAR systems melting high-end camera sensors at car shows, and some are even warning about potential eye damage. Given how fast we're moving with autonomous vehicles, you can see why the news cycle would be in high gear. However, before you go full tinfoil hat, I break down how the tech actually works, where the risks are real, and what's just headline hype. If you've got a phone, or eyeballs, you'll want to check this out.⸻Jobs at Risk: What SHRM Gets Right—and Misses CompletelySHRM dropped a new report claiming around 12% of jobs are at high or very high risk of automation. Depending on how you're defining it, that number could be generous or a gross underestimate. That's the problem. It doesn't tell the whole story. I unpack the data, share what I'm seeing in executive boardrooms, and challenge the idea that any job, including yours, is safe from change, at least as you know it today. Spoiler: It's not about who gets replaced; it's about who adapts.⸻Codex and the Collapse of Coding ComplacencyOpenAI's new specialized coding model, Codex, has some folks declaring the end of software engineers as we know them. Given how much companies have historically spent on these roles, I can understand why there'd be so much push to automate it. To be clear, I don't buy the doomsday hype. I think it's a more complicated mix that is tied to a larger market correction for an overinflated industry. However, if you're a developer, this is your wake-up call because the game is changing fast.⸻Duolingo and Klarna: When “AI-First” BackfiresThis week I wanted to close with a conversation that hopefully reduces some of people's anxiety about work, so here it is. Two big names went all in on AI and are changing course as a result of two very different kinds of pain. Klarna is quietly walking back their AI-first bravado after realizing it's not actually cheaper, or better. Meanwhile, Duolingo is getting publicly roasted by users and employees alike. I break down what went wrong and what it tells us about doing AI right.⸻If this episode challenged your thinking or helped you see something new, share it with someone who needs it. Leave a comment, drop a rating, and make sure you're following so you never miss what's coming next.—Show Notes:In this Weekly Update, host Christopher Lind examines the ripple effects of LIDAR technology on camera sensors and the public's rising concern around eye safety. He breaks down SHRM's automation risk report, arguing that every job is being reshaped by AI—even if it's not eliminated. He explores the rise of OpenAI's Codex and its implications for the future of software engineering, and wraps with cautionary tales from Klarna and Duolingo about the cost of going “AI-first” without a strategy rooted in people, not just platforms.00:00 Introduction 01:07 Overview of This Week's Topics01:54 LIDAR Technology Explained13:43 - SHRM Job Automation Report 30:26 - OpenAI Codex: The Future of Coding?41:33 - AI-First Companies: A Cautionary Tale45:40 - Encouragement and Final Thoughts#FutureOfWork #LIDAR #JobAutomation #OpenAI #AIEthics #TechLeadership

Heute dominieren die News der beiden großen Tech-Konferenzen der Woche: Google I/O 2025 und Microsoft Build!MicrosoftNLWeb soll als Open-Source Projekt AI-Interaktion mit Webseiten vereinfachenGitHub Copilot nun auch als eigenständiger Agent in der CloudGitHub Agent Mode kommt zu JetBrains und XcodeWindows AI Foundry vereinfacht das Ausführen und Trainieren von Modellen auf dem Gerät und in der CloudAzure AI Foundry: xAI-Modelle stehen nun auch zur Verfügung und können gehostet werden. Ein Model-Router sucht selbst das beste ModellMicrosoft 365 Copilot Tuning: Low-Code-Lösung zum Tunen eigener Modelle mit UnternehmensdatenBreite Unterstützung des MCP in GitHub, Copilot Studio, Windows 11 und weiteren ToolsEdge Browser AI APIs: Neue APIs, um auf lokale Modelle im Browser zuzugreifenGoogleGemini 2.5 Pro „Deep Think“ bietet eine neue ArchitekturGemini 2.5 Flash als Vorschau verfügbar mit starker Leistung bei reduzierten RessourcenGemma 3n als kleine Version des offenen Modells von Google, das die Nano-Reihe ablösen sollJede Menge Neuerungen für kreative AI: Mit Veo 3 kommt ein neues Videomodell, das nicht nur den Film, sondern auch gleich das Audio mitgeneriert.Imagen 4 ist ein besseres Bildmodell, inklusive Text-Rendering.Lyria 2 ist die Suno-Alternative zum Generieren von Musik. Den ganzen Flow fasst Google im neuen Tool Flow zusammen.Jules ist der GitHub Copilot Agent oder das OpenAI Codex von GoogleGoogle Meet kann jetzt Echtzeit-Übersetzungen – zunächst nur auf Englisch und SpanischGemini-App mit vielen Funktionen geflutet und Gemini erhält mehr Einzug in die Google SucheZwei neue Abo-Modelle für AI bei Google: Für $20 gibt es Google AI Pro und schlappe $250 werden für Google AI Ultra fälligAbseits davon: AlphaEvolve v

In the News §  Regeneron Pharmaceuticals Acquires 23andMe for $256M §  Apple Suggest Users Put Themselves at Risk Using Third Party Alternatives §  Google Assistant Losing Features Across Android Devices and Nest/Hub Speaker Products §  Running ChromeOS with Microsoft 365 §  What Actually Happens If You Don't Use Airplane Mode on Your  Phone During A Flight? §  Microsoft Launches Azure Image Testing for Linux as a Service   ITPro Series with Benjamin Rockwell §  What to do If Asked to Train Your Replacement From the Tech Corner §  What is OpenAI Codex? §  The Windows Subsystem for Linux (WSL) in Windows §  What is the Sweet Spot for How Much Windows PC Memory You Need? Technology Chatter with Benjamin Rockwell and Marty Winston Intumescent Fire-Preventative Vents

AI is moving faster than most people realize—and it's continuing to reshape the workforce. Paul Roetzer and Mike Kaput dig into Microsoft's 6,000 job cuts and what they signal about the future of AI-powered automation, they also explain the major copyright report that triggered a high-level firing and they break down new data from the 2025 State of Marketing AI Report.  The episode also covers OpenAI's autonomous coding agent, TikTok's new AI video tool, the rise of AI baby podcasters, what to watch for at Google I/O and more in our rapid fire section. Access the show notes and show links here Timestamps: 00:00:00 — Intro 00:06:49 —More Quiet AI Layoffs, Including at Microsoft 00:19:24 — Bombshell Copyright Decision and Drama 00:30:01 — 2025 State of Marketing AI Report Findings 00:39:18 — OpenAI Releases Codex 00:41:40 — Altman Wants to Build “Core AI Subscription” for Your Life 00:56:20 — Altman, Musk, and Grok Drama 01:01:22 — Are Chatbots Replacing Search? 01:05:36 — AI in Education Updates 01:11:15 — The Cost of AI 01:14:29 — AI Product and Funding Updates 01:20:04 — Listener Question This episode is brought to you by the AI for B2B Marketers Summit. Join us on Thursday, June 5th at 12 PM ET, and learn real-world strategies on how to use AI to grow better, create smarter content, build stronger customer relationships, and much more. Thanks to our sponsors, there's even a free ticket option. See the full lineup and register now at www.b2bsummit.ai. This week's episode is also brought to you by MAICON, our 6th annual Marketing AI Conference, happening in Cleveland, Oct. 14-16. The code POD100 saves $100 on all pass types. For more information on MAICON and to register for this year's conference, visit www.MAICON.ai. Visit our website Receive our weekly newsletter Join our community: Slack LinkedIn Twitter Instagram Facebook Looking for content and resources? Register for a free webinar Come to our next Marketing AI Conference Enroll in our AI Academy 

У свіжому дайджесті DOU News обговорюємо скорочення в Microsoft, CarPlay Ultra від Apple, VPN-компанію, яка скасувала довічні підписки клієнтів, закінчення ери Stack Overflow та інші новини українського ІТ та світового тек-сектору. ▶️ Навігація 00:00 Інтро  01:04 Чверть лідів звільнили через конфлікт з керівництвом. Аналітика про скорочення айтівців https://dou.ua/lenta/articles/job-market-2025-part-3/ 03:43 LLM вбивають Stack Overflow https://newsletter.pragmaticengineer.com/p/the-pulse-134?open=false#%C2%A7stack-overflow-almost-dead 06:03 Партнерський блок 07:03 Новий CarPlay Ultra від Apple готовий, але поки що лише в Aston Martins https://arstechnica.com/cars/2025/05/apples-new-carplay-ultra-is-ready-but-only-in-aston-martins-for-now/ 09:31 OpenAI випустили Codex https://dou.ua/forums/topic/53860/ 12:35 Попри скорочення, компанія Klarna знову наймає людей у службу підтримки https://slashdot.org/story/25/05/14/2339257/klarna-pivots-back-to-humans-after-ai-experiment-fails 14:04 Microsoft проводить одне з найбільших скорочення з 2023 року https://dou.ua/forums/topic/53815/ 15:09 Microsoft намагається уникнути штрафів в ЄС, відокремлюючи Teams від Office https://www.engadget.com/big-tech/microsoft-attemps-to-avoid-eu-fines-by-further-decoupling-teams-and-office-170519085.html?src=rss 17:01 Uber винайшов маршрутки

Timestamps: 0:00 See ya on Wed, May 21 0:09 Epic's plan for Apple to block Fortnite 3:29 Intel Arc Pro B60, RX 9060 XT 4:27 OpenAI Codex, Grok's breakdown 5:50 MSI! 6:41 QUICK BITS INTRO 6:47 Spotify podcast play counts 7:14 The Steam data breach that wasn't 7:41 Australian rocket top fell off 8:07 BREAKING: Vader is bad guy NEWS SOURCES: https://lmg.gg/oRJxT Learn more about your ad choices. Visit megaphone.fm/adchoices

More info: https://docs.anthropic.com/en/docs/claude-code/overviewThe AI coding wars have now split across four battlegrounds:1. AI IDEs: with two leading startups in Windsurf ($3B acq. by OpenAI) and Cursor ($9B valuation) and a sea of competition behind them (like Cline, Github Copilot, etc).2. Vibe coding platforms: Bolt.new, Lovable, v0, etc. all experiencing fast growth and getting to the tens of millions of revenue in months.3. The teammate agents: Devin, Cosine, etc. Simply give them a task, and they will get back to you with a full PR (with mixed results)4. The cli-based agents: after Aider's initial success, we are now seeing many other alternatives including two from the main labs: OpenAI Codex and Claude Code. The main draw is that 1) they are composable 2) they are pay as you go based on tokens used.Since we covered all three of the first categories, today's guests are Boris and Cat, the lead engineer and PM for Claude Code. If you only take one thing away from this episode, it's this piece from Boris: Claude Code is not a product as much as it's a Unix utility.This fits very well with Anthropic's product principle: “do the simple thing first.” Whether it's the memory implementation (a markdown file that gets auto-loaded) or the approach to prompt summarization (just ask Claude to summarize), they always pick the smallest building blocks that are useful, understandable, and extensible. Even major features like planning (“/think”) and memory (#tags in markdown) fit the same idea of having text I/O as the core interface. This is very similar to the original UNIX design philosophy:Claude Code is also the most direct way to consume Sonnet for coding, rather than going through all the hidden prompting and optimization than the other products do. You will feel that right away, as the average spend per user is $6/day on Claude Code compared to $20/mo for Cursor, for example. Apparently, there are some engineers inside of Anthropic that have spent >$1,000 in one day!If you're building AI developer tools, there's also a lot of alpha on how to design a cli tool, interactive vs non-interactive modes, and how to balance feature creation. Enjoy!Full Video EpisodeTimestamps[00:00:00] Intro[00:01:59] Origins of Claude Code[00:04:32] Anthropic's Product Philosophy[00:07:38] What should go into Claude Code?[00:09:26] Claude.md and Memory Simplification[00:10:07] Claude Code vs Aider[00:11:23] Parallel Workflows and Unix Utility Philosophy[00:12:51] Cost considerations and pricing model[00:14:51] Key Features Shipped Since Launch[00:16:28] Claude Code writes 80% of Claude Code[00:18:01] Custom Slash Commands and MCP Integration[00:21:08] Terminal UX and Technical Stack[00:27:11] Code Review and Semantic Linting[00:28:33] Non-Interactive Mode and Automation[00:36:09] Engineering Productivity Metrics[00:37:47] Balancing Feature Creation and Maintenance[00:41:59] Memory and the Future of Context[00:50:10] Sandboxing, Branching, and Agent Planning[01:01:43] Future roadmap[01:11:00] Why Anthropic Excels at Developer Tools Get full access to Latent.Space at www.latent.space/subscribe

More info: https://docs.anthropic.com/en/docs/claude-code/overview The AI coding wars have now split across four battlegrounds: 1. AI IDEs: with two leading startups in Windsurf ($3B acq. by OpenAI) and Cursor ($9B valuation) and a sea of competition behind them (like Cline, Github Copilot, etc). 2. Vibe coding platforms: Bolt.new, Lovable, v0, etc. all experiencing fast growth and getting to the tens of millions of revenue in months. 3. The teammate agents: Devin, Cosine, etc. Simply give them a task, and they will get back to you with a full PR (with mixed results) 4. The cli-based agents: after Aider's initial success, we are now seeing many other alternatives including two from the main labs: OpenAI Codex and Claude Code. The main draw is that 1) they are composable 2) they are pay as you go based on tokens used. Since we covered all three of the first categories, today's guests are Boris and Cat, the lead engineer and PM for Claude Code. If you only take one thing away from this episode, it's this piece from Boris: Claude Code is not a product as much as it's a Unix utility. This fits very well with Anthropic's product principle: “do the simple thing first.” Whether it's the memory implementation (a markdown file that gets auto-loaded) or the approach to prompt summarization (just ask Claude to summarize), they always pick the smallest building blocks that are useful, understandable, and extensible. Even major features like planning (“/think”) and memory (#tags in markdown) fit the same idea of having text I/O as the core interface. This is very similar to the original UNIX design philosophy: Claude Code is also the most direct way to consume Sonnet for coding, rather than going through all the hidden prompting and optimization than the other products do. You will feel that right away, as the average spend per user is $6/day on Claude Code compared to $20/mo for Cursor, for example. Apparently, there are some engineers inside of Anthropic that have spent >$1,000 in one day! If you're building AI developer tools, there's also a lot of alpha on how to design a cli tool, interactive vs non-interactive modes, and how to balance feature creation. Enjoy! Timestamps [00:00:00] Intro [00:01:59] Origins of Claude Code [00:04:32] Anthropic's Product Philosophy [00:07:38] What should go into Claude Code? [00:09:26] Claude.md and Memory Simplification [00:10:07] Claude Code vs Aider [00:11:23] Parallel Workflows and Unix Utility Philosophy [00:12:51] Cost considerations and pricing model [00:14:51] Key Features Shipped Since Launch [00:16:28] Claude Code writes 80% of Claude Code [00:18:01] Custom Slash Commands and MCP Integration [00:21:08] Terminal UX and Technical Stack [00:27:11] Code Review and Semantic Linting [00:28:33] Non-Interactive Mode and Automation [00:36:09] Engineering Productivity Metrics [00:37:47] Balancing Feature Creation and Maintenance [00:41:59] Memory and the Future of Context [00:50:10] Sandboxing, Branching, and Agent Planning [01:01:43] Future roadmap [01:11:00] Why Anthropic Excels at Developer Tools

Summary Large Language Models (LLMs) have rapidly captured the attention of the world with their impressive capabilities. Unfortunately, they are often unpredictable and unreliable. This makes building a product based on their capabilities a unique challenge. Jignesh Patel is building DataChat to bring the capabilities of LLMs to organizational analytics, allowing anyone to have conversations with their business data. In this episode he shares the methods that he is using to build a product on top of this constantly shifting set of technologies. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Your host is Tobias Macey and today I'm interviewing Jignesh Patel about working with LLMs; understanding how they work and how to build your own Interview Introduction How did you get involved in machine learning? Can you start by sharing some of the ways that you are working with LLMs currently? What are the business challenges involved in building a product on top of an LLM model that you don't own or control? In the current age of business, your data is often your strategic advantage. How do you avoid losing control of, or leaking that data while interfacing with a hosted LLM API? What are the technical difficulties related to using an LLM as a core element of a product when they are largely a black box? What are some strategies for gaining visibility into the inner workings or decision making rules for these models? What are the factors, whether technical or organizational, that might motivate you to build your own LLM for a business or product? Can you unpack what it means to "build your own" when it comes to an LLM? In your work at DataChat, how has the progression of sophistication in LLM technology impacted your own product strategy? What are the most interesting, innovative, or unexpected ways that you have seen LLMs/DataChat used? What are the most interesting, unexpected, or challenging lessons that you have learned while working with LLMs? When is an LLM the wrong choice? What do you have planned for the future of DataChat? Contact Info Website (https://jigneshpatel.org/) LinkedIn (https://www.linkedin.com/in/jigneshmpatel/) Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast (https://www.dataengineeringpodcast.com) covers the latest on modern data management. Podcast.__init__ () covers the Python language, its community, and the innovative ways it is being used. Visit the site (https://www.themachinelearningpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com (mailto:hosts@themachinelearningpodcast.com)) with your story. To help other people find the show please leave a review on iTunes (https://podcasts.apple.com/us/podcast/the-machine-learning-podcast/id1626358243) and tell your friends and co-workers. Links DataChat (https://datachat.ai/) CMU == Carnegie Mellon University (https://www.cmu.edu/) SVM == Support Vector Machine (https://en.wikipedia.org/wiki/Support_vector_machine) Generative AI (https://en.wikipedia.org/wiki/Generative_artificial_intelligence) Genomics (https://en.wikipedia.org/wiki/Genomics) Proteomics (https://en.wikipedia.org/wiki/Proteomics) Parquet (https://parquet.apache.org/) OpenAI Codex (https://openai.com/blog/openai-codex) LLama (https://en.wikipedia.org/wiki/LLaMA) Mistral (https://mistral.ai/) Google Vertex (https://cloud.google.com/vertex-ai) Langchain (https://www.langchain.com/) Retrieval Augmented Generation (https://blogs.nvidia.com/blog/what-is-retrieval-augmented-generation/) Prompt Engineering (https://en.wikipedia.org/wiki/Prompt_engineering) Ensemble Learning (https://en.wikipedia.org/wiki/Ensemble_learning) XGBoost (https://xgboost.readthedocs.io/en/stable/) Catboost (https://catboost.ai/) Linear Regression (https://en.wikipedia.org/wiki/Linear_regression) COGS == Cost Of Goods Sold (https://www.investopedia.com/terms/c/cogs.asp) Bruce Schneier - AI And Trust (https://www.schneier.com/blog/archives/2023/12/ai-and-trust.html) The intro and outro music is from Hitman's Lovesong feat. Paola Graziano (https://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Tales_Of_A_Dead_Fish/Hitmans_Lovesong/) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/)/CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0/)

Jason Gulya is a Professor of English at Berkeley College. In 2020, Jason received Berkeley's Faculty of the Year Award for Teaching Excellence. He is also a higher ed consultant who helps students and professors prepare for the future and gives advice on how to utilize artificial intelligence in and outside of the classroom.Jason has a wealth of knowledge and actionable advice for using A.I. He outlines many great resources that you can use immediately to make yourself more productive at work. We talk about the future of the humanities, white collar work, the idea of a second brain and the emergence of a new profession he calls an A.I. Prompt Engineer. He even shares A.I. hacks for creating online classes and training manuals in record time.If you are an educator, entrepreneur or just someone who is interested in A.I. and how to “work smarter, not harder” then you will enjoy this podcast.Discussion Topics:(1:06) A.I. tools you need to be using right now.(10:50) What A.I. means for the future of work.(13:00) A.I. and the future of the Humanities.(18:33) Second brains and offloading effects.(26:45) Using A.I. to build a business.(36:00) Who owns A.I. copyrights?(41:25) OpenAI Codex.(44:38) Advice for educators. Grade the interaction with A.I.(45:27) New careers as a prompt engineer.(49:34) Advice for colleges and universities.(54:42) What does the future look like?(59:19) Rapid fire questions.