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“We were still trying to change the world from the inside… and the empire struck back.” — Claire Stapleton on the Google Walkout Earlier this week, the New York Times published an op-ed on how to fight back against big tech which included a reading list from previous guests on the show like Karen Hao, Yanis Varoufakis and Astra Taylor. Not included was Claire Stapleton's Don't Be Evil: Bad Bosses, Fake Promises, and My Escape from Big Tech. Partly, I assume, because this anti-Google jeremiad only came out yesterday. An idealistic English major straight out of college, Stapleton joined Google in 2007, when few questioned Silicon Valley's utopian language. Her first job was in Google's ministry of truth, an internal communications department designed, as she now admits, to get everybody high on the mission. And that mission was to train Googlers into becoming obedient corporate cogs by neither blaming the system nor questioning authority. While not exactly Winston Smith, she nonetheless rose in the bureaucracy. Rachel Whetstone (Steve Hilton's wife) promoted Stapleton to the CEO's communications team where, as the archivist of the sayings of the company's two big brothers (Larry & Sergey), she became known as the “Bard” of Google. As an obedient corporate cog, she was both the message and the messenger. Until she wasn't. Like Orwell's Smith, Google's Claire Stapleton eventually turned her ministry of truth language on the ministry itself. In response to the $90 million exit package of a senior Google exec accused of sexual misconduct, she co-organized the Google Walkout of 20,000 employees. Two months later, her role was hollowed out. Then she lost her job. Not quite Room 101. No rats. But not a lot of fun. So is Google evil? Stapleton doesn't know. But the former Bard of Google acknowledges that Larry & Sergey's promises of making the world a better place are no longer credible. Today, idealistic young college grads want to fight back against Silicon Valley. Today, nobody outside the internal communications department of Google, OpenAI or Anthropic believes that technology is making the world a better place. Like Claire Stapleton, we all now want to escape from big tech. Five Takeaways • Getting High on the Mission. Google in 2007 wasn't selling a job; it was selling a worldview — and Stapleton's department, internal communications, existed to pump the mission, the values, and the founders' folksy TGIF theater into a workforce growing by tens of thousands. The perks were the philosophy made visible: laundry, haircuts, dentists, masseurs — “letting Google be your housewife.” The indoctrination had a curriculum, too: Fred Kofman's “radical accountability” training taught new hires to be players, not victims — which sounds empowering until you notice, as Stapleton did in a G-chat to her brother, that it means never blaming the system: “today I learned that if my team is dysfunctional, it's my fault.”• The Bard of Google. Rachel Whetstone — ex-London, politically shrewd, now married to California's Republican gubernatorial nominee — drafted Stapleton onto Larry Page's communications when Page retook the CEO job, making her the archivist of the founder's vision (until, in a twist she still can't quite believe, he literally lost his voice). From there: Creative Lab, “the alchemist lab of Google's branding,” wrapped in change-the-world gobbledygook; then YouTube's curation team, a job so dispensable that nobody on earth would notice if it stopped — David Graeber's bullshit jobs made flesh, complete with colleagues vanishing to the gym for hours. Her bad bosses, she notes, were workaday and familiar — which is precisely the point. Laszlo Bock's Work Rules explains why Google tried to constrain managers' power: given too much, they abuse it, pettily.• Normie, Not Whistleblower. Unlike Sarah Wynn-Williams' Careless People — a whistleblower's damning portrait of Zuckerberg and Sandberg — Don't Be Evil is a coming-of-age story from the rank and file: earnest, unjaded, “normie.” What's genuinely new in it is the workers'-eye view of the tech-labor moment: a company that hired idealists on the promise of changing the world, then watched the worker-management relationship transform with whiplash speed once those idealists asked what corporate accountability actually looks like. “We were still trying to change the world from the inside, from a different direction, as we lumbered our way through labor organizing 101… and the empire struck back.”• The Walkout and the Bill. The rupture came in 2018: the Me Too summer, an engineer's memo against diversity itself, and the Times' revelation of Andy Rubin's $90 million exit after credible harassment findings. The moms listserv lit up; at TGIF, management read talking points “designed to absorb dissent, project empathy, and move right along” — radicalizing the woman who used to write them. The Walkout drew 20,000 employees. The bill arrived two months later: her role hollowed out, transfers blocked across the organizer group, Meredith Whittaker's role dismantled, the rubber room. The group went public — the nuclear option — and Google offered severance. Stapleton, pregnant, asked for a year. An HR veteran's verdict, years later: “They sure got you out cheap.” And the villains of her story, she notes, were mostly women — her own management chain — while Susan Wojcicki offered the older generation's lament: you have no idea how hard it was for me.• Don't Outsource Your Moral Compass. Is Google better than the rest? There's no noxious star — Sundar Pichai harmonizes rather than dominates — but the company owns the infrastructure of the digital world, and “is it more evil? less evil? I don't know.” The deeper shift is industry-wide: Larry Page's “treat workers well and they'll contribute tenfold” has become “your job could easily go to AI, so be grateful.” Her advice to the young and idealistic: keep some skepticism; hold your own values in your own mind; let work be part of your life without letting it take over; build solidarity instead of “letting the overlords do their overlord thing.” The good news, she suspects, is that the spell is breaking anyway: audiences now boo the AI ads in movie theaters. About the Guest Claire Stapleton is a writer, communications consultant, and tech labor activist. She spent twelve years at Google in communications, marketing, and executive ghostwriting roles — becoming known as “the Bard of Google” — before co-organizing the 2018 Google Walkout, in which 20,000 employees protested the company's mishandling of sexual harassment; The New York Times called it a watershed moment in tech. She writes an existential advice column for tech workers on Substack, and her account of her Google years, “The Voice of Google,” appeared in The New Yorker. Her first book is
AI has handed marketers speed and scale they've never had before. But is this really a differentiator when everyone has access to the same models? Emma Fisher, VP of Marketing at RWS, and Sal Mohammed, Head of Answer Engine Optimization (AEO) at LangSync and former Googler, join our latest Cultural Intelligence podcast episode to explore what modern marketing looks like in the age of AI – and why the best campaigns have very little to do with how fast you can generate content. From "we're all on a rocket ship with no way to get off" to the CMO's evolving role as orchestrator, warrior, and tastemaker (curious yet?), just make sure you buckle up. Emma and Sal dig into the rise of AEO, what it means for brand visibility as search shifts to generative AI, and how Cultural Intelligence shapes whether your content gets surfaced… or ignored. You'll also learn why relevance, resonance and trust are the only valid metrics when everyone is publishing at the same speed, and why too many content leaders are still measuring success against the wrong scorecard. If you're interested in learning more about the ‘Content Unlocked' report, you can find it here: [https://www.rws.com/about/content-unlocked/lp/ ](https://www.rws.com/about/content-unlocked/lp/)
VLOG July 13 Today, Google official trades on prediction market case; Eric Adams' Herbert runs out of money: https://www.patreon.com/MatthewRussellLee/posts/bad-times-for-ex-163554478 OppFi FOIA win at FDIC, stonewalling by OCC. #NextSG race: @RafaelMGrossi sock puppet diversifies https://innercitypress.com/unbetrayals2sockpuppetguterresprofilebookicp071026.html
Have you ever hit a massive milestone—like selling the business you spent a decade building—only to feel an unexpected wave of grief instead of pure joy? In this episode of Truth, Lies and Work, we sit down with Richard Clarke, founder of software company Secret Source. He did what every entrepreneur dreams of: he grew his business, put team happiness at the absolute center of his culture, and successfully exited. But instead of finding ultimate contentment, Richard fell face-first into the ultimate happiness trap: "I'll be happy when..." Richard opens up about the bittersweet reality of exiting a business, the sudden loss of community, and how he had to use his own behavioral science background to climb out of the post-exit slump. Plus, he breaks down the 5 essential needs every human requires to be truly happy at work and thoroughly busts the famous myth surrounding Google's "20% time." Key Takeaways from This Episode The 5 Workplace Needs: Based on the US Surgeon General's framework, everyone requires five core elements to thrive: Safety, Community, Autonomy, Growth, and Purpose. The Post-Exit Slump: Founders often find that while an exit checks off the "safety" and "autonomy" boxes, it completely strips away their daily sense of "community" and "belonging." Ask, Don't Guess: Richard shares a brilliant case study where simply mapping out his team's anonymous scores on these five needs revealed a massive, easily fixable issue with scheduling meetings. The Google 20% Myth: Think Google employees spend a fifth of their week working on passion projects? Richard talks to ex-Googlers who have a very different story to tell. Connect with Richard Clarke
The latest episode of the Cambridge Tech Podcast reveals a fascinating trend in the tech ecosystem: some of the most impactful people supporting startups today are themselves former founders who've made the leap into major tech companies. In this week's episode, hosts James Parton and Faye Holland speak with Alexandre Béliard and Kimoon Kim from Google who are transforming how early-stage companies access world-class technical and financial support.Whether you're bootstrapping or scaling with serious funding, this conversation will show you exactly where to find the support you need.Listen to the full episode on the Cambridge Tech Podcast and find Alex and Kim on LinkedIn to explore how Google can support your startup's growth.And for aspiring Googlers, the pair offered refreshingly honest insights into the notoriously competitive interview process.Headline sponsor Holden Polestar Produced by Cambridge TV #CamTechPod Hosted on Acast. See acast.com/privacy for more information.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover Apple‘s uncertain path beyond the iPhone. They also discuss Google‘s agentic pivot at Google I/O, a surge in DuckDuckGo traffic following Google’s default switch to AI mode, and payroll platform Remote surpassing 300 million in ARR with flat headcount. Gregor and Sean also dig into why consumer subscriptions don’t seem to correspond to actual costs, how enterprise is quietly subsidizing the AI economy, why the true moat has shifted from model quality to context management and agentic harness, and what the coming wave of token cost optimization might look like as companies start scrutinizing their AI bills. Finally, they highlight standout threads from Hacker News including Doom running on a travel router touchscreen, a viral post asking whether AI productivity gains should translate to a day off, YouTube‘s move to automatically label AI-generated content, and SimCity 3000 running in 4K. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: Apple's AI Problem, The Real Business Model of AI, and Token Cost Reckoning appeared first on Software Engineering Daily.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover Apple‘s uncertain path beyond the iPhone. They also discuss Google‘s agentic pivot at Google I/O, a surge in DuckDuckGo traffic following Google’s default switch to AI mode, and payroll platform Remote surpassing 300 million in ARR with flat headcount. Gregor and Sean also dig into why consumer subscriptions don’t seem to correspond to actual costs, how enterprise is quietly subsidizing the AI economy, why the true moat has shifted from model quality to context management and agentic harness, and what the coming wave of token cost optimization might look like as companies start scrutinizing their AI bills. Finally, they highlight standout threads from Hacker News including Doom running on a travel router touchscreen, a viral post asking whether AI productivity gains should translate to a day off, YouTube‘s move to automatically label AI-generated content, and SimCity 3000 running in 4K. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: Apple's AI Problem, The Real Business Model of AI, and Token Cost Reckoning appeared first on Software Engineering Daily.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover Apple‘s uncertain path beyond the iPhone. They also discuss Google‘s agentic pivot at Google I/O, a surge in DuckDuckGo traffic following Google’s default switch to AI mode, and payroll platform Remote surpassing 300 million in ARR with flat headcount. Gregor and Sean also dig into why consumer subscriptions don’t seem to correspond to actual costs, how enterprise is quietly subsidizing the AI economy, why the true moat has shifted from model quality to context management and agentic harness, and what the coming wave of token cost optimization might look like as companies start scrutinizing their AI bills. Finally, they highlight standout threads from Hacker News including Doom running on a travel router touchscreen, a viral post asking whether AI productivity gains should translate to a day off, YouTube‘s move to automatically label AI-generated content, and SimCity 3000 running in 4K. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: Apple's AI Problem, The Real Business Model of AI, and Token Cost Reckoning appeared first on Software Engineering Daily.
Formal methods are a branch of mathematics and computer science focused on proving the correctness of systems, and they have long promised a more rigorous foundation for software. However, their complexity has kept them confined to a small community of specialists. That is now changing as agentic AI systems take on increasingly autonomous roles. The question of how to define, enforce, and verify what those agents are allowed to do has become urgent, and automated reasoning is emerging as a critical part of the answer. Byron Cook is a VP and Distinguished Scientist at AWS, a professor at University College London, and a program manager at DARPA. He founded the Automated Reasoning Group at AWS over a decade ago, where his team built the foundations behind products like IAM Access Analyzer, VPC Reachability Analyzer, and Bedrock Guardrails. In this episode, Byron joins Sean Falconer to discuss how automated reasoning works and why it scales so well with AI, the rise of neurosymbolic approaches that combine formal logic with large language models, what it means to formally specify agent behavior using temporal logic, and why the convergence of agentic AI and formal methods may represent one of the most significant shifts in how software is built and verified. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Formal Methods as Agent Guardrails appeared first on Software Engineering Daily.
Formal methods are a branch of mathematics and computer science focused on proving the correctness of systems, and they have long promised a more rigorous foundation for software. However, their complexity has kept them confined to a small community of specialists. That is now changing as agentic AI systems take on increasingly autonomous roles. The question of how to define, enforce, and verify what those agents are allowed to do has become urgent, and automated reasoning is emerging as a critical part of the answer. Byron Cook is a VP and Distinguished Scientist at AWS, a professor at University College London, and a program manager at DARPA. He founded the Automated Reasoning Group at AWS over a decade ago, where his team built the foundations behind products like IAM Access Analyzer, VPC Reachability Analyzer, and Bedrock Guardrails. In this episode, Byron joins Sean Falconer to discuss how automated reasoning works and why it scales so well with AI, the rise of neurosymbolic approaches that combine formal logic with large language models, what it means to formally specify agent behavior using temporal logic, and why the convergence of agentic AI and formal methods may represent one of the most significant shifts in how software is built and verified. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Formal Methods as Agent Guardrails appeared first on Software Engineering Daily.
Formal methods are a branch of mathematics and computer science focused on proving the correctness of systems, and they have long promised a more rigorous foundation for software. However, their complexity has kept them confined to a small community of specialists. That is now changing as agentic AI systems take on increasingly autonomous roles. The question of how to define, enforce, and verify what those agents are allowed to do has become urgent, and automated reasoning is emerging as a critical part of the answer. Byron Cook is a VP and Distinguished Scientist at AWS, a professor at University College London, and a program manager at DARPA. He founded the Automated Reasoning Group at AWS over a decade ago, where his team built the foundations behind products like IAM Access Analyzer, VPC Reachability Analyzer, and Bedrock Guardrails. In this episode, Byron joins Sean Falconer to discuss how automated reasoning works and why it scales so well with AI, the rise of neurosymbolic approaches that combine formal logic with large language models, what it means to formally specify agent behavior using temporal logic, and why the convergence of agentic AI and formal methods may represent one of the most significant shifts in how software is built and verified. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Formal Methods as Agent Guardrails appeared first on Software Engineering Daily.
On Episode #13 of of the Tedi Talks Podcast, Tedi welcomes his special guest, Rachel Dawson, a Strategic Executive, Attorney & Certified Leadership Coach. Rachel is located in Ann Arbor, Michigan. Rachel and Tedi talk about Rachel's book: ‘Bold, Black & Brilliant: A Black Woman's Guide to Career Confidence and Power'. Rachel shares with us why she wrote this book and encourages ALL women to read it, as well as men. Tedi shares a lot of data after hitting the Googler again and he and Rachel break it all down. Rachel shares with us the reason so many black women are overlooked in the workplace and ways we address the systematic and systemic racism that is ever present in today's workplace. This is a very informative and educational episode, one that you def do not want to miss. To learn more about Rachel, you can connect with her at:Website: https://www.racheldawsonconsulting.com/ Facebook: https://www.facebook.com/profile.php?id=61581707561310LinkedIn: https://www.linkedin.com/in/racheldawson5RESOURCESBold, Black & Brilliant A Black Woman's Guide to Career Confidence & Power (book by Rachel Dawson, JD)First Alumna in Space (Article by the University of Michigan)Professor Timothy SnyderPONSORS:7C LingoSuccessful Coaches EnterpriseThe opinions and statements made on the Tedi TalksPodcast are/or do not necessarily reflect those of the Tedi Talks Podcast or Tedi Parsons. To learn more, please visit: https://www.teditalks2.com/The music used for this podcast was provided by: chill-house-vol-9-by-sascha-ende-from-filmmusic-io. https://filmmusic.io/standard-license. License (CC BY 4.0):
Vector search has risen to become a foundational tool in modern search and retrieval systems, including the RAG pipelines that power many AI applications. However, the demands on retrieval systems are growing more sophisticated, which is revealing the limits of relying on a single vector similarity score. Vespa is a popular open source search and data serving engine. Central to Vespa’s architecture is tensor-based retrieval, which is an approach that represents data as tensors rather than simple vectors. Tensor-based retrieval enables richer mathematical operations and more flexible ranking functions that can surmount the limitations of a single vector similarity score. Radu Gheorghe is a software engineer at Vespa with a background spanning nearly 12 years of consulting and training on Elasticsearch and Solr. In this episode, Radu joins Sean Falconer to discuss why vector similarity alone falls short in production, how tensor-based retrieval generalizes to support richer ranking functions, the trade-offs in chunking and multi-stage re-ranking architectures, and where AI search is headed next. Full Disclosure: This episode is sponsored by Vespa. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Vespa AI and Surpassing the Limits of Vector Search appeared first on Software Engineering Daily.
Vector search has risen to become a foundational tool in modern search and retrieval systems, including the RAG pipelines that power many AI applications. However, the demands on retrieval systems are growing more sophisticated, which is revealing the limits of relying on a single vector similarity score. Vespa is a popular open source search and data serving engine. Central to Vespa’s architecture is tensor-based retrieval, which is an approach that represents data as tensors rather than simple vectors. Tensor-based retrieval enables richer mathematical operations and more flexible ranking functions that can surmount the limitations of a single vector similarity score. Radu Gheorghe is a software engineer at Vespa with a background spanning nearly 12 years of consulting and training on Elasticsearch and Solr. In this episode, Radu joins Sean Falconer to discuss why vector similarity alone falls short in production, how tensor-based retrieval generalizes to support richer ranking functions, the trade-offs in chunking and multi-stage re-ranking architectures, and where AI search is headed next. Full Disclosure: This episode is sponsored by Vespa. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Vespa AI and Surpassing the Limits of Vector Search appeared first on Software Engineering Daily.
Vector search has risen to become a foundational tool in modern search and retrieval systems, including the RAG pipelines that power many AI applications. However, the demands on retrieval systems are growing more sophisticated, which is revealing the limits of relying on a single vector similarity score. Vespa is a popular open source search and data serving engine. Central to Vespa’s architecture is tensor-based retrieval, which is an approach that represents data as tensors rather than simple vectors. Tensor-based retrieval enables richer mathematical operations and more flexible ranking functions that can surmount the limitations of a single vector similarity score. Radu Gheorghe is a software engineer at Vespa with a background spanning nearly 12 years of consulting and training on Elasticsearch and Solr. In this episode, Radu joins Sean Falconer to discuss why vector similarity alone falls short in production, how tensor-based retrieval generalizes to support richer ranking functions, the trade-offs in chunking and multi-stage re-ranking architectures, and where AI search is headed next. Full Disclosure: This episode is sponsored by Vespa. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Vespa AI and Surpassing the Limits of Vector Search appeared first on Software Engineering Daily.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover Anthropic's controversial “Mythos” security model and what it means for vulnerability discovery at scale. They also discuss recent layoffs at Snap and Meta, and how AI investment pressures are reshaping hiring, organizational priorities, and the economics of big tech. Gregor and Sean then zoom out to examine the massive wave of AI infrastructure spending—hundreds of billions in capex across Amazon, Google, Microsoft, and Meta, and what it signals about the future of cloud platforms, model providers, and the engineers who build on top of them. They explore the emerging entanglement between model labs and infrastructure providers, the evolving role of engineers in an AI-native world, and the growing gap between rapid AI adoption and security readiness. Finally, they highlight standout threads from Hacker News, including creative uses of AI coding tools to revive abandoned side projects, new approaches to training smaller yet highly capable models, surprising demographic data visualizations, and even the mathematics of “cheating” at Tetris. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: Anthropic's Mythos, Supply Chain Hacks, and the AI Spending Surge appeared first on Software Engineering Daily.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover Anthropic's controversial “Mythos” security model and what it means for vulnerability discovery at scale. They also discuss recent layoffs at Snap and Meta, and how AI investment pressures are reshaping hiring, organizational priorities, and the economics of big tech. Gregor and Sean then zoom out to examine the massive wave of AI infrastructure spending—hundreds of billions in capex across Amazon, Google, Microsoft, and Meta, and what it signals about the future of cloud platforms, model providers, and the engineers who build on top of them. They explore the emerging entanglement between model labs and infrastructure providers, the evolving role of engineers in an AI-native world, and the growing gap between rapid AI adoption and security readiness. Finally, they highlight standout threads from Hacker News, including creative uses of AI coding tools to revive abandoned side projects, new approaches to training smaller yet highly capable models, surprising demographic data visualizations, and even the mathematics of “cheating” at Tetris. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: Anthropic's Mythos, Supply Chain Hacks, and the AI Spending Surge appeared first on Software Engineering Daily.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover Anthropic's controversial “Mythos” security model and what it means for vulnerability discovery at scale. They also discuss recent layoffs at Snap and Meta, and how AI investment pressures are reshaping hiring, organizational priorities, and the economics of big tech. Gregor and Sean then zoom out to examine the massive wave of AI infrastructure spending—hundreds of billions in capex across Amazon, Google, Microsoft, and Meta, and what it signals about the future of cloud platforms, model providers, and the engineers who build on top of them. They explore the emerging entanglement between model labs and infrastructure providers, the evolving role of engineers in an AI-native world, and the growing gap between rapid AI adoption and security readiness. Finally, they highlight standout threads from Hacker News, including creative uses of AI coding tools to revive abandoned side projects, new approaches to training smaller yet highly capable models, surprising demographic data visualizations, and even the mathematics of “cheating” at Tetris. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: Anthropic's Mythos, Supply Chain Hacks, and the AI Spending Surge appeared first on Software Engineering Daily.
In this bonus episode recorded live at the ASU+GSV summit in San Diego, we spoke with Geordie Hyland from the American College of Education about building a higher ed model centered on affordability, efficiency, and student outcomes. They explore how a fully online, in-house curriculum approach—combined with a focus on employer alignment and continuous improvement—can deliver high-quality education while keeping costs low and minimizing student debt. The conversation highlights the importance of rethinking traditional higher ed structures, emphasizing that institutions must prioritize clear ROI, student value, and sustainable operating models to remain relevant and accessible in today's evolving landscape. Guest Name: Geordie Hyland - President & Chief Executive Officer at American College of Education Guest Social: LinkedIn Guest Bio: Geordie Hyland is the President and Chief Executive Officer of the American College of Education (ACE) and is passionate about strengthening human capital and communities. Geordie's education management experience spans Higher Ed, K12, workforce development, allied health, clinical healthcare, continuing medical education and remedial training in online, virtual reality, simulated, hybrid and in-person modalities. Geordie is a former Googler and graduate of Harvard University, where he received a bachelor's degree in English and American literature as well as a master's in business administration from Harvard Business School. He also received a master's degree in industrial relations and personnel management from The London School of Economics and Political Science. - - - -Connect With Our Host:Dustin Ramsdellhttps://www.linkedin.com/in/dustinramsdell/About The Enrollify Podcast Network:The Higher Ed Geek is a part of the Enrollify Podcast Network. If you like this podcast, chances are you'll like other Enrollify shows too!Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Adam Coelho stood on stage presenting to Google's CEO at a leadership conference, the culmination of his 14-year career training thousands of Googlers in mindfulness and emotional intelligence. One week later, he was placed on a performance improvement plan—the corporate equivalent of being told your time is up. His story reveals a fundamental truth about financial independence that most people miss until it's too late: having enough money to walk away isn't the same as knowing where to walk toward. Key Topics Discussed [00:00:00] Introduction and Adam's Return Brad welcomes Adam back to explore his transition from Google and introduce the central question: if FI life started tomorrow, what would you actually do? [00:03:30] The Necessary vs. Sufficient Framework Adam introduces the concept that FU money alone isn't enough for true resilience. Unexpected life events can thrust anyone into early retirement without warning, and financial preparedness without life preparedness leaves you directionless. [00:08:15] Identity Beyond Work How much of your identity is tied to prestigious roles and external markers of success? The challenge of discovering who you are when those markers disappear. [00:14:00] Adam's Story: From Peak to Performance Warning The journey from presenting at Google CEO's leadership conference to being placed on a performance improvement plan illustrates how quickly circumstances can change—and why preparation matters. [00:22:00] The Power of Vision and Envisioning The neuroscience behind envisioning: neuroplasticity, how our brains are prediction machines, and why the future we expect is the one we tend to create. [00:32:00] Practical Envisioning Exercises Step-by-step guidance on envisioning your FI life, including the FI Life Jumpstart exercise, journaling practices, and thinking bigger than your current constraints. [00:40:00] Client Success Story: Nick the Flight Doc How one client transformed his life by thinking bigger about his vision, leading to international medical mission trips and better work-life balance. [00:46:00] Planting Seeds: Vision Practices Specific practices for reinforcing your vision: visualization, mindset affirmations, talking about your vision, and mini experiments. [00:54:00] Day One of FI Life Adam describes his actual first day after leaving Google, the importance of giving yourself grace, and transitioning from corporate pace to entrepreneurial freedom. [01:02:00] Final Lessons and Closing Key takeaways about mourning old identities, avoiding the trap of hitting a number without a plan, and starting to live your FI life now. Notable Quotes "FU money is absolutely necessary, but not sufficient on its own. There's actually a second half to true resilience." — Adam Coelho "If FI life started tomorrow, what would you do? We're all on this path to financial independence, but if that life started tomorrow morning, are you ready to start living it?" — Adam Coelho "FU money gives you options and security, but vision gives you direction and momentum." — Adam Coelho "Our story creates our reality. Everything you think, feel, and pay attention to changes the structure and function of your brain." — Adam Coelho "FI number is necessary but not sufficient for a great financially independent life. I think the money without the plan of what does life look like, without the experimentation, without the resilience to take the ups and downs of how life throws things at you, I think if it's just the money, I think you're hopelessly lacking." — Brad Barrett Key Takeaways Download the FI Life Jumpstart exercise at mindfulfire.org/choosefi and complete the envisioning journaling prompt this week Identify one mini experiment you can try this month that aligns with your vision for FI life—something low-risk and low-cost Create 3-5 mindset affirmations based on who you want to become and practice them during meditation or quiet reflection Talk to at least one person about your vision for FI life this week t…
It doesn't matter how many times you've made a pivot in life, when you are craving making a big change, it will ROCK your world. This is why, for months now, I've been bringing women onto this podcast to talk about their pivots, transitions and seasons of big change. Who better to learn from than women who have gone before? In this episode, I'm joined by Kacia Ghetmiri: ex-Googler, top podcast host of empowerHER (we're talking .5% podcast here with 13+ MILLION downloads), turned million-dollar coach and now, recently pregnant with her 2nd baby, has started a brand-new career as a real estate agent.Speaking with Kacia felt like catching up with an old girlfriend who was just here to drop some wisdom. She shared her thoughts on: how she knows when it's time to pivotnavigating next steps in your career change when nothing feels clearhow ambition and career identity changes through motherhoodand she even gave us a mini-course on growing & monetizing a podcast (I took many many notes)!!! We also get into building a business as a mother, trusting the future version of yourself, and Kacia took us through her whole journey from coaching and podcasting into real estate.Listen to her podcast here: empowerHERFollow her on IG here: @kacia.ghetmiri
From building Applied Intuition from YC-era autonomy tooling into a $15B physical AI company, Qasar Younis and Peter Ludwig have spent the last decade living through the full arc of autonomy: from simulation and data infrastructure for robotaxi companies, to operating systems for safety-critical machines, to deploying AI onto cars, trucks, mining equipment, construction vehicles, agriculture, defense systems, and driverless L4 trucks running in Japan today. They join us to explain why “physical AI” is not just LLMs on wheels, why the real bottleneck is no longer model intelligence but deployment onto constrained hardware, and why the future of autonomy may look less like one-off demos and more like Android for every moving machine.We discuss:* Applied Intuition's mission: building physical AI for a safer, more prosperous world, powering cars, trucks, construction and mining equipment, agriculture, defense, and other moving machines* Why physical AI is different from screen-based AI: learned systems can make mistakes in chat or coding, but safety-critical machines like driverless trucks, autonomous vehicles, and robots need much higher reliability* The evolution from autonomy tooling to a broad physical AI platform: starting with simulation and data infrastructure for robotaxi companies, then expanding into 30+ products across simulation, operating systems, autonomy, and AI models* Why tooling companies came back into fashion: Qasar on why developer tooling looked unfashionable in 2016, why Applied Intuition still bet on it, and how the AI boom made workflows and tools central again* The three core buckets of Applied Intuition's technology: simulation and RL infrastructure, true operating systems for vehicles and machines, and fundamental AI models for autonomy and world understanding* Why vehicles need a real AI operating system: real-time control, sensor streaming, latency, memory management, fail-safes, reliable updates, and why “bricking a car” is much worse than bricking an iPad* Physical machines as “phones before Android and iOS”: Peter explains why today's vehicle and machine software stack is fragmented across many operating systems, and why Applied Intuition wants to consolidate the platform layer* Coding agents inside Applied Intuition: Cursor, Claude Code, internal adoption leaderboards, and how AI tools are changing engineering workflows even in embedded systems and safety-critical software* Verification and validation for physical AI: why evals get harder as models improve, how end-to-end autonomy changes simulation requirements, and why neural simulation has to be fast and cheap enough to make RL practical* From deterministic tests to statistical safety: why autonomy validation is shifting from binary pass/fail requirements toward “how many nines” of reliability and mean time between failures* Cruise, Waymo, and public trust: Qasar and Peter discuss why autonomy failures are not just technical issues, how companies interact with regulators, and why Waymo is setting a high bar for the industry* Simulation vs. reality: why no simulator perfectly represents the real world, how sim-to-real validation works, and why real-world testing will never disappear* World models for physical AI: hydroplaning, construction equipment, visual cues, cause-and-effect learning, and where world models help versus where they are not enough* Onboard vs. offboard AI: why data-center models can be huge and slow, but onboard vehicle models need millisecond-level latency, low power, small size, and distillation-like efficiency* Why physical AI is not constrained by model intelligence alone: the hard part is deploying models onto real hardware, under safety, latency, power, cost, and reliability constraints* Legacy autonomy vs. intelligent autonomy: RTK GPS in mining and agriculture, why hand-coded path-following worked for decades, and why modern systems need perception and dynamic intelligence* Planning for physical systems: how “plan mode” applies to robotaxis, mining, defense, and multi-step physical tasks where actions change the state of the world* Why robotics demos are not production: the brittle last 1%, humanoid reliability, DARPA Grand Challenge-style prize policy, and the advanced engineering gap between research and deployment* Applied Intuition's hard-earned lessons: after nearly a decade, Peter says they can look at a robotics demo and predict the next 20 problems the company will hit* Qasar's advice to founders: constrain the commercial problem, avoid copying mature-company strategies too early, and remember that compounding technology only matters if you survive long enough to see it compound* Why 2014 YC advice may not apply in 2026: capital markets, AI company dynamics, and the difference between building in stealth with a deep network versus building as a new founder today* What Applied is hiring for: operating systems, autonomy, dev tooling, model performance, evals, safety-critical systems, hardware/software boundaries, and engineers with deep curiosity about how things workApplied Intuition:* YouTube: https://www.youtube.com/@AppliedIntuitionInc* X: https://x.com/AppliedInt* LinkedIn: https://www.linkedin.com/company/applied-intuition-incQasar Younis:* X: https://x.com/qasar* LinkedIn: https://www.linkedin.com/in/qasar/Peter Ludwig:* LinkedIn: https://www.linkedin.com/in/peterwludwig/Timestamps00:00:00 Introduction: Applied Intuition, Physical AI, and 10 Years of Building00:01:37 Physical AI vs. Screen AI: Why Safety-Critical Changes Everything00:02:51 The Origin Story: Tooling, YC, and the Scale AI Comparison00:05:41 The Three Buckets: Simulation, Operating Systems, and Autonomy Models00:11:10 Hardware, Sensors, and the LiDAR Question00:14:26 The Operating System Layer: Why Vehicles Are Like Pre-Android Phones00:19:13 Customers, Licensing, and the Better-Together Stack00:21:19 AI Coding Adoption: Cursor, Claude Code, and the Bimodal Engineer00:26:41 Verifiable Rewards, Evals, and Neural Simulation00:31:04 Statistical Validation, Regulators, and the Cruise Lesson00:40:25 World Models, Hydroplaning, and Cause-Effect Learning00:43:34 Onboard vs. Offboard: Latency, Embedded ML, and Distillation00:50:57 Plan Mode for Physical Systems and Next-Token Prediction Universally00:53:04 Productionization: The 20 Problems Every Robotics Demo Will Hit00:58:00 Founder Advice: Constraints, Compounding Tech, and Mature-Company Mimicry01:05:41 Hiring Philosophy: Hardware/Software Boundary and Engineering Mindset01:08:50 General Motors Institute, Education, and the Curiosity MindsetTranscriptIntroduction: Applied Intuition, Physical AI, and 10 Years of BuildingAlessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: And today we're very honored to have the founders of Applied Intuition, Qasar and Peter. Welcome.Qasar [00:00:17]: You guys really know how to turn it on to podcast mode. That was, you guys are real pros at this.Qasar [00:00:23]: They were just joking around right before this, and then they flipped it pretty quick.Alessio [00:00:29]: Oh, yeah, it's good to have you guys. Maybe you just wanna introduce yourself so people know the voice on the mic and they'll know what they're hearing.Peter [00:00:33]: Oh, sure. Yeah, I'm Peter Ludwig. I'm the co-founder and CTO of Applied Intuition.Qasar [00:00:38]: And my name is Qasar Younis. I am the CEO and co-founder with Peter.Alessio [00:00:42]: Nice. Can you guys give the high-level overview of what Applied Intuition is? And I was reading through some of the Congress files, when you went out there, Peter, and eighteen of the top twenty global non-Chinese automakers, you two guys, you have customers in agriculture, defense, construction. I think most people have heard of Applied Intuition tied to YC when it was first started, and then you were kinda in stealth for a long time, so maybe just give people the high-level overview of what it is today, and then we'll dive into the different pieces.Peter [00:01:10]: Yeah. So at Applied Intuition, our mission is to build physical AI for a safer, more prosperous world. And so we work on physical AI for all different types of moving systems, everything from cars to trucks to construction and mining equipment, to defense technologies. And we're a true technology company, so we build and sell the technology, and we sell it to the companies that make the machines. We sell it to the government, really anyone that wants to buy a technology to make machines smart.Physical AI vs. Screen AI: Why Safety-Critical Changes EverythingQasar [00:01:38]: Yeah. And I think in the broader AI landscape, a lot of the focus, rightfully so in the last, three years has been on large language models, and so everything fits in a screen. Like, whether it's code complete products or things like that. And what's different about us is we're deploying intelligence onto a lot of things that don't have screens. they're physical machines. There are sometimes screens within the cabin or for example of a car or a truck or something like that, but most of the value we provide is putting intelligence that is in safety critical environments. So that those two words are really important because learn systems can make mistakes if you're asking for, like, some, so something like, “Tell me about these podcast hostsQasar [00:02:28]: that I'm about to go meet.” But you can't do that obviously when you run, like, as an example, we run driverless trucks in Japan right now, as we speak. We can't have errors. Those are L4 trucks. Yeah.Alessio [00:02:40]: Yeah. Was that always the mission? I remember initially, I think people put you and Scale AI very similarly for some things about being kinda like on the data infrastructure side of things. What was the evolution of the company?The Origin Story: Tooling, YC, and the Scale AI ComparisonPeter [00:02:51]: Well, from the very beginning, we always wanted to, really be a technology company that helped generally push forward the industrial sector. And so we started off working in autonomy. Our very first customers were robotaxi companies. And we started off doing a lot of work in simulation and data infrastructure. And then over the years, we've expanded our portfolios. Now we have, over thirty products, and it's a pretty broad technology play within the landscape of physical AI.Qasar [00:03:19]: Yeah, I think the Scale reason is because we're all YC Universe companies. But it was a very different company. Scale, was, is more of a services company, data labeling company fundamentally. We started and still are, do a lot of tooling. So like, you think developer tooling is now in vogue again, thanks to the AI boom. But honestly, ten years ago, it was out of vogue. It w Like, doing a tooling company in 2016, 2017 was not, like, the thing to do because, I don't know if you remember, the VCs generally, their views was that toolings are They're just workflows, and workflows ultimately are not really interesting. And we've gone and come, full circle with that. But when we started the company, our kind of it's kinda like in the periphery of what the company wants to be. It was like, from our earliest days, like, we wanna deploy software on physical machines, like on cars and on trucks and things like that. And obviously, we didn't know that the transformer boom was gonna happen. We didn't know that autonomy systems would become end-to-end. Those things we didn't know. And why that's important when autonomy systems become end-to-end, it is just now those models can be generalized to, multiple form factors. And so back nine, ten years ago, tooling was a great way, and still is a great way to, build the technology and sell technology to our end customers, a lot of them who wanna build this stuff themselves. And so we just offer like a spectrum of solutions from you can just use like one part of a development suite of tools all the way to buying the full thing. The way to think about the company, or at least the way we think about the company is, as Peter said, a technology provider. It's kinda like, what NVIDIA does or what an AMD, but we just don't do chips.Qasar [00:05:06]: We don't do silicon. But we're a technology provider fundamentally. And I think even, we used to joke when we started the company, like, we're not the guys to build, like, Instagram. Like that was just towards That's not our That's just not us in a most fundamental way. IAlessio [00:05:20]: You have thoughts.Qasar [00:05:21]: Yes.Qasar [00:05:22]: Well, it's, it's I mean, I think it's just like what And I mean, we worked on Maps and stuff, Google Maps. Consumer products are extremely difficult for a lot of different reasons. It just, I think doesn't scratch the itch. I think we're like Michigan guys who are kind of more of that traditional engineering kind of a realm, or lineage. we used to jokeThe Three Buckets: Simulation, Operating Systems, and Autonomy ModelsPeter [00:05:41]: I gotta say, though, what was clear ten years ago was that there was so much more that was possible with software and AI in vehiclesPeter [00:05:47]: and that was generally the space that we started in ten years ago.Peter [00:05:51]: And the precise path that we've taken over the years, I think we've been strategic, and we've adjusted to make sure that we're actually building stuff that's valuable to the market. And like, the technology has changed so much. Like our own technology stack has completely changed, I would say, roughly every two years. And so now we've probably done, let's say, four complete evolutions of our own technology stack. And I sort of see that cadence roughly keeping up.Peter [00:06:13]: And so the way even we think about engineering is almost on this two-year horizon, we're preparing ourselves that, hey, like, we wanna invest the appropriate amount, but then also be very dynamic as the research gets published and as our research team figures out new advancements and adapting to that.Qasar [00:06:27]: Yeah. One thing that has been consistent is the type of people we've, we've recruited. It's engineers who are fall into the sometimes very traditional, like, GoogleQasar [00:06:38]: -gen suite, but way different from, other companies. We are hiring folks who really know the intersection of hardware and software, who know really low-level systems. Obviously, traditional ML researchers and folks who've, actually, put ML systems into production. That's been pretty consistent. I think that, like, you look at the mix of our engineering, eighty-three percent of the company is engineering, so it's, like, a giant list.Qasar [00:07:05]: A lot of engineers.Alessio [00:07:06]: Which, by the way, a thousand engineersQasar [00:07:07]: Yeah. A thousand engineers.Alessio [00:07:08]: that's on your website, so I imagine it's up to date.Qasar [00:07:11]: It is, it is up to date, yes. Yes.Alessio [00:07:12]: okay. And then forty-plus founders.Qasar [00:07:15]: Yeah. We would tend to also, This was more luck than strategy. But we've recruited a lot of ex-founders. It's been a great place for founders, YC and non, ‘cause obviously I know a lot of the YC folks. It's kind of like we recruit a lot of Google people.Qasar [00:07:33]: For them to exercise both their technical and non-technical skills because, we're, we're, we're on the applied side. We have a research team that we do fundamental research, we publish, and we've, we've had great traction there. But fundamentally, the business wants to take this intelligence and deploy it into production and there's, like, a certain type of person that's more interested in that.Alessio [00:07:54]: Yeah. You mentioned the tech stack, Peter, so I just wanted to give you some rein to just go into it. I'm interested in where Wayve Nutrition, starts and ends in some sense, what won't you do? What, do you do that's common among all the verticals that you cover?Peter [00:08:10]: There's a few buckets of work that we do, and we've been at this for almost ten years now, so the technology's pretty broad. But we got startedQasar [00:08:17]: Yeah, with a thousand engineers, like, you could work on lots of things.Peter [00:08:19]: There's lots of stuff, yeah, espe-especially with AI tools to help.Peter [00:08:22]: So we got our start in simulation and simulation tooling and infrastructure. And so generally, if you're trying to build a very complex software system that involves moving machines, you need to test that, and the best way to test it is it's a combination of virtual developments, a simulation, and then also obviously real world testing.Peter [00:08:39]: And then there's a very careful process of that correlation between the simulation results and the real world results and ensuring that the simulator is in fact accurate to that. Simulation's a very deep topic.Peter [00:08:49]: We have a whole suite of products in that, and we could talk for many hours about that specifically. But that is one part of what we do as a company. Reinforcement learning as a subpart of that is also super critical. I think a lot of the a lot of the best advancements happening in a lot of these AI systems right now in some way relate to reinforcement learning, and with now we have lots of compute, and you can do tons of interesting things for reinforcement learning. The second bucket of work that we do is on operating systems technology. true operating systems. Like, think about, schedulers and memory management and middleware and message passing and highly reliable networking and data links. Like, the reality is, if you want to deploy AI onto vehicles, you need a really good operating system. And when we were getting deeper into that space, there wasn't really anything that we were happy with.Peter [00:09:39]: Like, things existed, absolutely, and we were using what was available in the market, and as an engineering organization, we roughly realized these things aren't great. We think we can do this better, and so let's, let's build something. And that was then the that was the moment of inspiration that started our operating systems business, which is now a very real business for us. And in order to write and run great AI, you need a great operating system, and so that-that's what got us into that. And then the third bucket that we work on, it's, it's true fundamental AI technology. Models, we do a lot of work in, as mentioned, the foundational research, but then the also the world models and the actual autonomy models that are running on these physical machines, and that's across cars, trucks, mining, construction, agriculture, and defense, and so that's both land, air, and sea.Qasar [00:10:31]: And also, a smaller subsector of that third bucket is the interaction of humans with those machines.Qasar [00:10:38]: So that's a multimodal, experience. Historically, if you're moving a dirt mover or any of these machines, there are, like, buttons you press, whether they're actual physical tactile buttons or something like a touch screen. That's just That fundamentally is changing to where you're just talking to the machine and the machine and you're teaming with the machine.Alessio [00:10:58]: Voice?Qasar [00:10:59]: Yeah, voice, absolutely, yeah.Alessio [00:11:00]: Oh.Qasar [00:11:00]: And also the machine just being aware of who is in the cabin, what their state is. you can think from a safety systems perspective, the most simple version of this is, like, the driver is tired, right? They're, they're if you get those alerts when you're driving your car and saysHardware, Sensors, and the LiDAR QuestionQasar [00:11:15]: -maybe take a coffee break, that take that times, a couple of order of magnitudes up. But this concept of teaming man and machine is important. When you think about running agents or just running, different instances of, Claude and doing work for you in the background, you can take that analogy out, almost copy and paste and put it into, like, a farm, where you have a farmer who's running a number of machines. So where they interact with the machine is where there's maybe a critical decision or a disengagement or something like that, but generally speaking, the agent on the physical machine is running and making decisions on the behalf of the farmer until there's something maybe critical. And that's also what we work on. So that's not pure autonomy. It's a little bit of a mix, but it falls under, autonomy. In the automotive sense, that's typically defined in SAE levels as an L2++ systemQasar [00:12:05]: -with a human in the loop. But just take that idea, to other verticals.Alessio [00:12:09]: Yeah. You've not mentioned hardware at all, like sensors or obviously we you mentioned you don't do chips. I think even in AV there's, like, a big, cameras versus lidars. Like, what are, like, in your space maybe some of those design decisions that you made, and are they driven by the OEM's ability to put things on the machinery? And like, how much influence do you guys have on co-designing those?Peter [00:12:32]: Yeah. So we don't make sensors. Like, we're, we're not a manufacturer. Obviously, we use a lot of sensors in our autonomy products. in terms of what actually goes on the vehicles, we have a preferred set of sensors that we, let's say fully support, and then our customers, they can sort of choose from those. And obviously if there's a very strong opinion on supporting something else, we'll add that to the platform as well. And the lidar question is at this point sort of the age-old,Peter [00:12:59]: topic in autonomy, and the state of the industry right now is lidar is hands down a useful sensor, specifically for data collection and the R&D phase of autonomy development. if you see, for example, a Tesla R&D vehicle, it actually has lidar on itPeter [00:13:17]: to this day, right? In the Bay Area we see these. you'll see, like, Model Ys or Cybercab that have lidars on them just driving around. So it's, it's useful because it gives you per pixel depth information. So if you can pair a lidar with a camerand you can say that, well, this camera's looking this direction, this lidar's looking this direction, and now for each pixel of the camera I can see how far away is that pixel. you can actually then use that as a part of your model training, and then the that depth information then becomes a learned, a learned state of the camera data. And then when you're doing the production system, you can now remove the lidarPeter [00:13:52]: and now you can actually get depth with just the camera. And so that difference between, like, a highly sensored R&D vehicle and then the down-costed production vehicle, we use that across our whole portfolio of products. And of course the end goal is you want super low cost and super reliable.Peter [00:14:08]: And then in certain use cases you have some more, bespoke things. Like in defense as an example, you do things at night oftentimes, and so you care about sensors like infrared, more so than And you don't, you don't wanna be putting energy out, so you don't wanna use lidar or radar.Peter [00:14:23]: but you still need to be able to see at nighttime. So yeah, we work the whole gamut.The Operating System Layer: Why Vehicles Are Like Pre-Android PhonesAlessio [00:14:27]: Cool. So that's kinda like on the hardware level. Then on the OS level, how does that look like? What is, like, unique? my drive- I drive a Tesla. Whenever I drive some other car that has a screen, it always sucks.Alessio [00:14:38]: It's on, like, cheap Android tablet. It's like, it's laggy and all of that. What does the OS of, like, the autonomy future look like?Peter [00:14:46]: When most people, it's really what you just described. When you think about operating system in a vehicle, you're thinking about the HMI, right? The human machine interface, and absolutely that's a an important part of it, but that's actually only one thin layer on top. So when we talk about operating systems for, like, AI in vehicles, there's many layers that go deep into the CPU critical realm and embedded systems, and you're talking about the real time control ofPeter [00:15:13]: let's say the electric motors or the engine and the actuators, and you have different redundancies for different, let's say, the steering actuation in the vehicle. And all of these things, need very core support in the in the operating system. And then of course for autonomy you have real time sensor data that's streaming in, and the latencies there are really important, right? If you try to Imagine you try to run Microsoft WindowsPeter [00:15:35]: like streaming your sensor data in or controlling the vehicle. Like, the latencies are gonna be absurd. Like, you can never do that. And so what's special about what we do is we really have this system level thinking, right? So we're looking at, we care about every performance characteristics of the entire system, and then we also, because we're doing a lot of the software or all of that software, we can fine-tune and control all of those things. So we can very carefully tune in the latencies for every aspect of the system. We can carefully tune in the memory management. We can have the right, fail-safes and fallbacks, for different things. ‘Cause you have to account for what if, what if there is a critical failure? What if there's a cosmic ray that flipsPeter [00:16:14]: a bit in the middle of the processor that causes some, malfunction? And you have to have a fail-safe to all of that, and so the core operating system is a part of that. And then the one last thing, which is a lot less exciting but is, actually a very big topic, is reliability of updates.Peter [00:16:30]: so the I have a Tesla and you get updates fairly frequently, right?Peter [00:16:36]: Once a month. Most companies that are making vehiclesPeter [00:16:40]: are basically never doing updates, and they're And even if they are doing updates, they're usually only updating maybe one module. Maybe they're updating the HMI module. But they're not able to update, let's say, the CPU critical parts of the system.Peter [00:16:51]: You have to go into the dealer for that. And so with our operating system now we can actually enable highly reliable updates of any system in the vehicle, and that's way easier said than done. Like, there's lots of technical, technically deep stuff, in the tech stack to do that in a way that you're not going to accidentally brick a vehicle.Peter [00:17:08]: And right? If, imagine yourAlessio [00:17:10]: That would be bad.Alessio [00:17:11]: Bad.Peter [00:17:11]: Bricking a car is a very expensivePeter [00:17:13]: and honestly, like across the industry maybe one of the most just pure impactful things that we've done is we've just, we're, we're now enabling the industry to actually do software updates.Alessio [00:17:22]: Just to clarify as well, who is the customer for this? Like, I assume a lot of hardware manufacturers have their own firmware, and I'm sure some of them would just have you write it for them because you're experts. And others would have their own. Like, who pays for this? Who invites you into the house? Is it, is it the end user, or is it, is it the manufacturer?Peter [00:17:41]: Yeah. So let me make an analogy firstly on the on the fragmentation of software. So physical machines today are more akin to the state of the phone market before Android and iOS existed, right? So I worked on Android at Google by the way many years ago, and part of the reason that Larry at Google decided to get into Android was they wanted to run Google products on a bunch of phones, and they bought all of these phones from the industry, and it turned out they had like 50 different operating systems on these phones. And it was virtually impossiblePeter [00:18:17]: for Google to make their app run on all 50 devices equally well. And so the solution was, well, actually what if, what if they created-A really great operating system and made it attractive to all of these phone makers, and that was sort of the genesis for what Android was and why Android existed. It was a way for Google to get their products onto really wide diversity of devices. The state of the physical, industry right now, it's a little bit like that. Like, there's yes, these companies have firmware, but they have so many different operating systems, it's so fragmented, and to actually get a modern AI application to run on these vehicles, you actually, you first have to consolidate the operating system, and so that's, that's why we've done that. And then, your specific question was who are our customers? It's, it's, generally it's the companies that are making these machines.Peter [00:19:06]: And we're, we're, we're selling our technology to them to really simplify the architecture and then enable these AI applications to run on them.Customers, Licensing, and the Better-Together StackSwyx [00:19:13]: How much is reusable across? Like, do you have, like, one OS that is just configured for everything, or is there some more customization that is needed?Peter [00:19:22]: Yeah, highly reusable. So the fundamental technology is quite universal, right? So things that we do have to think about though are, like, chipset support. And so if you're, if you're coding, let's say, an LLM and you have start with an assumption that, “Hey, oh, I'm gonna, I'm gonna use CUDA, and I'm gonna run this, on an NVIDIA chip,” then you don't really have to think about the hardware in that sense. Like, you're just, “Okay, I'm just I'm in the CUDA/NVIDIA ecosystem, and I'm, I'm going to use that.” But the hardware, especially in safety critical systems, it's a lot more diverse. There's not one or one or two players. There's a bunch of different chipsets that we have to support. And so our operating system doesn't just run on, like, the equivalent of X86. It has to, it has to run on a number of different architectures from chips from a bunch of different companies. But again, we've been working on this for a long time now, so we have, we have support for all of those chipsets. And then when you want to then run the AI applications, we can then do that reliably across now a variety of providers.Qasar [00:20:19]: And I think that is, like, heavily inspired by Android, right? Android has a huge suite of testing and it's a reliable operating system that runs on thousands of devices. And we think we can, we can do the same in all these physical moving machines, with the difference that we're really in a safety critical realm. Android isn't.Alessio [00:20:40]: So on Android, I don't need to use Gmail, I can use Superhuman. Like, what about your machinery? Like, can people bring somebody else's automation to it, or is it kinda like all-in-one?Qasar [00:20:50]: You have to use us. No. Yeah. we're If, Yeah. Yeah, it's totally open. Yeah.Peter [00:20:56]: Yeah. our philosophy is that we are a technology company, and so we license our technology to customers to use how they want. And so if a customer wants to If they wanna license our autonomy tech and our operating system, then great, we'll license those. If they just wanna license the operating system and then use different autonomy tech, that's fine also, and we have great documentation andSwyx [00:21:17]: Or if they wanna use developer tooling.Peter [00:21:18]: Yeah, exactly.AI Coding Adoption: Cursor, Claude Code, and the Bimodal EngineerSwyx [00:21:19]: It's, like, a better together if, obviously, if you, if they work together. Is it all C++ I assume is with different compile targets?Peter [00:21:27]: We use a lot of C++.Peter [00:21:28]: Rust is sort of a hot, the new hot kid on the blockPeter [00:21:32]: for a bunch of things as well. But yeah, the lower level you get, especially when you get to real-time constraints, you hit C++ at some point, and at some point maybe you work your way into assembly when needed.Swyx [00:21:44]: Oh, damn.Alessio [00:21:46]: I'm curious about the coding agent adoption, just, like, since you're mentioning more esoteric languages. Like, what's the adoption internally? What have you learned?Peter [00:21:55]: Yeah. We use everything. So Cursor was, I think the hottest tool in the company for a good while. Now Claude Code, I think has taken the reign on that. We have a internal leader, leaderboard that we use just to sort of encourage adoptionPeter [00:22:09]: with-within the company. And yeah, it's, they're phenomenally useful. it's, Honestly, we take inspiration from some of those tools also in how we're adapting some of that mindset of thinking to the physical realm. Like if it's so easy to build an app for this or that thing that lives just on a screen, we can We're taking now a lot of the same ideas and applying that to, “Okay, well, if you wanted a physical machine to do something, how easy can we make that, using our own tooling and platform as well?”Alessio [00:22:40]: Are you changing any of, like, the OS architecture, kinda like the way you expose services to, like, be more AI friendly or?Peter [00:22:48]: Yeah, absolutely. The in the early days of our tools infrastructure work, it was a lot about, You had engineers that were experts in certain topics, but the things that you're dealing with, they're oftentimes more mathematical or more abstract, where actually GUI tools are very useful for certain things. Like as an example, we have a product we call Sensor Studio, which is, it helps you design the sensor suite for your autonomous vehicle, whether, again, it could be a car, it could be a drone, could be a mining equipment, could be a robot. And you place sensors in different places. You There's different, There's a library. You can understand what are the trade-offs that you're making in the design of that system, and that was, like, a very, a very GUI intensive, thing ‘cause it's a little more like a CAD tool in that senseSwyx [00:23:37]: YepPeter [00:23:37]: if you've seen CAD tools. Nowadays, though, right, we expose all of the underlying APIs for that and now using, AI agents, you can actually configure a sensor suite with just text and likely reach a better result than you could've through the GUI in the past, and we're taking that thinking now through the whole product portfolio.Swyx [00:23:57]: Another thing I was thinking about is just in terms of, like, AI, adoption, does it change your hiring at least a little bit, or how do you, how do you sort of manage engineers, differently?Peter [00:24:08]: Yeah. absolutely, it does. we, I think like every company in the Valley right now, are evolving our hiring practicesPeter [00:24:16]: because the skills required to be effective are changing so fast, right? you used to really select for just rote implementation ability and now it is more the AI engineer skill set, right? Where it's like, yeah, how to implement, but actually-Just banging out code is no longer the core job, right? It's, it's actually knowing what questions to ask, knowing how to tie, how to tie together these different AI tools. And so the interviews that we give now I think are way harder than they've ever been.Peter [00:24:46]: But we also allow, right, selective use of AI tools to solve the problems. And I think in that you start to see more of a bimodal distribution of engineers, right? You start to see like wow, there's, there's this subset of people that they really get it. Like they're, they're all in and they've, they've clearly invested the hours needed to learn these tools and how to be effective.Peter [00:25:09]: And then there's sort of the group of people that haven't done that, and that the productivity gap is just enormous. And so we're, we're trying to obviously select for the people that are really into this.Qasar [00:25:20]: I first wrote the my AI engineer piece three years ago, and when I first wrote about it, I was like, “Actually, not everyone should be an AI engineer,” ‘cause I think there's a there's an extremist stance where well, every software is an engineer is an AI engineer. And my actual example of people who should not be adopting AI was embedded systems and operating systems, and database people. Are they adopting AI?Peter [00:25:41]: I think it's the classic bitter lesson, topic, which is the Six months ago I would've said the same thing, but it's, it's becoming super useful for every domain.Qasar [00:25:53]: I'm sure.Peter [00:25:54]: Right? Like,Peter [00:25:56]: there was, I think six months ago, or maybe a year ago, if you tried to use, let's say the latest Claude model for writing shaders, GPU shaders, the results were probably underwhelming. And if you use the latest model now to do that kind of task, you're a little bit blown away, like, “Wow, that actually worked. That's amazing.” And we see the same thing in the embedded realm. No question though, especially when you get into safety critical systems, the human validation isPeter [00:26:25]: is 100% key. Like I You're not gonna trust your life to a an AI written software that's, that's not been very carefully, checked by humans. And so I think now the really the challenge is about that appropriate level of human validation for these safety critical systems.Verifiable Rewards, Evals, and Neural SimulationAlessio [00:26:41]: How do you think about, yeah, touching on the simulation side, I think verifiable reward and reinforcement learning is, like, the hottest thing. What have you done internally to build around that? And like, what gives you What makes you sleep at night? Like, if somebody's like, just web coding something or likeAlessio [00:26:57]: wants to try something new, you have like a good enough system. Because I think the opposite is also true, is like if it's super easy to write anythingAlessio [00:27:04]: then it puts a lot of work on like the verifiableAlessio [00:27:07]: side of it. Like, what does that look like for people?Peter [00:27:10]: Yeah. So verifiability, a broader bucket of like evaluations, right? Like how do you evaluate the results that you're, you're getting? I think this is probably the hardest problem right now, because the As the models get better, it can be harder and harder to find the faults on the system.Peter [00:27:29]: And so like the problem of doing proper eval to find those faults, like that problem also keeps getting harder as the models get better. But it's no less important than it's ever been, right? You still there are still going to be edge cases that are not met and whatnot. And so it's, it's a big area of investment for us. On the reinforcement learning topic, the key thing is there's all these new requirements that come to be in the latest generation of these technologies. So for example, end-to-end is the big thing right now in autonomy and physical AI, which is you can now train these models that can effectively take sensor data in and then put control signals out, and get really good results out of that. But the way that you train and improve those models is really different from the previous generations. And so to do reinforcement learning on an end-to-end model, you now need to actually simulate all the sensor data, right? So then this becomes a we call our, work in this neural simulation, but it'sPeter [00:28:26]: think of it like a hybrid of Gaussian, splatting and diffusion methods, and where you really care about performance. Like performance is everything. If you can't do enough simulation fast enough and cheap enough, you actually can't get results that are worthwhile, in the end. It also gets to a lot of our work in embedded systems, which is like performance critical work, and that performance optimization, performance criticality, it carries over to a lot of the model training work. because, like, the only way to make it affordable is it has to be really fast.Qasar [00:28:58]: I think it's worth a few minutes talking about our own, evolving thoughts on verification and validation withinQasar [00:29:05]: kind of, traditional simulators, which are, you can think of like vehicle dynamics or something like that, which you're just taking textbooks and taking those formulasQasar [00:29:13]: and putting them into software, to like now this neural sim/world model universe. I think that's an interesting topic.Peter [00:29:20]: Yeah. So in more traditional development, right, you oftentimes would have, more black-and-white answers to questions.Peter [00:29:28]: And so the in Europe as an example, there's, a regulatory, system, it's called Euro NCAP. It's the European New Car Assessment Program, and as part of that, the vehicles have to pass a bunch of tests, and those tests actually, include, safety systems. So automatic emergency braking for a child that runs in front of a carPeter [00:29:51]: or let's say an occluded child that runs out and you hit it. And so you have You end up with sort of these binary answers of like, well, did the car under test pass this specific test? And there's a very well-known set of test casesPeter [00:30:05]: that the vehicle has to pass. And that was how the industry worked, let's say, until 10-ish years ago. But what's changed now is with these models, everything is statistics, right? Like you no longer have a black-and-white answer, but it's like, well, how many orders of magnitude or how many nines of reliability can I get in the system, and how can I, how can I prove that to be true? And the big unlock honestly for physical AI as an industry is that these models are just becoming much more reliable. Right? Things like things actually work a lot better. It's like the number of nines you can get out of these systems are now good enough that it actually becomes cost effective to really deploy these things. And so the big shift in, so verification and validation has been from a little bit more of a Again the past it was strictly requirements, and are you meeting or not? And now it's more of a statistical, verification and validation case where it's all about how many nines of reliability and meantime between failures, that sort of thing.Statistical Validation, Regulators, and the Cruise LessonSwyx [00:31:04]: And is the target audience regulators or even the customers are yeah, if you I imagine the customers are bought in, and it's mostly regulators that need to be satisfied.Peter [00:31:15]: We do work with the US government, we do work of course with the European governments and the government of Japan, and the government is not like an AI lab by any means.Peter [00:31:25]: So Swyx [00:31:26]: They just care about the outcome.Peter [00:31:27]: They care about the outcome.Peter [00:31:28]: And so we do education, in that regard, and like so sort of teaching about, “Hey, this is how we think validation should be done, and this is an approach that we think is reasonable,” and how to think about like when is a driverless system actually safe enough to go on the roads and that sort of thing. But I wouldn't say that the government is asking for it. It's like we're more teaching the government in that, in that sense. It's honestly, it's more so for our own, our own comfort, right? Like, we want to build very safe systems, and then of course our customers care deeply about that as well. But in that context we're also typically educating our customers.Qasar [00:32:01]: Yeah. Our first, our first core value is on round safety. So I think we can't underline enough that, us also verifying and validating that the systems that we're deploying are safe to us is probably as important as, like, some regulator or a customer saying,Swyx [00:32:19]: Of course. Okay. Yeah.Swyx [00:32:20]: You have to satisfy yourselves.Peter [00:32:22]: As I say, as a whole across the world, regulation oftentimes it's like a almost lowest common denominator. But like, you really have to substantially exceed what the regulators are expecting to make good products.Swyx [00:32:33]: Yeah. One thing I often talk about, I think and I try to make this relatable to the audience also, is Cruise, where they had an accident that basically ended the company. I wonder if people overreact to single incidents, because incidents are going to happen regardless, right? ‘Cause it's a statistical thing, but as long I don't know if regulators understand that, you cannot extrapolate from a single incident, but we do because that's all we have to go on. And your sample sizes are necessarily gonna be lower than, I don't knowSwyx [00:33:00]: consumer driving.Qasar [00:33:01]: Yeah. I think the Cruise example wasn't a technology failure. there was The real, compounding issue there was just how did the company talk to the regulators and what was their kind of behavior, and I think that became more of the issue. If you look,Peter [00:33:19]: It isn't It definitely was a technology failure, but it was made much worse by theSwyx [00:33:23]: Put the car back on the woman.Qasar [00:33:25]: Yeah. And let me put it another way. There is a version where Cruise still exists.Swyx [00:33:29]: right. Right.Qasar [00:33:30]: Right. It'sSwyx [00:33:30]: It was like the last strawQasar [00:33:31]: ItSwyx [00:33:31]: in like a long chain ofSwyx [00:33:33]: like issues.Qasar [00:33:33]: So do you feel like ATG had that horrific accident or someone actually dying, because, that was a homeless person crossing the street? So yeah, I think we can't understate enough that ultimately, like, statistical validation of something, that's one part of it, but it's not the only part of it. Like, consumer and let's say, mainstream adoption of these technologies is also gonna be part of that conversation. I think companies like Waymo are doing a lot of service positively to the industry in the sense of they're, they're setting a high benchmark and they're showing, kind of in a very responsible way how to, how to deal with these. There have been Waymo incidences as well. They've just not been as significant as the Cruise one that you mentioned. But yeah, so I think you'll just continue to see that. I think probably the long term question is really gonna be, again, around Like it is very clear humans are way worse drivers statistically.Qasar [00:34:29]: Like, there's no, there's no debate. And so at what point But we're emotional animals.Swyx [00:34:34]: Yeah. So my thing is, like, we have to get to a point as a society where we accept horrific accidents that would never happen by a human because statistically we understand that it is safer overall. In the same way that planes, they're safer, than I think they're the safest mode of transport that we have.Qasar [00:34:50]: Yeah. it's more dangerous to drive to the airport than it is to get on a flight.Qasar [00:34:53]: So if you're everQasar [00:34:54]: if you're ever getting nervous about getting on a plane, just think “I just gotta get to the airport.”Swyx [00:34:58]: Yes, we're flying.Qasar [00:34:59]: If I get to the airportQasar [00:35:00]: I'll be good.Swyx [00:35:00]: But then it's, planes also concentrate the tail risk if planesQasar [00:35:03]: Yeah. AndPeter [00:35:04]: And I was, I don't think we honestly have to worry about there ever being, accidents from these systems that are like much worse than what humans would cause, ‘cause humans do terrible things.Peter [00:35:14]: Like, people fall asleep at the wheel all the time.Swyx [00:35:16]: I have.Swyx [00:35:17]: Like, I'll call, I've been a drowsy driver.Peter [00:35:19]: Kinda drunk drivers, and that'sPeter [00:35:20]: that's the extreme end of the example. But these AI systems, you have redundancies, you have fallbacks. Like, there's many things have to go wrong for there to actually be a something catastrophic because there's, there's so many, fallbacks that these systems have.Alessio [00:35:36]: your simulation is like so vast because there's so many use cases. What are, like, maybe things that worked in a simulation and then you put it out and it's like, “F**k, this isAlessio [00:35:45]: this just did not work at all?”Peter [00:35:47]: Yes.Alessio [00:35:47]: IsPeter [00:35:47]: That's maybe a bit of a misconception, about simulation there. So let me go a little bit, more technical on this. So at first go, no simulation is going to represent the real world. There's always a process of this, sim to real matchingPeter [00:36:02]: where you actually, you need the real world feedback to basically feed into the parameters that are being used in the simulator, and you have to do that, it's like this validation flow, a number of times until you can get some confidence that, like I think the simulator is now accurately representingPeter [00:36:19]: what's gonna happen in the real world. Now, if you have a situation where you've done that full validation and you thought that it was accurate and then there's something different, those are much trickier cases, and that's, that absolutely can happen, but really I think the validation process is a really important part. You can never skip the simulation validation process, like where you're actually ensuring that, hey, the actual, my sim to real gap here is small enough that I can trust these simulation results. And there's, there's so many fun things that you can do when you get into it. Like, I'll, I'll give one fun example that came up recently is like in these humanoid robotics, systemsOverheating actuators is a real problem, right? So obviously phenomenal demos. IPeter [00:37:01]: The most amazingAlessio [00:37:02]: For 10 minutes.Peter [00:37:03]: The most amazing I can get. I love, I love watching robots do acrobatics like everybody but the these systems actually overheat, right? If, like, And one of the ways you can use simulation though is you can actually have that, the temperature of those actuators be one of the parameters that's representedPeter [00:37:18]: in the simulation. And if you're doing reinforcement learning over a certain task, then the robot can actually adjust its motions in the simulation to account for the fact that, oh, it knows that as it's moving, it's actually beginning to overheat this motor. But if you didn't have that parameter of, let's say, the heat of that motor represented in the simulation initially, then your RL policy might It will disregard that. And now you run that on the robot and the robot will overheat and fail.Alessio [00:37:43]: I guess the question is, like, how do you have all of these parameters taken care of while also understanding the deployment environment? Like, temperature is like a great example, right? WellAlessio [00:37:53]: why did you make my robot worse when it runs in like a freezer?Alessio [00:37:57]: So it actually shouldn't worry about that. it's like, yeah, how do you design these simulations?Peter [00:38:02]: This is honestly the This is what makes simulation so hard, right? it's because you Simulation is fundamentally about you're trying to optimize the development of a system, right? Like, how can I build this system faster and better and cheaper and what are all the levers that I have to actually accomplish that? And because simulation's just a software program, you can, you can change it a lot more easily than you can hardware systems. And then what's particularly awesome about the let's say, world models and using that as a part of simulation is now the simulation doesn't just scale with, let's say, adding new math equations inPeter [00:38:36]: but we can actually scale the simulation environment now with additional real world data and that also unlocks a whole new field of robotics.Qasar [00:38:46]: There is a meniscus line where you cross where still doing real world testing is better. there's, in this, sim-to-real gap, you can reproduce reality at exceedingly expensive costs and this So nothing is free. So really you have to you're finding that line where you're getting great performance, you're getting great feedback, whether it's on the training side or on the eval side, but it's way cheaper than doing it in the real world. At some point it, that doesn't make sense. And so even, from our earliest days in autonomy, our view was you're still gonna do real world testing. You There's, there's not, there's not this, magical land where you're not gonna do that. And maybe even like a more nuanced version of this in like traditional software development is, most of your testing for software in a vehicle, 95% of that can be like traditional CI/CD kind of, flows that you would have in traditional web development. But once you have Now you, let's say you have a truck. Well, you can do like 4% of those in like a rig which has all the components, the electrical and electronics of a truck, but doesn't have, it doesn't have the tires and it doesn't have the And then you have the 1%, which is actually the vehicle. There's something There's a similar analogy in terms of using simulation for intelligent systems. You can do a lot in a simulator, but in using world models, but ultimately it's, it's physical AI. So you're gonna deploy it on physical machines andQasar [00:40:17]: the freezer example comes to, comes to light.Alessio [00:40:20]: The world model thing has been to me the hardest thing toAlessio [00:40:22]: wrap my head around. Like we have Faith Eliyon on the podcast.World Models, Hydroplaning, and Cause-Effect LearningQasar [00:40:25]: We've been doing a small series with like another Intuition company, General Intuition as well.Qasar [00:40:31]: yeah, and I mean, lots of, lots of coverage on NeRFs and yes.Alessio [00:40:34]: Yeah. It feels like we talk with about, the heliocentric system, right? It's like in a world model, if you just feed visual data, the model might learn that the sun spins around the Earth. It makes sense, right? And it's like, well, not really. And I think what are like some of these other things that like hydroplaning is one thing I think about, is like can a world model understand hydroplaning and like what amount of water like causes it to happen? And it's like, yeah, to me it's like I don't understand how you guys do it. I guess it's like the real thing is like when you're doing both cars and the highway in Japan versus the excavator in a mine in,Qasar [00:41:13]: ArizonaAlessio [00:41:13]: wherever you're Arizona, wherever you're deploying them.Alessio [00:41:15]: How much of it are you relying on the world models to like generate the simulations for you and then try and close the gap after versus like giving the world models as a tool to your engineers to like curate the simulations if that makes sense?Peter [00:41:28]: Yeah, totally. So yeah, I can say at a pure engineering level, I think if you're hoping to do real world deploys and you're purely relying on a world model approach, you probably won't get to something that works, before you go bankrupt. So there is just a very practical mindset of like, world models are amazing and they're extremely useful for a lot of use cases, but there are a lot of other things that you need to do to actually get something started and something deployed and working. most fundamentally, world models are all about It's understanding the world, but also understanding what's going to happen. It's like the cause-effect relationship.Peter [00:42:01]: Right? And so like it, right, if you have a take some sort of construction tool, and that construction tool is gonna be doing some work on the Earth in some way, it's gonna be moving earth, the world model needs to understand that cause-effect relationship. Like, okay, when I, when I take this material from here and put it over there and now I have things that are over here and not over there anymore and that cause-effect, relationship. data obviously is a is a big problem. The hydroplaningPeter [00:42:26]: one is actually a really great example because it's actually quite non-obvious sometimes. Right? It's like, well, it's, it's raining and well this road, has, let's say the appropriate curvature to it so the water is running off the road and cars are driving faster here and then you approach a road that's very flat and water is now puddling on that road and all of a sudden cars are driving slower because when they were driving faster they were starting to lose control. And there are a lot of visual nuance, very nuanced visual cues in the scene and so I do think in the world model concept there's a good chance that the model actually would learn that you should just drive slower when these visual cues exist, and that's obviously the beautiful-The beauty of, these kinds of models where they just, they learn these non-obvious things.Swyx [00:43:14]: It doesn't need to know about hydroplaning to know that it needs to drive slower.Peter [00:43:17]: Yes.Swyx [00:43:17]: I guess it's Yeah. I wanna ask questions about, also deploying models. I presume, like, you use a lot of these world models for training data and simulation, but what about deploying it onto the systems in production? Presumably you have you have, like, GPUs on deviceOnboard vs. Offboard: Latency, Embedded ML, and DistillationSwyx [00:43:36]: but they're I keep saying on device. What's the what's the right term for that?Peter [00:43:40]: On machine.Swyx [00:43:41]: On machine.Peter [00:43:41]: Or embedded, yeah.Swyx [00:43:42]: Yeah. What is the embedded world like? because for people who are not used to that world, this is very alien.Peter [00:43:49]: Yeah. So it's actually We call it onboard and off board.Peter [00:43:52]: So like, onboard software and off board software.Peter [00:43:54]: And the great thing about off board software is you don't have to care about time, and you can run really large models, right? So you can, you can say, “Well, this model, I don't care if it takes one second for it to give me a result or 10 seconds for it to give me a result, because we have time.” And the models can be really big, and they can run, in a data center or on a on a huge GPU and you can obviously have distribute to compute, et cetera. But onboard you don't have any of those benefits. You're like, “Well, I need I have this many milliseconds where I need an answer from this model.” And so a lot more of the energy then is about, think of it more like distillation and it's like truly efficiency and like, literally every fraction of a millisecond counts. And you can't have a situation where the model takes too long because then the vehicle can't actually function.Peter [00:44:42]: And so you can, you can still use a lot of the same techniques, and the models themselves you can think of as like a derivative of larger models that you can run offline, and then you're, you're trying to just get a model that is still performs really well but it's, it's a it's smaller, small enough version that you can then run on this embedded system where you care about latency and power.Qasar [00:45:03]: Yeah. And I think like, the broader point I think which, maybe is not obvious but it's worth saying is in physical AI world, we're not really constrained right now by, like, the intelligence of the models. It's actually what Peter's talking about, it's actually deploying them inSwyx [00:45:19]: The hardware they give you.Qasar [00:45:21]: Yeah. On the hardware you give you.Qasar [00:45:22]: And so And there's just a reality is of safety critical systems. So those end up being the your limiting factorsQasar [00:45:29]: rather than, let's say, a limiting factor for, a foundation model companyQasar [00:45:34]: is gonna be just capital maybe or researchers.Qasar [00:45:38]: So we're, we're in that way dealing with, for us as people who kind of come in that realm with like a very interesting Those constraints force creativity.Swyx [00:45:47]: And I imagine, nobody was deploying or giving you the hardware for transformers back in 2018, whatever, but now they are. What's the evolution like? just peel back the curtains a little bit.Peter [00:45:59]: Yeah. Transformers first off, I think the paper was originally published in 2017.Swyx [00:46:02]: 2017.Swyx [00:46:02]: So there's no time.Peter [00:46:04]: And ISwyx [00:46:05]: But I'm just saying I guess I'm saying, like, embedded ML systems usually, like, a lot less parameters, a lot less compute, and now, like, orders of magnitude more.Peter [00:46:14]: Yeah. absolutely. what I was gonna say though was I think in the in the original paper in 2017, maybe it's in the last paragraph, somewhere in the paper they talk about, like, “Oh, by the way, this technique might be useful for, like, images and videos as well.”Peter [00:46:30]: These last subjects.Peter [00:46:31]: And it took a few years for that impact to really hit. But like, now, we're seeing transformers are everywhere.Swyx [00:46:39]: Yeah. Vision transformers.Peter [00:46:40]: And then then the compute just keeps getting better and better. But you do have this fundamental trade-off, right? It's like you have power, you have cost, and performance and like, getting the right, getting the right mix of those things in an embedded package that can also be, like, shaken and baked in all thePeter [00:47:00]: conditions that these things have to have to operate in. But yeah, I think that they're only going to keep getting better and so we also try to plan our strategy understanding that, we know the rate of improvements of these systems.Swyx [00:47:11]: Yeah. So like, Google just released the Gemma 2B modelSwyx [00:47:15]: that effective 2B model. Is that useful to you guys or is that too big?Peter [00:47:18]: You can run that model on an embedded system, definitely.Peter [00:47:21]: the So yes, it's, it's useful in that regard. The bigger question is, like, what do you use it for in an embedded system? Like, you actually need to customize it quite a bit to make it useful for something. But yeah, you could run a two billion parameter model, definitely.Swyx [00:47:35]: It also interesting, like, what percent is a custom ML model that only does that thing versus a generalist LLMSwyx [00:47:41]: which probably is not that useful actually for your context.Peter [00:47:46]: Like, you, like, you can imagine different use cases, right?Peter [00:47:48]: So theSwyx [00:47:49]: The voice stuff, yes.Peter [00:47:49]: Yeah, the voice test. Totally, yes.Peter [00:47:51]: So for the actual, autonomy elements, that's 100% in-house. We do every bit of that, the data simulation, the model, everything. But when you get into the more generic use cases like voice or voice assistant kind of thing, that's where these more generalist models like Gemma actually can be quite, can be quite useful.Swyx [00:48:09]: Yeah. And then there's also obviously a trade-off between, like, what percent must you do on machine, versus just call home.Peter [00:48:16]: Yeah. It's all about latency.Swyx [00:48:17]: Latency.Peter [00:48:17]: It's all about latency. Yeah.Swyx [00:48:18]: Yeah. Well, like, I think actually in a lot of contexts, especially in the US, you can just have a connection to the web.Qasar [00:48:26]: Yeah. I think though most of our universe is everything has to be fairly, embedded and local because just the nature of Even in the US there's a lot of likeSwyx [00:48:39]: PatchinessQasar [00:48:40]: don't haveQasar [00:48:41]: have coverage, right? And if you look at, like, the old world of autonomy within mining, which is, like, long before transformers and kind of, neural networks, in the like CNN and kind of a universe, they were really just hand-coded, systems. They were just like, this machine is gonna run to that place with thisPeter [00:49:03]: That was our GPS, like very accurate GPS.Qasar [00:49:05]: Yeah. And so that worked, and that worked for 20 years, so why would we actually need to use transformers or kind of more modern end-to-end systems? Mainly because you can only really run a path and run backwards. That provided a lot of value, but m-Not as much as you get when the machine is actually intelligent. It's, it's seeing, it's perceiving, it's acting in a dynamic world.Alessio [00:49:28]: I looked up RTK, real-time kinematic, one to two-centimeter accuracy.Qasar [00:49:32]: Yeah. Fantastic. But the and fantastic in faraway lands where there's not gonna be cell phone coverage.Peter [00:49:39]: Yeah, so it's widely used on the legacy mining and agricultural autonomy systems today. So like, for example, a combine that can be precise within one or two centimeters as it's driving down the field, they use RTK.Qasar [00:49:53]: Yes.Peter [00:49:53]: But it's, it's expensive.Qasar [00:49:54]: Yeah. And it's, it's, it's autonomy, but it's not intelligent in the way that I think all of usQasar [00:49:58]: if in twenty-six we'd be talking about intelligence.Alessio [00:50:00]: In one of your blog posts, you mentioned research on large scale transformers that are similar to those doing modern generative AI. What are, like, the big differences other than, “You're absolutely right. I should steer the car, so you probably wanna remove that?”Peter [00:50:14]: We have a diversified bet strategy internally, and the reason we've done that is because we operate in now a bunch of industries, a bunch of geographies, and each of the approaches has, obviously a different risk to them.Peter [00:50:27]: And so like, we're not going to put all of our eggs in a single basket for a single approach because that approach may no
Ever wrestled with a question that felt too big to answer—something that Google couldn't quite help with, and the Bible app just gave a list of verses that didn't really land? We live in a world of instant answers, but spiritual wisdom takes more than a search bar. So, how do we actually find answers to hard questions using God's Word first, not last? Today's episode is called “Bible First: Finding Real Answers to Questions”, and we're talking about how to study, search, and investigate hard topics using Scripture, not just shortcuts. And the episode is less about specific questions and more about methods to use when searching for answers. When you have a question, where do you usually start? Why do you take this approach? Be honest! Here are more questions to consider: Why is our default to Google or search in the Bible app? And is that always bad? What does it look like to actually investigate using Scripture alone? What types of resources can we use when searching for answers? What makes this kind of study so hard for most of us? What fruit comes from doing it “the hard way”—the Bible-first way? What do we really need when we're studying? TIME and PATIENCE! I hope our listeners know that that Google is not our enemy, but we should still question the root, and the effect, of getting quick answers that we seldom meditate upon. How do you need to slow down, read, reread, and ponder God's word? This is a challenge for me, as well. We don't learn everything all at once; growth takes time. We are always learning! We encourage you to keep reading, praying, and talking with the Lord about your questions. Then, speak with mature Christians who have navigated similar questions and know their Bibles well. Subscribe so you don’t miss an episode! UNEDITED TRANSCRIPTION: 00:00:00 Patricia: Have you ever wrestled with a question that felt too big to answer? Something that Google couldn’t quite help you with? And the Bible app just gave a list of verses that didn’t really land. We live in a world of instant answers, but gaining spiritual wisdom takes more than just searching in a search bar. So today’s podcast is about using the Bible first finding real answers to our questions. Welcome to our Patterns of Truth podcast. I’m Patricia, your host, and today we are talking about how to study, search and investigate hard topics using the scriptures and not just shortcuts. Shortcuts are not a bad thing. We’ll talk about that. Um, but we want to kind of reexamine the practices that we engage in when we’re searching for answers. So this episode is about is not really about specific questions, specific hard questions that we seek to answer, but more about the methods that we can use when searching for those answers. So hello to everyone on the podcast today. Hello, Peter. Hello, Roy. Hello, Bethel. How are you all doing today? 00:01:05 Peter: Hello, hello. 00:01:07 Roy: Hey, great. Rainy and cold in Oregon. Oh it’s raining. Yeah. Rainy. 00:01:15 Bethel: Not humid here. 00:01:17 Patricia: Yeah. 00:01:17 Peter: Whereas here Bethel. 00:01:19 Bethel: Right now it’s Jersey. 00:01:21 Patricia: Yeah. 00:01:22 Bethel: It’s not Philly. It’s Jersey today. 00:01:24 Patricia: Jersey. Welcome back. All right. So um I’ll start with a panel question for all of us. So when any of us have a question, something popped into your mind. Somebody talks about something. Where do you usually start to find the answer? It can be any resource. It could be Google, it could be another. Right. So where do you start and why do you take this approach? 00:01:52 Bethel: I’m a Googler. 00:01:54 Patricia: All right. Yeah. 00:01:55 Bethel: Everybody and everybody makes fun of me that I even use Google because everybody just uses AI. Like everybody’s just like, just ask ChatGPT. Just ask ChatGPT. Um, so even googling is like outdated at this point, but depending on how deep I might text my dad. 00:02:11 Patricia: Oh, nice. All right. Cool. Roy? Peter. 00:02:17 Roy: Um, I asked my wife. 00:02:19 Patricia: Okay. 00:02:21 Roy: Um, good place to start. That’s good intuition. Um, my daughter, um, who also has very good insight. Um, and then it depends on what kind of a question. And I appreciate the Google answer. Um, in fact, I did, I used Google just the other day when I wanted to know the initial, um, area that was assigned to the tribe of Dan and I got a pretty good answer. So if the question is specific enough, um, then I think, um, Google is fine or I don’t know about chat, I haven’t used chat GP so I don’t know how that works, but I know Google uses AI underneath. So Google basically a, a front end to an AI program. Yeah. But it has to be specific. It depends on the type of question. 00:03:18 Patricia: Yeah, I like that you mentioned that because sometimes you could do like a broad question and then who knows what you’re going to get just just how Google works. Right. Sorry, Peter. 00:03:28 Peter: Yeah. I, I would say I try to find the shortest article I find, usually from kind of the same circle of church community. Amen. Um, um, and uh, definitely Google. Like sometimes it’s like a specific website that I go to other than, uh, I find got questions sometimes is a website that would help a lot in like general questions. Uh, if it’s something specific, more doctrine, I go back to the like some brief, uh, article and then control F to find. Yeah, the article. So, uh, yeah, I do that. 00:04:12 Patricia: Yeah. All right. That’s practical, I like it. I tend to start with the Bible app for some reason, right? There’s just, I don’t know, it’s, uh, it’s easy and I don’t know, there’s something I like Google, but I feel like So I really slow down and I think about like, what I feel when I Google something, I usually feel fear because I think that there are questions that I may have that when I Google it, there are harmful or anti-God, anti-Christian things that seem to pop up at the top. And I honestly just don’t want to see that when I’m searching out something. I don’t know what it is, but it just really disturbs me. Um, I know some people can see it and just discard it, but for me, it just, it really unsettles me. So I tend to like not want to go to Google for some reason. So maybe the Bible app, I’m trying to protect myself in some way. I’m not sure. But, um, our first question really is about like, why do we think that, um, a more popular default for searching for any question will be Google or a search in the Bible app? Why is that something that we tend to do these days? And is that always a bad thing? 00:05:21 Peter: Well, convenience. 00:05:24 Patricia: Um. 00:05:25 Roy: It depends a lot on the question. 00:05:28 Patricia: Do you ever feel like. Or maybe I should ask it this way? Is there a scenario where you find something on Google or a different tool, and it makes you immediately stop searching? Like you don’t go back to your Bible? Or does the opposite happen? You find what you need and then you say, oh, I want to go deeper. What does that look like? 00:05:50 Roy: Really depends upon the subject matter and the question. Okay. Um, I think, you know. 00:05:56 Peter: Yeah. I mean, for, for Patricia’s point, um, that’s a good point because I think when I Google things, it does stop me from digging more into scripture because I found the solution or at least part of an answer, and then I’m satisfied with it. Um, so that’s a, that’s a good point. I mean, we’re definitely not against technology. We should use technology. Um, if it’s your favorite AI search, LLM or Google, uh, it can be useful. Um, but, um, I think studying scripture as we can talk soon about is and, uh, like changing your heart through studying scripture is more just knowledge. Um, and I think you reach just knowledge if you like, get the answer quickly. 00:06:55 Roy: Yes. That’s very important point. Uh, and I want to emphasize that we are talking about having a specific question or a question about something. We get an answer, but that should lead us to dig deeper. And that should even that even specific studies should not keep us from regular Bible reading. Um, and that’s where we gain a general knowledge of God’s character. Um, you know, there’s a, a rule, there’s apps and whatnot that lead you through the Bible? Genesis to revelation in a year? Well, you may or may not want to use one of those apps, but the point is you have to be generally familiar with your Bible. I found questions that are, quite surprisingly in books like Ecclesiastes or Proverbs or Chronicles, and that seem to have nothing to do with the subject matter, but they. But they’re put in a way that for trigger thinking about things in a different way. So general Bible reading needs to always be done on a regular basis. 00:08:03 Patricia: Yeah. So leading into that, um, or coming out of that point, I should say, uh, if we had no technology, right. I couldn’t use my phone. Google’s down. It does happen from time to time, right? We can’t get to the website that we want. Um, I’m thinking about that AWS blackout from a few weeks ago where people were panicking. They couldn’t find anything. So if we only had our Bible in front of us, the actual physical volume, what does it look like to investigate using Scripture alone? Where does it start? 00:08:38 Roy: Need to know the books of the Bible and where they are. 00:08:41 Patricia: Mhm. Mhm. 00:08:44 Speaker 6: And I think maybe a general gist of what’s happening in each one. 00:08:48 Patricia: Yeah. 00:08:49 Roy: Definitely the difference between the Old and New Testament. Mhm. Um, and it also helps to have a, a mental map like Bethel was saying of what generally goes together. And this is fairly obvious, and I think a lot of people, uh, talk about it. So maybe we don’t need to belabor the point, but there are prophetic books, there are poetry books, there are history books, and there’s the Pentateuch and there’s New Testament. That’s a general classification. But we should know generally how how the different books relate to one another. Like among the Gospels, Matthew presents the Lord Jesus as the King. And I’m not saying anything that is particularly remarkable. I mean, we I think we all know this quote. 00:09:44 Bethel: And maybe instead of just looking up, oh, what does the Bible say about this? Fill in the blank. We could use Google as a resource to say, hey, how is the Bible split up? What is the Old Testament about? What are the parts of the Old Testament? What makes it different from the New Testament? What makes the Gospels different from each other? And you can use the internet as that type of resource to dig deeper in that way. 00:10:10 Patricia: Yeah. I think also if someone is a new believer, I mean, it’s, it might feel like kind of steep, right? Like, oh, before you start, you got to memorize all these things. I think while you’re doing it, I think I’m looking at the front of my Bible. There’s a table of contents, right? So if you’re a new Christian, or maybe it’s been a little while, if you if you need the pages with the numbers, right, start with it, like where each book of the Bible is. And what’s great is like most Bibles, like mine is organized, it tells you what’s in the Old Testament, what’s in the New Testament, and that can help you with organizing. Um, we’re looking at the Bible like how it’s, how it’s organized. And I think that’s a good place to begin. Um, I. 00:10:52 Peter: Think it’s high yield to Patricia. Like knowing the books of the Bible can be very helpful and knowing like the sections that, like Roy was saying, and I can argue also like some of them maybe can, they’re not inspired the chapters, but knowing how many chapters, like, you know, like, oh, you know, for example, Ephesians and Galatians are six chapters. Colossians and Philippians are four chapters. Um, so help you kind of. you know, contain or have a hold of of the book and how, how long it is. 00:11:29 Patricia: Yeah, that’s really good. And I think too, it’s, um, it’s good to think of how while we learned about what the book of the Bible’s are and how the Bible’s organized, that we can still start reading it. I think sometimes it can feel like levels like, oh, I can’t, I can’t do this until I do that. But it’s like, no, start reading while you’re memorizing where the books of the Bible are. So we talked about, I guess, operationally speaking, knowing how the Bible’s organized, but is there another way that we can begin that helps us when we’re just looking at the scripture alone and trying to find an answer? 00:12:08 Peter: We need help from Roy on this one. 00:12:14 Roy: Well, it’s been a long time since I was, uh, first, uh, I was pretty much know where everything is right now, and I hope this is going to be cut out of the. That’s the final deal. Um, well, again, I have to go back to the kind of question, I guess, because questions about the church, for example, if I have a question about that, I’m going to have to look in the New Testament. And I have to start with acts because that’s where the church began. And then Paul’s epistles in particular. So having a knowledge of where things are talked about and explained in Scripture is almost essential. Um, if you need comfort, let me give a couple of examples. We often look to the Psalms for comfort and encouragement, but in doing that, you need to realize that it’s a Jewish book. And so there are things in the Psalms which do not apply to us. Um, the Imprecatory Psalms in particular, which are Psalms which call down judgment upon our enemies. Well, if you’re new to the Bible, you might get confused by some of that. If you haven’t read and absorbed Romans, for example, toward the end where it says, vengeance is mine, I will repay, saith the Lord. And if you haven’t really digested that. So I guess I’d have to say that we need to start looking through the New Testament to get a feel for the kinds of things that are particularly appropriate for the Christian. I’m thinking of a new believer now. Sometimes we say, okay, start reading John’s gospel. Well, that’s a good one. Um, if I say start reading Matthew, Then I may run across the kingdom of God, where servants are failed, and throw in thrown into outer darkness. And that kind of verses have led to the idea of we can lose our salvation if you don’t really understand what the kingdom of God is. So there is some basic knowledge that’s required. You know, if you keep reading, then you’ll get to John’s Gospel. And there you find out that if you’re in the hand of the Lord, no one can pluck you out. And so there’s the answer. But some of this can be confusing to a new person. So the only solution is, I think, to ask somebody that you can trust, give you a general feeling for what the different books talk about. And then you have to have your general knowledge to have scripture reading it through to, to come up with stuff. And I gotta say this right here too. There are several verses that emphasize that God is compassionate and he preserves the simple. And I think if actually, in my experience, the biggest hindrance is pride. So if we come to the Bible with the proper attitude that this is God’s word, then I think God can lead us. The Holy Spirit leads us to apply things in the right way. Um, striking verses in um, um, Psalm one hundred and sixteen six is perhaps just a good one. Um, and also in Proverbs there’s some. So God and God will guide us if we’re humble enough to learn from him. 00:16:15 Peter: Yeah. Just to add to what Roy was saying is when you’re studying the scripture, uh, it’s good to, uh, uh, look at the context of. 00:16:25 Roy: Right. 00:16:26 Peter: Uh, I think that’s what Roy’s saying also of the whole scripture and the book and the context of the chapter. What does it talk about? 00:16:36 Patricia: So then, okay, so we have the word of God, um, itself, and we have the Holy Spirit who will teach us and reveal things to us that we cannot learn just intellectually on our own. So when we’re Christians, we have that. We have him as a resource. But what about some other resources that we can use when searching for answers? I’m talking about things that other very mature Christians who have studied the Bible have put together. Um, and I’m thinking of a concordance. I’m thinking of biblical commentaries. Um, can we have some commentary on that? What type of resources can we use when searching for answers and how do we use them? 00:17:19 Roy: Concordance is really helpful. I use a concordance frequently. Usually there’s a concordance at the back of most Bibles that is tuned to the particular, um, um, uh, version that you’re using, uh, translation, but you can always do a cross-reference. You know, the standard concordance is ah, Strong’s and Young’s someone that says strongest for the weak and young is for the old. But be that as it may, um, they’re both both good, although they’re different. Um, um, and if you’re not using King James, both of those are based on King James. Maybe they’ve been upgraded, I don’t know, or changed. But anyway, you can always, um, if you have a particular verse in NIV, for example, look it up at the same verse, uh, in, uh, in the King James. Um, and figure out what verse, what word you want to look up and then go to the concordance with that. Now, I use Young’s a lot because it gives the Greek and Hebrew and, um, that can be helpful if you have a good, um, uh, uh, dictionary, uh, specific, you know, the, the, the old Testament, uh, dictionary I use is um, theological wordbook of the old Testament, which is good, good Hebrew, uh, analysis. I don’t know a word of Hebrew. So I just have to depend in that, uh, in Greek, uh, in Hebrew. 00:18:56 Speaker 7: Let me ask you, Roy. 00:18:57 Peter: Um, I, I don’t remember the last time I used the concordance. Bethel. Have you you. 00:19:05 Bethel: Really just just the one in the back of my Bible. 00:19:09 Peter: Uh, are we missing out a lot because we’re not using the concordance or when do you use it? Do you. When is the deep study verse by verse? 00:19:19 Patricia: Wait, so maybe I should define it and it will help to answer the question. Right? I’m thinking that the concordance is actually what the search bar is now in the Bible app. But all right, so the definition of concordance, it’s an alphabetical index of all the words in the Bible or any text. And it lists where each word appears. So it’s an alphabetical index of all the words in a text and lists where each word appears. 00:19:48 Roy: Now the problem is, and this is why I use Young’s analytical concordance, is that there are only about four thousand words in the Hebrew biblical Hebrew. Now, modern Hebrew is totally different, but we’re dealing with an Old Testament text. And if you think about the number of words that we have in the English language, It’s up in the. Millions and more are being added every year. So to have four thousand words in a language means that each word is going to have to do multiple jobs. And so context is really important. And you can get that information. You can look that information up, uh, in the, um, in the back of the Young’s concordance and figure out how the different words are being used in the context in which they’re being used. So you can kind of parse that out. So it is definitely for a deeper study of, of the words. But the basic idea is that it gives you a list of verses where the word is used. 00:20:56 Patricia: Mhm. 00:20:56 Roy: And so you can go and compare where the word is used or how the word is being used in these different verses. And in the back of Young’s Concordance, you also have a reverse cross reference so that you can look up the Hebrew word, for example, and see the different version, the different ways the translators have translated it. So you get a sense of how specific the word is and, um, and what the translators were thinking of when they translated it. You can sort of figure that out. So, um, those kinds of things have to do with puzzling verses that you run across and they just, why? What does that mean? And so if you’re trying to figure out what that means or what a particular verse means, then, uh, a concordance is really helpful. Okay. 00:22:00 Patricia: So on the most basic level, for example, if I have a question about world peace, does the Bible ever talk about world peace? I can look up the word peace in a physical concordance. And I know Strong’s is like big and blue. Maybe they updated it, but the one I grew up seeing was like baby blue. Um, and you could look up the word peace. And when you look it up, it’s got a list of every single place that the word peace is mentioned. And you have to go through each verse to decipher what the definition of peace, I guess you could say is being, or I should say like the part of speech is being used, right? Is it the peace that’s between that passes all understanding for Christians? Is it the peace between God and humans? Now, because of the Lord Jesus? Is it peace that God will establish in the future? So you have to really do some legwork to find out if what you’re looking for is the definition of the word that you found. I guess you would say, is that like how you start at the most basic level? 00:23:04 Roy: Yes. 00:23:05 Patricia: Okay. All right. But if you’re advanced, you’d be like, going towards more nuanced definitions of the word. Um, maybe if they’re in Greek or in Hebrew, there are different words for different types of peace, which I know, like for people who are bilingual, they understand that a lot better than I do. Like being monolingual. I only speak English, but I know there are multiple words. Say, for example, love. So you can’t just look up love. You can. But there’s so much variety in what you’ll find. So it takes effort, right? That’s what it sounds like. Effort. 00:23:42 Roy: Yeah, yeah. You have to do some study. Okay. Probably a real example would be, um, the word corruption in the New Testament. Okay. That has a certain, uh, mental image brought up. But the problem is that in Greek, which is what the base language that the New Testament was translated from, the Greek word uses the same word, same Greek word for two different kinds of corruption. Now we distinguish, for example, corruption from decay. Decay is what results from the law of physics. The entropy. You throw a pile of grass out in the in the backyard and after a while it decays. Um, on the other hand, um, immorality is also corruption. So this, this requires that you kind of look at the verse and try and figure out what is being meant by the word decay. But and some translators will translate them differently. Sometimes they won’t. Okay. 00:24:53 Patricia: So then I guess it’s good to just have a dictionary. Yes. Do I know what the words mean that I’m searching up? Right. That I think that would probably be useful. Like even in your own language, like, you know, the way we use certain words are not necessarily how they’re always used in other contexts? It would be good to have a dictionary as well. Okay. All right. So we got the concordance. So what about biblical commentaries? What are they? When should be the when should they be used and does the publication date matter? 00:25:27 Peter: I thought the use the commentary. 00:25:29 Bethel: Honestly, I’m big on commentaries. I am an enduring word person. Um, I don’t know how the saints feel about that, but I like it. Um, no, I just think it’s very helpful that like sometimes, honestly, I’ll sit and read a passage and I’m like, wow. Um, my reading comprehension is not with us today. I have no idea what I just read. And so sometimes enduring word does a good job of setting the scene of where are we in the chapter? What’s going on? Um, and it breaks it down like couple verses at a time. And then it’ll provide like texts of what certain authors have said about said portion. Um, so it’s very helpful to get a well-rounded picture. Of course, like anything else, we are trying to emphasize that using things as a resource is good. Using things as the source is not good. And so referring back to the Word of God and just kind of, you know, I think we said this, but to, to pray and ask the Lord for wisdom and help. Um, because that’s, that’s the main reason that we can understand any of this because of the help of the Holy Spirit and, um, to kind of be able to have a better understanding of the word of God, but using scriptures in itself to understand you look at a couple commentaries. I mean, like that’s, I really thought about like, how did I learn anything when I was applying for college? How did I learn how any of that process worked? I read a million articles and I read a million Reddit posts, and I read a million everything. And I gathered information on what is what are people saying? And so you can go about it like that, but ultimately approaching it prayerfully and using things, like we said, as a resource, not as the source. 00:27:16 Roy: Yes, that’s that’s a very important principle because no resource I haven’t I’ve been through lots of different translations, for example, and I don’t find any single one that’s perfect or that I, you know, isn’t without some complaint that I can come up with. Uh, and that’s doubly true of commentaries. We have to look at several. And it changes over the years. The commentaries that I looked at when I was, uh, twenty or thirty are quite different than the ones I look at today. But we have to look at different ones and think about what they’re saying in context. And we have to talk to different people to. MM. 00:27:58 Patricia: Oh, one thing I forgot to do was like, define what a commentary is. I know the word comment is in commentary, but there are some people who don’t use a commentary at all. Or maybe they’re nervous about it because it seems like, is it about the Bible? How am I supposed to know? So just by way of defining things, a biblical commentary is a written aid that provides explanations and sometimes interpretations of scriptures to help readers better understand a biblical text. So there are lots of different types. There are some that are about certain topics that are discussing certain topics. And then there are others that are, um, devotional, um, there are some that are historical, cultural. So Bethel, probably the one that you’re talking about. And I’ve seen some in some study Bibles where they give the context of the cultural Sauk, um, backdrop of a particular book of the Bible or a particular passage. And that’s really helpful to help to assist in how we can understand. But like I said, there’s lots of different types of commentaries that we. 00:29:06 Bethel: I think. 00:29:06 Patricia: It is. 00:29:06 Bethel: Helpful along the lines of what you’re saying. I took a class and it’s silly that I had to take a class about this in college to understand it. But always, always, always, no matter what you are looking up, know what the source is and knowing what the point of the source is like. For example, if I’m reading a commentary that is meant for daily encouragement, it’s always going to be not twisted, but the point pulled out of that portion will be to encourage me. And so maybe that’s not exactly what this portion is, or that’s not the point of this portion, or that’s not the context that this portion originally was in. So being able to read a resource and take a step back and put it back in the big picture, is this what the what the scripture is saying? Is this what our context is? Does this fit into what we’re understanding here? AM I getting this right? Always, always, always looking back at what is the source? 00:30:01 Roy: Yes, that’s extremely important. Um, if you pick up something from Legionnaire, for example, which is a reformed, uh, outlet, um, you’re going to have reformed theology woven in and some of what they said is going to be quite wrong. Uh, from my point of view, um, but a lot of it is going to be spot on. You know, I was once riding in a car. This really struck me because I was riding in, in the car listening to some religious program of some kind. It was just a general program. No, it was a Catholic priest, and it was one of the best explanations of a particular subject in Scripture that I had heard. I haven’t heard anything better since, but that was a Catholic priest, but it just happened to be a subject that was so universal that, uh, any denomination basically would, um, would agree to what he said. Uh, but it was, it was very sound and very well put. But if I’m going to listen to him about the remembrance meeting, as we call it, or can, um, confession or something like that, that’s not going to be reliable. So having the source, knowing the source is extremely important. 00:31:15 Patricia: What should people do if they, if the answer they are seeking, the support they’re seeking can be found in a commentary that was written a long time ago, but it just doesn’t make sense to you because we understand things a little bit differently now. What should they do? 00:31:33 Roy: That’s a really tough one. And the best advice that I can say is to talk to somebody about it. Um, an older person, uh, it’s really unfortunate. Uh, you know, it’s, it’s terrible because I, I see exactly what you’re, what you’re talking about. Um, some of these, some of these texts should be rewritten. Um, but who’s going to do that? We just don’t have the energy and the time anymore. Um, if you, if you really want to get into some of the best commentaries I remember, I tell you a funny story. I was in a Bible study at work for a while, and as a miscellaneous group of people there from all kinds of denominations. And, um, we were talking everything and I said, well, I don’t think anything useful has been written about the Bible in the last hundred years. 00:32:27 Patricia: Mhm. 00:32:29 Roy: Well, that was a good talking point. We got off on a real discussion about commentaries. Right. But the problem is it’s it’s almost true. And it’s sad. Um, if you really want to learn about these, then get a dictionary. Sit down and just work at it. MM. That’s all I can say. You know, it’s like if you want, if you want to be really good at something, If you want to be a great basketball player and always be able to sink that shot from beyond the third three shot line, three point line. That’s going to take concentration. It’s going to take work. It’s going to take effort. It’s going to take time. Yeah. So I’m I’m sorry. There’s just no other way. 00:33:18 Patricia: Yeah. No I don’t think you have to be sorry. I do think that there’s something there’s something in the effort that comes forth. And just on the literacy side, like I’ve always got two suggestions. Um, one is using technology and one is just reading out loud. So at times reading out loud, right, can help bring a certain clarity that the voice in your head may not be able to, um, and reading something repeatedly out loud in a conversational voice can be very helpful. Um, in terms of helping you to hear what the author is saying. My second suggestion is that particular sentences or passages you don’t understand, honestly, you can feed it into AI and ask AI, can you please change the level which is literacy? You could change the lexile level. That is what it’s called, or just the reading level of the passage. And you can put it down to like a ninth grade or tenth grade level. If you’re in nine states and it’s going to help you a lot. Just know that it may take away some of the original author’s voice and their particular writing style. Um, but that could be really helpful for you to get the gist of what they’re trying to say. But do be careful because those commentaries are commenting on the Bible, which is God’s Word and AI, and Google those resources. When they summarize, they can lose the original nuances of the words that the Lord intends. So always just know that the technology is not perfect either. Um, and it can also just be a way to just lose the true core meaning of a passage. So just be careful. Thank you, Peter Boy and Bethel for this important conversation about how to answer any question using the Bible. Of course, I’ll go back to the beginning. Knowing the books of the Bible and where they are is always a really great challenge to put upon yourself. Memorize them. We used to have competitions about this when we were younger. There’s some there are there are songs. Right? Exactly. But that’s a really good place to start. Um, I hope that our listeners know that Google is not our enemy. The internet is not our enemy. We love technology, but we should always question the root. The effect of getting quick answers. Um, when we seldom meditate on those answers. So let’s think about how we need to slow down, read, reread, and ponder God’s Word. It’s a challenge for me as well. And just know that we don’t need to learn everything all at once. Growth takes time as well. So we encourage you to keep reading, praying, and talking to the Lord about your questions. And then also, as has been mentioned so many times, talk to mature Christians who have navigated similar questions and they know their Bibles well. They can probably give you some really great supports as to how they have been helped too. For more on this topic, you can check out Patterns of Truth dot org and we will see you next time for another conversation about living this Christian life. 00:36:15 Speaker 1: Thank you for listening to the Patterns of Truth podcast. We invite you to join us for our next episode. And we also encourage you to check out Patterns of truth dot org, where we post articles every week for the encouragement and growth of Christ followers. If you have any questions, please don’t hesitate to submit them on our website. I’m Peter. Until next time. The post Using God's Word to Answer Hard Questions appeared first on Patterns of Truth.
AI agents are evolving from individual productivity tools into distributed systems components inside enterprises. The next frontier is coming into focus, and it involves large-scale ecosystems of collaborating agents embedded directly into business processes. However, multi-agent architectures introduce serious challenges around orchestration, state management, trust, governance, and observability. Eric Broda is a veteran of the software industry, and he's the co-author of the new O’Reilly book, Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem. In this episode, Eric joins Sean Falconer to discuss the architectural challenges of deploying agents as core infrastructure, how distributed computing principles apply to multi-agent systems, why trust and explainability are foundational, and what enterprises may look like as agents become full participants in business processes. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Agentic Mesh with Eric Broda appeared first on Software Engineering Daily.
AI agents are evolving from individual productivity tools into distributed systems components inside enterprises. The next frontier is coming into focus, and it involves large-scale ecosystems of collaborating agents embedded directly into business processes. However, multi-agent architectures introduce serious challenges around orchestration, state management, trust, governance, and observability. Eric Broda is a veteran of the software industry, and he's the co-author of the new O’Reilly book, Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem. In this episode, Eric joins Sean Falconer to discuss the architectural challenges of deploying agents as core infrastructure, how distributed computing principles apply to multi-agent systems, why trust and explainability are foundational, and what enterprises may look like as agents become full participants in business processes. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Agentic Mesh with Eric Broda appeared first on Software Engineering Daily.
AI agents are evolving from individual productivity tools into distributed systems components inside enterprises. The next frontier is coming into focus, and it involves large-scale ecosystems of collaborating agents embedded directly into business processes. However, multi-agent architectures introduce serious challenges around orchestration, state management, trust, governance, and observability. Eric Broda is a veteran of the software industry, and he's the co-author of the new O’Reilly book, Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem. In this episode, Eric joins Sean Falconer to discuss the architectural challenges of deploying agents as core infrastructure, how distributed computing principles apply to multi-agent systems, why trust and explainability are foundational, and what enterprises may look like as agents become full participants in business processes. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Agentic Mesh with Eric Broda appeared first on Software Engineering Daily.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover the resurgence of ARM and CPUs as serious compute infrastructure for running local AI agents, a supply chain attack on LiteLLM that exposed API credentials across thousands of developer environments, and the arrival of OpenCode as a fully open source alternative to Claude Code and Codex. They also discuss the diverging strategies of Anthropic and OpenAI following the Pentagon contract controversy, and what it signals about where each company is positioning itself in the enterprise and government markets. Gregor and Sean then dive deep into what the AI coding boom actually means for shipping software. Finally, they highlight standout threads from Hacker News, including Doom running entirely over DNS, the psychology of seafoam green in Cold War-era control rooms, a Tesla Model 3 computer assembled from salvaged crash components, and Apple’s quiet discontinuation of the Mac Pro. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: OpenCode, AI Code vs. Shipped Code, and the LiteLLM Breach appeared first on Software Engineering Daily.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover the resurgence of ARM and CPUs as serious compute infrastructure for running local AI agents, a supply chain attack on LiteLLM that exposed API credentials across thousands of developer environments, and the arrival of OpenCode as a fully open source alternative to Claude Code and Codex. They also discuss the diverging strategies of Anthropic and OpenAI following the Pentagon contract controversy, and what it signals about where each company is positioning itself in the enterprise and government markets. Gregor and Sean then dive deep into what the AI coding boom actually means for shipping software. Finally, they highlight standout threads from Hacker News, including Doom running entirely over DNS, the psychology of seafoam green in Cold War-era control rooms, a Tesla Model 3 computer assembled from salvaged crash components, and Apple’s quiet discontinuation of the Mac Pro. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: OpenCode, AI Code vs. Shipped Code, and the LiteLLM Breach appeared first on Software Engineering Daily.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover the resurgence of ARM and CPUs as serious compute infrastructure for running local AI agents, a supply chain attack on LiteLLM that exposed API credentials across thousands of developer environments, and the arrival of OpenCode as a fully open source alternative to Claude Code and Codex. They also discuss the diverging strategies of Anthropic and OpenAI following the Pentagon contract controversy, and what it signals about where each company is positioning itself in the enterprise and government markets. Gregor and Sean then dive deep into what the AI coding boom actually means for shipping software. Finally, they highlight standout threads from Hacker News, including Doom running entirely over DNS, the psychology of seafoam green in Cold War-era control rooms, a Tesla Model 3 computer assembled from salvaged crash components, and Apple’s quiet discontinuation of the Mac Pro. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: OpenCode, AI Code vs. Shipped Code, and the LiteLLM Breach appeared first on Software Engineering Daily.
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Retrieval-augmented generation, or RAG, has become a foundational approach to building production AI systems. However, deploying RAG in practice can be complex and costly. Developers typically have to manage vector databases, chunking strategies, embedding models, and indexing infrastructure. Designing effective RAG systems is also a moving target, as techniques and best practices evolve in step with rapidly advancing language models. Google DeepMind recently released the File Search Tool, a fully managed RAG system built directly into the Gemini API. File Search abstracts away the retrieval pipeline, allowing developers to upload documents, code, and other text data, automatically generate embeddings, and query their knowledge base. We wanted to understand how the DeepMind team designed a general-purpose RAG system that maintains high retrieval quality. Animesh Chatterji is a Software Engineer at Google DeepMind and Ivan Solovyev is a Product Manager at DeepMind, and they worked on File Search Tool. They joined the podcast with Sean Falconer to discuss the evolution of RAG, why simplicity and pricing transparency matter, how embedding models have improved retrieval quality, the tradeoffs between configurability and ease of use, and what's next for multimodal retrieval across text, images, and beyond. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post DeepMind's RAG System with Animesh Chatterji and Ivan Solovyev appeared first on Software Engineering Daily.
Retrieval-augmented generation, or RAG, has become a foundational approach to building production AI systems. However, deploying RAG in practice can be complex and costly. Developers typically have to manage vector databases, chunking strategies, embedding models, and indexing infrastructure. Designing effective RAG systems is also a moving target, as techniques and best practices evolve in step with rapidly advancing language models. Google DeepMind recently released the File Search Tool, a fully managed RAG system built directly into the Gemini API. File Search abstracts away the retrieval pipeline, allowing developers to upload documents, code, and other text data, automatically generate embeddings, and query their knowledge base. We wanted to understand how the DeepMind team designed a general-purpose RAG system that maintains high retrieval quality. Animesh Chatterji is a Software Engineer at Google DeepMind and Ivan Solovyev is a Product Manager at DeepMind, and they worked on File Search Tool. They joined the podcast with Sean Falconer to discuss the evolution of RAG, why simplicity and pricing transparency matter, how embedding models have improved retrieval quality, the tradeoffs between configurability and ease of use, and what's next for multimodal retrieval across text, images, and beyond. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post DeepMind's RAG System with Animesh Chatterji and Ivan Solovyev appeared first on Software Engineering Daily.
Retrieval-augmented generation, or RAG, has become a foundational approach to building production AI systems. However, deploying RAG in practice can be complex and costly. Developers typically have to manage vector databases, chunking strategies, embedding models, and indexing infrastructure. Designing effective RAG systems is also a moving target, as techniques and best practices evolve in step with rapidly advancing language models. Google DeepMind recently released the File Search Tool, a fully managed RAG system built directly into the Gemini API. File Search abstracts away the retrieval pipeline, allowing developers to upload documents, code, and other text data, automatically generate embeddings, and query their knowledge base. We wanted to understand how the DeepMind team designed a general-purpose RAG system that maintains high retrieval quality. Animesh Chatterji is a Software Engineer at Google DeepMind and Ivan Solovyev is a Product Manager at DeepMind, and they worked on File Search Tool. They joined the podcast with Sean Falconer to discuss the evolution of RAG, why simplicity and pricing transparency matter, how embedding models have improved retrieval quality, the tradeoffs between configurability and ease of use, and what's next for multimodal retrieval across text, images, and beyond. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post DeepMind's RAG System with Animesh Chatterji and Ivan Solovyev appeared first on Software Engineering Daily.
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SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover the viral rise of OpenClaw and its founder's move to OpenAI, OpenAI's exploration of ads inside ChatGPT, and Alibaba's push into agent-powered commerce during Lunar New Year. They also discuss Mistral's acquisition of Koyeb to deepen its compute stack, the growing competition between ChatGPT, Claude, and Gemini, and what these moves signal about monetization, infrastructure, and control in the AI arms race. Gregor and Sean then dive deep into the rapid acceleration of agentic engineering. They examine how tools like Claude Code and Codex are compressing the idea-to-production cycle, what multi-agent orchestration means for software teams, whether the era of the “10x engineer” is ending, and how organizational structures may need to evolve as coding shifts from manual craft to supervised automation. Finally, they highlight standout threads from Hacker News, including reverse engineering a 1990 DOS classic, a 3D reimagining of flight tracking data, old-school practical film effects using cloud tanks, and the privacy-focused GrapheneOS mobile operating system. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: OpenClaw Goes Viral, Mistral's Compute Play, and the Agent Arms Race appeared first on Software Engineering Daily.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover the viral rise of OpenClaw and its founder's move to OpenAI, OpenAI's exploration of ads inside ChatGPT, and Alibaba's push into agent-powered commerce during Lunar New Year. They also discuss Mistral's acquisition of Koyeb to deepen its compute stack, the growing competition between ChatGPT, Claude, and Gemini, and what these moves signal about monetization, infrastructure, and control in the AI arms race. Gregor and Sean then dive deep into the rapid acceleration of agentic engineering. They examine how tools like Claude Code and Codex are compressing the idea-to-production cycle, what multi-agent orchestration means for software teams, whether the era of the “10x engineer” is ending, and how organizational structures may need to evolve as coding shifts from manual craft to supervised automation. Finally, they highlight standout threads from Hacker News, including reverse engineering a 1990 DOS classic, a 3D reimagining of flight tracking data, old-school practical film effects using cloud tanks, and the privacy-focused GrapheneOS mobile operating system. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: OpenClaw Goes Viral, Mistral's Compute Play, and the Agent Arms Race appeared first on Software Engineering Daily.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover the viral rise of OpenClaw and its founder's move to OpenAI, OpenAI's exploration of ads inside ChatGPT, and Alibaba's push into agent-powered commerce during Lunar New Year. They also discuss Mistral's acquisition of Koyeb to deepen its compute stack, the growing competition between ChatGPT, Claude, and Gemini, and what these moves signal about monetization, infrastructure, and control in the AI arms race. Gregor and Sean then dive deep into the rapid acceleration of agentic engineering. They examine how tools like Claude Code and Codex are compressing the idea-to-production cycle, what multi-agent orchestration means for software teams, whether the era of the “10x engineer” is ending, and how organizational structures may need to evolve as coding shifts from manual craft to supervised automation. Finally, they highlight standout threads from Hacker News, including reverse engineering a 1990 DOS classic, a 3D reimagining of flight tracking data, old-school practical film effects using cloud tanks, and the privacy-focused GrapheneOS mobile operating system. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: OpenClaw Goes Viral, Mistral's Compute Play, and the Agent Arms Race appeared first on Software Engineering Daily.
Enterprise IT systems have grown into sprawling, highly distributed environments spanning cloud infrastructure, applications, data platforms, and increasingly AI-driven workloads. Observability tools have made it easier to collect metrics, logs, and traces, but understanding why systems fail and responding quickly remains a persistent challenge. As complexity continues to rise, the industry is looking beyond dashboards and alerts toward agentic AI systems that can reason about operational data, reduce toil, and take action when things go wrong. SolarWinds offers solutions to monitor, understand, and remediate issues across complex, distributed systems. The company began as a leader in network and infrastructure monitoring, and has evolved to support modern applications, cloud environments, containers, and AI workloads, with a growing focus on reducing operational toil. Krishna Sai is the Chief Technology Officer at SolarWinds. He joins the show with Sean Falconer to discuss how SolarWinds is rethinking observability in the age of AI, what it means to design agentic systems for mission-critical environments, how AI-assisted programming is reshaping engineering workflows, and why the future of operations depends on building platforms where humans and autonomous agents work together. Full Disclosure: This episode is sponsored by SolarWinds. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Engineering AI Systems for Autonomy and Resilience with Krishna Sai appeared first on Software Engineering Daily.
Enterprise IT systems have grown into sprawling, highly distributed environments spanning cloud infrastructure, applications, data platforms, and increasingly AI-driven workloads. Observability tools have made it easier to collect metrics, logs, and traces, but understanding why systems fail and responding quickly remains a persistent challenge. As complexity continues to rise, the industry is looking beyond dashboards and alerts toward agentic AI systems that can reason about operational data, reduce toil, and take action when things go wrong. SolarWinds offers solutions to monitor, understand, and remediate issues across complex, distributed systems. The company began as a leader in network and infrastructure monitoring, and has evolved to support modern applications, cloud environments, containers, and AI workloads, with a growing focus on reducing operational toil. Krishna Sai is the Chief Technology Officer at SolarWinds. He joins the show with Sean Falconer to discuss how SolarWinds is rethinking observability in the age of AI, what it means to design agentic systems for mission-critical environments, how AI-assisted programming is reshaping engineering workflows, and why the future of operations depends on building platforms where humans and autonomous agents work together. Full Disclosure: This episode is sponsored by SolarWinds. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Engineering AI Systems for Autonomy and Resilience with Krishna Sai appeared first on Software Engineering Daily.
Enterprise IT systems have grown into sprawling, highly distributed environments spanning cloud infrastructure, applications, data platforms, and increasingly AI-driven workloads. Observability tools have made it easier to collect metrics, logs, and traces, but understanding why systems fail and responding quickly remains a persistent challenge. As complexity continues to rise, the industry is looking beyond dashboards and alerts toward agentic AI systems that can reason about operational data, reduce toil, and take action when things go wrong. SolarWinds offers solutions to monitor, understand, and remediate issues across complex, distributed systems. The company began as a leader in network and infrastructure monitoring, and has evolved to support modern applications, cloud environments, containers, and AI workloads, with a growing focus on reducing operational toil. Krishna Sai is the Chief Technology Officer at SolarWinds. He joins the show with Sean Falconer to discuss how SolarWinds is rethinking observability in the age of AI, what it means to design agentic systems for mission-critical environments, how AI-assisted programming is reshaping engineering workflows, and why the future of operations depends on building platforms where humans and autonomous agents work together. Full Disclosure: This episode is sponsored by SolarWinds. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Engineering AI Systems for Autonomy and Resilience with Krishna Sai appeared first on Software Engineering Daily.
Python 3.14 is here and continues Python's evolution toward greater performance, scalability, and usability. The new release formally supports free-threaded, no-GIL mode, introduces template string literals, and implements deferred evaluation of type annotations. It also includes new debugging and profiling tools, along with many other features. Łukasz Langa is the CPython Developer in Residence at the Python Software Foundation, and he joins Sean Falconer to discuss the 3.14 release, the future of free threading, type system improvements, Python's growing role in AI, and how the language continues to evolve while maintaining its commitment to backward compatibility. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Python 3.14 with Łukasz Langa appeared first on Software Engineering Daily.
Python 3.14 is here and continues Python's evolution toward greater performance, scalability, and usability. The new release formally supports free-threaded, no-GIL mode, introduces template string literals, and implements deferred evaluation of type annotations. It also includes new debugging and profiling tools, along with many other features. Łukasz Langa is the CPython Developer in Residence at the Python Software Foundation, and he joins Sean Falconer to discuss the 3.14 release, the future of free threading, type system improvements, Python's growing role in AI, and how the language continues to evolve while maintaining its commitment to backward compatibility. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Python 3.14 with Łukasz Langa appeared first on Software Engineering Daily.
Python 3.14 is here and continues Python's evolution toward greater performance, scalability, and usability. The new release formally supports free-threaded, no-GIL mode, introduces template string literals, and implements deferred evaluation of type annotations. It also includes new debugging and profiling tools, along with many other features. Łukasz Langa is the CPython Developer in Residence at the Python Software Foundation, and he joins Sean Falconer to discuss the 3.14 release, the future of free threading, type system improvements, Python's growing role in AI, and how the language continues to evolve while maintaining its commitment to backward compatibility. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Python 3.14 with Łukasz Langa appeared first on Software Engineering Daily.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover Starlink's rapid rollout of free, high-speed in-flight internet, Tesla's move to deprecate Autopilot in favor of full self-driving, and Apple's reported decision to power Siri with Google's Gemini models. They also discuss Meta's $2B acquisition of Manus, Waymo's growing pains as autonomous vehicles scale, and the competitive shockwaves triggered by Google's advances in custom AI hardware. Gregor and Sean then dive deep into the state of the tech job market, examining OpenAI's decision to eliminate vesting cliffs, the escalating war for elite AI talent, and what recent layoffs really say about the future of software engineering. They explore how AI coding tools are reshaping the balance between junior and senior engineers, why fundamentals still matter, and what developers should focus on heading into 2026. Finally, they highlight standout threads from Hacker News, including Doom running on wireless earbuds, the enduring appeal of wildly over-engineered side projects, and why hacking for fun still matters in an age of industrial-scale AI. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: Apple Bets on Gemini, Google's AI Advantage, and the Talent Arms Race appeared first on Software Engineering Daily.
In this episode: Adam gets live coaching from Nicholas Whitaker on navigating early retirement and shifting from money to connection as his primary success metricEpisode SummaryAdam opens up about his first 14 months of early retirement—still seeking Google's validation, wrestling with outdated success metrics, and struggling to let go of money as his primary motivator. Through compassionate coaching, Nick helps Adam explore the tension between entrepreneurial revenue goals and simply enjoying life. This raw conversation tackles corporate identity hangover, the "not good enough" narrative, and what it means to fully own your retirement.Guest BioNicholas Whitaker is a coach, mindfulness facilitator, and founder of Rebellion Collective. He helps high performers navigate burnout, identity collapse, and life transitions—restoring their sense of self and building lives aligned with who they need to be. A fellow ex-Googler, Nick brings deep expertise in mindfulness and conscious leadership.ResourcesNick's previous episodes: Episode 11 and Episode 12"Love Money, Money Loves You" by Sarah Crumbrebellioncollective.com — Free journaling prompt guide availableLinkedIn: Nicholas WhitakerKey TakeawaysThe first year of retirement is harder than expected—corporate baggage and outdated metrics don't disappear overnightSeeking validation from your former employer keeps you stuck in the pastMoney doesn't need to be your primary metric after FIRE—connection, enjoyment, and fun are equally validThe "not good enough" narrative just takes new entrepreneurial forms after leaving corporatePS: Introducing the…
Modern software development is evolving rapidly. New tools, processes, and AI-powered systems are reshaping how teams collaborate and how engineers find satisfaction in their craft. At the same time, developer experience has become a critical function for helping organizations balance agility, security, and scale while maintaining the creativity and flow that make top tier engineering possible. Capital One is continuously transforming its developer culture, with a focus on faster development cycles, reducing operational overhead, and boosting productivity across the organization. Catherine McGarvey is the SVP of Developer Experience at Capital One. She joins the podcast with Sean Falconer to talk about what developer enablement means at enterprise scale, measuring developer productivity, being agile in a regulated environment, AI in enterprise development, the future for developers, and much more. Full Disclosure: This episode is sponsored by Capital One. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Developer Experience at Capital One with Catherine McGarvey appeared first on Software Engineering Daily.
Does your content prove that you're the ideal person to work with? In today's episode, I'm sharing 5 types of contacts to create content for, in order to make your business the ONLY option in your industry. I'm breaking down who these contacts are and why it's so important to include them in your content calendar. On Quianna Marie Weekly, we're chatting about business growing pains, finding genuine connections, and celebrating wins of all sizes through the lens of a photographer at heart. Sprinkled throughout stories and interviews with past clients, photographers and other business owners this podcast is designed to help you step into your purpose and to truly create a life you're proud of, a life worth photographing and sharing.Today's episode is brought to you by The Green House, my resource garden for photographers! Let me help you AMPLIFY your heart online and in real life to turn bridesmaids into future brides through templates, workshops, and freebies!Review The Show Notes:Get Crystal Clear About Your Content (5:01)Googlers (8:20)Current Clients (11:29)Dream Clients (13:46)Past Clients (15:58)Family And Friends (19:43)Keep-It-Real Moments (25:28)Mentioned In This EpisodeThe Green House Resource Garden: quiannamarie.com/shopConnect with Quianna:Website: quiannamarie.comInstagram: instagram.com/quiannamarie Hosted on Acast. See acast.com/privacy for more information.
This Week In Startups is made possible by:Sentry - http://sentry.io/twistLinkedIn Ads - http://linkedin.com/thisweekinstartupsPipedrive - pipedrive.com/twistToday's show:Netflix wants to gobble up Warner Bros. Do they just want to own Batman and Harry Potter, or is this secretly about destroying movie theaters?Sure, this is usually a startup show, but news THIS BIG warrants attention! So Lon stops by to tell Jason and Alex about the big Netflix acquisition news, why so many theatrical movie fans are terrified for the future, and why this might face particular regulatory scrutiny both at home and abroad.PLUS… are Googlers gaming Polymarket? This is one scenario in which prediction markets are NOT exactly like stocks.THEN we're looking at some of our favorite startups from the Fall ‘25 Y Combinator cohort (and asking Producer Claude for his picks)… Considering why Perplexity keeps getting sued and how they can stop it… and doing a victory lap for Jason's early investment in breakout AI training project Micro1.Timestamps:(02:05) Netflix buying Warner Bros! Jason, Lon and Alex react.(05:04) Jaytrade Update: J kind of missed the boat on this one(05:36) What does this mean for theatrical cinema?(08:42) Sentry - New users get 3 months free of the Business plan (covers 150k errors). Go to http://sentry.io/twist and use code TWIST(09:52) Jason's pitch to Disney CEO Bob Iger (please send this to him!)(19:36) LinkedIn Ads: Start converting your B2B audience into high quality leads today. Launch your first campaign and get $250 FREE when you spend at least $250. Go to http://linkedin.com/thisweekinstartups to claim your credit.(23:29) Is this deal going to get approval, at home and abroad?(25:52) Are Googlers gaming Polymarket?(28:02) Can you do “insider trading” on a prediction market?(29:23) Pipedrive - Bring your entire sales process into one elegant space. Get started with a 30 day free trial at pipedrive.com/twist(37:00) How accelerators like Y Combinator serve as “finishing schools” for startups(37:52) A Quick Look at some of our fav companies from YC's Fall '25 cohort(39:01) Why startups need to “skate to where the puck is going”(40:08) Why sometimes old ideas (like solar-powered aircraft) are often worth revisiting(45:29) Jason's advice for founders (and investors) in the “feel good” or activist space(50:48) Why Lon, Alex, and Claude ALL thought Hyperspell sounds like a hot startup(52:58) Perplexity getting sued again! Why can't they make friends!(57:51) Meanwhile, Meta's signing AI deals with news publications.(59:21) Micro1, which Jason helped to fund, has hit $100M ARR! Why do AI companies need so many experts?Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpFollow Lon:X: https://x.com/lonsFollow Alex:X: https://x.com/alexLinkedIn: https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisThank you to our partners:(8:42) Sentry - New users get 3 months free of the Business plan (covers 150k errors). Go to http://sentry.io/twist and use code TWIST(19:36) LinkedIn Ads: Start converting your B2B audience into high quality leads today. Launch your first campaign and get $250 FREE when you spend at least $250. Go to http://linkedin.com/thisweekinstartups to claim your credit.(29:23) Pipedrive - Bring your entire sales process into one elegant space. Get started with a 30 day free trial at pipedrive.com/twist
Send us a text!Watch this episode on YouTubeThis week, it's the biggest brain drain at Apple for decades — and a lot of Apple fans are celebrating! Also: Intel is coming back to the Mac (but it's not what you think!) and another pedantic Mac question only Griffin can answer. This episode supported by:Listeners like you. Your support helps us fund CultCast Off-Topic, a new weekly podcast of bonus content available for everyone; and helps us secure the future of the podcast. You also get access to The CultClub Discord, where you can chat with us all week long, give us show topics, and even end up on the show. Support The CultCast at support.thecultcast.com — or unsubscribe at unfork.thecultcast.comCultCloth will keep your iPhone, MacBook, display, guitars, glasses and lenses sparkling clean! For a limited time use code CULTCAST at checkout to score a two free CarryCloths with any order $20+ at CultCloth.coNordLayer is an easy to use and easy to set up security platform for businesses. Get the exclusive Black Friday offer: 28% off NordLayer yearly plans with the coupon code cultcast-28. Try it risk-free with a 14-day money-back guarantee at nordlayer.com/cultcast.This week's stories:Apple design chief quits for Meta. Some say good riddance!Social media users responded to big news that Alan Dye will join Meta with Liquid-Glass-focused sarcasm. Is it really such a big loss?Meet Apple's new UI chief, the man Steve Jobs called ‘Margaret'Meet Steve Lemay, the new head of user interface design at Apple, and learn why Steve Jobs called him “Margaret.”Apple replaces AI chief, taps ex-Googler to fix Apple IntelligenceApple's AI chief is out after a string of failures. Learn about the new leadership for the company's critical AI development efforts.Macs might soon have Intel inside again — but there's a twistIn a surprising shift in Apple's chip strategy, Intel will reportedly fabricate low-end M-series chips for future MacBook Air and iPad Pro.How to find your music stats with Apple Music Replay 2025Apple Music Replay is where you find your most-played songs, artists and albums from 2025. Here's how to find it.Griffin on Apple MusicLewis on Apple MusicLeander on Apple Music
Can being "too smart" actually hold you back socially? In this episode of Social Intelligence, AJ Harbinger and Johnny Dzubak sit down with Anne-Laure Le Cunff—founder of Ness Labs and a former Googler turned neuroscience researcher—to explore the psychology of overthinking, social fatigue, and emotional connection for high performers. If you've ever felt drained after socializing, struggled to connect in unstructured conversations, or defaulted to overanalyzing instead of just vibing, Anne-Laure's insights will change how you see your brain—and your relationships. What to Listen For [00:00:00] Meet Anne-Laure: Neuroscience researcher, entrepreneur, and former Googler [00:02:01] What inspired Anne-Laure to walk away from Silicon Valley [00:04:02] The science of mental fitness: training your mind like a muscle [00:05:42] Why smart people often struggle in social settings [00:07:50] Overthinking vs. high thinking: what's the difference? [00:09:20] Anne-Laure's framework for emotional granularity [00:11:03] The hidden impact of unspoken emotions on your connections [00:12:42] How journaling and reflection sharpen your emotional vocabulary [00:14:35] Real-time self-awareness: catching yourself before you spiral [00:16:04] Why smart people default to logic instead of connection—and how to fix it [00:18:20] The loneliness trap of overperformance [00:20:03] How Anne-Laure blends neuroscience with self-compassion [00:22:01] The importance of emotional bandwidth and recovery [00:24:40] What socially intelligent people do differently after high-effort conversations [00:27:20] Tools for restoring your energy after social drain [00:29:00] Why connection doesn't mean constant performance [00:31:05] Anne-Laure's advice for other deep thinkers navigating real relationships A Word From Our Sponsors Tired of awkward handshakes and collecting business cards without building real connections? Dive into our Free Social Capital Networking Masterclass. Learn practical strategies to make your interactions meaningful and boost your confidence in any social situation. Sign up for free at theartofcharm.com/sc and elevate your networking from awkward to awesome. Don't miss out on a network of opportunities! Unleash the power of covert networking to infiltrate high-value circles and build a 7-figure network in just 90 days. Ready to start? Check out our CIA-proven guide to networking like a spy! Indulge in affordable luxury with Quince—where high-end essentials meet unbeatable prices. Upgrade your wardrobe today at quince.com/charm for free shipping and hassle-free returns. Ready to turn your business idea into reality? Shopify makes it easy to start, scale, and succeed—whether you're launching a side hustle or building the next big brand. Sign up for your $1/month trial at shopify.com/charm. Need to hire top talent—fast? Skip the waiting game and get more qualified applicants with Indeed. Claim your $75 Sponsored Job Credit now at Indeed.com/charm. This year, skip breaking a sweat AND breaking the bank. Get your summer savings and shop premium wireless plans at mintmobile.com/charm Stop needlessly overpaying for car insurance. Before you renew your policy, do yourself a favor—download the Jerry app or head to JERRY.com/charm Connect with quality therapists and mental health experts who specialize in you at www.rula.com/charm Curious about your influence level? Get your Influence Index Score today! Take this 60-second quiz to find out how your influence stacks up against top performers at theartofcharm.com/influence. Episode resources: https://anne-laure.net/ Check in with AJ and Johnny! AJ on LinkedIn Johnny on LinkedIn AJ on Instagram Johnny on Instagram The Art of Charm on Instagram The Art of Charm on YouTube The Art of Charm on TikTok Learn more about your ad choices. Visit megaphone.fm/adchoices
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