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WARNING: Audio is bit cooked in this one. Apologies we're sorting studio stuff out.This week that lads are in sync as they have Pimp My Ride on the brain! That inevitably leads them to Shark Tank Australia and the beautiful culinary masterpiece - the Donug. There's other stuff too but hey you don't read this anyway. Do ya? Na Probs not. Let me know if you do though. DM us the secret code - Spanner - if you do read this far. Anyway. Enjoy the pod and come to a show. Hosted on Acast. See acast.com/privacy for more information.
Send us Fan MailFor more than 40 years, Larry Copponi has been working at the intersection of engineering talent and product innovation. Today, he serves as Vice President of Staffing Solutions at Spanner Product Development, where he helps companies across industries assemble the engineering teams they need to bring complex products to life. Larry's work spans sectors including consumer electronics, robotics, renewable energy, life sciences, and medical devices. His team specializes in placing highly skilled professionals—mechanical engineers, electrical engineers, product designers, industrial designers, and quality engineers—into organizations that are racing to transform ideas into real-world products. By deeply understanding both the technical landscape and the people who power it, Larry plays a critical role in helping companies scale their engineering capabilities. Before joining Spanner, Larry spent more than 14 years as Division Manager at Pro Source Inc., supporting companies with contract engineering talent and CAD professionals to keep product development projects on schedule. Earlier in his career, he helped build recruiting and sales teams at TriMech Solutions, where he launched new recruiting initiatives focused on engineering and technical sales professionals. Across decades in the recruiting and staffing industry, Larry has built a reputation for understanding the real needs of engineering organizations—matching the right talent to the right challenges and helping companies deliver products to market faster. His career offers a rare vantage point on how engineering teams evolve, how companies compete for talent, and what separates organizations that build strong technical cultures from those that struggle to grow. In this conversation, Larry shares lessons from decades of working alongside engineering leaders, insights into the hiring challenges facing technical organizations today, and practical advice for both companies looking to build great teams and engineers navigating their careers. LINKS: Larry Copponi LinkedIn: https://www.linkedin.com/in/larry-copponivpstaffingsolutionsspannerpd/ Spanner Website: https://www.spannerpd.com/ Aaron Moncur, host Subscribe to the show to get notified so you don't miss new episodes every Friday.The Being An Engineer podcast is brought to you by Pipeline Design & Engineering. Pipeline partners with medical & other device engineering teams who need turnkey equipment like cycle test machines, custom test fixtures, automation equipment, assembly jigs, inspection stations and more. You can find us at www.teampipeline.usWatch the show on YouTube: www.youtube.com/@TeamPipelineus
Tickets für Game Show #2 am 13.09.: KLICK FÜR TICKETS!Wen hättet ihr gerne als Gast dabei?-Max ist krank und Hinni dropt heute solo rein. Er empfiehlt ein paar Bücher, outet sich mal wieder als auditiver Spanner, erzählt vom nächsten Programm und gibt einen Einblick in seine Psyche. Obendrauf kommt eine extralange Sonderausgabe Dr. Möwe mit schwierigen Sujets wie Zwiebellook und Darmstadt! Seid euch gewiss: das Leben ist schön, man muss es sich nur immer wieder sagen! GuBe an Max, danke fürs Zuhören und nächste Woche wieder in alter Frische ♡
Neue Woche, neue Mails von euch: Wir starten mit einem Follow-up zum Bodycount-Thema und charmanten Lösungen, um die Frage zu umgehen. Danach wird's spannend: Eine Hörerin fragt sich, warum ihr Freund nach einem halben Jahr Beziehung meistens die Lust auf Sex verliert. Dann haben wir eine Mail, bei der wir kurz schlucken mussten: Ein Freund, der heimlich seine Nachbarin beobachtet – wo hört Fantasie auf und wo fängt übergriffiges Verhalten an? Außerdem geht's um Verlustangst, die eine Beziehung langsam kaputtmacht. Und zum Schluss: Fremdgehen, schlechtes Gewissen und ein mögliches Comeback mit der Ex. Soll er es beichten oder lieber nicht? Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte: https://linktr.ee/beste_freundinnen Du möchtest Werbung in diesem Podcast schalten? Dann erfahre hier mehr über die Werbemöglichkeiten bei Seven.One Audio: https://www.seven.one/portfolio/sevenone-audio
Der Streit um die Aufarbeitung der Spanner-Affäre an der Universität Freiburg landet vor Gericht. Die Psychiatrie in Emmendingen äußert sich zu entwichenen Patienten. Und die SC-Freiburg-Hymne ertönt erstmals auf den Orgeln im Freiburger Münster.
In this episode, Ray Cochrane breaks down a reversible conductive glue from Newcastle University that could replace solder and finally make electronics recycling work. Additional stories cover China widening its clean energy lead, DeepMind’s AlphaEvolve scoring wins from genomics to Google’s database, Anthropic’s $200 million partnership with the Gates Foundation, Intel teaming up with McLaren Racing, and end-to-end encrypted RCS rolling out in beta. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens the show with a deep dive into Newcastle University’s reversible conductive glue, a water-based adhesive that could finally make electronics recycling economically viable. He frames the e-waste problem first: 62 billion kilos a year, with less than a quarter ever recycled. Then he walks through the silver nanoparticle chemistry, the lead-free angle on traditional solder, and the geopolitical stakes of critical mineral recovery. From there the episode pivots through energy, AI, hardware, open source, data research, space, science, and consumer privacy. A Reversible Conductive Glue That Could Replace Solder A team at Newcastle University has developed a water-based glue that conducts electricity well enough to replace solder. Unlike solder, however, the glue releases cleanly with a quick rinse of acetone or an alkaline bath. The breakthrough relies on silver nanoparticles suspended in a water-based binder. Consequently, components can be recovered intact, opening a viable path to electronics recycling at scale. Co-investigator Volker Pickert framed the second prize directly: solder has the best conductivity, but the best formulations contain lead. China Widens Its Clean Energy Lead A new Atlas Public Policy report shows Chinese firms accounted for 55 percent of $1.1 trillion in global clean energy manufacturing investment between 2019 and 2025. Battery manufacturing alone pulled in nearly half of that money. Meanwhile, U.S. companies have actively retreated from those same industries. With the Strait of Hormuz currently closed, supply chain ownership in solar, wind, and batteries matters more than ever. A separate Ember analysis showed Chinese solar panel exports doubled in March alone. DeepMind’s AlphaEvolve Scores Real Wins DeepMind published an update on AlphaEvolve, its Gemini-powered AI coding agent. The system cut genomic variant detection errors by 30 percent. Additionally, it lifted AC Optimal Power Flow feasibility from 14 to over 88 percent on the electrical grid. AlphaEvolve also found a better cache replacement policy in two days that would have taken human engineers months. Furthermore, it reduced write amplification in Google’s Spanner database by 20 percent. The pattern shows applied AI sticking, not as a chatbot but as a quiet optimizer. Anthropic and Gates Foundation Commit $200 Million Anthropic announced a four-year, $200 million partnership with the Gates Foundation across three pillars. The biggest pillar targets global health and life sciences in low and middle-income countries. Notably, the research scope includes polio, HPV, and preeclampsia. A second pillar covers AI in education across the U.S., sub-Saharan Africa, and India, in partnership with the Global AI for Learning Alliance. Finally, an economic mobility pillar focuses on agricultural productivity and crop benchmarks. Google’s AI Educator Series Launches Free Google rolled out the first 20-plus sessions of its AI Educator Series this week. The free AI literacy training targets the roughly 6 million K-12 and higher education teachers across the U.S. Modules are designed as short, snackable trainings teachers can finish in a prep period or a lunch break. Additionally, stackable workshops let educators build credentials over time. Importantly, the program requires no institutional subscription. Amazon Bedrock Prompt Optimization Goes GA Amazon Bedrock dropped its Advanced Prompt Optimization tool, now generally available across most major regions. The feature rewrites prompts to perform better on specific models and automates prompt migration when switching between models. Furthermore, a built-in evaluation feedback loop lets users benchmark against up to five models side by side. The default judge model is Claude Sonnet 4.6. Consequently, teams can stop hand-tuning string templates and focus on product work. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. Arm AGI CPU and Red Hat Go Production-Ready on Agentic AI Arm and Red Hat expanded their collaboration around Arm’s AGI CPU, which is Arm’s branding for its agentic AI chip family. The deal brings Red Hat Enterprise Linux and OpenShift to the chip as a production-ready stack. Hardware specifications include 136 Neoverse V3 cores, 96 PCIe Gen6 lanes, and 12 channels of DDR5-8800 memory in a 300-watt thermal envelope. Availability lands in Q4 through Supermicro, Lenovo, and ASRock Rack. Intel Becomes McLaren Racing’s Official Compute Partner Intel announced a multi-year deal as the official compute partner for McLaren Racing. The agreement covers the McLaren Mastercard Formula 1 team, Arrow McLaren IndyCar, and McLaren F1 Sim Racing. Trackside edge compute will power real-time race decisions, while Xeon and Core Ultra silicon drive Computational Fluid Dynamics and digital twin work. Consequently, design iterations that once took weeks now collapse to days. The deal puts Intel silicon in front of every CTO watching a Grand Prix. Rust Lands 13 Google Summer of Code Projects The Rust Project landed 13 accepted projects in Google Summer of Code 2026. Out of 96 proposals, a 50 percent jump from last year, the project selected 13. Notably, three returning contributors from prior years are back. Mentors flagged a noticeable share of AI-generated submissions as a growing challenge. Furthermore, the real bottleneck remains mentor capacity rather than funding. GitHub Innovation Graph Maps Digital Complexity Researchers used GitHub Innovation Graph data to predict GDP, inequality, and emissions through the Economic Complexity Index, or ECI. Countries are compared to kitchens; the more variety and sophistication in software output, the higher the score. Germany ranks first, followed by Australia and Canada. The U.S. lands at sixth. However, the dataset only captures public GitHub activity, leaving most proprietary software invisible. NASA and Eta Space Prepare Cryogenic Fuel Demo NASA is teaming with Eta Space on an in-orbit demonstration called LOXSAT, short for Liquid Oxygen Flight Demonstration. The nine-month mission tests cryogenic fluid management techniques required for in-space propellant depots. Launch is no earlier than July 17 aboard a Rocket Lab Electron from the Mahia Peninsula in New Zealand. Successful refueling in orbit could reshape what is possible for deep-space missions to the Moon and Mars. Stealth Magma Surge Under São Jorge Surprises Researchers Researchers in the UK and Spain published in Nature Communications on a 2022 magma surge under São Jorge Island in the Azores. The surge climbed from more than 20 kilometers underground to 1.6 kilometers below the surface. Surprisingly, most of the thousands of earthquakes happened after the magma stalled, not during the climb. Consequently, scientists are calling it a stealth surge and a failed eruption. A primed magma chamber now sits closer to the surface than before. End-to-End Encrypted RCS Begins Rolling Out Apple and Google led a cross-industry effort to roll out end-to-end encryption for RCS messaging. As of May 11, the feature is rolling out in beta on both platforms. Importantly, encryption is on by default and auto-applies to new and existing conversations. A lock icon in the chat indicates active end-to-end encryption. This quietly raises baseline privacy for billions of cross-platform messages. Cochrane signs off with the usual ecosystem mentions: GNC Insider at geeknewscentral.com/insider, the show newsletter, and modern podcast app recommendations at podcastapps.com. The post A Reversible Glue that could Replace Solder #1865 appeared first on Geek News Central.
Das Wissenschaftsministerium beauftragt im Spanner-Fall an der Universität Freiburg eine unabhängige Expertin. Novartis möchte sich aus Wehr zurückziehen. Ein Mietshaus in Freiburg ist Teil eines Rechtsstreits.
Nach dem Spanner-Fall an der Uni Freiburg wurde die Personalchefin entlassen. Cerdia weist Vorwürfe zu Russland-Lieferungen zurück. Ein Hells-Angels-Mitglied wurde wegen Drogenhandels verurteilt.
In this special episode of Cloud Wars Live from Google Cloud Next, Bob Evans speaks with Andi Gutmans about Google Cloud's newly announced Agentic Data Cloud and what it means for enterprise customers entering the AI-driven future. Gutmans explains how businesses must rethink data platforms for an era where autonomous agents, not just people, need instant access to trusted enterprise knowledge. The New Data Foundation The Big Themes: The Agentic Data Cloud Is a Reinvention: Google Cloud is not simply rebranding its existing Data Cloud, it is fundamentally redesigning it for the agentic AI era. Gutmans explains that data must evolve from being a passive repository into active business knowledge that agents can reason over. He describes this as moving from a “system of intelligence” to a “system of action.” The newly announced Agentic Data Cloud includes innovations across databases, analytics, storage, and governance so agents can securely access and act on enterprise information. Culture Matters More Than Technology: According to Gutmans, the organizations moving fastest are the ones embracing cultural transformation, not just deploying models on top of old systems. Companies succeeding in the agentic era are rethinking how their data platforms work and how employees engage with AI. Instead of treating agents as copilots, they view every employee as an orchestrator of agents. That mindset shift drives faster ROI because it creates readiness for change and willingness to innovate. Google's Vertical Stack Is a Major Advantage: Gutmans says that Google Cloud is uniquely positioned because it owns the entire stack: AI infrastructure, models, and the data platform itself. This allows what he calls “closed-loop innovation” between models and data systems, where improvements in one directly enhance the other. He says many people underestimate how important that relationship is because model reasoning must evolve alongside the platform serving enterprise data. Products like BigQuery, Spanner, and Gemini benefit from Google's decades of operating at massive scale, including multiple billion-user businesses. The Big Quote: "We're moving from this reactive, agentic experience to agents truly being autonomous, being able to drive outcomes for the business, and that's also now steering how we're thinking about the data cloud." More from Google Cloud: Learn more about what's new in the Agentic Data Cloud and security in the AI era. Visit Cloud Wars for more.
Sexualisierte Gewalt im Netz ist ein Massenphänomen, das durch den Fall Collien Fernandes in den Fokus gerückt ist. Ein neuer Gesetzentwurf sieht härtere Strafen vor. Und: Waffenruhe zwischen Israel und Libanon: Chance auf echten Frieden? Schulz, Josephine
Sexualisierte Gewalt im Netz ist ein Massenphänomen, das durch den Fall Collien Fernandes in den Fokus gerückt ist. Ein neuer Gesetzentwurf sieht härtere Strafen vor. Und: Waffenruhe zwischen Israel und Libanon: Chance auf echten Frieden? Schulz, Josephine
Send us Fan MailJoin us April 23, 2026 at 9AM Pacific Great engineering alone does not guarantee product success.Achieving product-market fit—ensuring that a product truly meets user needs and expectations—requires integrating market insights, usability considerations, and business goals into the development process.But how can engineers quantify something that often seems subjective?In this PDX Webinar, Arne Lang-Ree, Chief Design Officer and Cofounder at Spanner, will demonstrate how product-market fit can be transformed into a practical engineering objective.Drawing on real-world tools and frameworks developed at Spanner, this session will show how teams can systematically evaluate user needs, prioritize design trade-offs, and make decisions that improve the likelihood of market success.Topics covered include:• Why Product-Market Fit is an Engineering Challenge• Turning Market & User Needs into Engineering Constraints• Tools & Frameworks for Measuring Product Success• Interactive Q&A and Application to Your ProjectsThis session is designed for engineers, product developers, and technical leaders involved in bringing new products to market.Sign up hereSubscribe to the show to get notified so you don't miss new episodes every Friday.The Being An Engineer podcast is brought to you by Pipeline Design & Engineering. Pipeline partners with medical & other device engineering teams who need turnkey equipment like cycle test machines, custom test fixtures, automation equipment, assembly jigs, inspection stations and more. You can find us at www.teampipeline.usWatch the show on YouTube: www.youtube.com/@TeamPipelineus
The whole gang is here to talk about the end of the Talay drama, it's finally over. Time to get ready for the PK era. We also discuss the women's team defeating Melbourne in their last home game of the season, what a finish to an unlucky season! Take a listen, hope you enjoy!
In den eigenen vier Wänden erlebt der nackte anredo eine Blamage, als er plötzlich einem mysteriösen Spanner gegenübersteht. Außerdem: Stress bei der Tesla-Rückgabe wegen angeblicher Schäden und Bastis völlig absurdes erstes Mal im Nagelstudio. Nach dem Joggen durch den Cruising-Wald läuft anredo nackt durch seine Erdgeschoss-Bude und fühlt sich sicher. Doch plötzlich merkt er, dass dieser Moment nicht so privat ist wie gedacht. Innerhalb von Sekunden wird aus entspannter Routine eine der peinlichsten Situationen seines Alltags. Während Basti erzählt, dass er zuhause gern wie Winnie Pooh herumläuft, beschreibt sich anredo eher als Quasimodo. Viel zu spät merkt er, dass der Safe Space Wohnung vielleicht doch keiner ist. Auch bei der Rückgabe des Raumschiff-Autos läuft es für den Ex-Internetstar nicht besser. Der Besuch auf dem KFZ-Schrottplatz wird zur Prüfung unter grellem Licht. Jede Felge wird untersucht. Jede Spur wird notiert. Aus kleinen Kratzern könnten teure Schäden werden. Parallel entdeckt Basti wieder seine ostdeutsche Identität. Ein Gespräch über Burger, Socken und alte Ost-West-Gefühle nimmt eine völlig unerwartete Richtung. Und dann geht Basti noch ins Nagelstudio. Zum ersten Mal. Schon die Terminvereinbarung sorgt für extreme Verwirrung. Vor Ort warten Sprachbarrieren, viele Nail Artists und ein neugieriges Publikum. Am Ende entsteht ein neues Naildesign, irgendwo zwischen Gothic Look und der rätselhaften „Alex UK“ Richtung. Ein Beauty-Termin, der länger dauert als geplant und den Basti so schnell nicht vergessen wird… Diese und alle anderen Episoden #rundfunk17 findet ihr unter anderem bei Apple Podcasts, Spotify, Deezer und als RSS-Feed.
Too Tall Johnson sues Dick Spanner over his loss of height, but he agrees to drop the lawsuit if Spanner can find the family's parrot Polly, so the detective heads to Ivywood.Intro special guest: Natalie Roles
In einer WG in Wiesbaden wundern sich die Bewohnerinnen, dass ihr Mitbewohner immer vor ihnen ins Bad will. Dann stellen sie fest: Im Warmwasserboiler ist ein Radiowecker mit eingebauter Kamera versteckt. Die filmt die Frauen beim Duschen. Als sie aufmerksam werden, zeigen sie ihren Mitbewohner, einen 23-Jährigen an. Und sie machen den Fall öffentlich. Es dauert drei Jahre, bis im Februar 2026 der Fall vorm Wiesbadener Amtsgericht verhandelt wird. Der Vorwurf: Verletzung des höchstpersönlichen Lebensbereichs und von Persönlichkeitsrechten durch Bildaufnahmen. Strafandrohung: Geldstrafe bis zwei Jahre Haft. Podcast-Tipp: Dark Matters Geheimdienste arbeiten im Verborgenen, aber manchmal geraten sie ins Licht. Doch auch ihre Welt verändert sich. Wie halten Geheimdienste Schritt in Zeiten von Cyberwar und globalen Spannungen? "Dark Matters" taucht ein in echte Fälle, zeigt Probleme, Erfolge und Methoden. Erfahrt, was ihr eigentlich nicht wissen solltet. Mit HintergrundWissen der Geheimdienst-Experten der ARD. Abonniert und folgt "Dark Matters" überall, wo es Podcasts gibt. https://www.ardaudiothek.de/sendung/dark-matters-geheimnisse-der-geheimdienste/urn:ard:show:870aeeecdf31b1b9/
Die Themen von Jan und Lisa am 27.02.2026: (00:00:00) 30 Jahre Pokémon: Warum Pikachu und Co. über Jahrzehnte Millionen begeistern. (00:02:26) Warnstreik: Wie Berufstätige ohne Bus und Bahn heute zur Arbeit kommen. (00:03:37) Ist die AfD gesichert rechtsextremistisch? Warum ein Gericht jetzt erstmal zu Gunsten der Partei entschieden hat und was das bedeutet. (00:07:53) Epstein-Files: Wurden Akten zurückgehalten, die US-Präsident Trump belasten? (00:13:30) Gaffen in der Sauna: Warum Filmen in der Sauna bisher nicht strafbar ist und wie Bundesjustizministerin Hubig das ändern will. (00:17:37) Equal Pay Day: Wie viel Frauen in Deutschland weniger verdienen als Männer - immer noch. Hier findet ihr die neuesten Infos zu den Epstein-Files: https://www.tagesschau.de/thema/epstein Und hier könnt ihr euch das Video von "Die andere Frage" zu Gaffen in der Sauna anschauen: https://1.ard.de/sauna-spanner Wer einen sexuellen Übergriff erlebt hat, kann sich hier Hilfe holen: https://1.ard.de/hilfe-sexuelle-uebergriffe Hat euch unsere Folge gefallen? Schickt uns gerne eine Sprachnachricht an 0151 15071635 oder ne Mail an 0630@wdr.de. Kennt ihr schon unseren WhatsApp Channel? Den findet ihr hier: https://1.ard.de/0630-Whatsapp-Kanal Oder einfach diesen QR-Code abscannen: https://1.ard.de/0630-bei-Whatsapp Von 0630.
From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: 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. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:
Ali Hackalife und Basti sprechen über die Bahn. Wasserkocher Hacks, die FDP in der Pleite. Mitdenken, Walfische und Walfakten. Und darüber wie teuer der Perso geworden ist.
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Der Luxusurlaub in Südafrika kippt plötzlich in ein mulmiges Gefühl, als anredo merkt, dass in seiner Unterkunft irgendetwas ganz und gar nicht stimmt. Basti sitzt derweil im eiskalten Deutschland und kämpft in seiner AI-Wohnung mit Lichtschaltern und innerem Kontrollverlust. Während anredo aus der Sonne Südafrikas sendet und eigentlich nur Urlaub machen wollte, entpuppt sich seine Unterkunft zunehmend als mysteriöser Plünderer-Palast mit sehr eigenwilligem Geschmack. Was zunächst nach High-End-Airbnb klingt, wirft moralische, ästhetische und leicht paranoide Fragen auf. Beobachtet da unten vielleicht ein perverser Spanner im Airbnb jede Bewegung? Oder spielt die Fantasie einfach verrückt, wenn Luxus auf schlechtes Gewissen trifft? Basti hingegen sitzt frierend in Deutschland und berichtet aus seiner neuen AI-Wohnung, in der Lichtschalter eher ein philosophisches Konzept als ein funktionales Element sind. Wer darf drücken, wer nicht: Und warum fühlt sich das Ganze an wie ein Escape Room für Gäste? Zwischen smartem Lichtmanagement und der Angst, aus Versehen das falsche Szenario zu aktivieren, wird klar: Auch Technik kann Persönlichkeit sein. Inspiriert durch die eigenwillige Einrichtung in anredos Elfenbein-Enklave driftet die Folge plötzlich in die Welt von Benjamin Blümchen ab. Was als harmlose Kindheitserinnerung beginnt, entwickelt sich schnell zu einer überraschend ernsten Debatte über Besitz, Verantwortung und die Frage, wem eigentlich was zusteht. Zwischen Nostalgie, Zoo-Logik und sehr erwachsenen Gedankenspielen wird Benjamin plötzlich mehr als nur eine Hörspielikone. Parallel klären die beiden einige wichtige Fragen: Welche Mappenfarbe hatte welches Schulfach? Was macht Basti im Café Extrablatt Mainz? Und wann wird anredo im „Salon Hair-einspaziert“ endlich seinen Fitzek-Gören-Zirkels ausrichten? Diese und alle anderen Episoden #rundfunk17 findet ihr unter anderem bei Apple Podcasts, Spotify, Deezer und als RSS-Feed.
Marta begleitet den Aal bei seinen Metamorphosen vom zarten Weidenblatt über den kletternden Fluss-Reisenden bis hin zum innerlich transformierten Ozeanrückkehrer. Kuba findet überall Piezokristalle, die mit ihren kleinen Bewegungen die halbe Technikwelt am Laufen halten. Und wir schauen Tom Walker dabei zu, wie man in einer virtuellen Welt voller 10.000 km/h schnellen Fahrzeuge überlebt. Kapitelchen & Tracklist 0:00:00 Evelyn Lark – The Noble Demon – Title Screen CC BY-NC-ND 0:01:04 BCBYNCSA Update 0:06:20 Pretty Bitter – Bodies Under The Rose Garden CC BY-NC 0:09:09 Muzyka Odnaleziona – Przyśpiewka „Dziad amerykański”. 1980 rok/Helena Szczur (ur. 1919) CC BY-NC-ND 0:09:50 M wie Metamorphose: Aale 1 0:29:55 WangleLine – Restful Day with the Rabbits CC BY 0:31:30 M wie Metamorphose: Aale 2 0:41:47 WORMSWORTH – Brainwaves CC BY-NC-SA 0:45:38 Un/mögliche Kristalle Teil 3.1 1:04:11 April Wilson – Single Digits CC BY-NC-SA 1:08:23 Un/mögliche Kristalle Teil 3.2 1:26:32 Jamie Paige – Space Center CC BY-NC-SA 1:28:17 Audiorätsel 1:41:16 Jane Garthson – You are a Horse CC BY-NC 1:45:04 Tom Walker, GTA Fast Cars 1:56:54 Ausschnitt, Can you fall in love when every car travels at 10,000 km⧸h？ von Tom Walker 1:58:17 Nochmal WangleLine Flyer 1 Animierter Bonus-Flyer! Shownotes Musik-Bot BCBYNCSA: Big list, 13622 Alben (Stand 1.11.2025) M wie Metamorphose Elizabeth Stanways Blog "Cosmic Stories", Folge "Sargassos of Space", Elizabeth Stanley auf Mastodon Die Sargassosee in der deutschen Wikipedia Die Geschichte der Aalforschung in der englischen Wikipedia Informationen von der Naturschutzorganisation BUND zur Gefährdung der Aale Kristalle: Piezoelektrizität bei Wikipedia Audiorätsel: Quelle und Hintergrundinformationen Tom Walker GTA4, Teil 3: Can you fall in love when every car travels at 10,000 km/h? Existenzielles ohne Autos: Ape Simulator Performance art: Eating An Entire Lemon Including The Rind, THE LEMON STREAM Australian Bake Off: Es gibt zur Zeit keinen offiziellen Trailer, nich tmal eine gut verlinkbare Seite, aber piratisch komplette Folgen auf YouTube lol Tom vs Vtubefilter Credits & Lizenz Animiertes GIF: Werkingsprincipe van een ‘bimorph piezo' motor. von LaurensvanLieshout via Wikimedia Commons. CC BY-SA 3.0 Flyer Strichzeichnungen: Wanderwellenmotor-Strichzeichung von Shinsei corporation via K. Spanner, Physik Instrumente, Weidenblattlarve PD via Wikimedia Commons, Truck GTA4 via Tom Walker Cover: basierend auf Robert Fludd Metaphysik und Natur- und Kunstgeschichte beider Welten, nämlich des Makro- und des Mikrokosmos, 1617; Public Domain via Wikimedia Commons Diese Folge erscheint unter CC BY-NC-SA 3.0, d.h. unsere Inhalte gerne teilen, remixen, aber uns bitte erwähnen und ja kein Geld verdienen! Musik siehe jeweilige Lizenzen.
Der hartnäckigste Podcast der Welt geht in die nächste Runde und es ist wieder so viel passiert...es liegt nicht nur ein absolut klebriges und emotionales Konzert hinter uns, sondern noch viel vor uns. Das lässt sich am besten bei einem leckeren McBernd und einer tschechischen Kaltschale besprechen. Bier gehört übrigens nicht in die Technik und Spanner nicht auf die Autobahn...da muss man mal ein Machtwort sprechen, so wie die Erwachsenen damals im Ferienlager...das waren noch Zeiten...während heute nur noch Schulden gesammelt werden verstauben bei uns waschechte und greifbare andere Dinge in den Regalen...einfach nur krank...aber noch lange keine Männergrippe...
Dans cet épisode, Arnaud et Guillaume discutent des dernières évolutions dans le monde de la programmation, notamment les nouveautés de Java 25, JUnit 6, et Jackson 3. Ils abordent également les récents développements en IA, les problèmes rencontrés dans le cloud, et l'état actuel de React et du web. Dans cette conversation, les intervenants abordent divers sujets liés à la technologie, notamment les spécifications de Wasteme, l'utilisation des UUID dans les bases de données, l'approche RAG en intelligence artificielle, les outils MCP, et la création d'images avec Nano Banana. Ils discutent également des complexités du format YAML, des récents dramas dans la communauté Ruby, de l'importance d'une bonne documentation, des politiques de retour au bureau, et des avancées de Cloud Code. Enfin, ils évoquent l'initiative de cafés IA pour démystifier l'intelligence artificielle. Enregistré le 24 octobre 2025 Téléchargement de l'épisode LesCastCodeurs-Episode-331.mp3 ou en vidéo sur YouTube. News Langages GraalVM se détache du release train de Java https://blogs.oracle.com/java/post/detaching-graalvm-from-the-java-ecosystem-train Un article de Loic Mathieu sur Java 25 et ses nouvelles fonctionalités https://www.loicmathieu.fr/wordpress/informatique/java-25-whats-new/ Sortie de Groovy 5.0 ! https://groovy-lang.org/releasenotes/groovy-5.0.html Groovy 5: Évolution des versions précédentes, nouvelles fonctionnalités et simplification du code. Compatibilité JDK étendue: Full support JDK 11-25, fonctionnalités JDK 17-25 disponibles sur les JDK plus anciens. Extension majeure des méthodes: Plus de 350 méthodes améliorées, opérations sur tableaux jusqu'à 10x plus rapides, itérateurs paresseux. Améliorations des transformations AST: Nouveau @OperatorRename, génération automatique de @NamedParam pour @MapConstructor et copyWith. REPL (groovysh) modernisé: Basé sur JLine 3, support multi-plateforme, coloration syntaxique, historique et complétion. Meilleure interopérabilité Java: Pattern Matching pour instanceof, support JEP-512 (fichiers source compacts et méthodes main d'instance). Standards web modernes: Support Jakarta EE (par défaut) et Javax EE (héritage) pour la création de contenu web. Vérification de type améliorée: Contrôle des chaînes de format plus robuste que Java. Additions au langage: Génération d'itérateurs infinis, variables d'index dans les boucles, opérateur d'implication logique ==>. Améliorations diverses: Import automatique de java.time.**, var avec multi-assignation, groupes de capture nommés pour regex (=~), méthodes utilitaires de graphiques à barres ASCII. Changements impactants: Plusieurs modifications peuvent nécessiter une adaptation du code existant (visibilité, gestion des imports, comportement de certaines méthodes). **Exigences JDK*: Construction avec JDK17+, exécution avec JDK11+. Librairies Intégration de LangChain4j dans ADK pour Java, permettant aux développeurs d'utiliser n'importe quel LLM avec leurs agents ADK https://developers.googleblog.com/en/adk-for-java-opening-up-to-third-party-language-models-via-langchain4j-integration/ ADK pour Java 0.2.0 : Nouvelle version du kit de développement d'agents de Google. Intégration LangChain4j : Ouvre ADK à des modèles de langage tiers. Plus de choix de LLM : En plus de Gemini et Claude, accès aux modèles d'OpenAI, Anthropic, Mistral, etc. Modèles locaux supportés : Utilisation possible de modèles via Ollama ou Docker Model Runner. Améliorations des outils : Création d'outils à partir d'instances d'objets, meilleur support asynchrone et contrôle des boucles d'exécution. Logique et mémoire avancées : Ajout de callbacks en chaîne et de nouvelles options pour la gestion de la mémoire et le RAG (Retrieval-Augmented Generation). Build simplifié : Introduction d'un POM parent et du Maven Wrapper pour un processus de construction cohérent. JUnit 6 est sorti https://docs.junit.org/6.0.0/release-notes/ :sparkles: Java 17 and Kotlin 2.2 baseline :sunrise_over_mountains: JSpecify nullability annotations :airplane_departure: Integrated JFR support :suspension_railway: Kotlin suspend function support :octagonal_sign: Support for cancelling test execution :broom: Removal of deprecated APIs JGraphlet, une librairie Java sans dépendances pour créer des graphes de tâches à exécuter https://shaaf.dev/post/2025-08-25-think-in-graphs-not-just-chains-jgraphlet-for-taskpipelines/ JGraphlet: Bibliothèque Java légère (zéro-dépendance) pour construire des pipelines de tâches. Principes clés: Simplicité, basée sur un modèle d'exécution de graphe. Tâches: Chaque tâche a une entrée/sortie, peut être asynchrone (Task) ou synchrone (SyncTask). Pipeline: Un TaskPipeline construit et exécute le graphe, gère les I/O. Modèle Graph-First: Le flux de travail est un Graphe Orienté Acyclique (DAG). Définition des tâches comme des nœuds, des connexions comme des arêtes. Support naturel des motifs fan-out et fan-in. API simple: addTask("id", task), connect("fromId", "toId"). Fan-in: Une tâche recevant plusieurs entrées reçoit une Map (clés = IDs des tâches parentes). Exécution: pipeline.run(input) retourne un CompletableFuture (peut être bloquant via .join() ou asynchrone). Cycle de vie: TaskPipeline est AutoCloseable, garantissant la libération des ressources (try-with-resources). Contexte: PipelineContext pour partager des données/métadonnées thread-safe entre les tâches au sein d'une exécution. Mise en cache: Option de mise en cache pour les tâches afin d'éviter les re-calculs. Au tour de Microsoft de lancer son (Microsoft) Agent Framework, qui semble être une fusion / réécriture de AutoGen et de Semnatic Kernel https://x.com/pyautogen/status/1974148055701028930 Plus de détails dans le blog post : https://devblogs.microsoft.com/foundry/introducing-microsoft-agent-framework-the-open-source-engine-for-agentic-ai-apps/ SDK & runtime open-source pour systèmes multi-agents sophistiqués. Unifie Semantic Kernel et AutoGen. Piliers : Standards ouverts (MCP, A2A, OpenAPI) et interopérabilité. Passerelle recherche-production (patterns AutoGen pour l'entreprise). Extensible, modulaire, open-source, connecteurs intégrés. Prêt pour la production (observabilité, sécurité, durabilité, "human in the loop"). Relation SK/AutoGen : S'appuie sur eux, ne les remplace pas, simplifie la migration. Intégrations futures : Alignement avec Microsoft 365 Agents SDK et Azure AI Foundry Agent Service. Sortie de Jackson 3.0 (bientôt les Jackson Five !!!) https://cowtowncoder.medium.com/jackson-3-0-0-ga-released-1f669cda529a Jackson 3.0.0 a été publié le 3 octobre 2025. Objectif : base propre pour le développement à long terme, suppression de la dette technique, architecture simplifiée, amélioration de l'ergonomie. Principaux changements : Baseline Java 17 requise (vs Java 8 pour 2.x). Group ID Maven et package Java renommés en tools.jackson pour la coexistence avec Jackson 2.x. (Exception: jackson-annotations ne change pas). Suppression de toutes les fonctionnalités @Deprecated de Jackson 2.x et renommage de plusieurs entités/méthodes clés. Modification des paramètres de configuration par défaut (ex: FAIL_ON_UNKNOWN_PROPERTIES désactivé). ObjectMapper et TokenStreamFactory sont désormais immutables, la configuration se fait via des builders. Passage à des exceptions de base non vérifiées (JacksonException) pour plus de commodité. Intégration des "modules Java 8" (pour les noms de paramètres, Optional, java.time) directement dans l'ObjectMapper par défaut. Amélioration du modèle d'arbre JsonNode (plus de configurabilité, meilleure gestion des erreurs). Testcontainers Java 2.0 est sorti https://github.com/testcontainers/testcontainers-java/releases/tag/2.0.0 Removed JUnit 4 support -> ups Grails 7.0 est sortie, avec son arrivée à la fondation Apache https://grails.apache.org/blog/2025-10-18-introducing-grails-7.html Sortie d'Apache Grails 7.0.0 annoncée le 18 octobre 2025. Grails est devenu un projet de premier niveau (TLP) de l'Apache Software Foundation (ASF), graduant d'incubation. Mise à jour des dépendances vers Groovy 4.0.28, Spring Boot 3.5.6, Jakarta EE. Tout pour bien démarrer et développer des agents IA avec ADK pour Java https://glaforge.dev/talks/2025/10/22/building-ai-agents-with-adk-for-java/ Guillaume a partagé plein de resources sur le développement d'agents IA avec ADK pour Java Un article avec tous les pointeurs Un slide deck et l'enregistrement vidéo de la présentation faite lors de Devoxx Belgique Un codelab avec des instructions pour démarrer et créer ses premiers agents Plein d'autres samples pour s'inspirer et voir les possibilités offertes par le framework Et aussi un template de projet sur GitHub, avec un build Maven et un premier agent d'exemple Cloud Internet cassé, du moins la partie hébergée par AWS #hugops https://www.theregister.com/2025/10/20/aws_outage_amazon_brain_drain_corey_quinn/ Panne majeure d'AWS (région US-EAST-1) : problème DNS affectant DynamoDB, service fondamental, causant des défaillances en cascade de nombreux services internet. Réponse lente : 75 minutes pour identifier la cause profonde; la page de statut affichait initialement "tout va bien". Cause sous-jacente principale : "fuite des cerveaux" (départ d'ingénieurs AWS seniors). Perte de connaissances institutionnelles : des décennies d'expertise critique sur les systèmes AWS et les modes de défaillance historiques parties avec ces départs. Prédictions confirmées : un ancien d'AWS avait anticipé une augmentation des pannes majeures en 2024. Preuves de la perte de talents : Plus de 27 000 licenciements chez Amazon (2022-2025). Taux élevé de "départs regrettés" (69-81%). Mécontentement lié à la politique de "Return to Office" et au manque de reconnaissance de l'expertise. Conséquences : les nouvelles équipes, plus réduites, manquent de l'expérience nécessaire pour prévenir les pannes ou réduire les temps de récupération. Perspective : Le marché pourrait pardonner cette fois, mais le problème persistera, rendant les futurs incidents plus probables. Web React a gagné "par défaut" https://www.lorenstew.art/blog/react-won-by-default/ React domine par défaut, non par mérite technique, étouffant ainsi l'innovation front-end. Choix par réflexe ("tout le monde connaît React"), freinant l'évaluation d'alternatives potentiellement supérieures. Fondations techniques de React (V-DOM, complexité des Hooks, Server Components) vues comme des contraintes actuelles. Des frameworks innovants (Svelte pour la compilation, Solid pour la réactivité fine, Qwik pour la "resumability") offrent des modèles plus performants mais sont sous-adoptés. La monoculture de React génère une dette technique (runtime, réconciliation) et centre les compétences sur le framework plutôt que sur les fondamentaux web. L'API React est complexe, augmentant la charge cognitive et les risques de bugs, contrairement aux alternatives plus simples. L'effet de réseau crée une "prison": offres d'emploi spécifiques, inertie institutionnelle, leaders choisissant l'option "sûre". Nécessité de choisir les frameworks selon les contraintes du projet et le mérite technique, non par inertie. Les arguments courants (maturité de l'écosystème, recrutement, bibliothèques, stabilité) sont remis en question; une dépendance excessive peut devenir un fardeau. La monoculture ralentit l'évolution du web et détourne les talents, nuisant à la diversité essentielle pour un écosystème sain et innovant. Promouvoir la diversité des frameworks pour un écosystème plus résilient et innovant. WebAssembly 3 est sortie https://webassembly.org/news/2025-09-17-wasm-3.0/ Data et Intelligence Artificielle UUIDv4 ou UUIDv7 pour vos clés primaires ? Ça dépend… surtout pour les bases de données super distribuées ! https://medium.com/google-cloud/understanding-uuidv7-and-its-impact-on-cloud-spanner-b8d1a776b9f7 UUIDv4 : identifiants entièrement aléatoires. Cause des problèmes de performance dans les bases de données relationnelles (ex: PostgreSQL, MySQL, SQL Server) utilisant des index B-Tree. Inserts aléatoires réduisent l'efficacité du cache, entraînent des divisions de pages et la fragmentation. UUIDv7 : nouveau standard conçu pour résoudre ces problèmes. Intègre un horodatage (48 bits) en préfixe de l'identifiant, le rendant ordonné temporellement et "k-sortable". Améliore la performance dans les bases B-Tree en favorisant les inserts séquentiels, la localité du cache et réduisant la fragmentation. Problème de UUIDv7 pour certaines bases de données distribuées et scalables horizontalement comme Spanner : La nature séquentielle d'UUIDv7 (via l'horodatage) crée des "hotspots d'écriture" (points chauds) dans Spanner. Spanner distribue les données en "splits" (partitions) basées sur les plages de clés. Les clés séquentielles concentrent les écritures sur un seul "split". Ceci empêche Spanner de distribuer la charge et de scaler les écritures, créant un goulot d'étranglement ("anti-pattern"). Quand ce n'est PAS un problème pour Spanner : Si le taux d'écriture total est inférieur à environ 3 500 écritures/seconde pour un seul "split". Le hotspot est "bénin" à cette échelle et n'entraîne pas de dégradation de performance. Solutions pour Spanner : Principe clé : S'assurer que la première partie de la clé primaire est NON séquentielle pour distribuer les écritures. UUIDv7 peut être utilisé, mais pas comme préfixe. Nouvelle conception ("greenfield") : ▪︎ Utiliser une clé primaire non-séquentielle (ex: UUIDv4 simple). Pour les requêtes basées sur le temps, créer un index secondaire sur la colonne d'horodatage, mais le SHARDER (ex: shardId) pour éviter les hotspots sur l'index lui-même. Migration (garder UUIDv7) : ▪︎ Ajouter un préfixe de sharding : Introduire une colonne `shard` calculée (ex: `MOD(ABS(FARM_FINGERPRINT(order_id_v7)), N)`) et l'utiliser comme PREMIER élément d'une clé primaire composite (`PRIMARY KEY (shard, order_id_v7)`). Réordonner les colonnes (si clé primaire composite existante) : Si la clé primaire est déjà composite (ex: (order_id_v7, tenant_id)), réordonner en (tenant_id, order_id_v7). Cela aide si tenant_id a une cardinalité élevée et distribue bien. (Un tenant_id très actif pourrait toujours nécessiter un préfixe de sharding supplémentaire). RAG en prod, comment améliorer la pertinence des résultats https://blog.abdellatif.io/production-rag-processing-5m-documents Démarrage rapide avec Langchain + Llamaindex: prototype fonctionnel, mais résultats de production jugés "subpar" par les utilisateurs. Ce qui a amélioré la performance (par ROI): Génération de requêtes: LLM crée des requêtes sémantiques et mots-clés multiples basées sur le fil de discussion pour une meilleure couverture. Reranking: La technique la plus efficace, modifie grandement le classement des fragments (chunks). Stratégie de découpage (Chunking): Nécessite beaucoup d'efforts, compréhension des données, création de fragments logiques sans coupures. Métadonnées à l'LLM: L'injection de métadonnées (titre, auteur) améliore le contexte et les réponses. Routage de requêtes: Détecte et traite les questions non-RAG (ex: résumer, qui a écrit) via API/LLM distinct. Outillage Créer un serveur MCP (mode HTTP Streamable) avec Micronaut et quelques éléments de comparaison avec Quarkus https://glaforge.dev/posts/2025/09/16/creating-a-streamable-http-mcp-server-with-micronaut/ Micronaut propose désormais un support officiel pour le protocole MCP. Exemple : un serveur MCP pour les phases lunaires (similaire à une version Quarkus pour la comparaison). Définition des outils MCP via les annotations @Tool et @ToolArg. Point fort : Micronaut gère automatiquement la validation des entrées (ex: @NotBlank, @Pattern), éliminant la gestion manuelle des erreurs. Génération automatique de schémas JSON détaillés pour les structures d'entrée/sortie grâce à @JsonSchema. Nécessite une configuration pour exposer les schémas JSON générés comme ressources statiques. Dépendances clés : micronaut-mcp-server-java-sdk et les modules json-schema. Testé avec l'inspecteur MCP et intégration avec l'outil Gemini CLI. Micronaut offre une gestion élégante des entrées/sorties structurées grâce à son support JSON Schema riche. Un agent IA créatif : comment utiliser le modèle Nano Banana pour générer et éditer des images (en Java, avec ADK) https://glaforge.dev/posts/2025/09/22/creative-ai-agents-with-adk-and-nano-banana/ Modèles de langage (LLM) deviennent multimodaux : traitent diverses entrées (texte, images, vidéo, audio). Nano Banana (gemini-2.5-flash-image-preview) : modèle Gemini, génère et édite des images, pas seulement du texte. ADK (Agent Development Kit pour Java) : pour configurer des agents IA créatifs utilisant ce type de modèle. Application : Base pour des workflows créatifs complexes (ex: agent de marketing, enchaînement d'agents pour génération d'assets). Un vieil article (6 mois) qui illustre les problèmes du format de fichier YAML https://ruudvanasseldonk.com/2023/01/11/the-yaml-document-from-hell YAML est extrêmement complexe malgré son objectif de convivialité humaine. Spécification volumineuse et versionnée (YAML 1.1, 1.2 diffèrent significativement). Comportements imprévisibles et "pièges" (footguns) courants : Nombres sexagésimaux (ex: 22:22 parsé comme 1342 en YAML 1.1). Tags (!.git) pouvant mener à des erreurs ou à l'exécution de code arbitraire. "Problème de la Norvège" : no interprété comme false en YAML 1.1. Clés non-chaînes de caractères (on peut devenir une clé booléenne True). Nombres accidentels si non-guillemets (ex: 10.23 comme flottant). La coloration syntaxique n'est pas fiable pour détecter ces subtilités. Le templating de documents YAML est une mauvaise idée, source d'erreurs et complexe à gérer. Alternatives suggérées : TOML : Similaire à YAML mais plus sûr (chaînes toujours entre guillemets), permet les commentaires. JSON avec commentaires (utilisé par VS Code), mais moins répandu. Utiliser un sous-ensemble simple de YAML (difficile à faire respecter). Générer du JSON à partir de langages de programmation plus puissants : ▪︎ Nix : Excellent pour l'abstraction et la réutilisation de configuration. Python : Facilite la création de JSON avec commentaires et logique. Gros binz dans la communauté Ruby, avec l'influence de grosses boîtes, et des pratiques un peu douteuses https://joel.drapper.me/p/rubygems-takeover/ Méthodologies Les qualités d'une bonne documentation https://leerob.com/docs Rapidité Chargement très rapide des pages (préférer statique). Optimisation des images, polices et scripts. Recherche ultra-rapide (chargement et affichage des résultats). Lisibilité Concise, éviter le jargon technique. Optimisée pour le survol (gras, italique, listes, titres, images). Expérience utilisateur simple au départ, complexité progressive. Multiples exemples de code (copier/coller). Utilité Documenter les solutions de contournement (workarounds). Faciliter le feedback des lecteurs. Vérification automatisée des liens morts. Matériel d'apprentissage avec un curriculum structuré. Guides de migration pour les changements majeurs. Compatible IA Trafic majoritairement via les crawlers IA. Préférer cURL aux "clics", les prompts aux tutoriels. Barre latérale "Demander à l'IA" référençant la documentation. Prêt pour les agents Faciliter le copier/coller de contenu en Markdown pour les chatbots. Possibilité de visualiser les pages en Markdown (ex: via l'URL). Fichier llms.txt comme répertoire de fichiers Markdown. Finition soignée Zones de clic généreuses (boutons, barres latérales). Barres latérales conservant leur position de défilement et état déplié. Bons états actifs/survol. Images OG dynamiques. Titres/sections lienables avec ancres stables. Références et liens croisés entre guides, API, exemples. Balises méta/canoniques pour un affichage propre dans les moteurs de recherche. Localisée Pas de /en par défaut dans l'URL. Routage côté serveur pour la langue. Localisation des chaînes statiques et du contenu. Responsive Excellents menus mobiles / support Safari iOS. Info-bulles sur desktop, popovers sur mobile. Accessible Lien "ignorer la navigation" vers le contenu principal. Toutes les images avec des balises alt. Respect des paramètres système de mouvement réduit. Universelle Livrer la documentation "en tant que code" (JSDoc, package). Livrer via des plateformes comme Context7, ou dans node_modules. Fichiers de règles (ex: AGENTS.md) avec le produit. Évaluations et modèles spécifiques recommandés pour le produit. Loi, société et organisation Microsoft va imposer une politique de Return To Office https://www.businessinsider.com/microsoft-execs-explain-rto-mandate-in-internal-meeting-2025-9 Microsoft impose 3 jours de présence au bureau par semaine à partir de février 2026, débutant par la région de Seattle Le CEO Satya Nadella explique que le télétravail a affaibli les liens sociaux nécessaires à l'innovation Les dirigeants citent des données internes montrant que les employés présents au bureau "prospèrent" davantage L'équipe IA de Microsoft doit être présente 4 jours par semaine, règles plus strictes pour cette division stratégique Les employés peuvent demander des exceptions jusqu'au 19 septembre 2025 pour trajets complexes ou absence d'équipe locale Amy Coleman (RH) affirme que la collaboration en personne améliore l'énergie et les résultats, surtout à l'ère de l'IA La politique s'appliquera progressivement aux 228 000 employés dans le monde après les États-Unis Les réactions sont mitigées, certains employés critiquent la perte d'autonomie et les bureaux inadéquats Microsoft rattrape ses concurrents tech qui ont déjà imposé des retours au bureau plus stricts Cette décision intervient après 15 000 licenciements en 2025, créant des tensions avec les employés Comment Claude Code est né ? (l'histoire de sa création) https://newsletter.pragmaticengineer.com/p/how-claude-code-is-built Claude Code : outil de développement "AI-first" créé par Boris Cherny, Sid Bidasaria et Cat Wu. Performance impressionnante : 500M$ de revenus annuels, utilisation multipliée par 10 en 3 mois. Adoption interne massive : Plus de 80% des ingénieurs d'Anthropic l'utilisent quotidiennement, y compris les data scientists. Augmentation de productivité : 67% d'augmentation des Pull Requests (PR) par ingénieur malgré le doublement de l'équipe. Origine : Commande CLI simple évoluant vers un outil accédant au système de fichiers, exploitant le "product overhang" du modèle Claude. Raison du lancement public : Apprendre sur la sécurité et les capacités des modèles d'IA. Pile technologique "on distribution" : TypeScript, React (avec Ink), Yoga, Bun. Choisie car le modèle Claude est déjà très performant avec ces technologies. "Claude Code écrit 90% de son propre code" : Le modèle prend en charge la majeure partie du développement. Architecture légère : Simple "shell" autour du modèle Claude, minimisant la logique métier et le code (suppression constante de code superflu). Exécution locale : Privilégiée pour sa simplicité, sans virtualisation. Sécurité : Système de permissions granulaire demandant confirmation avant chaque action potentiellement dangereuse (ex: suppression de fichiers). Développement rapide : Jusqu'à 100 releases internes/jour, 1 release externe/jour. 5 Pull Requests/ingénieur/jour. Prototypage ultra-rapide (ex: 20+ prototypes d'une fonctionnalité en quelques heures) grâce aux agents IA. Innovation UI/UX : Redéfinit l'expérience du terminal grâce à l'interaction LLM, avec des fonctionnalités comme les sous-agents, les styles de sortie configurables, et un mode "Learning". Le 1er Café IA publique a Paris https://www.linkedin.com/pulse/my-first-caf%25C3%25A9-ia-paris-room-full-curiosity-an[…]o-goncalves-r9ble/?trackingId=%2FPHKdAimR4ah6Ep0Qbg94w%3D%3D Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 30-31 octobre 2025 : Agile Tour Bordeaux 2025 - Bordeaux (France) 30-31 octobre 2025 : Agile Tour Nantais 2025 - Nantes (France) 30 octobre 2025-2 novembre 2025 : PyConFR 2025 - Lyon (France) 4-7 novembre 2025 : NewCrafts 2025 - Paris (France) 5-6 novembre 2025 : Tech Show Paris - Paris (France) 5-6 novembre 2025 : Red Hat Summit: Connect Paris 2025 - Paris (France) 6 novembre 2025 : dotAI 2025 - Paris (France) 6 novembre 2025 : Agile Tour Aix-Marseille 2025 - Gardanne (France) 7 novembre 2025 : BDX I/O - Bordeaux (France) 12-14 novembre 2025 : Devoxx Morocco - Marrakech (Morocco) 13 novembre 2025 : DevFest Toulouse - Toulouse (France) 15-16 novembre 2025 : Capitole du Libre - Toulouse (France) 19 novembre 2025 : SREday Paris 2025 Q4 - Paris (France) 19-21 novembre 2025 : Agile Grenoble - Grenoble (France) 20 novembre 2025 : OVHcloud Summit - Paris (France) 21 novembre 2025 : DevFest Paris 2025 - Paris (France) 24 novembre 2025 : Forward Data & AI Conference - Paris (France) 27 novembre 2025 : DevFest Strasbourg 2025 - Strasbourg (France) 28 novembre 2025 : DevFest Lyon - Lyon (France) 1-2 décembre 2025 : Tech Rocks Summit 2025 - Paris (France) 4-5 décembre 2025 : Agile Tour Rennes - Rennes (France) 5 décembre 2025 : DevFest Dijon 2025 - Dijon (France) 9-11 décembre 2025 : APIdays Paris - Paris (France) 9-11 décembre 2025 : Green IO Paris - Paris (France) 10-11 décembre 2025 : Devops REX - Paris (France) 10-11 décembre 2025 : Open Source Experience - Paris (France) 11 décembre 2025 : Normandie.ai 2025 - Rouen (France) 14-17 janvier 2026 : SnowCamp 2026 - Grenoble (France) 29-31 janvier 2026 : Epitech Summit 2026 - Paris - Paris (France) 2-5 février 2026 : Epitech Summit 2026 - Moulins - Moulins (France) 2-6 février 2026 : Web Days Convention - Aix-en-Provence (France) 3 février 2026 : Cloud Native Days France 2026 - Paris (France) 3-4 février 2026 : Epitech Summit 2026 - Lille - Lille (France) 3-4 février 2026 : Epitech Summit 2026 - Mulhouse - Mulhouse (France) 3-4 février 2026 : Epitech Summit 2026 - Nancy - Nancy (France) 3-4 février 2026 : Epitech Summit 2026 - Nantes - Nantes (France) 3-4 février 2026 : Epitech Summit 2026 - Marseille - Marseille (France) 3-4 février 2026 : Epitech Summit 2026 - Rennes - Rennes (France) 3-4 février 2026 : Epitech Summit 2026 - Montpellier - Montpellier (France) 3-4 février 2026 : Epitech Summit 2026 - Strasbourg - Strasbourg (France) 3-4 février 2026 : Epitech Summit 2026 - Toulouse - Toulouse (France) 4-5 février 2026 : Epitech Summit 2026 - Bordeaux - Bordeaux (France) 4-5 février 2026 : Epitech Summit 2026 - Lyon - Lyon (France) 4-6 février 2026 : Epitech Summit 2026 - Nice - Nice (France) 12-13 février 2026 : Touraine Tech #26 - Tours (France) 26-27 mars 2026 : SymfonyLive Paris 2026 - Paris (France) 31 mars 2026 : ParisTestConf - Paris (France) 16-17 avril 2026 : MiXiT 2026 - Lyon (France) 22-24 avril 2026 : Devoxx France 2026 - Paris (France) 23-25 avril 2026 : Devoxx Greece - Athens (Greece) 6-7 mai 2026 : Devoxx UK 2026 - London (UK) 22 mai 2026 : AFUP Day 2026 Lille - Lille (France) 22 mai 2026 : AFUP Day 2026 Paris - Paris (France) 22 mai 2026 : AFUP Day 2026 Bordeaux - Bordeaux (France) 22 mai 2026 : AFUP Day 2026 Lyon - Lyon (France) 17 juin 2026 : Devoxx Poland - Krakow (Poland) 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/
After a positive weekend for both Arsenal and Manchester United, Ste Howson and Joel Beya are with Rio to analyse the performances and focus on why Manchester United's attitude was different when competing against Sunderland.Mason Mount spoke about how they focused on bringing energy so Ste dissects how professional footballers approach games to ensure their mentality and application is sufficient in order to perform.After receiving praise for his recent performances, a listener gets in touch to ask who is in better form at the moment, Declan Rice or Moises Caicedo?Rio also analyses how Brentford's central defenders lost their battle with Erling Haaland who Rio says can be viewed as the Premier League's greatest ever goalscorer.Rio Presents Brought To You By CRAFTD Hosted on Acast. See acast.com/privacy for more information.
Voyeur-Aufnahmen im Park, Videos aus Kaufhaus-Umkleiden oder vom Ex-Freund hochgeladene Privatbilder im Internet – viele Frauen erleben diese Formen sexualisierter Gewalt alltäglich. Als Opfer fühlen sich die meisten allein gelassen; die Täter kommen sehr oft davon oder machen sich nicht strafbar. Helfen neue Gesetze gegen Spanner, Online-Bloßstellung und verbale Belästigung wie das sogenannte Cat-Calling? Wie sicher ist der öffentliche Raum für Frauen? Was treibt die Männer zu ihren Taten? Lukas Meyer-Blankenburg diskutiert mit Anna-Lena von Hodenberg – Journalistin, Gründerin von "Hate Aid"; Jonas Kneer – Psychologe bei „I can change“, Hannover; Jacqueline Sittig – Juristin, Uni Würzburg
Die Themen von Flo und Matthis am 25.08.2025: (00:00:00) Von Parfum bis Fußball-Trikots: Was Flo und Matthis schon so alles gesammelt haben. (00:02:07) Spanner-Videos: Wie die Kölnerin Yanni Gentsch mit einer Online-Petition das heimliche Filmen von Frauen strafbar machen will. (00:06:00) Kidnapping im Krieg: Warum Russland Kinder aus der Ukraine entführt und was mit ihnen passiert. (00:12:14) Ausbildungsreport: Was Azubis Sorgen macht und in welchen Berufen es die meisten Probleme gibt. (00:17:54) Lebensretter: Wie Hans Joachim Reinicke zwei Frauen vor dem Ertrinken gerettet hat. Wie sind eure Erfahrungen als Azubi? Seid ihr zufrieden in eurem Betrieb? Schickt uns gerne eine Sprachnachricht an 0151 15071635 oder ne Mail an 0630@wdr.de. Kennt ihr schon unseren WhatsApp Channel? Den findet ihr hier: https://1.ard.de/0630-Whatsapp-Kanal Oder einfach diesen QR-Code abscannen: https://1.ard.de/0630-bei-Whatsapp Von 0630.
Jim, Jack and Joe discuss the ongoing Europa League delay, the arrival of Walter Benitez, the impending departures of Marc Guehi and Ebere Eze and test their knowledge with another quiz. Get our FA Cup Winners t-shirts and mugs here: https://fypfanzine.myshopify.com/collections/fa-cup-winners twitter: @fypfanzinefacebook: FYPFanzineinstagram: @fypfanzinecontact@fypfanzine.uk Learn more about your ad choices. Visit podcastchoices.com/adchoices
Still giving away weird old comics to friends who support the show. Christopher picked a copy of Spanner's Galaxy from the DC six-issue series. See the details and what others picked out lately.Drink of the Week (2:02)Fritz Godard arrives in Starkville; we get gills-deep in some White Rascal (yes), Leinenkugel Berry Weiss (no) and Michter's US★1 Kentucky Straight Rye (yes). Chickens are smoked. Final Girls are cooked.Game of the Week (6:30)Got on Board Game Arena for the first time ever, playing Faraway with Dave from Dude! Take Your Turn and Race for the Galaxy with Noisy Andrew of partymeeple.Track of the Week (15:17)Up out of your chairs for the hip-hop energy that informs Blapps Posse's “Beat Dat's Hype.”
We all talk about #AI, but what good is it if your models are powered by stale, outdated data?In Episode 99 of Great Things with Great Tech, Deepti Srivastava, founder and CEO of Snow Leopard, and former founding PM of Google Spanner, calls out the broken state of enterprise AI. With decades of experience in distributed systems and data infrastructure, Deepti unveils how Snow Leopard is redefining how AI applications are built, by tapping into live, real-time data from SQL and APIs without the need for ETL or pipelines.Instead of relying on static snapshots or disconnected data lakes, Snow Leopard's #agentic platform queries native sources like PostgreSQL, Snowflake, and Salesforce on-demand, empowering AI to live directly in the critical decision path.In This Episode, We Cover:Deepti's journey from building Spanner at Google to founding Snow Leopard AI.Why most enterprise AI fails due to reliance on stale data and outdated pipelines. How Snow Leopard federates live data across SQL and APIs with zero ETL.The limitations of vector databases in structured, real-time business use cases.Why putting AI in the critical path of business decisions unlocks real value.Snow Leopard is a U.S.-based technology company founded in 2023 by and is Headquartered in San Francisco, CaliforniaSnow Leopard specializes in building a platform that enables the development of production-ready AI applications by leveraging live business data. The company's approach focuses on real-time data retrieval directly from sources like SQL databases and APIs, eliminating the need for traditional ETL processes and data pipelines. This innovation allows for more accurate and timely AI-driven business decision.PODCAST LINKSGreat Things with Great Tech Podcast: https://gtwgt.comGTwGT Playlist on YouTube: https://www.youtube.com/@GTwGTPodcastListen on Spotify: https://open.spotify.com/show/5Y1Fgl4DgGpFd5Z4dHulVXListen on Apple Podcasts: https://podcasts.apple.com/us/podcast/great-things-with-great-tech-podcast/id1519439787EPISODE LINKSSnow Leopard Web: https://www.snowleopard.ai/Deepti Srivastava on LinkedIn:https://www.linkedin.com/in/thedeepti/Snow Leopard on LinkedIn: https://www.linkedin.com/company/snow-leopard-ai/GTwGT LINKSSupport the Channel: https://ko-fi.com/gtwgtBe on #GTwGT: Contact via Twitter/X @GTwGTPodcast or visit https://www.gtwgt.comSubscribe to YouTube: https://www.youtube.com/@GTwGTPodcast?sub_confirmation=1Great Things with Great Tech Podcast Website: https://gtwgt.comSOCIAL LINKSFollow GTwGT on Social Media:Twitter/X: https://twitter.com/GTwGTPodcastInstagram: https://www.instagram.com/GTwGTPodcastTikTok: https://www.tiktok.com/@GTwGTPodcast
Military sabotage is a deliberate action aimed at weakening a government effort, or organization through subversion, obstruction, demoralization, destabilization, division, disruption, or destruction. It can take place left of bang or during war with the object the weakening of the military effort by an adversary. Military sabotage has been taking place since the early military adventures of men. The apocryphal story of the Trojan Horse is an ancient example and variations on the theme echo through historical warfare. References: Ian Jones Booby Traps!: The History of Deadly Devices, from World War I to Vietnam Gordon L. Rottman World War II Axis Booby Traps and Sabotage Tactics Gordon L. Rottman World War II Allied Sabotage Devices and Booby Traps Lester Grau and Michael Gress The Red Army's Do-it-Yourself, Nazi-Bashing Guerrilla Warfare Manual: The Partizan's Handbook, Updated and Revised Edition, 1942 Roman Mars The 99% Invisible City: A Field Guide to the Hidden World of Everyday Design Access All Areas: A User's Guide to the Art of Urban Exploration OSS Simple Sabotage Field Manual FM 5-31 Boobytraps TM 31-201-1 Unconventional Warfare Devices and Techniques: Incendiaries Eric Frank Russell The Wasp Michael Z. Williamson The Weapon (and the entire Freehold series) Robert Heinlein The Moon is a Harsh Mistress Robert Asprey War In The Shadows: The Guerrilla In History My Substack Email at cgpodcast@pm.me
Nein, das Sperma-Rennen in Los Angeles haben die Samstags-Crasher nicht gewonnen, dafür KOMMEN aber völlig versaute Witze aus ihnen heraus. Dann schalten wir nach Görlitz in Sachsen: Nachdem die AFD jetzt vom Bundesverfassungsschutz als gesichert rechtsextremistisch eingestuft wurde, hören wir uns um, wie das dort bei den Menschen ankommt...Obendrauf hat Stefan Kreutzer noch ein paar schlechte Witze geladen. Ob die Sebastian Schaffstein bei der Gag-Challenge zum Stöhnen, Weinen oder Lachen bringen?
Send us a textIn this episode of What's New in Cloud Phenops, Stephen Old and Frank discuss the latest updates in cloud computing, focusing on Azure, Google Cloud, and AWS. They cover the retirement of certain Azure virtual machines, the introduction of serverless GPUs, and the benefits of Amazon Bedrock for cost transparency. The conversation also touches on new features for Azure databases, insights from a Forrester study on Spanner, and the importance of calculating AI costs. Additionally, they discuss licensing changes for Amazon FSX, tiered storage for Spanner, and the deprecation of the AWS connector to Azure. The episode concludes with a look at sustainability efforts and upcoming events in the cloud computing space.takeawaysServerless GPUs enable on-demand AI workloads with automatic scaling.Amazon Bedrock introduces real-time cost transparency for custom models.Physical snapshots for Azure databases enhance backup flexibility.Forrester study shows significant ROI with Spanner.Understanding AI costs on Google Cloud is crucial for budgeting.Amazon FSX for NetApp removes SnapLock licensing fees.Tiered storage for Spanner optimizes cost and performance.AWS connector to Azure is deprecated, focusing on native solutions.Azure OpenAI service offers discounts for provisioned reservations.
We've got horrible people doing horrible things. We've got good people just trying to live. It's hard right now. Host Terri Doty attempts to keep things light in our April episode. The media rundown includes some amazing (or... Read More
In today's episode, the crew continue to search for the Kobold Spanner. Connect: Sounds Like Adventure on Twitch Sounds Like Adventure on YouTube Sounds Like Adventure on Instagram Sounds Like Adventure on Threads See omnystudio.com/listener for privacy information.
With no proper show this week I thought it unfair to leave you without anything - so as a throwback for the longer listeners and an introduction for the newer listeners, here's the best of our first year proper of doing Off track Podcast with Coopes, Hicky and Josh, Foggy, Leon Haslam, Niall Mackenzie, Tommy Bridewell, Tac Mackenzie, Shakery, Whit and Spanner!Enjoy!Send us a text Support the showWould you like early access to shows and the chance to ask questions of the guests? Well, you can, right here... https://www.patreon.com/join/9993138Off Track Merchandise: https://www.hmycustoms.co.uk/off-track-podcastRidinGraphics: https://www.instagram.com/ridingraphics/?hl=enhttps://www.facebook.com/demographics/?locale=en_GBFacebook: Off Track Podcast https://www.facebook.com/OffTrackTheMotorcycleRacingPodcast/ Instagram: @offtrackpodcastukhttps://www.instagram.com/offtrackpodcastuk/Twitter: @offtrack_https://twitter.com/OffTrack_ IG: @thedaveneal | Twitter: @daveneal | Facebook: Dave Neal
Er steht am Fenster und beobachtet die Nachbarin mit dem Fernglas. Ist das erlaubt?Lars Paulsen und Andreas Lingsch diskutieren. Außerdem: Eine Hörerin glaubt an dieheftigsten Verschwörungstheorien, Gaylor Swift und die Liebe von Lars und Florentin. Hosted on Acast. See acast.com/privacy for more information.
Send us a textWhat happens when you get Eyvonne, William, and our special guest Nick Eberts in the same conversation? You get a GKE party! In this episode, we dive deep into the world of multi-cluster Kubernetes management with Nick Eberts, Product Manager for GKE Fleets & Teams at Google. Nick shares his expertise on platform engineering, the evolution from traditional infrastructure to cloud-native platforms, and the challenges of managing multiple Kubernetes clusters at scale. We explore the parallels between enterprise architecture and modern platform teams, discuss the future of multi-cluster orchestration, and unpack Google's innovative work with Spanner database integration for GKE. Nick also shares his passion for contributing to open source through SIG Multi-Cluster and provides valuable guidance for those interested in getting involved with the Kubernetes community.Where to Find Nick EbertsLinkedIn: https://www.linkedin.com/in/nicholasebertsTwitter: https://twitter.com/nicholasebertsBluesky: @nickeberts.devShow LinksSIG Multi-Cluster: https://github.com/kubernetes/community/tree/master/sig-multiclusterGoogle Kubernetes Engine (GKE): https://cloud.google.com/kubernetes-engineSpanner Database: https://cloud.google.com/spannerKubernetes: https://kubernetes.io/KubeCon: https://events.linuxfoundation.org/kubecon-cloudnativecon-north-america/Argo CD: https://argoproj.github.io/cdFlux: https://fluxcd.io/CNCF: https://www.cncf.io/Follow, Like, and Subscribe!Podcast: https://www.thecloudgambit.com/YouTube: https://www.youtube.com/@TheCloudGambitLinkedIn: https://www.linkedin.com/company/thecloudgambitTwitter: https://twitter.com/TheCloudGambitTikTok: https://www.tiktok.com/@thecloudgambit
Have you ever felt like you weren't talented enough to serve God?
http://archive.org/download/jah-works-radio-11-17-2024-final/Jah%20Works%20Radio%2011-17-2024%20Final.mp3 Kingdom rise and kingdom fall, family… This week we come into the Ioneyez Studio for an uplifting broadcast of word, power, sound – and unity – bringing babywrong to its knees. Fire tunes coming in this week from artists like The Wailing Souls, Barrington Levy, Black Uhuru, Luciano and Louie Culture, Luciano and Spanner […]
Nach einer ausgedehnten Shoppingtour machen Samira und ihre Freundinnen noch einen Abstecher ins Fast-Food-Restaurant. Als Samira die Toilette nicht auf Anhieb findet, ist sofort ein engagierter Mitarbeiter zur Stelle. Doch der zeigt ihr nicht nur den Weg. Er begleitet die junge Frau noch weiter, als ihr lieb ist. ***Contentwarnung: Wir möchten darauf aufmerksam machen, dass dieser Podcast wahre Verbrechen und Kriminalfälle thematisiert und Schilderungen von Gewalt und Sex enthält. Das kann für einige von euch belastend sein. Dieser Podcast ist auf keinen Fall für Kinderohren geeignet. *****Podcast-Tipp in dieser Folge: "Killing Jack - Warum der Ripper-Mythos uns nicht loslässt”.https://www.ardaudiothek.de/sendung/killing-jack-warum-der-ripper-mythos-uns-nicht-loslaesst-wdr/13742551/
In der 86. Episode sprechen wir über überfüllte Gefriertruhen und wie gut Elina ihre Truhe einsortieren kann. Außerdem erzählt sie von der gruseligen Story mit dem Spanner aus ihrem Garten!
Officer Chloe Vincent returns! That's actor and podcaster Natalie Roles, remembering her time on Space Precinct and the years she spent on ITV's The Bill - she literally transferred to another precinct!Meanwhile, there's more Stingray news to celebrate the show's 60th year and the Randomiser continues where it left off last week - with part two of a Dick Spanner adventure. Another dumb move!00:28 Welcome to the Gerry Anderson Podcast! 03:40 The Gerry Anderson News!08:30 Natalie Roles - Part 230:13 The Voice Of The Podsterons36:27 The Randomiser!01:00:41 Wrapping things up! Links MentionedGuest LinksNews LinksNever Miss An EpisodeJoin the Podsterons Facebook groupSubscribe wherever you get your podcastsThe Randomiser with Chris DaleHelp The ShowLeave us a review on Apple PodcastsTweet about it! Use the hashtag #GerryAndersonPodcast@ImJamieAnderson / @RichardNJames / @ChrisDalekJoin the Anderson Insiders for Extra ContentStay In TouchEmail Podcast AT GerryAnderson.comJoin the Email Newsletter
It's not often Richard gets to interview one of his victims! This week, he relives more Space Precinct memories with Natalie Roles, Chloe Vincent in the classic episode Predator and Prey.In the news, the winners of the recent Space Precinct competition are revealed - and there's an update on the Bluray release, too. Apparently, it's to include exclusive footage from series two!Finally, after all that excitement, the team make another dum move in the Randomiser...00:25 Welcome to the Gerry Anderson Podcast! 01:10 The Gerry Anderson News! See links below08:37 Natalie Roles - Part 147:48 The Randomiser01:13:58 Wrapping things up! Links MentionedGuest LinksNews LinksNever Miss An EpisodeJoin the Podsterons Facebook groupSubscribe wherever you get your podcastsThe Randomiser with Chris DaleHelp The ShowLeave us a review on Apple PodcastsTweet about it! Use the hashtag #GerryAndersonPodcast@ImJamieAnderson / @RichardNJames / @ChrisDalekJoin the Anderson Insiders for Extra ContentStay In TouchEmail Podcast AT GerryAnderson.comJoin the Email Newsletter
The One where Dave and Rich are confused! Please support Signal of Doom & Legion Outpost on Patreon! Every single dollar helps the show! https://www.patreon.com/SignalofDoom Follow us on Instagram! Please like the Facebook Page! Follow us on X: @signalofdoom Dredd or Dead: @OrDredd Legion Outpost: @legionoutpost
Kamen Rider Gavv is getting closer and closer, and we get right into the cast reveal from this week's preview! They're a really fascinating group of characters, especially those villains… It's also our penultimate Gotchard podcast, and we'll be sad to see it go. There's chaos in the streets in episodes 46 & 47, but Spanner thinks he's got a solution. Hold onto your Chemy plushies! 0:54 - Intro 6:35 - Gavv Cast 50:33 - Gotchard 46 1:17:46 - Gotchard 47 Website: www.RiderLovePodcast.com Twitter: www.twitter.com/RiderLOVEcast Email: RiderLovePodcast@gmail.com
Stars of Back To Black are here to talk about the great Amy Winehouse and hear some of your Unpopular Opinions. Also, show legend Spanner is on for the quiz and Danni Diston gets a special surprise (sort of) from Greg.
Trier im Jahr 1988. Die 31-jährige Beatrix Hemmerle lebt mit ihrem elfjährigen Sohn in einer Zweizimmerwohnung im Erdgeschoss einer Hochhaussiedlung. Damals treibt sich ein Spanner in der Gegend herum, der es offensichtlich auf die junge Mutter abgesehen hat. Mehrmals steigt er auf ihren Balkon, einmal soll er sogar in ihre Wohnung eingedrungen sein. Ein Geschehen, dass Beatrix Hemmerle selbst jedoch nicht sonderlich beunruhigt. Am 10. August 1989 kommt spätabends Beatrix‘ Verlobter vorbei. Er bleibt bis tief in die Nacht. Um 03:00 Uhr - Beatrix ist seit rund einer Stunde wieder allein - steigt erneut ein Unbekannter über den Balkon in die Wohnung ein. Mit einem Messer geht er auf die schlafende Frau los, sticht mehrfach auf sie ein. Dann ergreift er die Flucht. Beatrix‘ Sohn ist von dem Kampfgeschehen im Nachbarzimmer aufgewacht und entdeckt seine schwerverletzte Mutter. Er holt Hilfe bei einem Nachbarn, dieser verständigt sofort den Notarzt und die Polizei. Doch für die erst 31-Jährige kommt jede Hilfe zu spät. Im Studio mit Rudi Cerne und Conny Neumeyer: Oberstaatsanwalt Dr. Eric Samel von der Staatsanwaltschaft in Trier. Er schildert, wie auf einem Parkplatz in der Nähe des Tatorts blutverschmierte Kleidung gefunden wurde: Ein T-Shirt, das der Täter aus der Wohnung des Opfers mitgenommen hatte, und eine Lederjacke. An der schwarzen Herrenjacke können später das Blut des Opfers und Hautschuppen einer unbekannten männlichen Person festgestellt werden. Doch auch eine DNA-Reihenuntersuchung mit mehreren hundert Teilnehmern liefert keine weiteren Erkenntnisse. Trotzdem ist die Jacke bis heute der wichtigste Anhaltspunkt in diesem Fall. Hoffnung setzt Dr. Eric Samel auch in die Suche nach einem Tellerwäscher. Er soll sich seinerzeit einem Koch anvertraut und von einer alleinerziehenden Mutter geschwärmt haben. Die Polizei vermutet, dass es sich bei der Frau um Beatrix Hemmerle gehandelt haben muss. Hat er etwas mit der Tat zu tun? Oder ist er möglicherweise ein wichtiger Zeuge? Im Interview: Rike Hemmerle. Sie hatte ein enges Verhältnis zu ihrer Schwester Beatrix. Bis heute hat sie die Hoffnung nicht aufgegeben, dass der Mörder früher oder später gefunden wird. *** Wenn ihr Kritik oder Anregungen zu Fällen habt, schreibt uns gerne eine E-Mail an xy@zdf.de. Die aktuelle Sendung und mehr findet ihr in der ZDFmediathek: aktenzeichenxy.zdf.de. *** Moderation: Rudi Cerne, Conny Neumeyer Gäste & Experten: OStA Dr. Eric Samel, Staatsanwaltschaft Trier, Rike Hemmerle Autor dieser Folge: Andy Klein Audioproduktion: Felix Wittmann Technik: Anja Rieß Produktionsleitung Securitel: Marion Biefeld Produktionsleitung Bumm Film: Melanie Graf, Nina Kuhn Produktionsmanagement ZDF: Carolin Klapproth, Julian Best Leitung Digitale Redaktion Securitel: Nicola Haenisch-Korus Redaktion Securitel: Corinna Prinz, Erich Grünbacher Produzent Securitel: René Carl Produzent Bumm Film: Nico Krappweis Redaktion ZDF: Sonja Roy, Kirsten Schönig Regie Bumm Film: Alexa Waschkau
Leeds United made a child swear and we're here for it. The latest clips from the football fan channels.
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