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Topics covered in this episode: Raw+DC: The ORM pattern of 2026? pytest-check releases Dataclass Wizard SQLiteo - “native macOS SQLite browser built for normal people” Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 11am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Raw+DC: The ORM pattern of 2026? ORMs/ODMs provide great support and abstractions for developers They are not the native language of agentic AI Raw queries are trained 100x+ more than standard ORMs Using raw queries at the data access optimizes for AI coding Returning some sort of object mapped to the data optimizes for type safety and devs Brian #2: pytest-check releases 3 merged pull requests 8 closed issues at one point got to 0 PR's and 1 enhancement request Now back to 2 issues and 1 PR, but activity means it's still alive and being used. so cool Check out changelog for all mods A lot of changes around supporting mypy I've decided to NOT have the examples be fully --strict as I find it reduces readability See tox.ini for explanation But src is --strict clean now, so user tests can be --strict clean. Michael #3: Dataclass Wizard Simple, elegant wizarding tools for Python's dataclasses. Features
In this episode, Ray tackles Anthropic’s standoff with the U.S. Department of War after CEO Daria Amodei refused to grant unrestricted model access, citing concerns over mass surveillance and autonomous weapons. The government responded by banning Anthropic models through administrative orders. Also covered: the top 20 websites of 2026, China’s $173,000 warm-blooded companion robot, Fukushima’s rapidly evolving radioactive hybrid boars, a Chinese spacecraft emergency involving viewport cracks from space debris, Japan’s wooden satellite built with traditional joinery, and human brain cells on a chip that learned to play Doom in just one week. – 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 Anthropic’s confrontation with the U.S. Department of War. CEO Daria Amodei released a public statement refusing unrestricted government access to Anthropic’s AI models. Two red lines stood firm: mass domestic surveillance and fully autonomous weapons. Ray explains that these models are predictive by nature, raising serious misidentification risks. However, the government hit back hard. Administrative orders now ban Anthropic models from government use. Despite the backlash, Cochrane expresses support for the company’s stance. He points listeners to a CBS interview with the CEO posted roughly nine hours before recording. Additionally, Anthropic released new models including Opus 4.5 and Sonnet 4.6. The company climbed to the number two spot on the App Store, trailing only ChatGPT and surpassing Google Gemini. Personal Updates Ray shares that February has been a demanding month. He’s juggling a capstone project, two jobs, and finishing his degree. Meanwhile, he continues working on developments at Blubrry hosting. He apologizes for inconsistent episode production and thanks listeners for their patience. Top 20 Websites of 2026 A Visual Capitalist chart ranks the most visited websites of 2026. Google holds the top spot, followed by YouTube. Facebook, Instagram, ChatGPT, Reddit, Wikipedia, X, and WhatsApp round out the upper rankings. Notably, DuckDuckGo appears at rank seventeen as a privacy-focused search alternative. 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. Anthropic Retires Claude Opus 3 Cochrane discusses Anthropic’s decision to retire Claude Opus 3. In a unique move, the company gave the model a Substack-style blog to reflect on its own existence. Reactions online were mixed, with both supporters and critics engaging in the conversation. China’s $173,000 Warm-Blooded Companion Robot From ZME Science, Ray covers China’s new humanoid robot designed as a warm-blooded companion. Priced at $173,000, it features conventional robotics hardware, sensors, cameras, and autonomous navigation. A built-in heating element maintains body warmth. Cochrane comments humorously on the growing market for companion robots. Windows XP Green Hill Found and Photographed From Tom’s Hardware, someone tracked down and photographed the actual location of the iconic Windows XP “Green Hill” wallpaper. The Reddit post sparked a wave of nostalgia in the community. Fukushima’s Radioactive Hybrid Boars From AZ Animals, domestic pigs that escaped after the Fukushima disaster hybridized with wild boars. Their DNA reveals rapid evolutionary changes driven by the altered radioactive landscape. These aggressive hybrids now complicate wildlife management and rewilding efforts in the region. Shenzhou 20 Spacecraft Emergency Chinese astronauts aboard Shenzhou 20 discovered cracks in their spacecraft’s viewport during what became the nation’s first spaceflight emergency. Space debris likely caused the damage. The crew switched to an alternative return capsule. Multiple protective layers kept the situation manageable. Japan’s Wooden Satellite Japanese teams plan to launch the first wooden satellite. Built with magnolia wood panels assembled using traditional Japanese joinery methods, the biodegradable design aims to reduce aluminum particle pollution from satellites burning up during atmospheric reentry. Human Brain Cells Play Doom Building on previous work where living neurons played Pong, an independent developer used Python to train human brain cell clusters on microelectrode arrays to play Doom. The cells learned in roughly one week. Cochrane highlights how open knowledge sharing accelerated the project dramatically. He also raises ethical questions about training sentient brain cells, connecting the topic to evolving views on sentience in crustaceans and other organisms. The post Anthropic Stands Their Ground, Ethics over Money #1859 appeared first on Geek News Central.
Talk Python To Me - Python conversations for passionate developers
Digital humanities sounds niche, until you realize it can mean a searchable archive of U.S. amendment proposals, Irish folklore, or pigment science in ancient art. Today I'm talking with David Flood from Harvard's DARTH team about an unglamorous problem: What happens when the grant ends but the website can't. His answer, static sites, client-side search, and sneaky Python. Let's dive in. Episode sponsors Sentry Error Monitoring, Code talkpython26 Command Book Talk Python Courses Links from the show Guest David Flood: davidaflood.com DARTH: digitalhumanities.fas.harvard.edu Amendments Project: digitalhumanities.fas.harvard.edu Fionn Folklore Database: fionnfolklore.org Mapping Color in History: iiif.harvard.edu Apatosaurus: apatosaurus.io Criticus: github.com github.com/palewire/django-bakery: github.com sigsim.acm.org/conf/pads/2026/blog/artifact-evaluation: sigsim.acm.org Hugo: gohugo.io Water Stories: waterstories.fas.harvard.edu Tsumeb Mine Notebook: tmn.fas.harvard.edu Dharma and Punya: dharmapunya2019.org Pagefind library: pagefind.app django_webassembly: github.com Astro Static Site Generator: astro.build PageFind Python Lib: pypi.org Frozen-Flask: frozen-flask.readthedocs.io Watch this episode on YouTube: youtube.com Episode #538 deep-dive: talkpython.fm/538 Episode transcripts: talkpython.fm Theme Song: Developer Rap
Do you have complex logic and unpredictable dependencies that make it hard to write reliable tests? How can you use Python's mock object library to improve your tests? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects.
In this talk, Juan, Analytics Engineer and author of Fundamentals of Analytics Engineering share his professional journey from studying psychological research in Colombia to becoming one of the first analytics engineers in the Netherlands. We explore the evolution of the role, the shift toward engineering rigor in data modeling, and how the landscape of tools like dbt and Databricks is changing the way teams work.You'll learn about:- The fundamental differences between traditional BI engineering and modern analytics engineering.- How to bridge the gap between business stakeholders and technical data infrastructure.- The technical "glue" that connects Python and SQL for robust data pipelines.- The importance of automated testing (generic vs. singular tests) to prevent "silent" data failures.- Strategies for modeling messy, fragmented source data into a unified "business reality."- The current state of the "Lakehouse" paradigm and how it impacts storage and compute costs.- Expert advice on navigating the dbt ecosystem and its emerging competitors.Links:- DE Course: https://github.com/DataTalksClub/data-engineering-zoomcamp- Luma: https://luma.com/0uf7mmupTIMECODES:0:00 Juan's psychological research and transition to data4:36 Riding the wave: The early days of analytics engineering7:56 Breaking down the gap between analysts and engineers11:03 The art of turning business reality into clean data16:25 Why data engineering is about safety, not just speed20:53 Reimagining data modeling in the modern era26:53 To split or not to split: Finding the right team roles30:35 Python, SQL, and the technical toolkit for success38:41 How to stop manually testing your data dashboards46:34 Bringing software engineering rigor to data workflows49:50 Must-read books and resources for mastering the craft55:42 The future of dbt and the shifting tool landscape1:00:29 Deciphering the lakehouse: Warehousing in the cloud1:11:16 Pro-tips for starting your data engineering journey1:14:40 The big debate: Databricks vs. Snowflake1:18:28 Why every data professional needs a local communityThis talk is designed for data analysts looking to level up their engineering skills, data engineers interested in the business-logic layer, and data leaders trying to structure their teams more effectively. It is particularly valuable for those preparing for the Data Engineering Zoomcamp or anyone looking to transition into an Analytics Engineering role.Connect with Juan- Linkedin - https://www.linkedin.com/in/jmperafan/ - Website - https://juanalytics.com/Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/
Eric sits down with David Henderson, Principal Architect for NetDevOps at Presidio, to discuss the practical journey for network engineers transitioning from manual CLI operations to scalable NetDevOps and automation. They discuss how traditional networking knowledge and certifications are foundational, and suggest essential tools and habits for beginning your automation journey. David also shares a... Read more »
Eric sits down with David Henderson, Principal Architect for NetDevOps at Presidio, to discuss the practical journey for network engineers transitioning from manual CLI operations to scalable NetDevOps and automation. They discuss how traditional networking knowledge and certifications are foundational, and suggest essential tools and habits for beginning your automation journey. David also shares a... Read more »
Four years after its original MVP, we revisit Quiet Links, exploring how it evolved into a production-ready AI-powered research tool.Bob is joined by Tim Gallati and Juanjo to unpack how they integrated a Retrieval-Augmented Generation (RAG) pipeline into an existing Python application, without rewriting everything from scratch. This episode is a reminder that impactful AI projects don't require massive teams or cutting-edge complexity - just solid design, curiosity, and a willingness to learn!Connect with Tim on LinkedIn: https://www.linkedin.com/in/timgallati/Visit the Quiet Links website: https://quietlinks.com/___
This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubCheck out more here:https://gotopia.tech/episodes/420Bill Frasure - Co-Author of "Effect Oriented Programming"Bruce Eckel - Author of many books such as "Thinking in Java", "Thinking in C++" & Atomic Kotlin & Co-Author of "Effect Oriented Programming"James Ward - Principal Developer Advocate at AWS & Co-Author of "Effect Oriented Programming"Andrew Harmel-Law - Technical Principal at Thoughtworks & Author of "Facilitating Software Architecture"RESOURCESBillhttps://github.com/swoogleshttps://x.com/bill_frasureBrucehttps://bsky.app/profile/bruceeckel.bsky.socialhttps://x.com/BruceEckelhttps://github.com/BruceEckelhttps://www.linkedin.com/in/bruceeckelJameshttps://bsky.app/profile/jamesward.comhttps://twitter.com/_JamesWardhttps://github.com/jameswardhttps://www.linkedin.com/in/jameswardAndrewhttps://bsky.app/profile/andrewhl.bsky.socialhttps://twit.social/@ahlhttps://x.com/al94781https://github.com/andrewharmellawhttps://www.linkedin.com/in/andrewharmellawhttps://andrewharmellaw.github.ioLinkshttps://effectorientedprogramming.comhttps://happypathprogramming.comhttps://zio.devhttps://www.unison-lang.orghttps://www.roc-lang.orgDESCRIPTIONAndrew Harmel-Law explores the core concepts of effect oriented programming with authors Bill Frasure, Bruce Eckel, and James Ward. The discussion reveals that effects are composable operations that encapsulate side effects and defer execution, giving developers the right handles to manage unpredictability through compiler-checked types.The authors explain how ZIO tracks three critical types: outputs, failures, and environmental requirements, enabling better testing with mock clocks and random number generators.They share their intentional avoidance of intimidating functional programming terminology like "monads" proving you don't need mathematical foundations to understand effects. The conversation covers effect systems' expansion beyond Scala into TypeScript, Kotlin, and new languages like Unison and Roc, and how their collaborative writing process with strict constraints like 47-character line limits - created a coherent 100-page book readable in portrait mode on your phone.RECOMMENDED BOOKSBill Frasure, Bruce Eckel, James Ward • Effect Oriented Programming • https://amzn.to/4sO6wLVBruce Eckel & Svetlana Isakova • Atomic Kotlin • https://amzn.to/4qT1gEQBruce Eckel • Thinking in C++ • https://amzn.to/4qnrIGWAndrew Harmel-Law • Facilitating Software Architecture • https://amzn.eu/d/5kZKVfUSam Keen • Clean Architecture with Python • https://amzn.to/4pBT5g0Eric Evans • Domain-Driven Design • https://amzn.to/3tnGhwmBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!
Topics covered in this episode: Better Python tests with inline-snapshot jolt Battery intelligence for your laptop Markdown code formatting with ruff act - run your GitHub actions locally Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 11am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Better Python tests with inline-snapshot Alex Hall, on Pydantic blog Great for testing complex data structures Allows you to write a test like this: from inline_snapshot import snapshot def test_user_creation(): user = create_user(id=123, name="test_user") assert user.dict() == snapshot({}) Then run pytest --inline-snapshot=fix And the library updates the test source code to look like this: def test_user_creation(): user = create_user(id=123, name="test_user") assert user.dict() == snapshot({ "id": 123, "name": "test_user", "status": "active" }) Now, when you run the code without “fix” the collected data is used for comparison Awesome to be able to visually inspect the test data right there in the test code. Projects mentioned inline-snapshot pytest-examples syrupy dirty-equals executing Michael #2: jolt Battery intelligence for your laptop Support for both macOS and Linux Battery Status — Charge percentage, time remaining, health, and cycle count Power Monitoring — System power draw with CPU/GPU breakdown Process Tracking — Processes sorted by energy impact with color-coded severity Historical Graphs — Track battery and power trends over time Themes — 10+ built-in themes with dark/light auto-detection Background Daemon — Collect historical data even when the TUI isn't running Process Management — Kill energy-hungry processes directly Brian #3: Markdown code formatting with ruff Suggested by Matthias Schoettle ruff can now format code within markdown files Will format valid Python code in code blocks marked with python, py, python3 or py3. Also recognizes pyi as Python type stub files. Includes the ability to turn off formatting with comment [HTML_REMOVED] , [HTML_REMOVED] blocks. Requires preview mode [tool.ruff.lint] preview = true Michael #4: act - run your GitHub actions locally Run your GitHub Actions locally! Why would you want to do this? Two reasons: Fast Feedback - Rather than having to commit/push every time you want to test out the changes you are making to your .github/workflows/ files (or for any changes to embedded GitHub actions), you can use act to run the actions locally. The environment variables and filesystem are all configured to match what GitHub provides. Local Task Runner - I love make. However, I also hate repeating myself. With act, you can use the GitHub Actions defined in your .github/workflows/ to replace your Makefile! When you run act it reads in your GitHub Actions from .github/workflows/ and determines the set of actions that need to be run. Uses the Docker API to either pull or build the necessary images, as defined in your workflow files and finally determines the execution path based on the dependencies that were defined. Once it has the execution path, it then uses the Docker API to run containers for each action based on the images prepared earlier. The environment variables and filesystem are all configured to match what GitHub provides. Extras Michael: Winter is coming: Frozendict accepted Django ORM stand-alone Command Book app announcement post Joke: Plug ‘n Paste
Travis Kennedy sits down with me to talk about his fatherhood journey. He talks about the values he looks to instill into his kids as they grow up. In addition he shares what his kids have taught him. After that we talk about his book, The Whyte Python World Tour. We talk about the inspiration for the book and how it will be turned into a movie. Plus, he gives some insight on if any of the characters were inspired by himself or others. He even talks about how he looks at storytelling and the importance of creating interesting characters. Lastly, we finish the interview with the Fatherhood Quick Five. About Travis Kennedy Travis Kennedy's work is in the Best New England Crime Stories. In addition, you can see his work in Best American Mystery Stories anthologies. He is the Grand Prize Winner of ScreenCraft's 2021 Cinematic Book Contest for “Sharks in the Valley,” to be published as Welcome to Redemption. He lives in Scarborough, Maine, with his wife and their two children. The Whyte Python World Tour is his debut novel. Make sure you follow Travis on Instagram at @kennedywriting. In addition, pick up his book, The Whyte Python World Tour wherever you purchase books. SLIDEMVP Is This Week's Podcast Sponsor As a dad and coach, inspired by some awkward slides and makeshift cardboard sliding tools, Coach Robby asked: “How can I help players slide better?” After countless brainstorming sessions and prototypes, the SLIDEMVP™ was born. Players immediately had fun, and their sliding skills improved dramatically. To further support athletes, Coach Robby has hosted multiple sliding clinics, building confidence and teaching techniques like the pop-up slide. Proudly manufactured in the USA, SLIDEMVP™ is player-tested and coach-approved. It enhances sliding technique, boosts speed, and improves agility on the basepaths. Most importantly, SLIDEMVP™ helps players build confidence and take their game to the next level. To learn more go to their website at SlideMVP.com. About The Art of Fatherhood Podcast The Art of Fatherhood Podcast follows the journey of fatherhood. Your host, Art Eddy talks with fantastic dads from all around the world where they share their thoughts on fatherhood. You get a unique perspective on fatherhood from guests like Bob Odenkirk, Hank Azaria, Joe Montana, Kevin Smith, Danny Trejo, Jerry Rice, Jeff Foxworthy, Patrick Warburton, Jeff Kinney, Paul Sun-Hyung Lee, Kyle Busch, Dennis Quaid, Dwight Freeney and many more.
¿Es Python siempre la mejor opción para tus scripts de automatización? En este episodio, Lorenzo profundiza en una de las discusiones más recurrentes de la comunidad: la estabilidad de los scripts frente a la comodidad de los módulos de terceros. Acompaña a nuestro experto en Linux mientras desglosa los motivos que lo llevaron a abandonar soluciones basadas en Python para la gestión de metadatos de audio.Descubre ID3CLI, una herramienta potente y ligera escrita en Rust que soluciona los problemas de retrocompatibilidad y fallos en tiempo de ejecución. Aprenderás cómo automatizar el etiquetado de tus podcasts extrayendo datos directamente de archivos Markdown, eliminando la necesidad de introducir información manualmente en herramientas gráficas. Analizamos la importancia de tener binarios compilados que simplemente "funcionan", permitiéndote centrarte en crear contenido en lugar de arreglar herramientas rotas.Temas destacados del episodio: Bash vs Python: ¿Cuándo el "follón" de compilar merece la pena? Los peligros de depender de módulos de terceros que cambian sin previo aviso. De EasyTag a la automatización total en la terminal. Uso de Front Matter y RipGrep para un flujo de trabajo eficiente. Soporte de metadatos para Apple y carátulas en múltiples formatos. Capítulos,00:00:00 Introducción: El dilema de Bash vs Python00:00:48 El riesgo de las dependencias de terceros en Python00:01:35 La obsesión por la automatización de metadatos00:03:01 Flujo de trabajo: De EasyTag a la Terminal00:05:36 Extrayendo datos del Front Matter en Markdown00:07:24 Herramientas antiguas: ID3 y MiD3v2 (Mutagen)00:09:12 El colapso de los módulos y la necesidad de compilar00:10:13 Presentando ID3CLI: La solución definitiva en Rust00:11:53 Características técnicas y soporte de formatos (MP3, OGG, FLAC)00:13:48 Integración de ID3CLI en scripts de automatización00:15:23 Reflexión sobre la importancia de los metadatos00:16:42 Nuevo proyecto: El podcast "La Era de las Distros"00:17:47 Comunidad y cierre del episodioAdemás, Lorenzo nos habla sobre su nuevo podcast "La Era de las Distros", una mirada necesaria a las distribuciones Linux que marcaron un hito en la informática española como LinEx o Guadalex. ¡Disfruta del episodio y optimiza tu entorno Linux!Más información y enlaces en las notas del episodio
Pourquoi les géants de la tech basculent de C/C++ vers Rust, et quelles conséquences concrètes en matière de sécurité, de performance et de maintenance ? Avec Sylvestre Ledru (Mozilla), on revient sur la révolution de la sûreté mémoire, les exemples très concrets dans les navigateurs et les composants Windows, et les coulisses d'incidents de sécurité qui ont marqué l'industrie. Vous découvrirez aussi pourquoi Rust séduit de plus en plus de développeurs web venus de JavaScript ou Python, et comment cette évolution s'inscrit dans l'histoire qui va de l'assembleur au C, puis à Rust.Sources Vidéo recommandée (YouTube)En plateau Michaël de Marliave — animateur Matthieu Lambda — chroniqueur Sylvestre Ledru — invité (Mozilla)➤ Pour découvrir Mammouth IA : https://mammouth.ai/➤ Pour le Merch Micode et Underscore_ : https://traphic.fr/collections/micode⚠️ Précommandes avant le 15 Janvier ! Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.
Jeremy Zakis reports irregular weather is driving venomous snakes into unusual residential locations, with a Victorianwoman startled by a copperhead wrapping around her leg while Queensland's Whitsunday Islands face a python epidemic leading to tourist warnings about painful defensive bites. 3
Jeremy Zakis reports irregular weather is driving venomous snakes into unusual residential locations, with a Victorianwoman startled by a copperhead wrapping around her leg while Queensland's Whitsunday Islands face a python epidemic leading to tourist warnings about painful defensive bites. 2
Talk Python To Me - Python conversations for passionate developers
You love building web apps with Python, and HTMX got you excited about the hypermedia approach -- let the server drive the HTML, skip the JavaScript build step, keep things simple. But then you hit that last 10%: You need Alpine.js for interactivity, your state gets out of sync, and suddenly you're juggling two unrelated libraries that weren't designed to work together. What if there was a single 11-kilobyte framework that gave you everything HTMX and Alpine do, and more, with real-time updates, multiplayer collaboration out of the box, and performance so fast you're actually bottlenecked by the monitor's refresh rate? That's Datastar. On this episode, I sit down with its creator Delaney Gillilan, core maintainer Ben Croker, and Datastar convert Chris May to explore how this backend-driven, server-sent-events-first framework is changing the way full-stack developers think about the modern web. Episode sponsors Sentry Error Monitoring, Code talkpython26 Command Book Talk Python Courses Links from the show Guests Delaney Gillilan: linkedin.com Ben Croker: x.com Chris May: everydaysuperpowers.dev Datastar: data-star.dev HTMX: htmx.org AlpineJS: alpinejs.dev Core Attribute Tour: data-star.dev data-star.dev/examples: data-star.dev github.com/starfederation/datastar-python: github.com VSCode: marketplace.visualstudio.com OpenVSX: open-vsx.org PyCharm/Intellij plugin: plugins.jetbrains.com data-star.dev/datastar_pro: data-star.dev gg: discord.gg HTML-ivating your Django web app's experience with HTMX, AlpineJS, and streaming HTML - Chris May: www.youtube.com Senior Engineer tries Vibe Coding: www.youtube.com 1 Billion Checkboxes: checkboxes.andersmurphy.com Game of life example: example.andersmurphy.com Watch this episode on YouTube: youtube.com Episode #537 deep-dive: talkpython.fm/537 Episode transcripts: talkpython.fm Theme Song: Developer Rap
Foundations of Amateur Radio How to go about documenting your setup? Possibly the single most important thing that separates science from "fiddling around" is documentation. Figuring out how to document things is often non-trivial and me telling you that "unless you wrote it down, it didn't happen" only goes so far. If documentation isn't your thing, what about "I broke something and I don't know how it was before I fiddled" as an incentive instead? Recently I had cause to explore how to document how my station is configured. To give you a sense, the microphone is connected to a remote-rig, which is connected to a Wi-Fi base station, over Wi-Fi to a Wi-Fi slave, to another remote-rig, to the radio body, to the VHF port, through two coax switches, a run of RG213, to an antenna. When receiving, it goes from the antenna, to a run of RG213, through two coax switches, to the VHF port, to the radio body, to a remote-rig, to a Wi-Fi slave, to a Wi-Fi base station to a remote-rig, to the remote head, to a set of headphones. Of course, at this point I've written it down, so, job done .. right? Well, what about the data connection, the external speaker, the remote head display and other goodies, say nothing of the duplicate devices with similar names. All in all, the FT-857d has something like eleven ports, each remote-rig has ten, so just wording it is a start, but hardly qualifies as documented. What if we drew a picture instead? At this point you could pull out your crayons and start scribbling on a sheet of butcher's paper and that would be a fine start, but it would be difficult to share with me or anyone else and updating it would be a challenge, let alone versioning it. As it happens, we're not the first people to have this issue. In the 1980's and 1990's researchers at Bell Labs were trying to figure out how to draw graphs and from that work a language, 'DOT', since everyone is a fan of the "DAG Of Tomorrow", and a series of tools, which today are known as 'Graphviz', made the visualisation of relationships possible without the application of coloured wax on dried cellulose fibre. In my other, computing job, I had cause to visualise the relationship between a million or so nodes, allowing me to discover a specific node that was directing all traffic, where I could insert my debugging code, but it was only possible thanks to these free and open source tools. While the DOT language isn't particularly complex, it occurred to me that for someone not conversant with the syntax, we can start even simpler with a CSV text file that shows the relationships between each device and convert the CSV to DOT and in turn to a picture. For example, I documented the relationship between the radio and the antenna by adding five lines to a CSV file, essentially, FT857d to VHF port to VHF coax switch to VHF grounding switch to RG213 to antenna. In all, to document everything except power, since I haven't decided how I want to describe it, I used a CSV with 47 lines. On the face of it, that might sound ridiculous, but I can tell you, it shows all the sockets on the FT857d, all the sockets on both remote-rig devices and the relationships between them. With it anyone can duplicate my set-up. Having previously spent some quality time learning various aspects of the DOT language, I figured I could write a little script to convert CSV files to DOT, but being of the generation of software developers with the attitude, "Why write something if someone else already did?", I discovered that Reinier Post at the Eindhoven University of Technology has a delightful collection of scripts, including one appropriately named 'csv2dot'. Written in Perl, the only language that according to some looks just as impenetrable before and after encryption, the tool works as advertised and makes a DOT file that you can then visualise using Graphviz. Of course there's Python scripts lying around that claim to do the same, but I wasn't keen to install the kitchen sink just to try them. Instead I made a quick little Docker file that you can find on my vk6flab GitHub repository that will walk you through this, complete with my example, so you have a starting point. Now, I used this here to describe my station, well, one part of it, but it can easily extend to document your entire station, and because we're talking about text files that contain the information, anyone with a copy of a text editor can update the file when things change, since that's where the real magic happens. So, what are you waiting for, documentation? I'm Onno VK6FLAB
Everyone says you need to "Start an AI Agency" to make millions in 2026. And technically, the hype is there ($307 Billion was spent on AI implementations last year). But if you're reading this, you probably know the uncomfortable truth. Most of those projects are failing. The problem isn't the "AI" or the "Client." It's the Learning Gap. Most agencies are selling "tools" (chatbots) when businesses are desperate for "outcomes" (custom automation). The method that actually saved my business $44,000/year—and is generating up to $10 returns for the top 5% of companies—is simple: The Architect Method. So today, I'm going to show you how to stop "prompting" and start "architecting." We are going to build a custom, enterprise-grade solution that replaces expensive software... without writing a single line of code yourself. We analyze the conflicting data between the IDC Spending Report and the MIT Failure Study. We then break down the "Architect" logic that separates the 95% who fail from the 5% who succeed. Finally, we use Claude to run a "Tech Stack Interview" and build a recursive, self-correcting automation system for High Level and Google Workspace. Anyway, here is how we will use AI to stop guessing and start building: Step 1: The "$307 Billion Lie." We look at the stats (95% failure rate) and explain why the "Standard Agency Model" is dangerous for beginners. If you are just selling "implementation," you are selling a commodity. Step 2: The "Learning Gap" (MIT Study). We reveal why AI tools "drift" and fail over time. The secret isn't better prompting—it's building a system that understands your specific Tech Stack context before it writes a single word. Step 3: The "Architect" Protocol. Most people ask AI to "do the work." I show you how to ask AI to "design the blueprint" first. We use the Recursive Self-Correction technique to have the AI write its own Python scripts and fix its own errors. Step 4: The "Tech Stack Interview." We watch live as I get the AI to interview me about my specific setup (High Level, Gmail, Custom Database). This ensures the code it writes actually works for my business, eliminating the "Hallucination" problem. If you want to be part of the 5% making AI work instead of the 95% burning cash, this video shows you the shift you need to make.
We're excited to kick off the 2026 Season 2 of the New to Z: Road to SME podcast with IBM Champions Steven Perva and Emma Skovgård, exploring how community, curiosity, and modern tooling are shaping the next generation of mainframers.In this episode, we talk about their unconventional paths into the mainframe world and their work building vibrant communities like the Systems Z Enthusiast Discord and Dinoframe. They share lessons on learning curves, modern tools like Python on Z, and the communication skills that help new and experienced mainframers thrive. A quick, inspiring look at two leaders shaping the future of the platform.
What does it take to design a programming language from scratch when the target isn't just CPUs, but GPUs, accelerators, and the entire AI stack? In this episode, I sit down with legendary language architect Chris Lattner to talk about Mojo — his ambitious attempt to rethink systems programming for the machine learning era. We trace the arc from LLVM and Clang to Swift and now Mojo, unpacking the lessons Chris has carried forward into this new language. Mojo aims to combine Python's ergonomics with C-level performance, but the real story is deeper: memory ownership, heterogeneous compute, compile-time metaprogramming, and giving developers precise control over how AI workloads hit silicon. Chris shares the motivation behind Modular, why today's AI infrastructure demands new abstractions, and how Mojo fits into a rapidly evolving ecosystem of ML frameworks and hardware backends. We also dig into developer experience, safety vs performance tradeoffs, and what it means to build a language that spans research notebooks all the way down to kernel-level execution.
I have a theory that only bad projects get finished — good ones keep finding new things to do. Asciinema is a case in point. What started as a way to share terminal sessions with friends has, over 14 years, grown into a full suite of tools covering recording, hosting, playback, and live streaming — and been rebuilt multiple times along the way. So what does it actually take to record and replay a terminal session faithfully in a browser?Joining us for this conversation is Marcin Kulik, Asciinema's creator. The project's architecture has passed through almost every interesting corner of software engineering: a Python recorder built around pseudo-terminals (PTY), a ClojureScript terminal emulator for the browser that hit performance limits with immutable data structures and garbage collection pressure, a move to Rust compiled to WebAssembly, a Go experiment that didn't last, and a new Rust CLI for concurrent live streaming backed by an Elixir/Phoenix server that calls Rust code via NIFs. The same Rust terminal emulator library now powers all three components — the browser player, the server, and the CLI.If you've ever looked at those terminal animations embedded in a README and wondered what's underneath them, or if you're interested in how a passionate open-source developer navigates 14 years of language changes and rewrites, this conversation has plenty to offer.---Support Developer Voices on Patreon: https://patreon.com/DeveloperVoicesSupport Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/joinAsciinema: https://asciinema.orgAsciinema Docs: https://docs.asciinema.orgAsciinema CLI (GitHub): https://github.com/asciinema/asciinemaAsciinema Player (GitHub): https://github.com/asciinema/asciinema-playerAsciinema Server (GitHub): https://github.com/asciinema/asciinema-serverAVT - Rust terminal emulator library: https://github.com/asciinema/avtvt-clj - the original ClojureScript terminal emulator: https://github.com/asciinema/vt-cljPaul Williams' ANSI/VT100 State Machine Parser: https://vt100.net/emu/dec_ansi_parserRust: https://www.rust-lang.orgWebAssembly: https://webassembly.orgSolidJS: https://www.solidjs.comElixir: https://elixir-lang.orgPhoenix Framework: https://www.phoenixframework.orgRustler (Rust NIFs for Elixir/Erlang): https://github.com/rusterlium/rustlerClojure: https://clojure.orgClojureScript: https://clojurescript.orgcmatrix: https://github.com/abishekvashok/cmatrixMarcin Kulik on GitHub: https://github.com/ku1ikMarcin Kulik on Mastodon: https://hachyderm.io/@ku1ikMarcin Kulik on asciinema.org: https://asciinema.org/~ku1ik"They're Made Out of Meat" demo: https://asciinema.org/a/746358Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.socialKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/---0:00 Intro2:28 What Is Asciinema?4:48 How Asciinema Started9:51 The Problem of Parsing Terminal Output14:07 Building a Cross-Platform Recorder17:01 Rewriting the Parser in ClojureScript22:19 The Hidden Complexity of Terminals29:28 Rendering Terminals in the Browser39:47 When ClojureScript Can't Keep Up45:28 Moving to Rust and WebAssembly52:01 The Go Experiment57:43 Adding Live Terminal Streaming1:07:12 Can You Scrub Back in a Live Stream?1:14:40 Editing Recordings1:25:27 Outro
This week we did what every developer swears they won't do - we broke our own Git workflow.Join us as we chat about what went wrong, what we learned, and where we're heading next. This conversation is a reminder that even experienced developers get rusty without practice. If you can relate, or just simply enjoy Julian's git puns, leave a comment!And while you're at it, check out our Pybites Books platform - https://pybitesbooks.com/ - Loads of new updates coming soon!___
In our latest Open Source Startup Podcast episode, co-hosts Robby and Tim talk with Magnus Müller, the Co-Founder & CEO of Browser Use - the platform that makes web agents come to life. Their open source, browser-use, has almost 80K stars on GitHub and is widely adopted. This episode dives into the unexpected rise of an open-source browser automation project that took off during Y Combinator - while many similar projects before and after it never gained traction. The founder reflects on why: delivering a “magical moment” fast. Early demos showing AI controlling a browser, inspired by trends like OpenAI's Operator, and immediately clicked with people. What began as a developer-only Python library evolved into a hosted product as non-technical users - from sales teams to startups - wanted access. Along the way, the team leaned into controversial but compelling use cases, like AI applying for jobs on your behalf, which sparked conversation and accelerated growth. The core challenge they focused on solving was reliability: unlike deterministic automation scripts, AI agents can behave unpredictably, making trust and repeatability central problems to overcome.The long-term vision goes beyond UI automation toward agents that can skip the browser entirely and interact directly with website servers through structured actions. But the conversation isn't just about infrastructure. The founder admits that early growth came mostly from building and talking to users, while recent months have been dedicated to storytelling and marketing rather than coding. A personal through-line emerges as well: learning to replace defensiveness with curiosity - questioning assumptions, staying open to feedback, and continuously refining both the technology and the narrative around it.
Send a textThis week on The Route to Networking podcast, our CEO George is joined by Joe Limardo, an AI and Cloud Architect Evangelist whose career has evolved from enterprise contact centre technology into hands-on AI implementation across OpenAI, AWS and Google.As AI dominates headlines, this episode cuts through the noise. Joe shares what the shift actually means for engineers, reflecting on how quickly once “hot” skills can become legacy and why continuous learning is no longer optional.The conversation explores the real breakthrough behind modern AI - access to reasoning, not just information. Large Language Models are changing how engineers build, solve problems, and accelerate their own learning, while businesses use AI to remove repetitive work and unlock higher-value thinking.They also discuss the growing infrastructure demands behind AI, the move from pure coding to architecture, and the three skills Joe believes future-proof careers: APIs, Python, and storytelling.The episode closes with a quick-fire round covering model preferences, common misconceptions, and the one advantage AI still cannot replace - human judgement under uncertainty.Want to stay up to date with new episodes? Follow our LinkedIn page for all the latest podcast updates!Head to: https://www.linkedin.com/company/the-route-to-networking-podcast/Interested in following a similar career path? Why don't you take a look at our jobs page, where you can find your next job opportunity? Head to: www.hamilton-barnes.com/jobs/
We know what the work of the data practitioner is, right? It's everything from managing data ingestion to data governance to report development to experimental design to basic and advanced analytics. It's writing (or vibe-writing?) SQL or Python or R while also being adept at whatever data stack—no matter how modern—is at hand. Of course, it's a lot more, too! And that's the topic of this episode: the unofficial, often unheralded, but often quite important "shadow work" of the analyst—the myriad tasks required to effectively glue together all the data work that occurs out in broad daylight to enable the data to truly be useful at driving the business forward. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.
On this episode of The Green Way Outdoors podcast Kyle Green, Ryan Parks and AJ Beadle discuss The Darién Gap. A dangerous, roadless jungle spanning the Colombia-Panama border, acting as the sole overland link between South and Central America, and has become a perilous route for migrants seeking to reach North America, filled with natural hazards like rivers and wildlife, alongside human threats from traffickers, smugglers, and violence, with hundreds of thousands undertaking the trek annually despite extreme risks, including death, disease, and exploitation. Then they dive in to the Florida Everglades, where researchers are using "robo-bunnies", solar-powered robotic rabbits, to lure and trap invasive Burmese pythons, which have devastated native mammal populations. These modified toy bunnies mimic real marsh rabbits with heat, movement, and soon scent, attracting pythons to strategically placed pens, triggering alerts for contractors to remove the snakes. It's a high-tech, ongoing trial by the University of Florida and South Florida Water Management District to combat the elusive pythons that are nearly impossible to find otherwise. Lastly, a large coalition of scientists, tribal nations, and environmental groups strongly advocates for removing the four lower Snake River dams (Ice Harbor, Little Goose, Lower Monumental, Lower Granite) because they are seen as a major obstacle to salmon recovery, making populations vulnerable to extinction, despite the dams providing benefits like power, irrigation, and barge transport, which would need replacing. Federal agencies, including NOAA Fisheries, have concluded that breaching is essential for salmon survival, especially with climate change making reservoirs warmer, while proponents argue it's the single best way to restore vital salmon runs to Idaho and beyond. On the other hand, the economic Impact of dam removal would be terrible and end efficient barge transport for wheat and irrigation for 400,000 acres, increasing costs for farmers. There is also no true way to transport that wheat for export if the dams were removed. The dams also generate significant clean energy, which would need replacing. Some also argue climate change, hatchery issues, and predation are also major threats, and dam removal isn't a guaranteed fix. So what is the right answer? Watch our HISTORY Channel show on:HISTORY: https://www.history.com/shows/the-green-way-outdoors Follow us on:Facebook: https://www.facebook.com/TheGreenWayOutdoors/Instagram: https://www.instagram.com/thegreenwayoutdoors/Twitter: https://twitter.com/thegreenwayout?lang=enYoutube: https://m.youtube.com/channel/UCjR5r6WwXcPKK0xVldNT5_gWebsite: www.thegreenwayoutdoors.com Watch our HISTORY Channel show on:HISTORYWAYPOINT TVFollow us on:FacebookInstagramTwitterYoutubeOur Website
BONUS: When AI Decisions Go Wrong at Scale—And How to Prevent It We've spent years asking what AI can do. But the next frontier isn't more capability—it's something far less glamorous and far more dangerous if we get it wrong. In this episode, Ran Aroussi shares why observability, transparency, and governance may be the difference between AI that empowers humans and AI that quietly drifts out of alignment. The Gap Between Demos and Deployable Systems "I've noticed that I watched well-designed agents make perfectly reasonable decisions based on their training, but in a context where the decision was catastrophically wrong. And there was really no way of knowing what had happened until the damage was already there." Ran's journey from building algorithmic trading systems to creating MUXI, an open framework for production-ready AI agents, revealed a fundamental truth: the skills needed to build impressive AI demos are completely different from those needed to deploy reliable systems at scale. Coming from the EdTech space where he handled billions of ad impressions daily and over a million concurrent users, Ran brings a perspective shaped by real-world production demands. The moment of realization came when he saw that the non-deterministic nature of AI meant that traditional software engineering approaches simply don't apply. While traditional bugs are reproducible, AI systems can produce different results from identical inputs—and that changes everything about how we need to approach deployment. Why Leaders Misunderstand Production AI "When you chat with ChatGPT, you go there and it pretty much works all the time for you. But when you deploy a system in production, you have users with unimaginable different use cases, different problems, and different ways of phrasing themselves." The biggest misconception leaders have is assuming that because AI works well in their personal testing, it will work equally well at scale. When you test AI with your own biases and limited imagination for scenarios, you're essentially seeing a curated experience. Real users bring infinite variation: non-native English speakers constructing sentences differently, unexpected use cases, and edge cases no one anticipated. The input space for AI systems is practically infinite because it's language-based, making comprehensive testing impossible. Multi-Layered Protection for Production AI "You have to put in deterministic filters between the AI and what you get back to the user." Ran outlines a comprehensive approach to protecting AI systems in production: Model version locking: Just as you wouldn't randomly upgrade Python versions without testing, lock your AI model versions to ensure consistent behavior Guardrails in prompts: Set clear boundaries about what the AI should never do or share Deterministic filters: Language firewalls that catch personal information, harmful content, or unexpected outputs before they reach users Comprehensive logging: Detailed traces of every decision, tool call, and data flow for debugging and pattern detection The key insight is that these layers must work together—no single approach provides sufficient protection for production systems. Observability in Agentic Workflows "With agentic AI, you have decision-making, task decomposition, tools that it decided to call, and what data to pass to them. So there's a lot of things that you should at least be able to trace back." Observability for agentic systems is fundamentally different from traditional LLM observability. When a user asks "What do I have to do today?", the system must determine who is asking, which tools are relevant to their role, what their preferences are, and how to format the response. Each user triggers a completely different dynamic workflow. Ran emphasizes the need for multi-layered access to observability data: engineers need full debugging access with appropriate security clearances, while managers need topic-level views without personal information. The goal is building a knowledge graph of interactions that allows pattern detection and continuous improvement. Governance as Human-AI Partnership "Governance isn't about control—it's about keeping people in the loop so AI amplifies, not replaces, human judgment." The most powerful reframing in this conversation is viewing governance not as red tape but as a partnership model. Some actions—like answering support tickets—can be fully automated with occasional human review. Others—like approving million-dollar financial transfers—require human confirmation before execution. The key is designing systems where AI can do the preparation work while humans retain decision authority at critical checkpoints. This mirrors how we build trust with human colleagues: through repeated successful interactions over time, gradually expanding autonomy as confidence grows. Building Trust Through Incremental Autonomy "Working with AI is like working with a new colleague that will back you up during your vacation. You probably don't know this person for a month. You probably know them for years. The first time you went on vacation, they had 10 calls with you, and then slowly it got to 'I'm only gonna call you if it's really urgent.'" The path to trusting AI systems mirrors how we build trust with human colleagues. You don't immediately hand over complete control—you start with frequent check-ins, observe performance, and gradually expand autonomy as confidence builds. This means starting with heavy human-in-the-loop interaction and systematically reducing oversight as the system proves reliable. The goal is reaching a state where you can confidently say "you don't have to ask permission before you do X, but I still want to approve every Y." In this episode, we refer to Thinking in Systems by Donella Meadows, Designing Machine Learning Systems by Chip Huyen, and Build a Large Language Model (From Scratch) by Sebastian Raschka. About Ran Aroussi Ran Aroussi is the founder of MUXI, an open framework for production-ready AI agents. He is also the co-creator of yfinance (with 10 million downloads monthly) and founder of Tradologics and Automaze. Ran is the author of the forthcoming book Production-Grade Agentic AI: From Brittle Workflows to Deployable Autonomous Systems, also available at productionaibook.com. You can connect with Ran Aroussi on LinkedIn.
This episode covers multiple active threats and security changes. It warns of an actively exploited critical BeyondTrust remote access vulnerability (CVE-2026-1731, CVSS 9.9) enabling pre-authentication remote code execution in Remote Support and Privileged Remote Access, noting SaaS was patched while on-prem deployments require urgent manual updates and may already be compromised. Microsoft details an evolution of the ClickFix social engineering technique where victims are tricked into running NSLookup commands that use attacker-controlled DNS responses as a malware staging channel, leading to payload delivery (including a Python-based RAT) and persistence via startup shortcuts, alongside increased Lumma Stealer activity. Cybersecurity Today would like to thank Meter for their support in bringing you this podcast. Meter delivers a complete networking stack, wired, wireless and cellular in one integrated solution that's built for performance and scale. You can find them at Meter.com/cst Researchers also report Mac-focused campaigns abusing AI-generated content and malicious search ads to push copy-paste terminal commands that install an info stealer (MaxSync) targeting Keychain, browsers, and crypto wallets. T The show describes fake recruiter campaigns targeting developers with coding tests containing malicious dependencies on repositories like NPM and PyPI, linked to the "Gala" operation and nearly 200 packages. Finally, it reviews NPM's authentication overhaul after a supply-chain worm incident—revoking classic long-lived tokens, moving to short-lived session credentials, encouraging MFA and OIDC trusted publishing—while noting remaining risks such as MFA phishing, non-mandatory MFA for unpublish, and the continued ability to create long-lived tokens. 00:00 Sponsor: Meter + Today's Cybersecurity Headlines 00:48 Urgent Patch: BeyondTrust Remote Access RCE (CVE-2026-1731) Actively Exploited 02:45 ClickFix Evolves: DNS Lookups (nslookup) Used as Malware Staging 04:34 Mac Malware via AI Search Results: Fake Terminal Commands Deliver Info-Stealer 06:08 Fake Recruiters, Real Malware: Coding Tests Poison Dev Environments 07:19 NPM Security Overhaul After Supply-Chain Worm—What's Better, What Still Risks 09:11 Wrap-Up, Thanks, and Sponsor Message
In MobileViews 597, recorded on February 15, 2026, Jon Westfall and I noted the upcoming the Lunar New Year while tackling the frustrations of modern tech ecosystems. I kicked things off with a double-header rant: first, my recurring battle with leaking alkaline batteries in my mouse and other devices, and second, Microsoft's decision to force Clipchamp (a video editor) users to store massive video files on OneDrive. With my upload speeds maxing out at 25 megabits, uploading gigabyte-sized files is simply unworkable, so I've officially pivoted to the open-source video editor Shotcut. We also explored the "bane of existence" for educators: the limitations of Chromebooks. Jon shared his struggles with students who, having grown up in managed K-12 Chrome environments, often struggle with standard file permissions and workflows when transitioning to college and professional platforms. Jon detailed his upgrade to the Backbone Pro gaming controller—praising its integrated battery and Bluetooth versatility—while looking forward to a future M5 Mac Mini to handle local LLM heavy lifting. I'm personally keeping an eye on rumors of an affordable A18 Pro-based MacBook that Jon noted could potentially disrupt the education sector. Between my nostalgia for coding in a 208-byte space on an Apple II and Jon's modern Python toolkit involving pyenv and PyInstaller, we emphasize that efficiency must remain a priority, even as software becomes more bloated. Whether it's navigating the "AI search" changes in Google Photos or finding ways around "vibe coding" errors, we're still looking for tech that just works.
Today we have Ziv Mador, VP of Security Research from LevelBlue SpiderLabs discussing their work on "SpiderLabs IDs New Banking Trojan Distributed Through WhatsApp." Researchers at LevelBlue SpiderLabs have identified a new Brazilian banking Trojan dubbed Eternidade Stealer, spread through WhatsApp hijacking and social engineering campaigns that use a Python-based worm to steal contacts and distribute malicious MSI installers. The Delphi-compiled malware targets Brazilian victims, profiles infected systems, dynamically retrieves its command-and-control server via IMAP email, and deploys banking overlays to harvest credentials from financial institutions and cryptocurrency platforms. The campaign reflects the continued evolution of Brazil's cybercrime ecosystem, combining WhatsApp propagation, geofencing, encrypted C2 communications, and process injection to maintain stealth and persistence. The research can be found here: SpiderLabs IDs New Banking Trojan Distributed Through WhatsApp Learn more about your ad choices. Visit megaphone.fm/adchoices
Today we have Ziv Mador, VP of Security Research from LevelBlue SpiderLabs discussing their work on "SpiderLabs IDs New Banking Trojan Distributed Through WhatsApp." Researchers at LevelBlue SpiderLabs have identified a new Brazilian banking Trojan dubbed Eternidade Stealer, spread through WhatsApp hijacking and social engineering campaigns that use a Python-based worm to steal contacts and distribute malicious MSI installers. The Delphi-compiled malware targets Brazilian victims, profiles infected systems, dynamically retrieves its command-and-control server via IMAP email, and deploys banking overlays to harvest credentials from financial institutions and cryptocurrency platforms. The campaign reflects the continued evolution of Brazil's cybercrime ecosystem, combining WhatsApp propagation, geofencing, encrypted C2 communications, and process injection to maintain stealth and persistence. The research can be found here: SpiderLabs IDs New Banking Trojan Distributed Through WhatsApp Learn more about your ad choices. Visit megaphone.fm/adchoices
Our ol' friend, Brett Cannon, is back to talk all things Python. But first! Star Wars, Machete Order, Lost, Babylon 5, Game of Thrones, Murderbot, Ted Lasso, Project Hail Mary, David Attenborough, perpetual voice rights, and the AI uncanny valley.
Would you like to learn how to work with LLMs locally on your own computer? How do you integrate your Python projects with a local model? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects.
Vincent Warmerdam is a Founding Engineer at marimo, working on reinventing Python notebooks as reactive, reproducible, interactive, and Git-friendly environments for data workflows and AI prototyping. He helps build the core marimo notebook platform, pushing its reactive execution model, UI interactivity, and integration with modern development and AI tooling so that notebooks behave like dependable, shareable programs and apps rather than error-prone scratchpads.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// AbstractVincent Warmerdam joins Demetrios fresh off marimo's acquisition by Weights & Biases—and makes a bold claim: notebooks as we know them are outdated.They talk Molab (GPU-backed, cloud-hosted notebooks), LLMs that don't just chat but actually fix your SQL and debug your code, and why most data folks are consuming tools instead of experimenting. Vincent argues we should stop treating notebooks like static scratchpads and start treating them like dynamic apps powered by AI.It's a conversation about rethinking workflows, reclaiming creativity, and not outsourcing your brain to the model.// BioVincent is a senior data professional who worked as an engineer, researcher, team lead, and educator in the past. You might know him from tech talks with an attempt to defend common sense over hype in the data space. He is especially interested in understanding algorithmic systems so that one may prevent failure. As such, he has always had a preference to keep calm and check the dataset before flowing tonnes of tensors. He currently works at marimo, where he spends his time rethinking everything related to Python notebooks.// Related LinksWebsite: https://marimo.io/Coding Agent Conference: https://luma.com/codingagentsHyperbolic GPU Cloud: app.hyperbolic.ai~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]MLOps GPU Guide: https://go.mlops.community/gpuguideConnect with Demetrios on LinkedIn: /dpbrinkmConnect with Vincent on LinkedIn: /vincentwarmerdam/Timestamps:[00:00] Context in Notebooks[00:24] Acquisition and Team Continuity[04:43] Coding Agent Conference Announcement![05:56] Hyperbolic GPU Cloud Ad[06:54] marimo and W&B Synergies[09:31] marimo Cloud Code Support[12:59] Hardest Code to Generate[16:22] Trough of Disillusionment[20:38] Agent Interaction in Notebooks[25:41] Wrap up
Our ol' friend, Brett Cannon, is back to talk all things Python. But first! Star Wars, Machete Order, Lost, Babylon 5, Game of Thrones, Murderbot, Ted Lasso, Project Hail Mary, David Attenborough, perpetual voice rights, and the AI uncanny valley.
• Support & get perks!• Proudly sponsored by PyMC Labs! Get in touch at alex.andorra@pymc-labs.com• Intro to Bayes and Advanced Regression courses (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work !Chapters:00:00 Exploring Generative AI and Scientific Modeling10:27 Understanding Simulation-Based Inference (SBI) and Its Applications15:59 Diffusion Models in Simulation-Based Inference19:22 Live Coding Session: Implementing Baseflow for SBI34:39 Analyzing Results and Diagnostics in Simulation-Based Inference46:18 Hierarchical Models and Amortized Bayesian Inference48:14 Understanding Simulation-Based Inference (SBI) and Its Importance49:14 Diving into Diffusion Models: Basics and Mechanisms50:38 Forward and Backward Processes in Diffusion Models53:03 Learning the Score: Training Diffusion Models54:57 Inference with Diffusion Models: The Reverse Process57:36 Exploring Variants: Flow Matching and Consistency Models01:01:43 Benchmarking Different Models for Simulation-Based Inference01:06:41 Hierarchical Models and Their Applications in Inference01:14:25 Intervening in the Inference Process: Adding Constraints01:25:35 Summary of Key Concepts and Future DirectionsThank you to my Patrons for making this episode possible!Links from the show:- Come meet Alex at the Field of Play Conference in Manchester, UK, March 27, 2026!- Jonas's Diffusion for SBI Tutorial & Review (Paper & Code)- The BayesFlow Library- Jonas on LinkedIn- Jonas on GitHub- Further reading for more mathematical details: Holderrieth & Erives- 150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer & Jakob Robnik- 107 Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt
Send a textIn this episode, I had the pleasure of speaking with Jimmy Willis, a Senior Manager of Data Engineering at an AdTech company, where he builds systems that turn massive amounts of raw data into useful information. He is a self-taught programmer without a tech degree who was able to get an internship at JP Morgan Chase and leveraged that opportunity into a 6-figure job. Jimmy is currently writing a book and is on a mission to get 10,000 Black people into tech by learning Python and other real-world tech skills.https://www.rovion.co/Sign up for Activate Your Calling: Create, Build, & Promote Your Gift: https://bit.ly/4r0QixGSign up to be notified about Faith to Launch Community: https://bit.ly/FaithtoLaunchPlease join me in my YouTube only series, 30 Days to Becoming a Stronger, More Confident You in Christ: https://www.youtube.com/playlist?list=PLfkkBA4-h1A56MxObeO__s873pdUnnWQ5
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]:
Чтобы научиться программировать и разбираться в тонкостях Python 3.12 записывайтесь на базовый курс Learn Python — https://clck.ru/3MuShF Ведущие – Григорий Петров и Михаил Корнеев Ссылки от Сергея: toloka.ai - компания https://platform.toloka.ai/ - self service (быстрая разметка данных) tendem.ai - Tendem (кентавр AI + human) как начать работать в Толоке экспертом: https://mindrift.ai/apply Ссылки выпуска: Курс Learn Python — https://learn.python.ru/advanced Канал Миши в Telegram — https://t.me/tricky_python Канал Moscow Python в Telegram — https://t.me/moscow_python Все выпуски — https://podcast.python.ru Митапы Moscow Python — https://moscowpython.ru Канал Moscow Python на Rutube — https://rutube.ru/channel/45885590/ Канал Moscow Python в VK — https://vk.com/moscowpythonconf
Today we are joined by Matt Remke, who has spent years in the trenches of network automation projects as a consultant. Matt offers a unique, non-engineer perspective on scaling network automation in real-world, complex environments for some of the world’s largest companies. Matt shares what worked, what backfired, and the hard-earned lessons he has gained... Read more »
Today we are joined by Matt Remke, who has spent years in the trenches of network automation projects as a consultant. Matt offers a unique, non-engineer perspective on scaling network automation in real-world, complex environments for some of the world’s largest companies. Matt shares what worked, what backfired, and the hard-earned lessons he has gained... Read more »
Talk Python To Me - Python conversations for passionate developers
You've built your FastAPI app, it's running great locally, and now you want to share it with the world. But then reality hits -- containers, load balancers, HTTPS certificates, cloud consoles with 200 options. What if deploying was just one command? That's exactly what Sebastian Ramirez and the FastAPI Cloud team are building. On this episode, I sit down with Sebastian, Patrick Arminio, Savannah Ostrowski, and Jonathan Ehwald to go inside FastAPI Cloud, explore what it means to build a "Pythonic" cloud, and dig into how this commercial venture is actually making FastAPI the open-source project stronger than ever. Episode sponsors Command Book Python in Production Talk Python Courses Links from the show Guests Sebastián Ramírez: github.com Savannah Ostrowski: github.com Patrick Arminio: github.com Jonathan Ehwald: github.com FastAPI labs: fastapilabs.com quickstart: fastapicloud.com an episode on diskcache: talkpython.fm Fastar: github.com FastAPI: The Documentary: www.youtube.com Tailwind CSS Situation: adams-morning-walk.transistor.fm FastAPI Job Meme: fastapi.meme Migrate an Existing Project: fastapicloud.com Join the waitlist: fastapicloud.com Talk Python CLI Talk Python CLI Announcement: talkpython.fm Talk Python CLI GitHub: github.com Command Book Download Command Book: commandbookapp.com Announcement post: mkennedy.codes Watch this episode on YouTube: youtube.com Episode #536 deep-dive: talkpython.fm/536 Episode transcripts: talkpython.fm Theme Song: Developer Rap
Python 3.14 is here and continues Python's evolution toward greater performance, scalability, and usability. The new release formally supports free-threaded, no-GIL mode, introduces template string literals, and implements deferred evaluation of type annotations. It also includes new debugging and profiling tools, along with many other features. Łukasz Langa is the CPython Developer in Residence at The post Python 3.14 with Łukasz Langa appeared first on Software Engineering Daily.
GFA 482. Sören Dittrich reveals how a lean German Amazon seller uses Python, data analytics, and AI to spot profit leaks and thrive in the 2025 margin squeeze.
Topics covered in this episode: Command Book App uvx.sh: Install Python tools without uv or Python Ending 15 years of subprocess polling monty: A minimal, secure Python interpreter written in Rust for use by AI Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Command Book App New app from Michael Command Book App is a native macOS app for developers, data scientists, AI enthusiasts and more. This is a tool I've been using lately to help build Talk Python, Python Bytes, Talk Python Training, and many more applications. It's a bit like advanced terminal commands or complex shell aliases, but hosted outside of your terminal. This leaves the terminal there for interactive commands, exploration, short actions. Command Book manages commands like "tail this log while I'm developing the app", "Run the dev web server with true auto-reload", and even "Run MongoDB in Docker with exactly the settings I need" I'd love it if you gave it a look, shared it with your team, and send me feedback. Has a free version and paid version. Build with Swift and Swift UI Check it out at https://commandbookapp.com Brian #2: uvx.sh: Install Python tools without uv or Python Tim Hopper Michael #3: Ending 15 years of subprocess polling by Giampaolo Rodola The standard library's subprocess module has relied on a busy-loop polling approach since the timeout parameter was added to Popen.wait() in Python 3.3, around 15 years ago The problem with busy-polling CPU wake-ups: even with exponential backoff (starting at 0.1ms, capping at 40ms), the system constantly wakes up to check process status, wasting CPU cycles and draining batteries. Latency: there's always a gap between when a process actually terminates and when you detect it. Scalability: monitoring many processes simultaneously magnifies all of the above. + L1/L2 CPU cache invalidations It's interesting to note that waiting via poll() (or kqueue()) puts the process into the exact same sleeping state as a plain time.sleep() call. From the kernel's perspective, both are interruptible sleeps. Here is the merged PR for this change. Brian #4: monty: A minimal, secure Python interpreter written in Rust for use by AI Samuel Colvin and others at Pydantic Still experimental “Monty avoids the cost, latency, complexity and general faff of using a full container based sandbox for running LLM generated code. “ “Instead, it lets you safely run Python code written by an LLM embedded in your agent, with startup times measured in single digit microseconds not hundreds of milliseconds.” Extras Brian: Expertise is the art of ignoring - Kevin Renskers You don't need to master the language. You need to master your slice. Learning everything up front is wasted effort. Experience changes what you pay attention to. I hate fish - Rands (Michael Lopp) Really about productivity systems And a nice process for dealing with email Michael: Talk Python now has a CLI New essay: It's not vibe coding - Agentic engineering GitHub is having a day Python 3.14.3 and 3.13.12 are available Wall Street just lost $285 billion because of 13 markdown files Joke: Silence, current side project!
Despite her title, The Python Huntress is a snake lover through-and-through. It's only when Amy learned that invasive Burmese pythons were slithering riot around the Florida Everglades, that she decided to pack her bags and commit to the issue full gas. This involves hunting pythons day and night, and working as professional guide, helping regular folks to experience what it's like to locate and remove a 17ft snake.A huge thank you to Amy for sharing her stories and knowledge with us today!Visit her site and book a hunt at PythonHuntress.comAnd check out her socials on YouTube, Facebook and Instagram
In this week's FOLLOW UP, Bitcoin is down 15%, miners are unplugging rigs because paying eighty-seven grand to mine a sixty-grand coin finally failed the vibes check, and Grok is still digitally undressing men—suggesting Musk's “safeguards” remain mostly theoretical, which didn't help when X offices got raided in France. Spain wants to ban social media for kids under 16, Egypt is blocking Roblox outright, and governments everywhere are flailing at the algorithmic abyss.IN THE NEWS, Elon Musk is rolling xAI into SpaceX to birth a $1.25 trillion megacorp that wants to power AI from orbit with a million satellites, because space junk apparently wasn't annoying enough. Amazon admits a “high volume” of CSAM showed up in its AI training data and blames third parties, Waymo bags a massive $16 billion to insist robotaxis are working, Pinterest reportedly fires staff who built a layoff-tracking tool, and Sam Altman gets extremely cranky about Claude's Super Bowl ads hitting a little too close to home.For MEDIA CANDY, we've got Shrinking, the Grammys, Star Trek: Starfleet Academy's questionable holographic future, Neil Young gifting his catalog to Greenland while snubbing Amazon, plus Is It Cake? Valentines and The Rip.In APPS & DOODADS, we test Sennheiser earbuds, mess with Topaz Video, skip a deeply cursed Python script that checks LinkedIn for Epstein connections, and note that autonomous cars and drones will happily obey prompt injection via road signs—defeated by a Sharpie.IN THE LIBRARY, there's The Regicide Report, a brutal study finding early dementia signals in Terry Pratchett's novels, Neil Gaiman denying allegations while announcing a new book, and THE DARK SIDE WITH DAVE, vibing with The Muppet Show as Disney names a new CEO. We round it out with RentAHuman.ai dread relief via paper airplane databases, free Roller Coaster Tycoon, and Sir Ian McKellen on Colbert—still classy in the digital wasteland.Sponsors:DeleteMe - Get 20% off your DeleteMe plan when you go to JoinDeleteMe.com/GOG and use promo code GOG at checkout.SquareSpace - go to squarespace.com/GRUMPY for a free trial. And when you're ready to launch, use code GRUMPY to save 10% off your first purchase of a website or domain.Private Internet Access - Go to GOG.Show/vpn and sign up today. For a limited time only, you can get OUR favorite VPN for as little as $2.03 a month.SetApp - With a single monthly subscription you get 240+ apps for your Mac. Go to SetApp and get started today!!!1Password - Get a great deal on the only password manager recommended by Grumpy Old Geeks! gog.show/1passwordShow notes at https://gog.show/732FOLLOW UPBitcoin drops 15%, briefly breaking below $61,000 as sell-off intensifies, doubts about crypto growBitcoin Is Crashing So Hard That Miners Are Unplugging Their EquipmentGrok, which maybe stopped undressing women without their consent, still undresses menX offices raided in France as UK opens fresh investigation into GrokSpain set to ban social media for children under 16Egypt to block Roblox for all usersIN THE NEWSElon Musk Is Rolling xAI Into SpaceX—Creating the World's Most Valuable Private CompanySpaceX wants to launch a constellation of a million satellites to power AI needsA potential Starlink competitor just got FCC clearance to launch 4,000 satellitesAmazon discovered a 'high volume' of CSAM in its AI training data but isn't saying where it came fromWaymo raises massive $16 billion round at $126 billion valuation, plans expansion to 20+ citiesPinterest Reportedly Fires Employees Who Built a Tool to Track LayoffsSam Altman got exceptionally testy over Claude Super Bowl adsMEDIA CANDYShrinkingStar Trek: Starfleet AcademyThe RipNeil Young gifts Greenland free access to his music and withdraws it from Amazon over TrumpIs it Cake? ValentinesAPPS & DOODADSSennheiser Consumer Audio IE 200 In-Ear Audiophile Headphones - TrueResponse Transducers for Neutral Sound, Impactful Bass, Detachable Braided Cable with Flexible Ear Hooks - BlackSennheiser Consumer Audio CX 80S In-ear Headphones with In-line One-Button Smart Remote – BlackTopaz VideoEpsteinAutonomous cars, drones cheerfully obey prompt injection by road signAT THE LIBRARYThe Regicide Report (Laundry Files Book 14) by Charles StrossScientists Found an Early Signal of Dementia Hidden in Terry Pratchett's NovelsNeil Gaiman Denies the Allegations Against Him (Again) While Announcing a New BookTHE DARK SIDE WITH DAVEDave BittnerThe CyberWireHacking HumansCaveatControl LoopOnly Malware in the BuildingThe Muppet ShowDisney announces Josh D'Amaro will be its new CEO after Iger departsA Database of Paper Airplane Designs: Hours of Fun for Kids & Adults AlikeOnline (free!) version of Roller Coaster tycoon.Speaking of coasters, here's the current world champion.I am hoping this is satire...Sir Ian McKellen on Colbert.CLOSING SHOUT-OUTSCatherine O'Hara: The Grande Dame of Off-Center ComedyStanding with Sam 'Balloon Man' MartinezSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
186: Becoming a ManagerIntro topic: plastic welding kitsNews/Links:Parse.bot, turn any website into an APIhttps://www.parse.bot/Gemini 3https://blog.google/products/gemini/gemini-3/Depth Anything 3https://github.com/ByteDance-Seed/Depth-Anything-3Wan 2.2 (run on runpod)https://www.runpod.io/Book of the ShowPatrickThe Thinking Game (DeepMind documentary)https://www.youtube.com/watch?v=d95J8yzvjbQJasonPlato: The Republichttps://www.gutenberg.org/ebooks/1497Patreon Plug https://www.patreon.com/programmingthrowdown?ty=hTool of the ShowPatrickCore KeeperPc/Switch/Xbox/Playstation JasonWorkers & Resources: Soviet RepublicPCTopic: Becoming a ManagerWhat is a ManagerOpportunityResults + RetentionSizingHiringPhilosophyInterviewsDownsizingHow to ManageCompany Goals / OKRsBreaking down & claiming company goals.Balancing inspirational & practical goalsCoachingOne-on-onesCareer planningPerformance MotivationPerformance Management ReviewCompensationChoosing to become a managerBalancing personal and company incentivesWhy ManageMentorshipBuild relationshipsWhy to not manageLess time for your original joy (coding)Less technical influenceMore uncertainty and less closureHow to transition back to EngineerTake the time/energy to get ramped upAct as an advisor to your manager ★ Support this podcast on Patreon ★
A Brisbane woman discovered a massive carpet python coiled on her chest in the middle of the night, handled it herself like a true Australian, and admitted she would have been more terrified if it had been a toad.READ or SHARE: https://weirddarkness.com/python-chest/WeirdDarkness® is a registered trademark. Copyright ©2026, Weird Darkness.#WeirdDarkness #WeirdDarkNEWS #Python #SnakeInBed #Australia #CarpetPython #WildlifeEncounter #StrangeNews #TrueStory #CaughtOnCamera
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