Podcasts about simon willison

Aujourd'hui, on parle des navigateurs d'intelligence artificielle, ces nouveaux outils censés révolutionner notre manière de naviguer sur le web.Mais sachez qu'ils font déjà frémir les experts en cybersécurité.La grande question c'est donc faut-il leur faire confiance ? Une nouvelle génération de navigateurs internetD'abord il faut bien comprendre qu'il s'agit d'une nouvelle génération de navigateurs internet. OpenAI par exemple vient tout juste de lancer ChatGPT Atlas, un navigateur qui intègre directement ChatGPT à l'intérieur de chaque onglet.L'idée paraît simple. Votre assistant IA vous accompagne partout sur le web, comprend ce que vous faites et peut même exécuter des actions pour vous, comme passer une commande en ligne, résumer un texte, ou encore analyser un dépôt GitHub.Atlas rejoint ainsi une nouvelle vague de navigateurs dits intelligents, comme Perplexity Comet, ou encore Dia. Mais derrière ces promesses d'efficacité se cache un vrai casse-tête côté sécurité et confidentialité.Le cauchemar des injections par des promptA commencer le cauchemar des injections par des prompt, en anglais on parle de prompt injection. Concrètement, cela signifie qu'un pirate peut tromper le modèle d'IA pour qu'il contourne ses propres garde-fous et exécute des actions non autorisées.Et comme ces assistants ont souvent les mêmes privilèges que l'utilisateur connecté, les dégâts peuvent être considérables. OpenAI assure investir « massivement » pour détecter ce type d'attaques. Mais comme le résume le développeur Simon Willison, « question sécurité, 99 % de réussite, c'est déjà un échec ».Mais au delà des attaques, la question de la vie privée est cardinale avec ces nouveaux navigateurs d'IA.La question du respect de la vie privéeCertes, OpenAI a prévu un mode déconnecté et un mode surveillance, censés redonner un peu de contrôle à l'utilisateur.Mais les experts restent sceptiques, car autoriser une IA à explorer tout votre historique de navigation, c'est lui ouvrir une fenêtre sur toute votre vie numérique.Eamonn Maguire, de Proton, va même plus loin. Selon lui, la navigation IA transforme la recherche en une forme de capitalisme de surveillance ultra-personnalisée parce que les IA ne voient plus seulement ce que vous cherchez, mais comment vous pensez et ce que vous ferez ensuite.Alors, faut-il adopter un navigateur IA ?Alors, faut-il adopter un navigateur IA ? Voici la réponse courte. Et bien pas encore.Et si vous voulez tester navigateur d'IA, évitez d'y connecter vos comptes sensibles, et surveillez de près les permissions que vous accordez.Le ZD Tech est sur toutes les plateformes de podcast ! Abonnez-vous !Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Hey everyone, Alex here! Welcome... to the browser war II - the AI edition! This week we chatted in depth about ChatGPT's new Atlas agentic browser, and the additional agentic powers Microsoft added to Edge with Copilot Mode (tho it didn't work for me) Also this week was a kind of crazy OCR week, with more than 4 OCR models releasing, and the crown one is DeepSeek OCR, that turned the whole industry on it's head (more later) Quite a few video updates as well, with real time lipsync from Decart, and a new update from LTX with 4k native video generation, it's been a busy AI week for sure! Additionally, I've had the pleasure to talk about AI Browsing agents with Paul from BrowserBase and real time video with Kwindla Kramer from Pipecat/Daily, so make sure to tune in for those interviews, buckle up, let's dive in! Thanks for reading ThursdAI - Recaps of the most high signal AI weekly spaces! This post is public so feel free to share it.Open Source: OCR is Not What You Think It Is (X, HF, Paper)The most important and frankly mind-bending release this week came from DeepSeek. They dropped DeepSeek-OCR, and let me tell you, this is NOT just another OCR model. The cohost were buzzing about this, and once I dug in, I understood why. This isn't just about reading text from an image; it's a revolutionary approach to context compression.We think that DeepSeek needed this as an internal tool, so we're really grateful to them for open sourcing this, as they did something crazy here. They are essentially turning text into a visual representation, compressing it, and then using a tiny vision decoder to read it back with incredible accuracy. We're talking about a compression ratio of up to 10x with 97% decoding accuracy. Even at 20x compression they are achieving 60% decoding accuracy! My head exploded live on the show when I read that. This is like the middle-out compression algorithm joke from Silicon Valley, but it's real. As Yam pointed out, this suggests our current methods of text tokenization are far from optimal.With only 3B and ~570M active parameters, they are taking a direct stab at long context inefficiency, imagine taking 1M tokens, encoding them into 100K visual tokens, and then feeding those into a model. Since the model is tiny, it's very cheap to run, for example, alphaXiv claimed they have OCRd' all of the papers on ArXiv with this model for $1000, a task that would have cost $7500 using MistalOCR - as per their paper, with DeepSeek OCR, on a single H100 GPU, its possible to scan up to 200K pages!

Csaba Okrona lays out exactly what Flow is (then shows you how to engineer your way back to it), a smart vacuum turned against an innocent hacker, Matz and the Ruby core team step up to steward RubyGems, Simon Willison things Claude Skills could be bigger than MCP, and Luke Plant looks at technical debt from a more positive perspective.

Csaba Okrona lays out exactly what Flow is (then shows you how to engineer your way back to it), a smart vacuum turned against an innocent hacker, Matz and the Ruby core team step up to steward RubyGems, Simon Willison things Claude Skills could be bigger than MCP, and Luke Plant looks at technical debt from a more positive perspective.

Csaba Okrona lays out exactly what Flow is (then shows you how to engineer your way back to it), a smart vacuum turned against an innocent hacker, Matz and the Ruby core team step up to steward RubyGems, Simon Willison things Claude Skills could be bigger than MCP, and Luke Plant looks at technical debt from a more positive perspective.

Hey folks, Alex here. Can you believe it's already the middle of October? This week's show was a special one, not just because of the mind-blowing news, but because we set a new ThursdAI record with four incredible interviews back-to-back!We had Jessica Gallegos from Google DeepMind walking us through the cinematic new features in VEO 3.1. Then we dove deep into the world of Reinforcement Learning with my new colleague Kyle Corbitt from OpenPipe. We got the scoop on Amp's wild new ad-supported free tier from CEO Quinn Slack. And just as we were wrapping up, Swyx ( from Latent.Space , now with Cognition!) jumped on to break the news about their blazingly fast SWE-grep models. But the biggest story? An AI model from Google and Yale made a novel scientific discovery about cancer cells that was then validated in a lab. This is it, folks. This is the “let's f*****g go” moment we've been waiting for. So buckle up, because this week was an absolute monster. Let's dive in!ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Open Source: An AI Model Just Made a Real-World Cancer DiscoveryWe always start with open source, but this week felt different. This week, open source AI stepped out of the benchmarks and into the biology lab.Our friends at Qwen kicked things off with new 3B and 8B parameter versions of their Qwen3-VL vision model. It's always great to see powerful models shrink down to sizes that can run on-device. What's wild is that these small models are outperforming last generation's giants, like the 72B Qwen2.5-VL, on a whole suite of benchmarks. The 8B model scores a 33.9 on OS World, which is incredible for an on-device agent that can actually see and click things on your screen. For comparison, that's getting close to what we saw from Sonnet 3.7 just a few months ago. The pace is just relentless.But then, Google dropped a bombshell. A 27-billion parameter Gemma-based model they developed with Yale, called C2S-Scale, generated a completely novel hypothesis about how cancer cells behave. This wasn't a summary of existing research; it was a new idea, something no human scientist had documented before. And here's the kicker: researchers then took that hypothesis into a wet lab, tested it on living cells, and proved it was true.This is a monumental deal. For years, AI skeptics like Gary Marcus have said that LLMs are just stochastic parrots, that they can't create genuinely new knowledge. This feels like the first, powerful counter-argument. Friend of the pod, Dr. Derya Unutmaz, has been on the show before saying AI is going to solve cancer, and this is the first real sign that he might be right. The researchers noted this was an “emergent capability of scale,” proving once again that as these models get bigger and are trained on more complex data—in this case, turning single-cell RNA sequences into “sentences” for the model to learn from—they unlock completely new abilities. This is AI as a true scientific collaborator. Absolutely incredible.Big Companies & APIsThe big companies weren't sleeping this week, either. The agentic AI race is heating up, and we're seeing huge updates across the board.Claude Haiku 4.5: Fast, Cheap Model Rivals Sonnet 4 Accuracy (X, Official blog, X)First up, Anthropic released Claude Haiku 4.5, and it is a beast. It's a fast, cheap model that's punching way above its weight. On the SWE-bench verified benchmark for coding, it hit 73.3%, putting it right up there with giants like GPT-5 Codex, but at a fraction of the cost and twice the speed of previous Claude models. Nisten has already been putting it through its paces and loves it for agentic workflows because it just follows instructions without getting opinionated. It seems like Anthropic has specifically tuned this one to be a workhorse for agents, and it absolutely delivers. The thing to note also is the very impressive jump in OSWorld (50.7%), which is a computer use benchmark, and at this price and speed ($1/$5 MTok input/output) is going to make computer agents much more streamlined and speedy! ChatGPT will loose restrictions; age-gating enables “adult mode” with new personality features coming (X) Sam Altman set X on fire with a thread announcing that ChatGPT will start loosening its restrictions. They're planning to roll out an “adult mode” in December for age-verified users, potentially allowing for things like erotica. More importantly, they're bringing back more customizable personalities, trying to recapture some of the magic of GPT-4.0 that so many people missed. It feels like they're finally ready to treat adults like adults, letting us opt-in to R-rated conversations while keeping strong guardrails for minors. This is a welcome change, and we've been advocating for this for a while, and it's a notable change from the XAI approach I covered last week. Opt in for adults with verification while taking precautions vs engagement bait in the form of a flirty animated waifu with engagement mechanics. Microsoft is making every windows 11 an AI PC with copilot voice input and agentic powers (Blog,X)And in breaking news from this morning, Microsoft announced that every Windows 11 machine is becoming an AI PC. They're building a new Copilot agent directly into the OS that can take over and complete tasks for you. The really clever part? It runs in a secure, sandboxed desktop environment that you can watch and interact with. This solves a huge problem with agents that take over your mouse and keyboard, locking you out of your own computer. Now, you can give the agent a task and let it run in the background while you keep working. This is going to put agentic AI in front of hundreds of millions of users, and it's a massive step towards making AI a true collaborator at the OS level.NVIDIA DGX - the tiny personal supercomputer at $4K (X, LMSYS Blog)NVIDIA finally delivered their promised AI Supercomputer, and while the excitement was in the air with Jensen hand delivering the DGX Spark to OpenAI and Elon (recreating that historical picture when Jensen hand delivered a signed DGX workstation while Elon was still affiliated with OpenAI). The workstation was sold out almost immediately. Folks from LMSys did a great deep dive into specs, all the while, folks on our feeds are saying that if you want to get the maximum possible open source LLMs inference speed, this machine is probably overpriced, compared to what you can get with an M3 Ultra Macbook with 128GB of RAM or the RTX 5090 GPU which can get you similar if not better speeds at significantly lower price points. Anthropic's “Claude Skills”: Your AI Agent Finally Gets a Playbook (Blog)Just when we thought the week couldn't get any more packed, Anthropic dropped “Claude Skills,” a huge upgrade that lets you give your agent custom instructions and workflows. Think of them as expertise folders you can create for specific tasks. For example, you can teach Claude your personal coding style, how to format reports for your company, or even give it a script to follow for complex data analysis.The best part is that Claude automatically detects which “Skill” is needed for a given task, so you don't have to manually load them. This is a massive step towards making agents more reliable and personalized, moving beyond just a single custom instruction and into a library of repeatable, expert processes. It's available now for all paid users, and it's a feature I've been waiting for. Our friend Simon Willison things skills may be a bigger deal than MCPs!

Hey everyone, Alex here

Twenty years after a scrappy newsroom team hacked together a framework to ship stories fast, Django remains the Python web framework that ships real apps, responsibly. In this anniversary roundtable with its creators and long-time stewards: Simon Willison, Adrian Holovaty, Will Vincent, Jeff Triplet, and Thibaud Colas, we trace the path from the Lawrence Journal-World to 1.0, DjangoCon, and the DSF; unpack how a BSD license and a culture of docs, tests, and mentorship grew a global community; and revisit lessons from deployments like Instagram. We talk modern Django too: ASGI and async, HTMX-friendly patterns, building APIs with DRF and Django Ninja, and how Django pairs with React and serverless without losing its batteries-included soul. You'll hear about Django Girls, Djangonauts, and the Django Fellowship that keep momentum going, plus where Django fits in today's AI stacks. Finally, we look ahead at the next decade of speed, security, and sustainability. Episode sponsors Talk Python Courses Python in Production Links from the show Guests Simon Willison: simonwillison.net Adrian Holovaty: holovaty.com Will Vincent: wsvincent.com Jeff Triplet: jefftriplett.com Thibaud Colas: thib.me Show Links Django's 20th Birthday Reflections (Simon Willison): simonwillison.net Happy 20th Birthday, Django! (Django Weblog): djangoproject.com Django 2024 Annual Impact Report: djangoproject.com Welcome Our New Fellow: Jacob Tyler Walls: djangoproject.com Soundslice Music Learning Platform: soundslice.com Djangonaut Space Mentorship for Django Contributors: djangonaut.space Wagtail CMS for Django: wagtail.org Django REST Framework: django-rest-framework.org Django Ninja API Framework for Django: django-ninja.dev Lawrence Journal-World: ljworld.com Watch this episode on YouTube: youtube.com Episode #518 deep-dive: talkpython.fm/518 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

In this milestone 150th episode, hosts Kelly Schuster-Paredes and Sean Tibor sit down with Simon Willison, co-creator of Django and creator of Datasette and LLM tools, for an in-depth conversation about artificial intelligence in Python education. The discussion covers the current landscape of LLMs in coding education, from the benefits of faster iteration cycles to the risks of students losing that crucial "aha moment" when they solve problems independently. Simon shares insights on prompt injection vulnerabilities, the importance of local models for privacy, and why he believes LLMs are much harder to use effectively than most people realize. Key topics include: Educational Strategy: When to introduce AI tools vs. building foundational skills first Security Concerns: Prompt injection attacks and their implications for educational tools Student Engagement: Maintaining motivation and problem-solving skills in an AI world Practical Applications: Using LLMs for code review, debugging, and rapid prototyping Privacy Issues: Understanding data collection and training practices of major AI companies Local Models: Running AI tools privately on personal devices The "Jagged Frontier": Why LLMs excel at some tasks while failing at others Simon brings 20 years of Django experience and deep expertise in both web development and AI tooling to discuss how educators can thoughtfully integrate these powerful but unpredictable tools into their classrooms. The conversation balances excitement about AI's potential with realistic assessments of its limitations and risks. Whether you're a coding educator trying to navigate the AI revolution or a developer interested in the intersection of education and technology, this episode provides practical insights for working with LLMs responsibly and effectively. Resources mentioned: - Simon's blog: simonwillison.net - Mission Encodable curriculum - Datasette and LLM tools - GitHub Codespaces for safe AI experimentation Special Guest: Simon Willison.

Why do Postgres developers, contributors, and users do what they do? In each episode of Talking Postgres, Claire Giordano talks to people from across the Postgres ecosystem—how they got started, what they've learned, and what they're still figuring out. This 3-minute trailer offers a fast-paced glimpse into the fun, surprising, and deeply human stories behind Postgres, including failures, wins, obstacles—and all the messy parts in between. New episodes monthly. Always on Fridays. Subscribe wherever you get your podcasts.Episodes from Talking Postgres with guests featured in the trailer (in order of appearance): Episode 01: Working in public on open source with Simon Willison and Marco SlotEpisode 18: How I got started as a developer (& in Postgres) with David RowleyEpisode 20: How I got started as a developer (& in Postgres) with Tom LaneEpisode 07: Why people care about PostGIS and Postgres with Paul Ramsey & Regina ObeEpisode 29: How I got started leading database teams with Shireesh ThotaEpisode 25: Why Python developers just use Postgres with Dawn WagesEpisode 19: Becoming a Postgres committer with Melanie PlagemanEpisode 24: Why mentor Postgres developers with Robert HaasEpisode 04: How I got started as a dev (& in Postgres) w/Melanie Plageman & Thomas Munro

AI is now definitely changing how we live our (geography) lives. Join Rachel, Dani, and Levi for a chat about how Artificial Intelligence is emerging in our GLAD lives. How is it being used "in the wilds" of teaching, research, coding, publishing, and beyond. Should you be farming out your peer reviews to a computer? Vibe coding with your friend Claude? Setting out on AI-generated running routes? And, as always, do we really need more papers faster than ever before? We chat through this, with some advice and reflections on where AI might change our own practice and how it's certainly changing the way academia (writ large) works.  Let us know what you think at thegladpodcast@gmail.com. Did we miss something about AI that you now can't live without? Have we missed your favorite anecdote about a deluge of digital dreck? Send us an email!  Resources we discussed:  Nature's recent editorial on peer review and AI https://www.nature.com/articles/d41586-025-00894-7 Harper Reed's post on how to code well with LLMs https://harper.blog/2025/04/17/an-llm-codegen-heros-journey/ Ethan Mollick's "reasoned optimism" about thinking with AI https://www.oneusefulthing.org/ Simon Willison's blog tracking the frontier of LLMs  https://simonwillison.org      

It's always a good day if you see a pelican. In Episode 30 of Talking Postgres with Claire Giordano, open source developer Simon Willison—creator of Datasette and co-creator of Django—joins to explore how AI is useful for data engineers today. We move past the hype and boosterism to dig into example after example: structured data extraction, alt text and accessibility, safety and security (aka the fiddly bits), and why Postgres's fine-grained permissions are such a good fit for AI-powered workflows. Also: Pulitzer-worthy data tooling, the science fiction of the 10X engineer, agents, MCP, RAG, the multitude of models, and why Simon spends so many waking hours on the jagged frontier of AI.Links mentioned in this episode:Blog: Simon Willison's WeblogBlog: Simon's Willison's TIL - Things I've LearnedPodcast episode: Working in public on open source with Simon Willison and Marco SlotProject page: Django Web FrameworkProject page: Datasette, for finding stories in data GitHub repo: llm CLI tool and Python libraryDemo: Language models on the command-line w/ Simon WillisonBlog post: OpenAI's new open weight (Apache 2) models are really good, by Simon Willison Podcast episode: Accessibility and Gen AI podcast with guest Simon WillisonBlog post: New dashboard: alt text for all my images, by Simon Willison Keynote talk: Big Opportunities in Small Data, by Simon Willison at Citus Con: An Event for Postgres 2023 Blog post: How OpenElections Uses LLMs, by Derek Willis Blog posts tagged with pelican-riding-a-bicycle on Simon Willison's Weblog Blog post: No, AI is not Making Engineers 10x as Productive, via Colton Voege, featured on Simon's weblogGitHub repo: pgvector extension to PostgresCal invite: LIVE recording of Ep31 of Talking Postgres to happen on Wed Sep 17, 2025

On this episode of Screaming in the Cloud, Corey Quinn talks with Simon Willison, founder of Datasette and creator of LLM CLI about AI's realities versus the hype. They dive into Simon's “lethal trifecta” of AI security risks, his prediction of a major breach within six months, and real-world use cases of his open source tools, from investigative journalism to OSINT sleuthing. Simon shares grounded insights on coding with AI, the real environmental impact, AGI skepticism, and why human expertise still matters. A candid, hype-free take from someone who truly knows the space.Highlights: 00:00 Introduction and Security Concerns02:32 Conversations and Kindness04:56 Niche Museums and Collecting06:52 Blogging as a Superpower08:01 Challenges of Writing and AI15:08 Unique Use Cases of Dataset19:33 The Evolution of Open Source21:09 Security Vulnerabilities in AI32:18 Future of AI and AGI Concerns37:10 Learning Programming with AI39:12 Vibe Coding and Its Risks41:49 Environmental Impact of AI46:34 AI in Legal and Creative Fields54:20 Voice AI and Ethical Concerns01:00:07 Monetizing Content CreativelyLinks: Simon Willison's BlogDatasette ProjectLLM command-line tool and Python libraryNiche MuseumsGitHub MCP prompt injection exampleHighlights from the Claude 4 system promptAI energy usage tagAI assisted search-based research actually works nowPOSSE: Publish on your own site, syndicate elsewhereBellingcatLawyer cites fake cases invented by ChatGPT, judge is not amused (May 2023)AI hallucination cases databaseSponsor Simon to get his monthly summary newsletterhttps://simonwillison.net/https://www.linkedin.com/in/simonwillisonhttps://datasette.io/

This episode covers a massive amount of AI-related news and research, especially developments over the past week, including ChatGPT's new "study mode," major platform announcements from Google, Microsoft, and OpenAI, generative video and music tools, and the implications of AI on jobs and education. The episode also highlights a new Stanford GenAI for Education hub and discusses current AI policy and access initiatives globally. News   ChatGPT Study Mode: https://openai.com/index/chatgpt-study-mode/  Simon Willison info on the system prompt for Study Mode: https://simonwillison.net/2025/Jul/29/openai-introducing-study-mode/  Dr Philippa Hardman's LinkedIn post on Study Mode: https://www.linkedin.com/posts/dr-philippa-hardman-057851120_as-a-member-of-openais-educator-advisor-ugcPost-7356234831929696256-OF6k   Google announced “Guided Learning” mode for Gemini https://blog.google/products/gemini/google-ai-pro-students-learning/    Open models by OpenAI https://openai.com/open-models/    Google released Gemini Storybooks https://gemini.google/overview/storybook/   ElevenLabs dropped a new multi-lingual music generation model https://elevenlabs.io/music    OpenAI giving ChatGPT Enterprise to every US Federal Government department for $1 a year https://openai.com/index/providing-chatgpt-to-the-entire-us-federal-workforce/   Microsoft Copilot released for 13+ students  https://techcommunity.microsoft.com/blog/EducationBlog/microsoft-365-copilot-chat-for-students-13/4412957    18 months. 12,000 questions. A whole lot of anxiety. What I learned from reading students' ChatGPT logs https://www.theguardian.com/technology/2025/jul/27/it-wants-users-hooked-and-jonesing-for-their-next-fix-are-young-people-becoming-too-reliant-on-ai  The Presidential AI Challenge https://orise.orau.gov/ai-challenge/    Research GenAI for Education Hub at Stanford University https://scale.stanford.edu/genai/repository  Working with AI: Measuring the Occupational Implications of Generative AI https://arxiv.org/abs/2507.07935  Prompting Science Report 3: I'll pay you or I'll kill you - but will you care? https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5375404 

Hey folks

Serious zero-day has been uncovered that is affecting everybody all around the world. There is a patch tho. Mark Gurman dishes on the foldable iPhone. TSMC joins the Trillion-Dollar-Club. If you're an expert in a given field you too can join the AI goldrush. And did we just take a big step toward AGI, or is this just the latest in the hype-cycle?Sponsors:AGNTCY.ORGLinks:Hackers Exploit Microsoft SharePoint as Firm Works to Patch (Bloomberg)The First Foldable iPhone Will Arrive Next Year in Un-Apple-Like Fashion (Bloomberg)Nvidia's CUDA platform now supports RISC-V — support brings open source instruction set to AI platforms, joining x86 and Arm (Tom's Hardware)TSMC's Taiwan Stock Value Surpasses $1 Trillion Amid AI Frenzy (Bloomberg)AI groups spend to replace low-cost ‘data labellers' with high-paid experts (FT)OpenAI's experimental model achieved gold at the International Math Olympiad (Engadget)OpenAI's gold medal performance on the International Math Olympiad. (Simon Willison's Weblog)See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Interview with Anil Dash egirl Grok x Claude Zuckerberg announces Meta's new AI data centers for superintelligence Meta Hires Two More OpenAI Researchers Reflections on OpenAI RSS is (not) dead (yet) (NED #3) – audra mcnamee Perplexity AI browser Perplexity CEO says its browser will track everything users do online to sell 'hyper personalized' ads Stewart Holbrook: Portland Mythmaker How I Learned to Stop Worrying and Have Fun With A.I. Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Anil Dash Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: helixsleep.com/twit agntcy.org Melissa.com/twit

Interview with Anil Dash egirl Grok x Claude Zuckerberg announces Meta's new AI data centers for superintelligence Meta Hires Two More OpenAI Researchers Reflections on OpenAI RSS is (not) dead (yet) (NED #3) – audra mcnamee Perplexity AI browser Perplexity CEO says its browser will track everything users do online to sell 'hyper personalized' ads Stewart Holbrook: Portland Mythmaker How I Learned to Stop Worrying and Have Fun With A.I. Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Anil Dash Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: helixsleep.com/twit agntcy.org Melissa.com/twit

Interview with Anil Dash egirl Grok x Claude Zuckerberg announces Meta's new AI data centers for superintelligence Meta Hires Two More OpenAI Researchers Reflections on OpenAI RSS is (not) dead (yet) (NED #3) – audra mcnamee Perplexity AI browser Perplexity CEO says its browser will track everything users do online to sell 'hyper personalized' ads Stewart Holbrook: Portland Mythmaker How I Learned to Stop Worrying and Have Fun With A.I. Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Anil Dash Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: helixsleep.com/twit agntcy.org Melissa.com/twit

Interview with Anil Dash egirl Grok x Claude Zuckerberg announces Meta's new AI data centers for superintelligence Meta Hires Two More OpenAI Researchers Reflections on OpenAI RSS is (not) dead (yet) (NED #3) – audra mcnamee Perplexity AI browser Perplexity CEO says its browser will track everything users do online to sell 'hyper personalized' ads Stewart Holbrook: Portland Mythmaker How I Learned to Stop Worrying and Have Fun With A.I. Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Anil Dash Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: helixsleep.com/twit agntcy.org Melissa.com/twit

Interview with Anil Dash egirl Grok x Claude Zuckerberg announces Meta's new AI data centers for superintelligence Meta Hires Two More OpenAI Researchers Reflections on OpenAI RSS is (not) dead (yet) (NED #3) – audra mcnamee Perplexity AI browser Perplexity CEO says its browser will track everything users do online to sell 'hyper personalized' ads Stewart Holbrook: Portland Mythmaker How I Learned to Stop Worrying and Have Fun With A.I. Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Anil Dash Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: helixsleep.com/twit agntcy.org Melissa.com/twit

Interview with Anil Dash egirl Grok x Claude Zuckerberg announces Meta's new AI data centers for superintelligence Meta Hires Two More OpenAI Researchers Reflections on OpenAI RSS is (not) dead (yet) (NED #3) – audra mcnamee Perplexity AI browser Perplexity CEO says its browser will track everything users do online to sell 'hyper personalized' ads Stewart Holbrook: Portland Mythmaker How I Learned to Stop Worrying and Have Fun With A.I. Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Anil Dash Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: helixsleep.com/twit agntcy.org Melissa.com/twit

In episode 39 of Generationship, Rachel speaks with Simon Willison, founder of Datasette and co-creator of Django. Simon discusses the surprising resurgence of blogging, his coining of the term “prompt injection,” the power of learning in public, and how he uses GitHub issues as an external brain to manage hundreds of projects. This quick-witted and humorous conversation offers a pragmatic look at leveraging today's tools for maximum productivity and impact.

In episode 39 of Generationship, Rachel speaks with Simon Willison, founder of Datasette and co-creator of Django. Simon discusses the surprising resurgence of blogging, his coining of the term “prompt injection,” the power of learning in public, and how he uses GitHub issues as an external brain to manage hundreds of projects. This quick-witted and humorous conversation offers a pragmatic look at leveraging today's tools for maximum productivity and impact.

Thank you to the folks at Sustain (https://sustainoss.org/) for providing the hosting account for CHAOSSCast! CHAOSScast – Episode 112 In this episode of CHAOSScast, host Georg Link, along with Nicole Huesman and Ruth Ikegah, welcome guest Chrys Wu to discuss the findings from the “State of Open Infrastructure” report by Invest in Open Infrastructure (IOI). The episode dives into how community health frameworks shape open infrastructure decisions, highlighting various frameworks like CHAOSS metrics, the FOREST framework, and POSI. Chrys talks about the Infra Finder tool and the importance of trust in decision-making for open infrastructure. The conversation also touches on metrics for understanding community engagement, the challenges of resource allocation, and the impact of open infrastructure on research ecosystems. Press download now to find out more! [00:01:01] Chrys explains her role of product lead at IOI and leading the research for the “State of Open Infrastructure” report. [00:02:08] Why study community health frameworks? IOI heard repeated mentions of frameworks in conversations but wasn't sure of their awareness or impact. [00:03:20] Chrys talks about the focus on three frameworks: CHAOSS, the FOREST framework (scholarly publishing), and POSI (The Principles of Open Scholarly Infrastructure). [00:06:20] Chrys defines “Open Infrastructure” that includes software, tools, standards, protocols used across the research lifecycle, and does not include hardware. [00:07:15] She shares some research findings that include a general low awareness of community health frameworks, trust is the key theme, and goes in depth with some primary concerns. [00:10:00] Ruth elaborates into CHAOSS metrics context explaining CHAOSS uses working groups to develop context-aware metrics and metrics and models help projects focus efforts, like identifying organizational diversity or adoption levels. [00:14:48] Who are CHAOSS metrics for? Ruth explains that metrics are made for the community: maintainers, users, contributors, and funders, and Georg talks about the origins of CHAOSS. [00:17:55] Chrys dives into the Infra Finder breakdown. She goes in depth what it gathers information on and how it allows organizations to assess readiness and sustainability of open infrastructure projects. [00:21:42] The Infra Finder has been used successfully in procurement processes. Chrys turns the conversation to talking about decision making. Users depend on peer feedback and case studies due to lack of formal sales/support in open source and she shares some key questions. [00:26:34] Nicole brings up the topic of supporting new contributors and there's a discussion on how CHAOSS includes metrics to identify and support new contributors and resources include project badging, practitioner guides, and metric models. Also, Ruth talks about challenges for small projects. [00:31:35] Chrys details some information on how IOI helps improve project visibility by sharing entries and providing communications support. [00:32:38] As far as what's next for the research they are doing, Chrys shares they're doing more in-depth sharing of report findings coming soon and IOI invites collaborators to help build governance, strategy, and respond to community needs. Value Adds (Picks) of the week: [00:35:16] Georg's pick is electric vehicle infrastructure. [00:36:00] Nicole's pick is the concept of slow travel. [00:37:03] Ruth's pick is spontaneously hanging out with friends. [00:38:19] Chrys's pick is Simon Willison's Newsletter and her favorite musical group, SHINee. *Panelists: * Georg Link Nicole Huesman Ruth Ikegah Guest: Chrys Wu Links: CHAOSS (https://chaoss.community/) CHAOSS Project X (https://twitter.com/chaossproj?lang=en) CHAOSScast Podcast (https://podcast.chaoss.community/) CHAOSS YouTube (https://www.youtube.com/@CHAOSStube/videos) podcast@chaoss.community (mailto:podcast@chaoss.community) Georg Link Website (https://georg.link/) Nicole Huesman X (https://twitter.com/uoduckswtd) Ruth Ikegah X (https://x.com/ikegahruth?lang=en) Chrys Wu LinkedIn (https://www.linkedin.com/in/chryswu/) Invest in Open Infrastructure (IOI) (https://investinopen.org/) Invest in Open Infrastructure Strategic Support (https://investinopen.org/strategic-support/) Invest in Open Infrastructure Bluesky (https://bsky.app/profile/investinopen.bsky.social) Invest in Open Infrastructure Mastodon (https://mastodon.social/@investinopen@indieweb.social) Invest in Open Infrastructure LinkedIn (https://www.linkedin.com/company/invest-in-open/) IOI's 2025 State of Open Infrastructure Report section, “Trust, transparency, and technology: Do community health frameworks shape open infrastructure decisions?” (https://investinopen.org/state-of-open-infrastructure-2025/sooi-signals-from-the-field-2025/#trust-transparency-and-technology-do-community-health-frameworks-shape-open-infrastructure-decisions) Infra Finder (https://infrafinder.investinopen.org/solutions) Infra Finder Expression of Interest (https://infrafinder.investinopen.org/solutions) CHAOSS Project DEI Metrics (https://github.com/chaoss/community/blob/main/DEI.md) FOREST Framework (https://www.nextgenlibpub.org/forest-framework) The Principles of Open Scholarly Infrastructure (https://openscholarlyinfrastructure.org/) 2025 State of Open Infrastructure: Trends in characteristics, funding, policy and community health (Zenodo) (https://zenodo.org/records/15198874) Simon Willison's Newsletter (https://simonw.substack.com/) SHINee's new single “Poet | Artist” (YouTube) (https://www.youtube.com/watch?v=QF6P6BSPDRw) Special Guest: Chrys Wu.

Hi #AmWriting listeners, Jennie here! Today, I'm talking to Jane Friedman, who is one of the most trusted voices in the world of publishing.She has advised and served organizations such as Writers Digest, The Chicago Manual of Style, The Editorial Freelancers Association, the Alliance of Independent Authors, and the National Endowment for the Arts, among others. She writes two must-read newsletters for industry professionals. One is her personal newsletter, and the other is The Bottom Line (previously called The Hot Sheet), where she provides nuanced market intelligence to thousands of authors and industry professionals. The reason I wanted to speak with Jane on the podcast today is that she has just released an updated version of her book, The Business of Being a Writer, which digs into the nuts and bolts of the writing life, including the work of getting published and choosing how to do that, and the work of making money. It is one of those must-read books for writers who are serious about making a mark.Jane offers so much information, some tough love, and also a reason for hope, and I'm so excited I'm talking to Jane about her own writing process, and her advice for writers.Links from the PodJane's Trademark situation via Writer's DigestJane's The Bottom Line Newsletter The Author's Guild (for AI info)Simon Willison's Things We Learned About LLMs in 2024 (via Substack)Make Art Make Money, Elizabeth Hyde StevensHow to Reform Capitalism, Alain de BottonThe Gift, Lewis Hyde Dana GioiaAlan Watt's Out of Your MindFind Jane via her website: www.janefriendman.com, or on Instagram at @janefriedman This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit amwriting.substack.com/subscribe

What if building software felt more like composing than coding? In this episode, Hugo and Greg explore how LLMs are reshaping the way we think about software development—from deterministic programming to a more flexible, prompt-driven, and collaborative style of building. It's not just hype or grift—it's a real shift in how we express intent, reason about systems, and collaborate across roles. Hugo speaks with Greg Ceccarelli—co-founder of SpecStory, former CPO at Pluralsight, and Director of Data Science at GitHub—about the rise of software composition and how it changes the way individuals and teams create with LLMs. We dive into: - Why software composition is emerging as a serious alternative to traditional coding - The real difference between vibe coding and production-minded prototyping - How LLMs are expanding who gets to build software—and how - What changes when you focus on intent, not just code - What Greg is building with SpecStory to support collaborative, traceable AI-native workflows - The challenges (and joys) of debugging and exploring with agentic tools like Cursor and Claude We've removed the visual demos from the audio—but you can catch our live-coded Chrome extension and JFK document explorer on YouTube. Links below. JFK Docs Vibe Coding Demo (YouTube) (https://youtu.be/JpXCkuV58QE) Chrome Extension Vibe Coding Demo (YouTube) (https://youtu.be/ESVKp37jDwc) Meditations on Tech (Greg's Substack) (https://www.meditationsontech.com/) Simon Willison on Vibe Coding (https://simonwillison.net/2025/Mar/19/vibe-coding/) Johnno Whitaker: On Vibe Coding (https://johnowhitaker.dev/essays/vibe_coding.html) Tim O'Reilly – The End of Programming (https://www.oreilly.com/radar/the-end-of-programming-as-we-know-it/) Vanishing Gradients YouTube Channel (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Greg Ceccarelli on LinkedIn (https://www.linkedin.com/in/gregceccarelli/) Greg's Hacker News Post on GOOD (https://news.ycombinator.com/item?id=43557698) SpecStory: GOOD – Git Companion for AI Workflows (https://github.com/specstoryai/getspecstory/blob/main/GOOD.md)

When I found out that Django developer and Python Software Foundation chair Dawn Wages has a chapter in her upcoming Domain-Driven Django book called “Just Use Postgres”, I knew we had to get her on the show. In this episode of Talking Postgres with Claire Giordano, Dawn breaks down why so many Python and Django developers have such an affinity for Postgres. And we dive into the Djangonaut Space mentoring program (where contributors launch), learn why “free as in puppies” beats “free as in cake” for open source vibes, and dig into why Python is the second-best language for everything.Links mentioned in this episode:Project page: psycopgDocumentation: Psycopg 3 – PostgreSQL database adapter for PythonProject page: PostgreSQL open source projectGit repo: code for PostgreSQL.org websiteConference: PyCon US 2025, happening May 14-22 in PittsburghConference: PGConf.dev 2025 Schedule, happening May 13-16 in Montreal CanadaConference: Prague PostgreSQL Developer Day 2025 (P2D2) Schedule, which took place Jan 28-29Wikipedia page: Model-view-controller software design patternBook: Professional ASP.NET MVC 1.0, affectionately called “the four heads book”Podcast episode: Working in Public with Simon Willison & Marco SlotBlog: Simon Willison's TILs, aka Things I've learnedSimon Willison's Weblog: Here's how I use LLMs to help me write codeSimon Willison's Weblog: How I make annotated presentationsSurvey: Python Developers Survey 2023 ResultsPython Docs: What's new in Python 3.14Mentorship program: Djangonaut SpaceMentorship program: Media & Talks about Djangonaut SpacePodcast episode: Why mentor Postgres developers with Robert HaasSlides: PGConf EU 2024 talk by Claire Giordano about Contributions to Postgres, including maps showing how global the Postgres project isVideo of POSETTE 2024 talk by Paolo Melchiorre: Semantic search with Django, PostgreSQL, & pgvectorVideo of Citus Con 2023 talk: Maps with Django (and PostGIS), by Paolo MelchiorreVideo of Citus Con 2022 talk: Django with PostgreSQL superpowers, by Paolo MelchiorreConference: DjangoCon Africa 2025, happening August 11-15 in Tanzania Calendar invite: LIVE recording of Ep26 of Talking Postgres to happen on Wed Apr 02, 2025 with guest Bruce Momjian, to talk about Open Source Leadership 

Apple Intelligence, annoncé comme une révolution lors de la WWDC 2024, peine à se concrétiser. Le déploiement est lent, les fonctionnalités arrivent au compte-gouttes, et surtout, la nouvelle version de Siri, initialement prévue pour 2025, ne sortirait pas avant 2026.Un problème majeur semble bloquer la firme de Cupertino : la sécurité. D'après le développeur Simon Willison, Siri 2.0 serait vulnérable aux attaques par injection de prompt, ce qui pourrait compromettre gravement la protection des données des utilisateurs. Apple, dont la promesse de confidentialité est un pilier de son image, se retrouve face à un dilemme : innover ou garantir une sécurité infaillible ?Apple Intelligence pourrait bien être une arme à double tranchant. L'intégration poussée de l'IA dans les systèmes Apple, censée être son atout majeur, risque de devenir son plus grand défi. La quête de perfection freine-t-elle Apple dans la course à l'IA ?-----------♥️ Soutenez Monde Numérique : https://donorbox.org/monde-numerique

What are the current large language model (LLM) tools you can use to develop Python? What prompting techniques and strategies produce better results? This week on the show, we speak with Simon Willison about his LLM research and his exploration of writing Python code with these rapidly evolving tools.

Due to overwhelming demand (>15x applications:slots), we are closing CFPs for AI Engineer Summit NYC today. Last call! Thanks, we'll be reaching out to all shortly!The world's top AI blogger and friend of every pod, Simon Willison, dropped a monster 2024 recap: Things we learned about LLMs in 2024. Brian of the excellent TechMeme Ride Home pinged us for a connection and a special crossover episode, our first in 2025. The target audience for this podcast is a tech-literate, but non-technical one. You can see Simon's notes for AI Engineers in his World's Fair Keynote.Timestamp* 00:00 Introduction and Guest Welcome* 01:06 State of AI in 2025* 01:43 Advancements in AI Models* 03:59 Cost Efficiency in AI* 06:16 Challenges and Competition in AI* 17:15 AI Agents and Their Limitations* 26:12 Multimodal AI and Future Prospects* 35:29 Exploring Video Avatar Companies* 36:24 AI Influencers and Their Future* 37:12 Simplifying Content Creation with AI* 38:30 The Importance of Credibility in AI* 41:36 The Future of LLM User Interfaces* 48:58 Local LLMs: A Growing Interest* 01:07:22 AI Wearables: The Next Big Thing* 01:10:16 Wrapping Up and Final ThoughtsTranscript[00:00:00] Introduction and Guest Welcome[00:00:00] Brian: Welcome to the first bonus episode of the Tech Meme Write Home for the year 2025. I'm your host as always, Brian McCullough. Listeners to the pod over the last year know that I have made a habit of quoting from Simon Willison when new stuff happens in AI from his blog. Simon has been, become a go to for many folks in terms of, you know, Analyzing things, criticizing things in the AI space.[00:00:33] Brian: I've wanted to talk to you for a long time, Simon. So thank you for coming on the show. No, it's a privilege to be here. And the person that made this connection happen is our friend Swyx, who has been on the show back, even going back to the, the Twitter Spaces days but also an AI guru in, in their own right Swyx, thanks for coming on the show also.[00:00:54] swyx (2): Thanks. I'm happy to be on and have been a regular listener, so just happy to [00:01:00] contribute as well.[00:01:00] Brian: And a good friend of the pod, as they say. Alright, let's go right into it.[00:01:06] State of AI in 2025[00:01:06] Brian: Simon, I'm going to do the most unfair, broad question first, so let's get it out of the way. The year 2025. Broadly, what is the state of AI as we begin this year?[00:01:20] Brian: Whatever you want to say, I don't want to lead the witness.[00:01:22] Simon: Wow. So many things, right? I mean, the big thing is everything's got really good and fast and cheap. Like, that was the trend throughout all of 2024. The good models got so much cheaper, they got so much faster, they got multimodal, right? The image stuff isn't even a surprise anymore.[00:01:39] Simon: They're growing video, all of that kind of stuff. So that's all really exciting.[00:01:43] Advancements in AI Models[00:01:43] Simon: At the same time, they didn't get massively better than GPT 4, which was a bit of a surprise. So that's sort of one of the open questions is, are we going to see huge, but I kind of feel like that's a bit of a distraction because GPT 4, but way cheaper, much larger context lengths, and it [00:02:00] can do multimodal.[00:02:01] Simon: is better, right? That's a better model, even if it's not.[00:02:05] Brian: What people were expecting or hoping, maybe not expecting is not the right word, but hoping that we would see another step change, right? Right. From like GPT 2 to 3 to 4, we were expecting or hoping that maybe we were going to see the next evolution in that sort of, yeah.[00:02:21] Brian: We[00:02:21] Simon: did see that, but not in the way we expected. We thought the model was just going to get smarter, and instead we got. Massive drops in, drops in price. We got all of these new capabilities. You can talk to the things now, right? They can do simulated audio input, all of that kind of stuff. And so it's kind of, it's interesting to me that the models improved in all of these ways we weren't necessarily expecting.[00:02:43] Simon: I didn't know it would be able to do an impersonation of Santa Claus, like a, you know, Talked to it through my phone and show it what I was seeing by the end of 2024. But yeah, we didn't get that GPT 5 step. And that's one of the big open questions is, is that actually just around the corner and we'll have a bunch of GPT 5 class models drop in the [00:03:00] next few months?[00:03:00] Simon: Or is there a limit?[00:03:03] Brian: If you were a betting man and wanted to put money on it, do you expect to see a phase change, step change in 2025?[00:03:11] Simon: I don't particularly for that, like, the models, but smarter. I think all of the trends we're seeing right now are going to keep on going, especially the inference time compute, right?[00:03:21] Simon: The trick that O1 and O3 are doing, which means that you can solve harder problems, but they cost more and it churns away for longer. I think that's going to happen because that's already proven to work. I don't know. I don't know. Maybe there will be a step change to a GPT 5 level, but honestly, I'd be completely happy if we got what we've got right now.[00:03:41] Simon: But cheaper and faster and more capabilities and longer contexts and so forth. That would be thrilling to me.[00:03:46] Brian: Digging into what you've just said one of the things that, by the way, I hope to link in the show notes to Simon's year end post about what, what things we learned about LLMs in 2024. Look for that in the show notes.[00:03:59] Cost Efficiency in AI[00:03:59] Brian: One of the things that you [00:04:00] did say that you alluded to even right there was that in the last year, you felt like the GPT 4 barrier was broken, like IE. Other models, even open source ones are now regularly matching sort of the state of the art.[00:04:13] Simon: Well, it's interesting, right? So the GPT 4 barrier was a year ago, the best available model was OpenAI's GPT 4 and nobody else had even come close to it.[00:04:22] Simon: And they'd been at the, in the lead for like nine months, right? That thing came out in what, February, March of, of 2023. And for the rest of 2023, nobody else came close. And so at the start of last year, like a year ago, the big question was, Why has nobody beaten them yet? Like, what do they know that the rest of the industry doesn't know?[00:04:40] Simon: And today, that I've counted 18 organizations other than GPT 4 who've put out a model which clearly beats that GPT 4 from a year ago thing. Like, maybe they're not better than GPT 4. 0, but that's, that, that, that barrier got completely smashed. And yeah, a few of those I've run on my laptop, which is wild to me.[00:04:59] Simon: Like, [00:05:00] it was very, very wild. It felt very clear to me a year ago that if you want GPT 4, you need a rack of 40, 000 GPUs just to run the thing. And that turned out not to be true. Like the, the, this is that big trend from last year of the models getting more efficient, cheaper to run, just as capable with smaller weights and so forth.[00:05:20] Simon: And I ran another GPT 4 model on my laptop this morning, right? Microsoft 5. 4 just came out. And that, if you look at the benchmarks, it's definitely, it's up there with GPT 4. 0. It's probably not as good when you actually get into the vibes of the thing, but it, it runs on my, it's a 14 gigabyte download and I can run it on a MacBook Pro.[00:05:38] Simon: Like who saw that coming? The most exciting, like the close of the year on Christmas day, just a few weeks ago, was when DeepSeek dropped their DeepSeek v3 model on Hugging Face without even a readme file. It was just like a giant binary blob that I can't run on my laptop. It's too big. But in all of the benchmarks, it's now by far the best available [00:06:00] open, open weights model.[00:06:01] Simon: Like it's, it's, it's beating the, the metalamas and so forth. And that was trained for five and a half million dollars, which is a tenth of the price that people thought it costs to train these things. So everything's trending smaller and faster and more efficient.[00:06:15] Brian: Well, okay.[00:06:16] Challenges and Competition in AI[00:06:16] Brian: I, I kind of was going to get to that later, but let's, let's combine this with what I was going to ask you next, which is, you know, you're talking, you know, Also in the piece about the LLM prices crashing, which I've even seen in projects that I'm working on, but explain Explain that to a general audience, because we hear all the time that LLMs are eye wateringly expensive to run, but what we're suggesting, and we'll come back to the cheap Chinese LLM, but first of all, for the end user, what you're suggesting is that we're starting to see the cost come down sort of in the traditional technology way of Of costs coming down over time,[00:06:49] Simon: yes, but very aggressively.[00:06:51] Simon: I mean, my favorite thing, the example here is if you look at GPT-3, so open AI's g, PT three, which was the best, a developed model in [00:07:00] 2022 and through most of 20 2023. That, the models that we have today, the OpenAI models are a hundred times cheaper. So there was a 100x drop in price for OpenAI from their best available model, like two and a half years ago to today.[00:07:13] Simon: And[00:07:14] Brian: just to be clear, not to train the model, but for the use of tokens and things. Exactly,[00:07:20] Simon: for running prompts through them. And then When you look at the, the really, the top tier model providers right now, I think, are OpenAI, Anthropic, Google, and Meta. And there are a bunch of others that I could list there as well.[00:07:32] Simon: Mistral are very good. The, the DeepSeq and Quen models have got great. There's a whole bunch of providers serving really good models. But even if you just look at the sort of big brand name providers, they all offer models now that are A fraction of the price of the, the, of the models we were using last year.[00:07:49] Simon: I think I've got some numbers that I threw into my blog entry here. Yeah. Like Gemini 1. 5 flash, that's Google's fast high quality model is [00:08:00] how much is that? It's 0. 075 dollars per million tokens. Like these numbers are getting, So we just do cents per million now,[00:08:09] swyx (2): cents per million,[00:08:10] Simon: cents per million makes, makes a lot more sense.[00:08:12] Simon: Yeah they have one model 1. 5 flash 8B, the absolute cheapest of the Google models, is 27 times cheaper than GPT 3. 5 turbo was a year ago. That's it. And GPT 3. 5 turbo, that was the cheap model, right? Now we've got something 27 times cheaper, and the Google, this Google one can do image recognition, it can do million token context, all of those tricks.[00:08:36] Simon: But it's, it's, it's very, it's, it really is startling how inexpensive some of this stuff has got.[00:08:41] Brian: Now, are we assuming that this, that happening is directly the result of competition? Because again, you know, OpenAI, and probably they're doing this for their own almost political reasons, strategic reasons, keeps saying, we're losing money on everything, even the 200.[00:08:56] Brian: So they probably wouldn't, the prices wouldn't be [00:09:00] coming down if there wasn't intense competition in this space.[00:09:04] Simon: The competition is absolutely part of it, but I have it on good authority from sources I trust that Google Gemini is not operating at a loss. Like, the amount of electricity to run a prompt is less than they charge you.[00:09:16] Simon: And the same thing for Amazon Nova. Like, somebody found an Amazon executive and got them to say, Yeah, we're not losing money on this. I don't know about Anthropic and OpenAI, but clearly that demonstrates it is possible to run these things at these ludicrously low prices and still not be running at a loss if you discount the Army of PhDs and the, the training costs and all of that kind of stuff.[00:09:36] Brian: One, one more for me before I let Swyx jump in here. To, to come back to DeepSeek and this idea that you could train, you know, a cutting edge model for 6 million. I, I was saying on the show, like six months ago, that if we are getting to the point where each new model It would cost a billion, ten billion, a hundred billion to train that.[00:09:54] Brian: At some point it would almost, only nation states would be able to train the new models. Do you [00:10:00] expect what DeepSeek and maybe others are proving to sort of blow that up? Or is there like some sort of a parallel track here that maybe I'm not technically, I don't have the mouse to understand the difference.[00:10:11] Brian: Is the model, are the models going to go, you know, Up to a hundred billion dollars or can we get them down? Sort of like DeepSeek has proven[00:10:18] Simon: so I'm the wrong person to answer that because I don't work in the lab training these models. So I can give you my completely uninformed opinion, which is, I felt like the DeepSeek thing.[00:10:27] Simon: That was a bomb shell. That was an absolute bombshell when they came out and said, Hey, look, we've trained. One of the best available models and it cost us six, five and a half million dollars to do it. I feel, and they, the reason, one of the reasons it's so efficient is that we put all of these export controls in to stop Chinese companies from giant buying GPUs.[00:10:44] Simon: So they've, were forced to be, go as efficient as possible. And yet the fact that they've demonstrated that that's possible to do. I think it does completely tear apart this, this, this mental model we had before that yeah, the training runs just keep on getting more and more expensive and the number of [00:11:00] organizations that can afford to run these training runs keeps on shrinking.[00:11:03] Simon: That, that's been blown out of the water. So yeah, that's, again, this was our Christmas gift. This was the thing they dropped on Christmas day. Yeah, it makes me really optimistic that we can, there are, It feels like there was so much low hanging fruit in terms of the efficiency of both inference and training and we spent a whole bunch of last year exploring that and getting results from it.[00:11:22] Simon: I think there's probably a lot left. I think there's probably, well, I would not be surprised to see even better models trained spending even less money over the next six months.[00:11:31] swyx (2): Yeah. So I, I think there's a unspoken angle here on what exactly the Chinese labs are trying to do because DeepSea made a lot of noise.[00:11:41] swyx (2): so much for joining us for around the fact that they train their model for six million dollars and nobody quite quite believes them. Like it's very, very rare for a lab to trumpet the fact that they're doing it for so cheap. They're not trying to get anyone to buy them. So why [00:12:00] are they doing this? They make it very, very obvious.[00:12:05] swyx (2): Deepseek is about 150 employees. It's an order of magnitude smaller than at least Anthropic and maybe, maybe more so for OpenAI. And so what's, what's the end game here? Are they, are they just trying to show that the Chinese are better than us?[00:12:21] Simon: So Deepseek, it's the arm of a hedge, it's a, it's a quant fund, right?[00:12:25] Simon: It's an algorithmic quant trading thing. So I, I, I would love to get more insight into how that organization works. My assumption from what I've seen is it looks like they're basically just flexing. They're like, hey, look at how utterly brilliant we are with this amazing thing that we've done. And it's, it's working, right?[00:12:43] Simon: They but, and so is that it? Are they, is this just their kind of like, this is, this is why our company is so amazing. Look at this thing that we've done, or? I don't know. I'd, I'd love to get Some insight from, from within that industry as to, as to how that's all playing out.[00:12:57] swyx (2): The, the prevailing theory among the Local Llama [00:13:00] crew and the Twitter crew that I indexed for my newsletter is that there is some amount of copying going on.[00:13:06] swyx (2): It's like Sam Altman you know, tweet, tweeting about how they're being copied. And then also there's this, there, there are other sort of opening eye employees that have said, Stuff that is similar that DeepSeek's rate of progress is how U. S. intelligence estimates the number of foreign spies embedded in top labs.[00:13:22] swyx (2): Because a lot of these ideas do spread around, but they surprisingly have a very high density of them in the DeepSeek v3 technical report. So it's, it's interesting. We don't know how much, how many, how much tokens. I think that, you know, people have run analysis on how often DeepSeek thinks it is cloud or thinks it is opening GPC 4.[00:13:40] swyx (2): Thanks for watching! And we don't, we don't know. We don't know. I think for me, like, yeah, we'll, we'll, we basically will never know as, as external commentators. I think what's interesting is how, where does this go? Is there a logical floor or bottom by my estimations for the same amount of ELO started last year to the end of last year cost went down by a thousand X for the [00:14:00] GPT, for, for GPT 4 intelligence.[00:14:02] swyx (2): Would, do they go down a thousand X this year?[00:14:04] Simon: That's a fascinating question. Yeah.[00:14:06] swyx (2): Is there a Moore's law going on, or did we just get a one off benefit last year for some weird reason?[00:14:14] Simon: My uninformed hunch is low hanging fruit. I feel like up until a year ago, people haven't been focusing on efficiency at all. You know, it was all about, what can we get these weird shaped things to do?[00:14:24] Simon: And now once we've sort of hit that, okay, we know that we can get them to do what GPT 4 can do, When thousands of researchers around the world all focus on, okay, how do we make this more efficient? What are the most important, like, how do we strip out all of the weights that have stuff in that doesn't really matter?[00:14:39] Simon: All of that kind of thing. So yeah, maybe that was it. Maybe 2024 was a freak year of all of the low hanging fruit coming out at once. And we'll actually see a reduction in the, in that rate of improvement in terms of efficiency. I wonder, I mean, I think we'll know for sure in about three months time if that trend's going to continue or not.[00:14:58] swyx (2): I agree. You know, I [00:15:00] think the other thing that you mentioned that DeepSeq v3 was the gift that was given from DeepSeq over Christmas, but I feel like the other thing that might be underrated was DeepSeq R1,[00:15:11] Speaker 4: which is[00:15:13] swyx (2): a reasoning model you can run on your laptop. And I think that's something that a lot of people are looking ahead to this year.[00:15:18] swyx (2): Oh, did they[00:15:18] Simon: release the weights for that one?[00:15:20] swyx (2): Yeah.[00:15:21] Simon: Oh my goodness, I missed that. I've been playing with the quen. So the other great, the other big Chinese AI app is Alibaba's quen. Actually, yeah, I, sorry, R1 is an API available. Yeah. Exactly. When that's really cool. So Alibaba's Quen have released two reasoning models that I've run on my laptop.[00:15:38] Simon: Now there was, the first one was Q, Q, WQ. And then the second one was QVQ because the second one's a vision model. So you can like give it vision puzzles and a prompt that these things, they are so much fun to run. Because they think out loud. It's like the OpenAR 01 sort of hides its thinking process. The Query ones don't.[00:15:59] Simon: They just, they [00:16:00] just churn away. And so you'll give it a problem and it will output literally dozens of paragraphs of text about how it's thinking. My favorite thing that happened with QWQ is I asked it to draw me a pelican on a bicycle in SVG. That's like my standard stupid prompt. And for some reason it thought in Chinese.[00:16:18] Simon: It spat out a whole bunch of like Chinese text onto my terminal on my laptop, and then at the end it gave me quite a good sort of artistic pelican on a bicycle. And I ran it all through Google Translate, and yeah, it was like, it was contemplating the nature of SVG files as a starting point. And the fact that my laptop can think in Chinese now is so delightful.[00:16:40] Simon: It's so much fun watching you do that.[00:16:43] swyx (2): Yeah, I think Andrej Karpathy was saying, you know, we, we know that we have achieved proper reasoning inside of these models when they stop thinking in English, and perhaps the best form of thought is in Chinese. But yeah, for listeners who don't know Simon's blog he always, whenever a new model comes out, you, I don't know how you do it, but [00:17:00] you're always the first to run Pelican Bench on these models.[00:17:02] swyx (2): I just did it for 5.[00:17:05] Simon: Yeah.[00:17:07] swyx (2): So I really appreciate that. You should check it out. These are not theoretical. Simon's blog actually shows them.[00:17:12] Brian: Let me put on the investor hat for a second.[00:17:15] AI Agents and Their Limitations[00:17:15] Brian: Because from the investor side of things, a lot of the, the VCs that I know are really hot on agents, and this is the year of agents, but last year was supposed to be the year of agents as well. Lots of money flowing towards, And Gentic startups.[00:17:32] Brian: But in in your piece that again, we're hopefully going to have linked in the show notes, you sort of suggest there's a fundamental flaw in AI agents as they exist right now. Let me let me quote you. And then I'd love to dive into this. You said, I remain skeptical as to their ability based once again, on the Challenge of gullibility.[00:17:49] Brian: LLMs believe anything you tell them, any systems that attempt to make meaningful decisions on your behalf, will run into the same roadblock. How good is a travel agent, or a digital assistant, or even a research tool, if it [00:18:00] can't distinguish truth from fiction? So, essentially, what you're suggesting is that the state of the art now that allows agents is still, it's still that sort of 90 percent problem, the edge problem, getting to the Or, or, or is there a deeper flaw?[00:18:14] Brian: What are you, what are you saying there?[00:18:16] Simon: So this is the fundamental challenge here and honestly my frustration with agents is mainly around definitions Like any if you ask anyone who says they're working on agents to define agents You will get a subtly different definition from each person But everyone always assumes that their definition is the one true one that everyone else understands So I feel like a lot of these agent conversations, people talking past each other because one person's talking about the, the sort of travel agent idea of something that books things on your behalf.[00:18:41] Simon: Somebody else is talking about LLMs with tools running in a loop with a cron job somewhere and all of these different things. You, you ask academics and they'll laugh at you because they've been debating what agents mean for over 30 years at this point. It's like this, this long running, almost sort of an in joke in that community.[00:18:57] Simon: But if we assume that for this purpose of this conversation, an [00:19:00] agent is something that, Which you can give a job and it goes off and it does that thing for you like, like booking travel or things like that. The fundamental challenge is, it's the reliability thing, which comes from this gullibility problem.[00:19:12] Simon: And a lot of my, my interest in this originally came from when I was thinking about prompt injections as a source of this form of attack against LLM systems where you deliberately lay traps out there for this LLM to stumble across,[00:19:24] Brian: and which I should say you have been banging this drum that no one's gotten any far, at least on solving this, that I'm aware of, right.[00:19:31] Brian: Like that's still an open problem. The two years.[00:19:33] Simon: Yeah. Right. We've been talking about this problem and like, a great illustration of this was Claude so Anthropic released Claude computer use a few months ago. Fantastic demo. You could fire up a Docker container and you could literally tell it to do something and watch it open a web browser and navigate to a webpage and click around and so forth.[00:19:51] Simon: Really, really, really interesting and fun to play with. And then, um. One of the first demos somebody tried was, what if you give it a web page that says download and run this [00:20:00] executable, and it did, and the executable was malware that added it to a botnet. So the, the very first most obvious dumb trick that you could play on this thing just worked, right?[00:20:10] Simon: So that's obviously a really big problem. If I'm going to send something out to book travel on my behalf, I mean, it's hard enough for me to figure out which airlines are trying to scam me and which ones aren't. Do I really trust a language model that believes the literal truth of anything that's presented to it to go out and do those things?[00:20:29] swyx (2): Yeah I definitely think there's, it's interesting to see Anthropic doing this because they used to be the safety arm of OpenAI that split out and said, you know, we're worried about letting this thing out in the wild and here they are enabling computer use for agents. Thanks. The, it feels like things have merged.[00:20:49] swyx (2): You know, I'm, I'm also fairly skeptical about, you know, this always being the, the year of Linux on the desktop. And this is the equivalent of this being the year of agents that people [00:21:00] are not predicting so much as wishfully thinking and hoping and praying for their companies and agents to work.[00:21:05] swyx (2): But I, I feel like things are. Coming along a little bit. It's to me, it's kind of like self driving. I remember in 2014 saying that self driving was just around the corner. And I mean, it kind of is, you know, like in, in, in the Bay area. You[00:21:17] Simon: get in a Waymo and you're like, Oh, this works. Yeah, but it's a slow[00:21:21] swyx (2): cook.[00:21:21] swyx (2): It's a slow cook over the next 10 years. We're going to hammer out these things and the cynical people can just point to all the flaws, but like, there are measurable or concrete progress steps that are being made by these builders.[00:21:33] Simon: There is one form of agent that I believe in. I believe, mostly believe in the research assistant form of agents.[00:21:39] Simon: The thing where you've got a difficult problem and, and I've got like, I'm, I'm on the beta for the, the Google Gemini 1. 5 pro with deep research. I think it's called like these names, these names. Right. But. I've been using that. It's good, right? You can give it a difficult problem and it tells you, okay, I'm going to look at 56 different websites [00:22:00] and it goes away and it dumps everything to its context and it comes up with a report for you.[00:22:04] Simon: And it's not, it won't work against adversarial websites, right? If there are websites with deliberate lies in them, it might well get caught out. Most things don't have that as a problem. And so I've had some answers from that which were genuinely really valuable to me. And that feels to me like, I can see how given existing LLM tech, especially with Google Gemini with its like million token contacts and Google with their crawl of the entire web and their, they've got like search, they've got search and cache, they've got a cache of every page and so forth.[00:22:35] Simon: That makes sense to me. And that what they've got right now, I don't think it's, it's not as good as it can be, obviously, but it's, it's, it's, it's a real useful thing, which they're going to start rolling out. So, you know, Perplexity have been building the same thing for a couple of years. That, that I believe in.[00:22:50] Simon: You know, if you tell me that you're going to have an agent that's a research assistant agent, great. The coding agents I mean, chat gpt code interpreter, Nearly two years [00:23:00] ago, that thing started writing Python code, executing the code, getting errors, rewriting it to fix the errors. That pattern obviously works.[00:23:07] Simon: That works really, really well. So, yeah, coding agents that do that sort of error message loop thing, those are proven to work. And they're going to keep on getting better, and that's going to be great. The research assistant agents are just beginning to get there. The things I'm critical of are the ones where you trust, you trust this thing to go out and act autonomously on your behalf, and make decisions on your behalf, especially involving spending money, like that.[00:23:31] Simon: I don't see that working for a very long time. That feels to me like an AGI level problem.[00:23:37] swyx (2): It's it's funny because I think Stripe actually released an agent toolkit which is one of the, the things I featured that is trying to enable these agents each to have a wallet that they can go and spend and have, basically, it's a virtual card.[00:23:49] swyx (2): It's not that, not that difficult with modern infrastructure. can[00:23:51] Simon: stick a 50 cap on it, then at least it's an honor. Can't lose more than 50.[00:23:56] Brian: You know I don't, I don't know if either of you know Rafat Ali [00:24:00] he runs Skift, which is a, a travel news vertical. And he, he, he constantly laughs at the fact that every agent thing is, we're gonna get rid of booking a, a plane flight for you, you know?[00:24:11] Brian: And, and I would point out that, like, historically, when the web started, the first thing everyone talked about is, You can go online and book a trip, right? So it's funny for each generation of like technological advance. The thing they always want to kill is the travel agent. And now they want to kill the webpage travel agent.[00:24:29] Simon: Like it's like I use Google flight search. It's great, right? If you gave me an agent to do that for me, it would save me, I mean, maybe 15 seconds of typing in my things, but I still want to see what my options are and go, yeah, I'm not flying on that airline, no matter how cheap they are.[00:24:44] swyx (2): Yeah. For listeners, go ahead.[00:24:47] swyx (2): For listeners, I think, you know, I think both of you are pretty positive on NotebookLM. And you know, we, we actually interviewed the NotebookLM creators, and there are actually two internal agents going on internally. The reason it takes so long is because they're running an agent loop [00:25:00] inside that is fairly autonomous, which is kind of interesting.[00:25:01] swyx (2): For one,[00:25:02] Simon: for a definition of agent loop, if you picked that particularly well. For one definition. And you're talking about the podcast side of this, right?[00:25:07] swyx (2): Yeah, the podcast side of things. They have a there's, there's going to be a new version coming out that, that we'll be featuring at our, at our conference.[00:25:14] Simon: That one's fascinating to me. Like NotebookLM, I think it's two products, right? On the one hand, it's actually a very good rag product, right? You dump a bunch of things in, you can run searches, that, that, it does a good job of. And then, and then they added the, the podcast thing. It's a bit of a, it's a total gimmick, right?[00:25:30] Simon: But that gimmick got them attention, because they had a great product that nobody paid any attention to at all. And then you add the unfeasibly good voice synthesis of the podcast. Like, it's just, it's, it's, it's the lesson.[00:25:43] Brian: It's the lesson of mid journey and stuff like that. If you can create something that people can post on socials, you don't have to lift a finger again to do any marketing for what you're doing.[00:25:53] Brian: Let me dig into Notebook LLM just for a second as a podcaster. As a [00:26:00] gimmick, it makes sense, and then obviously, you know, you dig into it, it sort of has problems around the edges. It's like, it does the thing that all sort of LLMs kind of do, where it's like, oh, we want to Wrap up with a conclusion.[00:26:12] Multimodal AI and Future Prospects[00:26:12] Brian: I always call that like the the eighth grade book report paper problem where it has to have an intro and then, you know But that's sort of a thing where because I think you spoke about this again in your piece at the year end About how things are going multimodal and how things are that you didn't expect like, you know vision and especially audio I think So that's another thing where, at least over the last year, there's been progress made that maybe you, you didn't think was coming as quick as it came.[00:26:43] Simon: I don't know. I mean, a year ago, we had one really good vision model. We had GPT 4 vision, was, was, was very impressive. And Google Gemini had just dropped Gemini 1. 0, which had vision, but nobody had really played with it yet. Like Google hadn't. People weren't taking Gemini [00:27:00] seriously at that point. I feel like it was 1.[00:27:02] Simon: 5 Pro when it became apparent that actually they were, they, they got over their hump and they were building really good models. And yeah, and they, to be honest, the video models are mostly still using the same trick. The thing where you divide the video up into one image per second and you dump that all into the context.[00:27:16] Simon: So maybe it shouldn't have been so surprising to us that long context models plus vision meant that the video was, was starting to be solved. Of course, it didn't. Not being, you, what you really want with videos, you want to be able to do the audio and the images at the same time. And I think the models are beginning to do that now.[00:27:33] Simon: Like, originally, Gemini 1. 5 Pro originally ignored the audio. It just did the, the, like, one frame per second video trick. As far as I can tell, the most recent ones are actually doing pure multimodal. But the things that opens up are just extraordinary. Like, the the ChatGPT iPhone app feature that they shipped as one of their 12 days of, of OpenAI, I really can be having a conversation and just turn on my video camera and go, Hey, what kind of tree is [00:28:00] this?[00:28:00] Simon: And so forth. And it works. And for all I know, that's just snapping a like picture once a second and feeding it into the model. The, the, the things that you can do with that as an end user are extraordinary. Like that, that to me, I don't think most people have cottoned onto the fact that you can now stream video directly into a model because it, it's only a few weeks old.[00:28:22] Simon: Wow. That's a, that's a, that's a, that's Big boost in terms of what kinds of things you can do with this stuff. Yeah. For[00:28:30] swyx (2): people who are not that close I think Gemini Flashes free tier allows you to do something like capture a photo, one photo every second or a minute and leave it on 24, seven, and you can prompt it to do whatever.[00:28:45] swyx (2): And so you can effectively have your own camera app or monitoring app that that you just prompt and it detects where it changes. It detects for, you know, alerts or anything like that, or describes your day. You know, and, and, and the fact that this is free I think [00:29:00] it's also leads into the previous point of it being the prices haven't come down a lot.[00:29:05] Simon: And even if you're paying for this stuff, like a thing that I put in my blog entry is I ran a calculation on what it would cost to process 68, 000 photographs in my photo collection, and for each one just generate a caption, and using Gemini 1. 5 Flash 8B, it would cost me 1. 68 to process 68, 000 images, which is, I mean, that, that doesn't make sense.[00:29:28] Simon: None of that makes sense. Like it's, it's a, for one four hundredth of a cent per image to generate captions now. So you can see why feeding in a day's worth of video just isn't even very expensive to process.[00:29:40] swyx (2): Yeah, I'll tell you what is expensive. It's the other direction. So we're here, we're talking about consuming video.[00:29:46] swyx (2): And this year, we also had a lot of progress, like probably one of the most excited, excited, anticipated launches of the year was Sora. We actually got Sora. And less exciting.[00:29:55] Simon: We did, and then VO2, Google's Sora, came out like three [00:30:00] days later and upstaged it. Like, Sora was exciting until VO2 landed, which was just better.[00:30:05] swyx (2): In general, I feel the media, or the social media, has been very unfair to Sora. Because what was released to the world, generally available, was Sora Lite. It's the distilled version of Sora, right? So you're, I did not[00:30:16] Simon: realize that you're absolutely comparing[00:30:18] swyx (2): the, the most cherry picked version of VO two, the one that they published on the marketing page to the, the most embarrassing version of the soa.[00:30:25] swyx (2): So of course it's gonna look bad, so, well, I got[00:30:27] Simon: access to the VO two I'm in the VO two beta and I've been poking around with it and. Getting it to generate pelicans on bicycles and stuff. I would absolutely[00:30:34] swyx (2): believe that[00:30:35] Simon: VL2 is actually better. Is Sora, so is full fat Sora coming soon? Do you know, when, when do we get to play with that one?[00:30:42] Simon: No one's[00:30:43] swyx (2): mentioned anything. I think basically the strategy is let people play around with Sora Lite and get info there. But the, the, keep developing Sora with the Hollywood studios. That's what they actually care about. Gotcha. Like the rest of us. Don't really know what to do with the video anyway. Right.[00:30:59] Simon: I mean, [00:31:00] that's my thing is I realized that for generative images and images and video like images We've had for a few years and I don't feel like they've broken out into the talented artist community yet Like lots of people are having fun with them and doing and producing stuff. That's kind of cool to look at but what I want you know that that movie everything everywhere all at once, right?[00:31:20] Simon: One, one ton of Oscars, utterly amazing film. The VFX team for that were five people, some of whom were watching YouTube videos to figure out what to do. My big question for, for Sora and and and Midjourney and stuff, what happens when a creative team like that starts using these tools? I want the creative geniuses behind everything, everywhere all at once.[00:31:40] Simon: What are they going to be able to do with this stuff in like a few years time? Because that's really exciting to me. That's where you take artists who are at the very peak of their game. Give them these new capabilities and see, see what they can do with them.[00:31:52] swyx (2): I should, I know a little bit here. So it should mention that, that team actually used RunwayML.[00:31:57] swyx (2): So there was, there was,[00:31:57] Simon: yeah.[00:31:59] swyx (2): I don't know how [00:32:00] much I don't. So, you know, it's possible to overstate this, but there are people integrating it. Generated video within their workflow, even pre SORA. Right, because[00:32:09] Brian: it's not, it's not the thing where it's like, okay, tomorrow we'll be able to do a full two hour movie that you prompt with three sentences.[00:32:15] Brian: It is like, for the very first part of, of, you know video effects in film, it's like, if you can get that three second clip, if you can get that 20 second thing that they did in the matrix that blew everyone's minds and took a million dollars or whatever to do, like, it's the, it's the little bits and pieces that they can fill in now that it's probably already there.[00:32:34] swyx (2): Yeah, it's like, I think actually having a layered view of what assets people need and letting AI fill in the low value assets. Right, like the background video, the background music and, you know, sometimes the sound effects. That, that maybe, maybe more palatable maybe also changes the, the way that you evaluate the stuff that's coming out.[00:32:57] swyx (2): Because people tend to, in social media, try to [00:33:00] emphasize foreground stuff, main character stuff. So you really care about consistency, and you, you really are bothered when, like, for example, Sorad. Botch's image generation of a gymnast doing flips, which is horrible. It's horrible. But for background crowds, like, who cares?[00:33:18] Brian: And by the way, again, I was, I was a film major way, way back in the day, like, that's how it started. Like things like Braveheart, where they filmed 10 people on a field, and then the computer could turn it into 1000 people on a field. Like, that's always been the way it's around the margins and in the background that first comes in.[00:33:36] Brian: The[00:33:36] Simon: Lord of the Rings movies were over 20 years ago. Although they have those giant battle sequences, which were very early, like, I mean, you could almost call it a generative AI approach, right? They were using very sophisticated, like, algorithms to model out those different battles and all of that kind of stuff.[00:33:52] Simon: Yeah, I know very little. I know basically nothing about film production, so I try not to commentate on it. But I am fascinated to [00:34:00] see what happens when, when these tools start being used by the real, the people at the top of their game.[00:34:05] swyx (2): I would say like there's a cultural war that is more that being fought here than a technology war.[00:34:11] swyx (2): Most of the Hollywood people are against any form of AI anyway, so they're busy Fighting that battle instead of thinking about how to adopt it and it's, it's very fringe. I participated here in San Francisco, one generative AI video creative hackathon where the AI positive artists actually met with technologists like myself and then we collaborated together to build short films and that was really nice and I think, you know, I'll be hosting some of those in my events going forward.[00:34:38] swyx (2): One thing that I think like I want to leave it. Give people a sense of it's like this is a recap of last year But then sometimes it's useful to walk away as well with like what can we expect in the future? I don't know if you got anything. I would also call out that the Chinese models here have made a lot of progress Hyde Law and Kling and God knows who like who else in the video arena [00:35:00] Also making a lot of progress like surprising him like I think maybe actually Chinese China is surprisingly ahead with regards to Open8 at least, but also just like specific forms of video generation.[00:35:12] Simon: Wouldn't it be interesting if a film industry sprung up in a country that we don't normally think of having a really strong film industry that was using these tools? Like, that would be a fascinating sort of angle on this. Mm hmm. Mm hmm.[00:35:25] swyx (2): Agreed. I, I, I Oh, sorry. Go ahead.[00:35:29] Exploring Video Avatar Companies[00:35:29] swyx (2): Just for people's Just to put it on people's radar as well, Hey Jen, there's like there's a category of video avatar companies that don't specifically, don't specialize in general video.[00:35:41] swyx (2): They only do talking heads, let's just say. And HeyGen sings very well.[00:35:45] Brian: Swyx, you know that that's what I've been using, right? Like, have, have I, yeah, right. So, if you see some of my recent YouTube videos and things like that, where, because the beauty part of the HeyGen thing is, I, I, I don't want to use the robot voice, so [00:36:00] I record the mp3 file for my computer, And then I put that into HeyGen with the avatar that I've trained it on, and all it does is the lip sync.[00:36:09] Brian: So it looks, it's not 100 percent uncanny valley beatable, but it's good enough that if you weren't looking for it, it's just me sitting there doing one of my clips from the show. And, yeah, so, by the way, HeyGen. Shout out to them.[00:36:24] AI Influencers and Their Future[00:36:24] swyx (2): So I would, you know, in terms of like the look ahead going, like, looking, reviewing 2024, looking at trends for 2025, I would, they basically call this out.[00:36:33] swyx (2): Meta tried to introduce AI influencers and failed horribly because they were just bad at it. But at some point that there will be more and more basically AI influencers Not in a way that Simon is but in a way that they are not human.[00:36:50] Simon: Like the few of those that have done well, I always feel like they're doing well because it's a gimmick, right?[00:36:54] Simon: It's a it's it's novel and fun to like Like that, the AI Seinfeld thing [00:37:00] from last year, the Twitch stream, you know, like those, if you're the only one or one of just a few doing that, you'll get, you'll attract an audience because it's an interesting new thing. But I just, I don't know if that's going to be sustainable longer term or not.[00:37:11] Simon: Like,[00:37:12] Simplifying Content Creation with AI[00:37:12] Brian: I'm going to tell you, Because I've had discussions, I can't name the companies or whatever, but, so think about the workflow for this, like, now we all know that on TikTok and Instagram, like, holding up a phone to your face, and doing like, in my car video, or walking, a walk and talk, you know, that's, that's very common, but also, if you want to do a professional sort of talking head video, you still have to sit in front of a camera, you still have to do the lighting, you still have to do the video editing, versus, if you can just record, what I'm saying right now, the last 30 seconds, If you clip that out as an mp3 and you have a good enough avatar, then you can put that avatar in front of Times Square, on a beach, or whatever.[00:37:50] Brian: So, like, again for creators, the reason I think Simon, we're on the verge of something, it, it just, it's not going to, I think it's not, oh, we're going to have [00:38:00] AI avatars take over, it'll be one of those things where it takes another piece of the workflow out and simplifies it. I'm all[00:38:07] Simon: for that. I, I always love this stuff.[00:38:08] Simon: I like tools. Tools that help human beings do more. Do more ambitious things. I'm always in favor of, like, that, that, that's what excites me about this entire field.[00:38:17] swyx (2): Yeah. We're, we're looking into basically creating one for my podcast. We have this guy Charlie, he's Australian. He's, he's not real, but he pre, he opens every show and we are gonna have him present all the shorts.[00:38:29] Simon: Yeah, go ahead.[00:38:30] The Importance of Credibility in AI[00:38:30] Simon: The thing that I keep coming back to is this idea of credibility like in a world that is full of like AI generated everything and so forth It becomes even more important that people find the sources of information that they trust and find people and find Sources that are credible and I feel like that's the one thing that LLMs and AI can never have is credibility, right?[00:38:49] Simon: ChatGPT can never stake its reputation on telling you something useful and interesting because That means nothing, right? It's a matrix multiplication. It depends on who prompted it and so forth. So [00:39:00] I'm always, and this is when I'm blogging as well, I'm always looking for, okay, who are the reliable people who will tell me useful, interesting information who aren't just going to tell me whatever somebody's paying them to tell, tell them, who aren't going to, like, type a one sentence prompt into an LLM and spit out an essay and stick it online.[00:39:16] Simon: And that, that to me, Like, earning that credibility is really important. That's why a lot of my ethics around the way that I publish are based on the idea that I want people to trust me. I want to do things that, that gain credibility in people's eyes so they will come to me for information as a trustworthy source.[00:39:32] Simon: And it's the same for the sources that I'm, I'm consulting as well. So that's something I've, I've been thinking a lot about that sort of credibility focus on this thing for a while now.[00:39:40] swyx (2): Yeah, you can layer or structure credibility or decompose it like so one thing I would put in front of you I'm not saying that you should Agree with this or accept this at all is that you can use AI to generate different Variations and then and you pick you as the final sort of last mile person that you pick The last output and [00:40:00] you put your stamp of credibility behind that like that everything's human reviewed instead of human origin[00:40:04] Simon: Yeah, if you publish something you need to be able to put it on the ground Publishing it.[00:40:08] Simon: You need to say, I will put my name to this. I will attach my credibility to this thing. And if you're willing to do that, then, then that's great.[00:40:16] swyx (2): For creators, this is huge because there's a fundamental asymmetry between starting with a blank slate versus choosing from five different variations.[00:40:23] Brian: Right.[00:40:24] Brian: And also the key thing that you just said is like, if everything that I do, if all of the words were generated by an LLM, if the voice is generated by an LLM. If the video is also generated by the LLM, then I haven't done anything, right? But if, if one or two of those, you take a shortcut, but it's still, I'm willing to sign off on it.[00:40:47] Brian: Like, I feel like that's where I feel like people are coming around to like, this is maybe acceptable, sort of.[00:40:53] Simon: This is where I've been pushing the definition. I love the term slop. Where I've been pushing the definition of slop as AI generated [00:41:00] content that is both unrequested and unreviewed and the unreviewed thing is really important like that's the thing that elevates something from slop to not slop is if A human being has reviewed it and said, you know what, this is actually worth other people's time.[00:41:12] Simon: And again, I'm willing to attach my credibility to it and say, hey, this is worthwhile.[00:41:16] Brian: It's, it's, it's the cura curational, curatorial and editorial part of it that no matter what the tools are to do shortcuts, to do, as, as Swyx is saying choose between different edits or different cuts, but in the end, if there's a curatorial mind, Or editorial mind behind it.[00:41:32] Brian: Let me I want to wedge this in before we start to close.[00:41:36] The Future of LLM User Interfaces[00:41:36] Brian: One of the things coming back to your year end piece that has been a something that I've been banging the drum about is when you're talking about LLMs. Getting harder to use. You said most users are thrown in at the deep end.[00:41:48] Brian: The default LLM chat UI is like taking brand new computer users, dropping them into a Linux terminal and expecting them to figure it all out. I mean, it's, it's literally going back to the command line. The command line was defeated [00:42:00] by the GUI interface. And this is what I've been banging the drum about is like, this cannot be.[00:42:05] Brian: The user interface, what we have now cannot be the end result. Do you see any hints or seeds of a GUI moment for LLM interfaces?[00:42:17] Simon: I mean, it has to happen. It absolutely has to happen. The the, the, the, the usability of these things is turning into a bit of a crisis. And we are at least seeing some really interesting innovation in little directions.[00:42:28] Simon: Just like OpenAI's chat GPT canvas thing that they just launched. That is at least. Going a little bit more interesting than just chat, chats and responses. You know, you can, they're exploring that space where you're collaborating with an LLM. You're both working in the, on the same document. That makes a lot of sense to me.[00:42:44] Simon: Like that, that feels really smart. The one of the best things is still who was it who did the, the UI where you could, they had a drawing UI where you draw an interface and click a button. TL draw would then make it real thing. That was spectacular, [00:43:00] absolutely spectacular, like, alternative vision of how you'd interact with these models.[00:43:05] Simon: Because yeah, the and that's, you know, so I feel like there is so much scope for innovation there and it is beginning to happen. Like, like, I, I feel like most people do understand that we need to do better in terms of interfaces that both help explain what's going on and give people better tools for working with models.[00:43:23] Simon: I was going to say, I want to[00:43:25] Brian: dig a little deeper into this because think of the conceptual idea behind the GUI, which is instead of typing into a command line open word. exe, it's, you, you click an icon, right? So that's abstracting away sort of the, again, the programming stuff that like, you know, it's, it's a, a, a child can tap on an iPad and, and make a program open, right?[00:43:47] Brian: The problem it seems to me right now with how we're interacting with LLMs is it's sort of like you know a dumb robot where it's like you poke it and it goes over here, but no, I want it, I want to go over here so you poke it this way and you can't get it exactly [00:44:00] right, like, what can we abstract away from the From the current, what's going on that, that makes it more fine tuned and easier to get more precise.[00:44:12] Brian: You see what I'm saying?[00:44:13] Simon: Yes. And the this is the other trend that I've been following from the last year, which I think is super interesting. It's the, the prompt driven UI development thing. Basically, this is the pattern where Claude Artifacts was the first thing to do this really well. You type in a prompt and it goes, Oh, I should answer that by writing a custom HTML and JavaScript application for you that does a certain thing.[00:44:35] Simon: And when you think about that take and since then it turns out This is easy, right? Every decent LLM can produce HTML and JavaScript that does something useful. So we've actually got this alternative way of interacting where they can respond to your prompt with an interactive custom interface that you can work with.[00:44:54] Simon: People haven't quite wired those back up again. Like, ideally, I'd want the LLM ask me a [00:45:00] question where it builds me a custom little UI, For that question, and then it gets to see how I interacted with that. I don't know why, but that's like just such a small step from where we are right now. But that feels like such an obvious next step.[00:45:12] Simon: Like an LLM, why should it, why should you just be communicating with, with text when it can build interfaces on the fly that let you select a point on a map or or move like sliders up and down. It's gonna create knobs and dials. I keep saying knobs and dials. right. We can do that. And the LLMs can build, and Claude artifacts will build you a knobs and dials interface.[00:45:34] Simon: But at the moment they haven't closed the loop. When you twiddle those knobs, Claude doesn't see what you were doing. They're going to close that loop. I'm, I'm shocked that they haven't done it yet. So yeah, I think there's so much scope for innovation and there's so much scope for doing interesting stuff with that model where the LLM, anything you can represent in SVG, which is almost everything, can now be part of that ongoing conversation.[00:45:59] swyx (2): Yeah, [00:46:00] I would say the best executed version of this I've seen so far is Bolt where you can literally type in, make a Spotify clone, make an Airbnb clone, and it actually just does that for you zero shot with a nice design.[00:46:14] Simon: There's a benchmark for that now. The LMRena people now have a benchmark that is zero shot app, app generation, because all of the models can do it.[00:46:22] Simon: Like it's, it's, I've started figuring out. I'm building my own version of this for my own project, because I think within six months. I think it'll just be an expected feature. Like if you have a web application, why don't you have a thing where, oh, look, the, you can add a custom, like, so for my dataset data exploration project, I want you to be able to do things like conjure up a dashboard, just via a prompt.[00:46:43] Simon: You say, oh, I need a pie chart and a bar chart and put them next to each other, and then have a form where submitting the form inserts a row into my database table. And this is all suddenly feasible. It's, it's, it's not even particularly difficult to do, which is great. Utterly bizarre that these things are now easy.[00:47:00][00:47:00] swyx (2): I think for a general audience, that is what I would highlight, that software creation is becoming easier and easier. Gemini is now available in Gmail and Google Sheets. I don't write my own Google Sheets formulas anymore, I just tell Gemini to do it. And so I think those are, I almost wanted to basically somewhat disagree with, with your assertion that LMS got harder to use.[00:47:22] swyx (2): Like, yes, we, we expose more capabilities, but they're, they're in minor forms, like using canvas, like web search in, in in chat GPT and like Gemini being in, in Excel sheets or in Google sheets, like, yeah, we're getting, no,[00:47:37] Simon: no, no, no. Those are the things that make it harder, because the problem is that for each of those features, they're amazing.[00:47:43] Simon: If you understand the edges of the feature, if you're like, okay, so in Google, Gemini, Excel formulas, I can get it to do a certain amount of things, but I can't get it to go and read a web. You probably can't get it to read a webpage, right? But you know, there are, there are things that it can do and things that it can't do, which are completely undocumented.[00:47:58] Simon: If you ask it what it [00:48:00] can and can't do, they're terrible at answering questions about that. So like my favorite example is Claude artifacts. You can't build a Claude artifact that can hit an API somewhere else. Because the cause headers on that iframe prevents accessing anything outside of CDNJS. So, good luck learning cause headers as an end user in order to understand why Like, I've seen people saying, oh, this is rubbish.[00:48:26] Simon: I tried building an artifact that would run a prompt and it couldn't because Claude didn't expose an API with cause headers that all of this stuff is so weird and complicated. And yeah, like that, that, the more that with the more tools we add, the more expertise you need to really, To understand the full scope of what you can do.[00:48:44] Simon: And so it's, it's, I wouldn't say it's, it's, it's, it's like, the question really comes down to what does it take to understand the full extent of what's possible? And honestly, that, that's just getting more and more involved over time.[00:48:58] Local LLMs: A Growing Interest[00:48:58] swyx (2): I have one more topic that I, I [00:49:00] think you, you're kind of a champion of and we've touched on it a little bit, which is local LLMs.[00:49:05] swyx (2): And running AI applications on your desktop, I feel like you are an early adopter of many, many things.[00:49:12] Simon: I had an interesting experience with that over the past year. Six months ago, I almost completely lost interest. And the reason is that six months ago, the best local models you could run, There was no point in using them at all, because the best hosted models were so much better.[00:49:26] Simon: Like, there was no point at which I'd choose to run a model on my laptop if I had API access to Cloud 3. 5 SONNET. They just, they weren't even comparable. And that changed, basically, in the past three months, as the local models had this step changing capability, where now I can run some of these local models, and they're not as good as Cloud 3.[00:49:45] Simon: 5 SONNET, but they're not so far away that It's not worth me even using them. The other, the, the, the, the continuing problem is I've only got 64 gigabytes of RAM, and if you run, like, LLAMA370B, it's not going to work. Most of my RAM is gone. So now I have to shut down my Firefox tabs [00:50:00] and, and my Chrome and my VS Code windows in order to run it.[00:50:03] Simon: But it's got me interested again. Like, like the, the efficiency improvements are such that now, if you were to like stick me on a desert island with my laptop, I'd be very productive using those local models. And that's, that's pretty exciting. And if those trends continue, and also, like, I think my next laptop, if when I buy one is going to have twice the amount of RAM, At which point, maybe I can run the, almost the top tier, like open weights models and still be able to use it as a computer as well.[00:50:32] Simon: NVIDIA just announced their 3, 000 128 gigabyte monstrosity. That's pretty good price. You know, that's that's, if you're going to buy it,[00:50:42] swyx (2): custom OS and all.[00:50:46] Simon: If I get a job, if I, if, if, if I have enough of an income that I can justify blowing $3,000 on it, then yes.[00:50:52] swyx (2): Okay, let's do a GoFundMe to get Simon one it.[00:50:54] swyx (2): Come on. You know, you can get a job anytime you want. Is this, this is just purely discretionary .[00:50:59] Simon: I want, [00:51:00] I want a job that pays me to do exactly what I'm doing already and doesn't tell me what else to do. That's, thats the challenge.[00:51:06] swyx (2): I think Ethan Molik does pretty well. Whatever, whatever it is he's doing.[00:51:11] swyx (2): But yeah, basically I was trying to bring in also, you know, not just local models, but Apple intelligence is on every Mac machine. You're, you're, you seem skeptical. It's rubbish.[00:51:21] Simon: Apple intelligence is so bad. It's like, it does one thing well.[00:51:25] swyx (2): Oh yeah, what's that? It summarizes notifications. And sometimes it's humorous.[00:51:29] Brian: Are you sure it does that well? And also, by the way, the other, again, from a sort of a normie point of view. There's no indication from Apple of when to use it. Like, everybody upgrades their thing and it's like, okay, now you have Apple Intelligence, and you never know when to use it ever again.[00:51:47] swyx (2): Oh, yeah, you consult the Apple docs, which is MKBHD.[00:51:49] swyx (2): The[00:51:51] Simon: one thing, the one thing I'll say about Apple Intelligence is, One of the reasons it's so disappointing is that the models are just weak, but now, like, Llama 3b [00:52:00] is Such a good model in a 2 gigabyte file I think give Apple six months and hopefully they'll catch up to the state of the art on the small models And then maybe it'll start being a lot more interesting.[00:52:10] swyx (2): Yeah. Anyway, I like This was year one And and you know just like our first year of iPhone maybe maybe not that much of a hit and then year three They had the App Store so Hey I would say give it some time, and you know, I think Chrome also shipping Gemini Nano I think this year in Chrome, which means that every app, every web app will have for free access to a local model that just ships in the browser, which is kind of interesting.[00:52:38] swyx (2): And then I, I think I also wanted to just open the floor for any, like, you know, any of us what are the apps that, you know, AI applications that we've adopted that have, that we really recommend because these are all, you know, apps that are running on our browser that like, or apps that are running locally that we should be, that, that other people should be trying.[00:52:55] swyx (2): Right? Like, I, I feel like that's, that's one always one thing that is helpful at the start of the [00:53:00] year.[00:53:00] Simon: Okay. So for running local models. My top picks, firstly, on the iPhone, there's this thing called MLC Chat, which works, and it's easy to install, and it runs Llama 3B, and it's so much fun. Like, it's not necessarily a capable enough novel that I use it for real things, but my party trick right now is I get my phone to write a Netflix Christmas movie plot outline where, like, a bunch of Jeweller falls in love with the King of Sweden or whatever.[00:53:25] Simon: And it does a good job and it comes up with pun names for the movies. And that's, that's deeply entertaining. On my laptop, most recently, I've been getting heavy into, into Olama because the Olama team are very, very good at finding the good models and patching them up and making them work well. It gives you an API.[00:53:42] Simon: My little LLM command line tool that has a plugin that talks to Olama, which works really well. So that's my, my Olama is. I think the easiest on ramp to to running models locally, if you want a nice user interface, LMStudio is, I think, the best user interface [00:54:00] thing at that. It's not open source. It's good.[00:54:02] Simon: It's worth playing with. The other one that I've been trying with recently, there's a thing called, what's it called? Open web UI or something. Yeah. The UI is fantastic. It, if you've got Olama running and you fire this thing up, it spots Olama and it gives you an interface onto your Olama models. And t

The great Simon Willison joins SWYX and I to talk about everything we learned about LLMs in 2024, and what the state of AI is generally, as we go into 2025.Here is Simon's blog post we keep referring to:https://simonwillison.net/2024/Dec/31...00:00 The State of AI in 202510:05 The Evolution of AI Models19:54 Challenges in AI Agents30:07 The Future of AI in Creative Industries38:29 The Rise of AI Influencers40:54 Credibility in the Age of AI43:15 The Future of User Interfaces for LLMs51:17 Local LLMs and Desktop AI Applications55:17 AI Tools and Applications for Everyday Use01:01:26 The Future of OpenAI and AI Regulation01:08:08 The Need for Better Criticism of LLMs01:10:41 The Future of Wearables and AI IntegrationSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The annual predictions tradition returns for 2025! Bryan and Adam were joined by Simon Willison, Mike Cafarella, Steve Tuck, and Steve Klabnik to review past predictions and look 1-, 3-, and 6-years into the future.See the table of predictions on GitHub.

M.G. Siegler goes way out on a limb with some BIG predictions of things that could happen this year, Simon Willison's year-end roundup is a must-read and perhaps the only thing you have to read to get up-to-speed on the state of the LLM, Allen Pike describes a method for magic, Tom Critchlow thinks small databases are magic & James Stanier agrees with me about Parkinson's Law and the usefulness of deadlines.

M.G. Siegler goes way out on a limb with some BIG predictions of things that could happen this year, Simon Willison's year-end roundup is a must-read and perhaps the only thing you have to read to get up-to-speed on the state of the LLM, Allen Pike describes a method for magic, Tom Critchlow thinks small databases are magic & James Stanier agrees with me about Parkinson's Law and the usefulness of deadlines.

M.G. Siegler goes way out on a limb with some BIG predictions of things that could happen this year, Simon Willison's year-end roundup is a must-read and perhaps the only thing you have to read to get up-to-speed on the state of the LLM, Allen Pike describes a method for magic, Tom Critchlow thinks small databases are magic & James Stanier agrees with me about Parkinson's Law and the usefulness of deadlines.

More fallout from the whole Cruise wind-down. What it's like to use some of the new Gemini 2.0 features. Has Apple, quite belatedly, finally done a feature update that provides the Vision Pro with a “killer app?” An Instagram-like app from China I had never heard of. And one singular, eye-popping datapoint from the CHIPS Act.Links:The end of Cruise is the beginning of a risky new phase for autonomous vehicles (The Verge)Gemini 2.0 Flash: An outstanding multi-modal LLM with a sci-fi streaming model (Simon Willison's Blog)FCC Opens Entire 6-GHz Band to Very-Low-Power Device Operations (TV Tech)The Vision Pro's ultrawide Mac display is very close to being a killer app (The Verge)China's Instagram-Style Xiaohongshu Crosses $1 Billion in Profit (Bloomberg)Harvard Is Releasing a Massive Free AI Training Dataset Funded by OpenAI and Microsoft (Wired)US chipmaking boom in doubt after Biden's defeat (Financial Times)See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

We're making some big Changelog changes in 2025, the previously featured Stanford study on ghost engineers doesn't live up to the hype, Git ingest is a simple service that turns any GitHub repository into a simple text ingest of its codebase, Simon Willison dishes out some hard-earned wisdom he acquired by working at Lanyrd / Eventbrite & Matheus Lima warns us about six mistakes that new managers make.

We're making some big Changelog changes in 2025, the previously featured Stanford study on ghost engineers doesn't live up to the hype, Git ingest is a simple service that turns any GitHub repository into a simple text ingest of its codebase, Simon Willison dishes out some hard-earned wisdom he acquired by working at Lanyrd / Eventbrite & Matheus Lima warns us about six mistakes that new managers make.

We're making some big Changelog changes in 2025, the previously featured Stanford study on ghost engineers doesn't live up to the hype, Git ingest is a simple service that turns any GitHub repository into a simple text ingest of its codebase, Simon Willison dishes out some hard-earned wisdom he acquired by working at Lanyrd / Eventbrite & Matheus Lima warns us about six mistakes that new managers make.

Bitcoin crosses the big $100k mark for the first time. We now know who Trump wants to fill the roles Silicon Valley cares about the most. Is Amazon about to become a top tier AI model player? Two new models from Google DeepMind, one of them promises to revolutionize weather prediction. And Waymo says bienvenido a Miami.Sponsors:WashingtonPost.com/rideLinks:Crypto Trading Volume Surged to $10 Trillion for the First Time in November (Bloomberg)Trump Picks Paul Atkins to Run the S.E.C. (NYTimes)Trump Taps Vance Aide Gail Slater as Top DOJ Antitrust Cop (Bloomberg)Amazon announces Nova, a new family of multimodal AI models (TechCrunch)First impressions of the new Amazon Nova LLMs (via a new llm-bedrock plugin) (Simon Willison's Blog)DeepMind's Genie 2 can generate interactive worlds that look like video games (TechCrunch)Google Introduces A.I. Agent That Aces 15-Day Weather Forecasts (NYTimes)Key leaders behind Google's viral NotebookLM are leaving to create their own startup (TechCrunch)Waymo to expand to Miami, aims to launch robotaxi service there in 2026 (CNBC)See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this conversation, Simon Willison discusses the intersection of AI, Open Source, and journalism, emphasizing the importance of tools like Dataset in enhancing data journalism. He reflects on his journey from developing Django to his current work with AI, highlighting the role of open source in software development. Willison shares insights on how AI can augment human capabilities rather than replace them, and he expresses concerns about the future of AI, particularly regarding AGI. The discussion also touches on the evolving landscape of programming and the need for better onboarding processes for new developers.

If you've listened to the podcast for a while, you might have heard our ElevenLabs-powered AI co-host Charlie a few times. Text-to-speech has made amazing progress in the last 18 months, with OpenAI's Advanced Voice Mode (aka “Her”) as a sneak peek of the future of AI interactions (see our “Building AGI in Real Time” recap). Yet, we had yet to see a real killer app for AI voice (not counting music).Today's guests, Raiza Martin and Usama Bin Shafqat, are the lead PM and AI engineer behind the NotebookLM feature flag that gave us the first viral AI voice experience, the “Deep Dive” podcast:The idea behind the “Audio Overviews” feature is simple: take a bunch of documents, websites, YouTube videos, etc, and generate a podcast out of them. This was one of the first demos that people built with voice models + RAG + GPT models, but it was always a glorified speech-to-text. Raiza and Usama took a very different approach:* Make it conversational: when you listen to a NotebookLM audio there are a ton of micro-interjections (Steven Johnson calls them disfluencies) like “Oh really?” or “Totally”, as well as pauses and “uh…”, like you would expect in a real conversation. These are not generated by the LLM in the transcript, but they are built into the the audio model. See ~28:00 in the pod for more details. * Listeners love tension: if two people are always in agreement on everything, it's not super interesting. They tuned the model to generate flowing conversations that mirror the tone and rhythm of human speech. They did not confirm this, but many suspect the 2 year old SoundStorm paper is related to this model.* Generating new insights: because the hosts' goal is not to summarize, but to entertain, it comes up with funny metaphors and comparisons that actually help expand on the content rather than just paraphrasing like most models do. We have had listeners make podcasts out of our podcasts, like this one.This is different than your average SOTA-chasing, MMLU-driven model buildooor. Putting product and AI engineering in the same room, having them build evals together, and understanding what the goal is lets you get these unique results. The 5 rules for AI PMsWe always focus on AI Engineers, but this episode had a ton of AI PM nuggets as well, which we wanted to collect as NotebookLM is one of the most successful products in the AI space:1. Less is more: the first version of the product had 0 customization options. All you could do is give it source documents, and then press a button to generate. Most users don't know what “temperature” or “top-k” are, so you're often taking the magic away by adding more options in the UI. Since recording they added a few, like a system prompt, but those were features that users were “hacking in”, as Simon Willison highlighted in his blog post.2. Use Real-Time Feedback: they built a community of 65,000 users on Discord that is constantly reporting issues and giving feedback; sometimes they noticed server downtime even before the Google internal monitoring did. Getting real time pings > aggregating user data when doing initial iterations. 3. Embrace Non-Determinism: AI outputs variability is a feature, not a bug. Rather than limiting the outputs from the get-go, build toggles that you can turn on/off with feature flags as the feedback starts to roll in.4. Curate with Taste: if you try your product and it sucks, you don't need more data to confirm it. Just scrap that and iterate again. This is even easier for a product like this; if you start listening to one of the podcasts and turn it off after 10 seconds, it's never a good sign. 5. Stay Hands-On: It's hard to build taste if you don't experiment. Trying out all your competitors products as well as unrelated tools really helps you understand what users are seeing in market, and how to improve on it.Chapters00:00 Introductions01:39 From Project Tailwind to NotebookLM09:25 Learning from 65,000 Discord members12:15 How NotebookLM works18:00 Working with Steven Johnson23:00 How to prioritize features25:13 Structuring the data pipelines29:50 How to eval34:34 Steering the podcast outputs37:51 Defining speakers personalities39:04 How do you make audio engaging?45:47 Humor is AGI51:38 Designing for non-determinism53:35 API when?55:05 Multilingual support and dialect considerations57:50 Managing system prompts and feature requests01:00:58 Future of NotebookLM01:04:59 Podcasts for your codebase01:07:16 Plans for real-time chat01:08:27 Wrap upShow Notes* Notebook LM* AI Test Kitchen* Nicholas Carlini* Steven Johnson* Wealth of Nations* Histories of Mysteries by Andrej Karpathy* chicken.pdf Threads* Area 120* Raiza Martin* Usama Bin ShafqatTranscriptNotebookLM [00:00:00]: Hey everyone, we're here today as guests on Latent Space. It's great to be here, I'm a long time listener and fan, they've had some great guests on this show before. Yeah, what an honor to have us, the hosts of another podcast, join as guests. I mean a huge thank you to Swyx and Alessio for the invite, thanks for having us on the show. Yeah really, it seems like they brought us here to talk a little bit about our show, our podcast. Yeah, I mean we've had lots of listeners ourselves, listeners at Deep Dive. Oh yeah, we've made a ton of audio overviews since we launched and we're learning a lot. There's probably a lot we can share around what we're building next, huh? Yeah, we'll share a little bit at least. The short version is we'll keep learning and getting better for you. We're glad you're along for the ride. So yeah, keep listening. Keep listening and stay curious. We promise to keep diving deep and bringing you even better options in the future. Stay curious.Alessio [00:00:52]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Residence at Decibel Partners. And I'm joined by my co-host, Swyx, founder of Smol.ai.Swyx [00:01:01]: Hey, and today we're back in the studio with our special guest, Raiza Martin. And Raiza, I forgot to get your last name, Shafqat.Raiza [00:01:10]: Yes.Swyx [00:01:10]: Okay, welcome.Raiza [00:01:12]: Hello, thank you for having us.Swyx [00:01:14]: So AI podcasters meet human podcasters, always fun. Congrats on the success of Notebook LM. I mean, how does it feel?Raiza [00:01:22]: It's been a lot of fun. A lot of it, honestly, was unexpected. But my favorite part is really listening to the audio overviews that people have been making.Swyx [00:01:29]: Maybe we should do a little bit of intros and tell the story. You know, what is your path into the sort of Google AI org? Or maybe, actually, I don't even know what org you guys are in.Raiza [00:01:39]: I can start. My name is Raisa. I lead the Notebook LM team inside of Google Labs. So specifically, that's the org that we're in. It's called Google Labs. It's only about two years old. And our whole mandate is really to build AI products. That's it. We work super closely with DeepMind. Our entire thing is just, like, try a bunch of things and see what's landing with users. And the background that I have is, really, I worked in payments before this, and I worked in ads right before, and then startups. I tell people, like, at every time that I changed orgs, I actually almost quit Google. Like, specifically, like, in between ads and payments, I was like, all right, I can't do this. Like, this is, like, super hard. I was like, it's not for me. I'm, like, a very zero-to-one person. But then I was like, okay, I'll try. I'll interview with other teams. And when I interviewed in payments, I was like, oh, these people are really cool. I don't know if I'm, like, a super good fit with this space, but I'll try it because the people are cool. And then I really enjoyed that, and then I worked on, like, zero-to-one features inside of payments, and I had a lot of fun. But then the time came again where I was like, oh, I don't know. It's like, it's time to leave. It's time to start my own thing. But then I interviewed inside of Google Labs, and I was like, oh, darn. Like, there's definitely, like—Alessio [00:02:48]: They got you again.Raiza [00:02:49]: They got me again. And so now I've been here for two years, and I'm happy that I stayed because especially with, you know, the recent success of Notebook LM, I'm like, dang, we did it. I actually got to do it. So that was really cool.Usama [00:03:02]: Kind of similar, honestly. I was at a big team at Google. We do sort of the data center supply chain planning stuff. Google has, like, the largest sort of footprint. Obviously, there's a lot of management stuff to do there. But then there was this thing called Area 120 at Google, which does not exist anymore. But I sort of wanted to do, like, more zero-to-one building and landed a role there. We were trying to build, like, a creator commerce platform called Kaya. It launched briefly a couple years ago. But then Area 120 sort of transitioned and morphed into Labs. And, like, over the last few years, like, the focus just got a lot clearer. Like, we were trying to build new AI products and do it in the wild and sort of co-create and all of that. So, you know, we've just been trying a bunch of different things. And this one really landed, which has felt pretty phenomenal. Really, really landed.Swyx [00:03:53]: Let's talk about the brief history of Notebook LM. You had a tweet, which is very helpful for doing research. May 2023, during Google I.O., you announced Project Tailwind.Raiza [00:04:03]: Yeah.Swyx [00:04:03]: So today is October 2024. So you joined October 2022?Raiza [00:04:09]: Actually, I used to lead AI Test Kitchen. And this was actually, I think, not I.O. 2023. I.O. 2022 is when we launched AI Test Kitchen, or announced it. And I don't know if you remember it.Swyx [00:04:23]: That's how you, like, had the basic prototype for Gemini.Raiza [00:04:26]: Yes, yes, exactly. Lambda.Swyx [00:04:28]: Gave beta access to people.Raiza [00:04:29]: Yeah, yeah, yeah. And I remember, I was like, wow, this is crazy. We're going to launch an LLM into the wild. And that was the first project that I was working on at Google. But at the same time, my manager at the time, Josh, he was like, hey, I want you to really think about, like, what real products would we build that are not just demos of the technology? That was in October of 2022. I was sitting next to an engineer that was working on a project called Talk to Small Corpus. His name was Adam. And the idea of Talk to Small Corpus is basically using LLM to talk to your data. And at the time, I was like, wait, there's some, like, really practical things that you can build here. And just a little bit of background, like, I was an adult learner. Like, I went to college while I was working a full-time job. And the first thing I thought was, like, this would have really helped me with my studying, right? Like, if I could just, like, talk to a textbook, especially, like, when I was tired after work, that would have been huge. We took a lot of, like, the Talk to Small Corpus prototypes, and I showed it to a lot of, like, college students, particularly, like, adult learners. They were like, yes, like, I get it, right? Like, I didn't even have to explain it to them. And we just continued to iterate the prototype from there to the point where we actually got a slot as part of the I.O. demo in 23.Swyx [00:05:42]: And Corpus, was it a textbook? Oh, my gosh.Raiza [00:05:45]: Yeah. It's funny. Actually, when he explained the project to me, he was like, talk to Small Corpus. It was like, talk to a small corpse?Swyx [00:05:51]: Yeah, nobody says Corpus.Raiza [00:06:00]: It was like, a small corpse? This is not AI. Yeah, yeah. And it really was just, like, a way for us to describe the amount of data that we thought, like, it could be good for.Swyx [00:06:02]: Yeah, but even then, you're still, like, doing rag stuff. Because, you know, the context length back then was probably, like, 2K, 4K.Raiza [00:06:08]: Yeah, it was basically rag.Raiza [00:06:09]: That was essentially what it was.Raiza [00:06:10]: And I remember, I was like, we were building the prototypes. And at the same time, I think, like, the rest of the world was. Right? We were seeing all of these, like, chat with PDF stuff come up. And I was like, come on, we gotta go. Like, we have to, like, push this out into the world. I think if there was anything, I wish we would have launched sooner because I wanted to learn faster. But I think, like, we netted out pretty well.Alessio [00:06:30]: Was the initial product just text-to-speech? Or were you also doing kind of, like, synthesizing of the content, refining it? Or were you just helping people read through it?Raiza [00:06:40]: Before we did the I.O. announcement in 23, we'd already done a lot of studies. And one of the first things that I realized was the first thing anybody ever typed was, summarize the thing. Right?Raiza [00:06:53]: Summarize the document.Raiza [00:06:54]: And it was, like, half like a test and half just like, oh, I know the content. I want to see how well it does this. So it was part of the first thing that we launched. It was called Project Tailwind back then. It was just Q&A, so you could chat with the doc just through text, and it would automatically generate a summary as well. I'm not sure if we had it back then.Raiza [00:07:12]: I think we did.Raiza [00:07:12]: It would also generate the key topics in your document, and it could support up to, like, 10 documents. So it wasn't just, like, a single doc.Alessio [00:07:20]: And then the I.O. demo went well, I guess. And then what was the discussion from there to where we are today? Is there any, maybe, intermediate step of the product that people missed between this was launch or?Raiza [00:07:33]: It was interesting because every step of the way, I think we hit, like, some pretty critical milestones. So I think from the initial demo, I think there was so much excitement of, like, wow, what is this thing that Google is launching? And so we capitalized on that. We built the wait list. That's actually when we also launched the Discord server, which has been huge for us because for us in particular, one of the things that I really wanted to do was to be able to launch features and get feedback ASAP. Like, the moment somebody tries it, like, I want to hear what they think right now, and I want to ask follow-up questions. And the Discord has just been so great for that. But then we basically took the feedback from I.O., we continued to refine the product.Raiza [00:08:12]: So we added more features.Raiza [00:08:13]: We added sort of, like, the ability to save notes, write notes. We generate follow-up questions. So there's a bunch of stuff in the product that shows, like, a lot of that research. But it was really the rolling out of things. Like, we removed the wait list, so rolled out to all of the United States. We rolled out to over 200 countries and territories. We started supporting more languages, both in the UI and, like, the actual source stuff. We experienced, like, in terms of milestones, there was, like, an explosion of, like, users in Japan. This was super interesting in terms of just, like, unexpected. Like, people would write to us and they would be like, this is amazing. I have to read all of these rules in English, but I can chat in Japanese. It's like, oh, wow. That's true, right? Like, with LLMs, you kind of get this natural, it translates the content for you. And you can ask in your sort of preferred mode. And I think that's not just, like, a language thing, too. I think there's, like, I do this test with Wealth of Nations all the time because it's, like, a pretty complicated text to read. The Evan Smith classic.Swyx [00:09:11]: It's, like, 400 pages or something.Raiza [00:09:12]: Yeah. But I like this test because I'm, like, asking, like, Normie, you know, plain speak. And then it summarizes really well for me. It sort of adapts to my tone.Swyx [00:09:22]: Very capitalist.Raiza [00:09:25]: Very on brand.Swyx [00:09:25]: I just checked in on a Notebook LM Discord. 65,000 people. Yeah.Raiza [00:09:29]: Crazy.Swyx [00:09:29]: Just, like, for one project within Google. It's not, like, it's not labs. It's just Notebook LM.Raiza [00:09:35]: Just Notebook LM.Swyx [00:09:36]: What do you learn from the community?Raiza [00:09:39]: I think that the Discord is really great for hearing about a couple of things.Raiza [00:09:43]: One, when things are going wrong. I think, honestly, like, our fastest way that we've been able to find out if, like, the servers are down or there's just an influx of people being, like, it saysRaiza [00:09:53]: system unable to answer.Raiza [00:09:54]: Anybody else getting this?Raiza [00:09:56]: And I'm, like, all right, let's go.Raiza [00:09:58]: And it actually catches it a lot faster than, like, our own monitoring does.Raiza [00:10:01]: It's, like, that's been really cool. So, thank you.Swyx [00:10:03]: Canceled eat a dog.Raiza [00:10:05]: So, thank you to everybody. Please keep reporting it. I think the second thing is really the use cases.Raiza [00:10:10]: I think when we put it out there, I was, like, hey, I have a hunch of how people will use it, but, like, to actually hear about, you know, not just the context of, like, the use of Notebook LM, but, like, what is this person's life like? Why do they care about using this tool?Raiza [00:10:23]: Especially people who actually have trouble using it, but they keep pushing.Raiza [00:10:27]: Like, that's just so critical to understand what was so motivating, right?Raiza [00:10:31]: Like, what was your problem that was, like, so worth solving? So, that's, like, a second thing.Raiza [00:10:34]: The third thing is also just hearing sort of, like, when we have wins and when we don't have wins because there's actually a lot of functionality where I'm, like, hmm, IRaiza [00:10:42]: don't know if that landed super well or if that was actually super critical.Raiza [00:10:45]: As part of having this sort of small project, right, I want to be able to unlaunch things, too. So, it's not just about just, like, rolling things out and testing it and being, like, wow, now we have, like, 99 features. Like, hopefully we get to a place where it's, like, there's just a really strong core feature set and the things that aren't as great, we can just unlaunch.Swyx [00:11:02]: What have you unlaunched? I have to ask.Raiza [00:11:04]: I'm in the process of unlaunching some stuff, but, for example, we had this idea that you could highlight the text in your source passage and then you could transform it. And nobody was really using it and it was, like, a very complicated piece of our architecture and it's very hard to continue supporting it in the context of new features. So, we were, like, okay, let's do a 50-50 sunset of this thing and see if anybody complains.Raiza [00:11:28]: And so far, nobody has.Swyx [00:11:29]: Is there, like, a feature flagging paradigm inside of your architecture that lets you feature flag these things easily?Raiza [00:11:36]: Yes, and actually...Raiza [00:11:37]: What is it called?Swyx [00:11:38]: Like, I love feature flagging.Raiza [00:11:40]: You mean, like, in terms of just, like, being able to expose things to users?Swyx [00:11:42]: Yeah, as a PM. Like, this is your number one tool, right?Raiza [00:11:44]: Yeah, yeah.Swyx [00:11:45]: Let's try this out. All right, if it works, roll it out. If it doesn't, roll it back, you know?Raiza [00:11:49]: Yeah, I mean, we just run Mendel experiments for the most part. And, actually, I don't know if you saw it, but on Twitter, somebody was able to get around our flags and they enabled all the experiments.Raiza [00:11:58]: They were, like, check out what the Notebook LM team is cooking.Raiza [00:12:02]: I was, like, oh!Raiza [00:12:03]: And I was at lunch with the rest of the team and I was, like, I was eating. I was, like, guys, guys, Magic Draft League!Raiza [00:12:10]: They were, like, oh, no!Raiza [00:12:12]: I was, like, okay, just finish eating and then let's go figure out what to do.Raiza [00:12:15]: Yeah.Alessio [00:12:15]: I think a post-mortem would be fun, but I don't think we need to do it on the podcast now. Can we just talk about what's behind the magic? So, I think everybody has questions, hypotheses about what models power it. I know you might not be able to share everything, but can you just get people very basic? How do you take the data and put it in the model? What text model you use? What's the text-to-speech kind of, like, jump between the two? Sure.Raiza [00:12:42]: Yeah.Raiza [00:12:42]: I was going to say, SRaiza, he manually does all the podcasts.Raiza [00:12:46]: Oh, thank you.Usama [00:12:46]: Really fast. You're very fast, yeah.Raiza [00:12:48]: Both of the voices at once.Usama [00:12:51]: Voice actor.Raiza [00:12:52]: Good, good.Usama [00:12:52]: Yeah, so, for a bit of background, we were building this thing sort of outside Notebook LM to begin with. Like, just the idea is, like, content transformation, right? Like, we can do different modalities. Like, everyone knows that. Everyone's been poking at it. But, like, how do you make it really useful? And, like, one of the ways we thought was, like, okay, like, you maybe, like, you know, people learn better when they're hearing things. But TTS exists, and you can, like, narrate whatever's on screen. But you want to absorb it the same way. So, like, that's where we sort of started out into the realm of, like, maybe we try, like, you know, two people are having a conversation kind of format. We didn't actually start out thinking this would live in Notebook, right? Like, Notebook was sort of, we built this demo out independently, tried out, like, a few different sort of sources. The main idea was, like, go from some sort of sources and transform it into a listenable, engaging audio format. And then through that process, we, like, unlocked a bunch more sort of learnings. Like, for example, in a sense, like, you're not prompting the model as much because, like, the information density is getting unrolled by the model prompting itself, in a sense. Because there's two speakers, and they're both technically, like, AI personas, right? That have different angles of looking at things. And, like, they'll have a discussion about it. And that sort of, we realized that's kind of what was making it riveting, in a sense. Like, you care about what comes next, even if you've read the material already. Because, like, people say they get new insights on their own journals or books or whatever. Like, anything that they've written themselves. So, yeah, from a modeling perspective, like, it's, like Reiza said earlier, like, we work with the DeepMind audio folks pretty closely. So, they're always cooking up new techniques to, like, get better, more human-like audio. And then Gemini 1.5 is really, really good at absorbing long context. So, we sort of, like, generally put those things together in a way that we could reliably produce the audio.Raiza [00:14:52]: I would add, like, there's something really nuanced, I think, about sort of the evolution of, like, the utility of text-to-speech. Where, if it's just reading an actual text response, and I've done this several times. I do it all the time with, like, reading my text messages. Or, like, sometimes I'm trying to read, like, a really dense paper, but I'm trying to do actual work. I'll have it, like, read out the screen. There is something really robotic about it that is not engaging. And it's really hard to consume content in that way. And it's never been really effective. Like, particularly for me, where I'm, like, hey, it's actually just, like, it's fine for, like, short stuff. Like, texting, but even that, it's, like, not that great. So, I think the frontier of experimentation here was really thinking about there is a transform that needs to happen in between whatever.Raiza [00:15:38]: Here's, like, my resume, right?Raiza [00:15:39]: Or here's, like, a 100-page slide deck or something. There is a transform that needs to happen that is inherently editorial. And I think this is where, like, that two-person persona, right, dialogue model, they have takes on the material that you've presented. That's where it really sort of, like, brings the content to life in a way that's, like, not robotic. And I think that's, like, where the magic is, is, like, you don't actually know what's going to happen when you press generate.Raiza [00:16:08]: You know, for better or for worse.Raiza [00:16:09]: Like, to the extent that, like, people are, like, no, I actually want it to be more predictable now. Like, I want to be able to tell them. But I think that initial, like, wow was because you didn't know, right? When you upload your resume, what's it about to say about you? And I think I've seen enough of these where I'm, like, oh, it gave you good vibes, right? Like, you knew it was going to say, like, something really cool. As we start to shape this product, I think we want to try to preserve as much of that wow as much as we can. Because I do think, like, exposing, like, all the knobs and, like, the dials, like, we've been thinking about this a lot. It's like, hey, is that, like, the actual thing?Raiza [00:16:43]: Is that the thing that people really want?Alessio [00:16:45]: Have you found differences in having one model just generate the conversation and then using text-to-speech to kind of fake two people? Or, like, are you actually using two different kind of system prompts to, like, have a conversation step-by-step? I'm always curious, like, if persona system prompts make a big difference? Or, like, you just put in one prompt and then you just let it run?Usama [00:17:05]: I guess, like, generally we use a lot of inference, as you can tell with, like, the spinning thing takes a while. So, yeah, there's definitely, like, a bunch of different things happening under the hood. We've tried both approaches and they have their, sort of, drawbacks and benefits. I think that that idea of, like, questioning, like, the two different personas, like, persists throughout, like, whatever approach we try. It's like, there's a bit of, like, imperfection in there. Like, we had to really lean into the fact that, like, to build something that's engaging, like, it needs to be somewhat human and it needs to be just not a chatbot. Like, that was sort of, like, what we need to diverge from. It's like, you know, most chatbots will just narrate the same kind of answer, like, given the same sources, for the most part, which is ridiculous. So, yeah, there's, like, experimentation there under the hood, like, with the model to, like, make sure that it's spitting out, like, different takes and different personas and different, sort of, prompting each other is, like, a good analogy, I guess.Swyx [00:18:00]: Yeah, I think Steven Johnson, I think he's on your team. I don't know what his role is. He seems like chief dreamer, writer.Raiza [00:18:08]: Yeah, I mean, I can comment on Steven. So, Steven joined, actually, in the very early days, I think before it was even a fully funded project. And I remember when he joined, I was like, Steven Johnson's going to be on my team? You know, and for folks who don't know him, Steven is a New York Times bestselling author of, like, 14 books. He has a PBS show. He's, like, incredibly smart, just, like, a true, sort of, celebrity by himself. And then he joined Google, and he was like, I want to come here, and I want to build the thing that I've always dreamed of, which is a tool to help me think. I was like, a what? Like, a tool to help you think? I was like, what do you need help with? Like, you seem to be doing great on your own. And, you know, he would describe this to me, and I would watch his flow. And aside from, like, providing a lot of inspiration, to be honest, like, when I watched Steven work, I was like, oh, nobody works like this, right? Like, this is what makes him special. Like, he is such a dedicated, like, researcher and journalist, and he's so thorough, he's so smart. And then I had this realization of, like, maybe Steven is the product. Maybe the work is to take Steven's expertise and bring it to, like, everyday people that could really benefit from this. Like, just watching him work, I was like, oh, I could definitely use, like, a mini-Steven, like, doing work for me. Like, that would make me a better PM. And then I thought very quickly about, like, the adjacent roles that could use sort of this, like, research and analysis tool. And so, aside from being, you know, chief dreamer, Steven also represents, like, a super workflow that I think all of us, like, if we had access to a tool like it, would just inherently, like, make us better.Swyx [00:19:46]: Did you make him express his thoughts while he worked, or you just silently watched him, or how does this work?Raiza [00:19:52]: Oh, now you're making me admit it. But yes, I did just silently watch him.Swyx [00:19:57]: This is a part of the PM toolkit, right? They give user interviews and all that.Raiza [00:20:00]: Yeah, I mean, I did interview him, but I noticed, like, if I interviewed him, it was different than if I just watched him. And I did the same thing with students all the time. Like, I followed a lot of students around. I watched them study. I would ask them, like, oh, how do you feel now, right?Raiza [00:20:15]: Or why did you do that? Like, what made you do that, actually?Raiza [00:20:18]: Or why are you upset about, like, this particular thing? Why are you cranky about this particular topic? And it was very similar, I think, for Steven, especially because he was describing, he was in the middle of writing a book. And he would describe, like, oh, you know, here's how I research things, and here's how I keep my notes. Oh, and here's how I do it. And it was really, he was doing this sort of, like, self-questioning, right? Like, now we talk about, like, chain of, you know, reasoning or thought, reflection.Raiza [00:20:44]: And I was like, oh, he's the OG.Raiza [00:20:46]: Like, I watched him do it in real time. I was like, that's, like, L-O-M right there. And to be able to bring sort of that expertise in a way that was, like, you know, maybe, like, costly inference-wise, but really have, like, that ability inside of a tool that was, like, for starters, free inside of NotebookLM, it was good to learn whether or not people really did find use out of it.Swyx [00:21:05]: So did he just commit to using NotebookLM for everything, or did you just model his existing workflow?Raiza [00:21:12]: Both, right?Raiza [00:21:12]: Like, in the beginning, there was no product for him to use. And so he just kept describing the thing that he wanted. And then eventually, like, we started building the thing. And then I would start watching him use it. One of the things that I love about Steven is he uses the product in ways where it kind of does it, but doesn't quite. Like, he's always using it at, like, the absolute max limit of this thing. But the way that he describes it is so full of promise, where he's like, I can see it going here. And all I have to do is sort of, like, meet him there and sort of pressure test whether or not, you know, everyday people want it. And we just have to build it.Swyx [00:21:47]: I would say OpenAI has a pretty similar person, Andrew Mason, I think his name is. It's very similar, like, just from the writing world and using it as a tool for thought to shape Chachabitty. I don't think that people who use AI tools to their limit are common. I'm looking at my NotebookLM now. I've got two sources. You have a little, like, source limit thing. And my bar is over here, you know, and it stretches across the whole thing. I'm like, did he fill it up?Raiza [00:22:09]: Yes, and he has, like, a higher limit than others, I think. He fills it up.Raiza [00:22:14]: Oh, yeah.Raiza [00:22:14]: Like, I don't think Steven even has a limit, actually.Swyx [00:22:17]: And he has Notes, Google Drive stuff, PDFs, MP3, whatever.Raiza [00:22:22]: Yes, and one of my favorite demos, he just did this recently, is he has actually PDFs of, like, handwritten Marie Curie notes. I see.Swyx [00:22:29]: So you're doing image recognition as well. Yeah, it does support it today.Raiza [00:22:32]: So if you have a PDF that's purely images, it will recognize it.Raiza [00:22:36]: But his demo is just, like, super powerful.Raiza [00:22:37]: He's like, okay, here's Marie Curie's notes. And it's like, here's how I'm using it to analyze it. And I'm using it for, like, this thing that I'm writing.Raiza [00:22:44]: And that's really compelling.Raiza [00:22:45]: It's like the everyday person doesn't think of these applications. And I think even, like, when I listen to Steven's demo, I see the gap. I see how Steven got there, but I don't see how I could without him. And so there's a lot of work still for us to build of, like, hey, how do I bring that magic down to, like, zero work? Because I look at all the steps that he had to take in order to do it, and I'm like, okay, that's product work for us, right? Like, that's just onboarding.Alessio [00:23:09]: And so from an engineering perspective, people come to you and it's like, hey, I need to use this handwritten notes from Marie Curie from hundreds of years ago. How do you think about adding support for, like, data sources and then maybe any fun stories and, like, supporting more esoteric types of inputs?Raiza [00:23:25]: So I think about the product in three ways, right? So there's the sources, the source input. There's, like, the capabilities of, like, what you could do with those sources. And then there's the third space, which is how do you output it into the world? Like, how do you put it back out there? There's a lot of really basic sources that we don't support still, right? I think there's sort of, like, the handwritten notes stuff is one, but even basic things like DocX or, like, PowerPoint, right? Like, these are the things that people, everyday people are like, hey, my professor actually gave me everything in DocX. Can you support that? And then just, like, basic stuff, like images and PDFs combined with text. Like, there's just a really long roadmap for sources that I think we just have to work on.Raiza [00:24:04]: So that's, like, a big piece of it.Raiza [00:24:05]: On the output side, and I think this is, like, one of the most interesting things that we learned really early on, is, sure, there's, like, the Q&A analysis stuff, which is like, hey, when did this thing launch? Okay, you found it in the slide deck. Here's the answer. But most of the time, the reason why people ask those questions is because they're trying to make something new. And so when, actually, when some of those early features leaked, like, a lot of the features we're experimenting with are the output types. And so you can imagine that people care a lot about the resources that they're putting into NotebookLM because they're trying to create something new. So I think equally as important as, like, the source inputs are the outputs that we're helping people to create. And really, like, you know, shortly on the roadmap, we're thinking about how do we help people use NotebookLM to distribute knowledge? And that's, like, one of the most compelling use cases is, like, shared notebooks. It's, like, a way to share knowledge. How do we help people take sources and, like, one-click new documents out of it, right? And I think that's something that people think is, like, oh, yeah, of course, right? Like, one push a document. But what does it mean to do it right? Like, to do it in your style, in your brand, right?Raiza [00:25:08]: To follow your guidelines, stuff like that.Raiza [00:25:09]: So I think there's a lot of work, like, on both sides of that equation.Raiza [00:25:13]: Interesting.Swyx [00:25:13]: Any comments on the engineering side of things?Usama [00:25:16]: So, yeah, like I said, I was mostly working on building the text to audio, which kind of lives as a separate engineering pipeline, almost, that we then put into NotebookLM. But I think there's probably tons of NotebookLM engineering war stories on dealing with sources. And so I don't work too closely with engineers directly. But I think a lot of it does come down to, like, Gemini's native understanding of images really well with the latest generation.Raiza [00:25:39]: Yeah, I think on the engineering and modeling side, I think we are a really good example of a team that's put a product out there, and we're getting a lot of feedback from the users, and we return the data to the modeling team, right? To the extent that we say, hey, actually, you know what people are uploading, but we can't really support super well?Raiza [00:25:56]: Text plus image, right?Raiza [00:25:57]: Especially to the extent that, like, NotebookLM can handle up to 50 sources, 500,000 words each. Like, you're not going to be able to jam all of that into, like, the context window. So how do we do multimodal embeddings with that? There's really, like, a lot of things that we have to solve that are almost there, but not quite there yet.Alessio [00:26:16]: On then turning it into audio, I think one of the best things is it has so many of the human... Does that happen in the text generation that then becomes audio? Or is that a part of, like, the audio model that transforms the text?Usama [00:26:27]: It's a bit of both, I would say. The audio model is definitely trying to mimic, like, certain human intonations and, like, sort of natural, like, breathing and pauses and, like, laughter and things like that. But yeah, in generating, like, the text, we also have to sort of give signals on, like, where those things maybe would make sense.Alessio [00:26:45]: And on the input side, instead of having a transcript versus having the audio, like, can you take some of the emotions out of it, too? If I'm giving, like, for example, when we did the recaps of our podcast, we can either give audio of the pod or we can give a diarized transcription of it. But, like, the transcription doesn't have some of the, you know, voice kind of, like, things.Raiza [00:27:05]: Yeah, yeah.Alessio [00:27:05]: Do you reconstruct that when people upload audio or how does that work?Raiza [00:27:09]: So when you upload audio today, we just transcribe it. So it is quite lossy in the sense that, like, we don't transcribe, like, the emotion from that as a source. But when you do upload a text file and it has a lot of, like, that annotation, I think that there is some ability for it to be reused in, like, the audio output, right? But I think it will still contextualize it in the deep dive format. So I think that's something that's, like, particularly important is, like, hey, today we only have one format.Raiza [00:27:37]: It's deep dive.Raiza [00:27:38]: It's meant to be a pretty general overview and it is pretty peppy.Raiza [00:27:42]: It's just very upbeat.Raiza [00:27:43]: It's very enthusiastic, yeah.Raiza [00:27:45]: Yeah, yeah.Raiza [00:27:45]: Even if you had, like, a sad topic, I think they would find a way to be, like, silver lining, though.Raiza [00:27:50]: Really?Raiza [00:27:51]: Yeah.Raiza [00:27:51]: We're having a good chat.Raiza [00:27:54]: Yeah, that's awesome.Swyx [00:27:54]: One of the ways, many, many, many ways that deep dive went viral is people saying, like, if you want to feel good about yourself, just drop in your LinkedIn. Any other, like, favorite use cases that you saw from people discovering things in social media?Raiza [00:28:08]: I mean, there's so many funny ones and I love the funny ones.Raiza [00:28:11]: I think because I'm always relieved when I watch them. I'm like, haha, that was funny and not scary. It's great.Raiza [00:28:17]: There was another one that was interesting, which was a startup founder putting their landing page and being like, all right, let's test whether or not, like, the value prop is coming through. And I was like, wow, that's right.Raiza [00:28:26]: That's smart.Usama [00:28:27]: Yeah.Raiza [00:28:28]: And then I saw a couple of other people following up on that, too.Raiza [00:28:32]: Yeah.Swyx [00:28:32]: I put my about page in there and, like, yeah, if there are things that I'm not comfortable with, I should remove it. You know, so that it can pick it up. Right.Usama [00:28:39]: I think that the personal hype machine was, like, a pretty viral one. I think, like, people uploaded their dreams and, like, some people, like, keep sort of dream journals and it, like, would sort of comment on those and, like, it was therapeutic. I didn't see those.Raiza [00:28:54]: Those are good. I hear from Googlers all the time, especially because we launched it internally first. And I think we launched it during the, you know, the Q3 sort of, like, check-in cycle. So all Googlers have to write notes about, like, hey, you know, what'd you do in Q3? And what Googlers were doing is they would write, you know, whatever they accomplished in Q3 and then they would create an audio overview. And these people they didn't know would just ping me and be like, wow, I feel really good, like, going into a meeting with my manager.Raiza [00:29:25]: And I was like, good, good, good, good. You really did that, right?Usama [00:29:29]: I think another cool one is just, like, any Wikipedia article. Yeah. Like, you drop it in and it's just, like, suddenly, like, the best sort of summary overview.Raiza [00:29:38]: I think that's what Karpathy did, right? Like, he has now a Spotify channel called Histories of Mysteries, which is basically, like, he just took, like, interesting stuff from Wikipedia and made audio overviews out of it.Swyx [00:29:50]: Yeah, he became a podcaster overnight.Raiza [00:29:52]: Yeah.Raiza [00:29:53]: I'm here for it. I fully support him.Raiza [00:29:55]: I'm racking up the listens for him.Swyx [00:29:58]: Honestly, it's useful even without the audio. You know, I feel like the audio does add an element to it, but I always want, you know, paired audio and text. And it's just amazing to see what people are organically discovering. I feel like it's because you laid the groundwork with NotebookLM and then you came in and added the sort of TTS portion and made it so good, so human, which is weird. Like, it's this engineering process of humans. Oh, one thing I wanted to ask. Do you have evals?Raiza [00:30:23]: Yeah.Swyx [00:30:23]: Yes.Raiza [00:30:24]: What? Potatoes for chefs.Swyx [00:30:27]: What is that? What do you mean, potatoes?Raiza [00:30:29]: Oh, sorry.Raiza [00:30:29]: Sorry. We were joking with this, like, a couple of weeks ago. We were doing, like, side-by-sides. But, like, Raiza sent me the file and it was literally called Potatoes for Chefs. And I was like, you know, my job is really serious, but you have to laugh a little bit. Like, the title of the file is, like, Potatoes for Chefs.Swyx [00:30:47]: Is it like a training document for chefs?Usama [00:30:50]: It's just a side-by-side for, like, two different kind of audio transcripts.Swyx [00:30:54]: The question is really, like, as you iterate, the typical engineering advice is you establish some kind of test or benchmark. You're at, like, 30 percent. You want to get it up to 90, right?Raiza [00:31:05]: Yeah.Swyx [00:31:05]: What does that look like for making something sound human and interesting and voice?Usama [00:31:11]: We have the sort of formal eval process as well. But I think, like, for this particular project, we maybe took a slightly different route to begin with. Like, there was a lot of just within the team listening sessions. A lot of, like, sort of, like... Dogfooding.Raiza [00:31:23]: Yeah.Usama [00:31:23]: Like, I think the bar that we tried to get to before even starting formal evals with raters and everything was much higher than I think other projects would. Like, because that's, as you said, like, the traditional advice, right? Like, get that ASAP. Like, what are you looking to improve on? Whatever benchmark it is. So there was a lot of just, like, critical listening. And I think a lot of making sure that those improvements actually could go into the model. And, like, we're happy with that human element of it. And then eventually we had to obviously distill those down into an eval set. But, like, still there's, like, the team is just, like, a very, very, like, avid user of the product at all stages.Raiza [00:32:02]: I think you just have to be really opinionated.Raiza [00:32:05]: I think that sometimes, if you are, your intuition is just sharper and you can move a lot faster on the product.Raiza [00:32:12]: Because it's like, if you hold that bar high, right?Raiza [00:32:15]: Like, if you think about, like, the iterative cycle, it's like, hey, we could take, like, six months to ship this thing. To get it to, like, mid where we were. Or we could just, like, listen to this and be like, yeah, that's not it, right? And I don't need a rater to tell me that. That's my preference, right? And collectively, like, if I have two other people listen to it, they'll probably agree. And it's just kind of this step of, like, just keep improving it to the point where you're like, okay, now I think this is really impressive. And then, like, do evals, right? And then validate that.Swyx [00:32:43]: Was the sound model done and frozen before you started doing all this? Or are you also saying, hey, we need to improve the sound model as well? Both.Usama [00:32:51]: Yeah, we were making improvements on the audio and just, like, generating the transcript as well. I think another weird thing here was, like, we needed to be entertaining. And that's much harder to quantify than some of the other benchmarks that you can make for, like, you know, Sweebench or get better at this math.Swyx [00:33:10]: Do you just have people rate one to five or, you know, or just thumbs up and down?Usama [00:33:14]: For the formal rater evals, we have sort of like a Likert scale and, like, a bunch of different dimensions there. But we had to sort of break down what makes it entertaining into, like, a bunch of different factors. But I think the team stage of that was more critical. It was like, we need to make sure that, like, what is making it fun and engaging? Like, we dialed that as far as it goes. And while we're making other changes that are necessary, like, obviously, they shouldn't make stuff up or, you know, be insensitive.Raiza [00:33:41]: Hallucinations. Safety.Swyx [00:33:42]: Other safety things.Raiza [00:33:43]: Right.Swyx [00:33:43]: Like a bunch of safety stuff.Raiza [00:33:45]: Yeah, exactly.Usama [00:33:45]: So, like, with all of that and, like, also just, you know, following sort of a coherent narrative and structure is really important. But, like, with all of this, we really had to make sure that that central tenet of being entertaining and engaging and something you actually want to listen to. It just doesn't go away, which takes, like, a lot of just active listening time because you're closest to the prompts, the model and everything.Swyx [00:34:07]: I think sometimes the difficulty is because we're dealing with non-deterministic models, sometimes you just got a bad roll of the dice and it's always on the distribution that you could get something bad. Basically, how many do you, like, do ten runs at a time? And then how do you get rid of the non-determinism?Raiza [00:34:23]: Right.Usama [00:34:23]: Yeah, that's bad luck.Raiza [00:34:25]: Yeah.Swyx [00:34:25]: Yeah.Usama [00:34:26]: I mean, there still will be, like, bad audio overviews. There's, like, a bunch of them that happens. Do you mean for, like, the raider? For raiders, right?Swyx [00:34:34]: Like, what if that one person just got, like, a really bad rating? You actually had a great prompt, you actually had a great model, great weights, whatever. And you just, you had a bad output.Usama [00:34:42]: Like, and that's okay, right?Raiza [00:34:44]: I actually think, like, the way that these are constructed, if you think about, like, the different types of controls that the user has, right? Like, what can the user do today to affect it?Usama [00:34:54]: We push a button.Raiza [00:34:55]: You just push a button.Swyx [00:34:56]: I have tried to prompt engineer by changing the title. Yeah, yeah, yeah.Raiza [00:34:59]: Changing the title, people have found out.Raiza [00:35:02]: Yeah.Raiza [00:35:02]: The title of the notebook, people have found out. You can add show notes, right? You can get them to think, like, the show has changed. Someone changed the language of the output. Changing the language of the output. Like, those are less well-tested because we focused on, like, this one aspect. So it did change the way that we sort of think about quality as well, right? So it's like, quality is on the dimensions of entertainment, of course, like, consistency, groundedness. But in general, does it follow the structure of the deep dive? And I think when we talk about, like, non-determinism, it's like, well, as long as it follows, like, the structure of the deep dive, right? It sort of inherently meets all those other qualities. And so it makes it a little bit easier for us to ship something with confidence to the extent that it's like, I know it's going to make a deep dive. It's going to make a good deep dive. Whether or not the person likes it, I don't know. But as we expand to new formats, as we open up controls, I think that's where it gets really much harder. Even with the show notes, right? Like, people don't know what they're going to get when they do that. And we see that already where it's like, this is going to be a lot harder to validate in terms of quality, where now we'll get a greater distribution. Whereas I don't think we really got, like, varied distribution because of, like, that pre-process that Raiza was talking about. And also because of the way that we'd constrain, like, what were we measuring for? Literally, just like, is it a deep dive?Swyx [00:36:18]: And you determine what a deep dive is. Yeah. Everything needs a PM. Yeah, I have, this is very similar to something I've been thinking about for AI products in general. There's always like a chief tastemaker. And for Notebook LM, it seems like it's a combination of you and Steven.Raiza [00:36:31]: Well, okay.Raiza [00:36:32]: I want to take a step back.Swyx [00:36:33]: And Raiza, I mean, presumably for the voice stuff.Raiza [00:36:35]: Raiza's like the head chef, right? Of, like, deep dive, I think. Potatoes.Raiza [00:36:40]: Of potatoes.Raiza [00:36:41]: And I say this because I think even though we are already a very opinionated team, and Steven, for sure, very opinionated, I think of the audio generations, like, Raiza was the most opinionated, right? And we all, like, would say, like, hey, I remember, like, one of the first ones he sent me.Raiza [00:36:57]: I was like, oh, I feel like they should introduce themselves. I feel like they should say a title. But then, like, we would catch things, like, maybe they shouldn't say their names.Raiza [00:37:04]: Yeah, they don't say their names.Usama [00:37:05]: That was a Steven catch, like, not give them names.Raiza [00:37:08]: So stuff like that is, like, we all injected, like, a little bit of just, like, hey, here's, like, my take on, like, how a podcast should be, right? And I think, like, if you're a person who, like, regularly listens to podcasts, there's probably some collective preference there that's generic enough that you can standardize into, like, the deep dive format. But, yeah, it's the new formats where I think, like, oh, that's the next test. Yeah.Swyx [00:37:30]: I've tried to make a clone, by the way. Of course, everyone did. Yeah. Everyone in AI was like, oh, no, this is so easy. I'll just take a TTS model. Obviously, our models are not as good as yours, but I tried to inject a consistent character backstory, like, age, identity, where they work, where they went to school, what their hobbies are. Then it just, the models try to bring it in too much.Raiza [00:37:49]: Yeah.Swyx [00:37:49]: I don't know if you tried this.Raiza [00:37:51]: Yeah.Swyx [00:37:51]: So then I'm like, okay, like, how do I define a personality? But it doesn't keep coming up every single time. Yeah.Raiza [00:37:58]: I mean, we have, like, a really, really good, like, character designer on our team.Raiza [00:38:02]: What?Swyx [00:38:03]: Like a D&D person?Raiza [00:38:05]: Just to say, like, we, just like we had to be opinionated about the format, we had to be opinionated about who are those two people talking.Raiza [00:38:11]: Okay.Raiza [00:38:12]: Right.Raiza [00:38:12]: And then to the extent that, like, you can design the format, you should be able to design the people as well.Raiza [00:38:18]: Yeah.Swyx [00:38:18]: I would love, like, a, you know, like when you play Baldur's Gate, like, you roll, you roll like 17 on Charisma and like, it's like what race they are. I don't know.Raiza [00:38:27]: I recently, actually, I was just talking about character select screens.Raiza [00:38:30]: Yeah. I was like, I love that, right.Raiza [00:38:32]: And I was like, maybe there's something to be learned there because, like, people have fallen in love with the deep dive as a, as a format, as a technology, but also as just like those two personas.Raiza [00:38:44]: Now, when you hear a deep dive and you've heard them, you're like, I know those two.Raiza [00:38:48]: Right.Raiza [00:38:48]: And people, it's so funny when I, when people are trying to find out their names, like, it's a, it's a worthy task.Raiza [00:38:54]: It's a worthy goal.Raiza [00:38:55]: I know what you're doing. But the next step here is to sort of introduce, like, is this like what people want?Raiza [00:39:00]: People want to sort of edit the personas or do they just want more of them?Swyx [00:39:04]: I'm sure you're getting a lot of opinions and they all, they all conflict with each other. Before we move on, I have to ask, because we're kind of on this topic. How do you make audio engaging? Because it's useful, not just for deep dive, but also for us as podcasters. What is, what does engaging mean? If you could break it down for us, that'd be great.Usama [00:39:22]: I mean, I can try. Like, don't, don't claim to be an expert at all.Swyx [00:39:26]: So I'll give you some, like variation in tone and speed. You know, there's this sort of writing advice where, you know, this sentence is five words. This sentence is three, that kind of advice where you, where you vary things, you have excitement, you have laughter, all that stuff. But I'd be curious how else you break down.Usama [00:39:42]: So there's the basics, like obviously structure that can't be meandering, right? Like there needs to be sort of a, an ultimate goal that the voices are trying to get to, human or artificial. I think one thing we find often is if there's just too much agreement between people, like that's not fun to listen to. So there needs to be some sort of tension and build up, you know, withholding information. For example, like as you listen to a story unfold, like you're going to learn more and more about it. And audio that maybe becomes even more important because like you actually don't have the ability to just like skim to the end of something. You're driving or something like you're going to be hooked because like there's, and that's how like, that's how a lot of podcasts work. Like maybe not interviews necessarily, but a lot of true crime, a lot of entertainment in general. There's just like a gradual unrolling of information. And that also like sort of goes back to the content transformation aspect of it. Like maybe you are going from, let's say the Wikipedia article of like one of the History of Mysteries, maybe episodes. Like the Wikipedia article is going to state out the information very differently. It's like, here's what happened would probably be in the very first paragraph. And one approach we could have done is like maybe a person's just narrating that thing. And maybe that would work for like a certain audience. Or I guess that's how I would picture like a standard history lesson to unfold. But like, because we're trying to put it in this two-person dialogue format, like there, we inject like the fact that, you know, there's, you don't give everything at first. And then you set up like differing opinions of the same topic or the same, like maybe you seize on a topic and go deeper into it and then try to bring yourself back out of it and go back to the main narrative. So that's, that's mostly from like the setting up the script perspective. And then the audio, I was saying earlier, it's trying to be as close to just human speech as possible. I think was the, what we found success with so far.Raiza [00:41:40]: Yeah. Like with interjections, right?Raiza [00:41:41]: Like I think like when you listen to two people talk, there's a lot of like, yeah, yeah, right. And then there's like a lot of like that questioning, like, oh yeah, really?Raiza [00:41:49]: What did you think?Swyx [00:41:50]: I noticed that. That's great.Raiza [00:41:52]: Totally.Usama [00:41:54]: Exactly.Swyx [00:41:55]: My question is, do you pull in speech experts to do this? Or did you just come up with it yourselves? You can be like, okay, talk to a whole bunch of fiction writers to, to make things engaging or comedy writers or whatever, stand up comedy, right? They have to make audio engaging, but audio as well. Like there's professional fields of studying where people do this for a living, but us as AI engineers are just making this up as we go.Raiza [00:42:19]: I mean, it's a great idea, but you definitely didn't.Raiza [00:42:22]: Yeah.Swyx [00:42:24]: My guess is you didn't.Raiza [00:42:25]: Yeah.Swyx [00:42:26]: There's a, there's a certain field of authority that people have. They're like, oh, like you can't do this because you don't have any experience like making engaging audio. But that's what you literally did.Raiza [00:42:35]: Right.Usama [00:42:35]: I mean, I was literally chatting with someone at Google earlier today about how some people think that like you need a linguistics person in the room for like making a good chatbot. But that's not actually true because like this person went to school for linguistics. And according to him, he's an engineer now. According to him, like most of his classmates were not actually good at language. Like they knew how to analyze language and like sort of the mathematical patterns and rhythms and language. But that doesn't necessarily mean they were going to be eloquent at like while speaking or writing. So I think, yeah, a lot of we haven't invested in specialists in audio format yet, but maybe that would.Raiza [00:43:13]: I think it's like super interesting because I think there is like a very human question of like what makes something interesting. And there's like a very deep question of like what is it, right? Like what is the quality that we are all looking for? Is it does somebody have to be funny? Does something have to be entertaining? Does something have to be straight to the point? And I think when you try to distill that, this is the interesting thing I think about our experiment, about this particular launch is first, we only launched one format. And so we sort of had to squeeze everything we believed about what an interesting thing is into one package. And as a result of it, I think we learned it's like, hey, interacting with a chatbot is sort of novel at first, but it's not interesting, right? It's like humans are what makes interacting with chatbots interesting.Raiza [00:43:59]: It's like, ha ha ha, I'm going to try to trick it. It's like, that's interesting.Raiza [00:44:02]: Spell strawberry, right?Raiza [00:44:04]: This is like the fun that like people have with it. But like that's not the LLM being interesting.Raiza [00:44:08]: That's you just like kind of giving it your own flavor. But it's like, what does it mean to sort of flip it on its head and say, no, you be interesting now, right? Like you give the chatbot the opportunity to do it. And this is not a chatbot per se. It is like just the audio. And it's like the texture, I think, that really brings it to life. And it's like the things that we've described here, which is like, okay, now I have to like lead you down a path of information about like this commercialization deck.Raiza [00:44:36]: It's like, how do you do that?Raiza [00:44:38]: To be able to successfully do it, I do think that you need experts. I think we'll engage with experts like down the road, but I think it will have to be in the context of, well, what's the next thing we're building, right? It's like, what am I trying to change here? What do I fundamentally believe needs to be improved? And I think there's still like a lot more studying that we have to do in terms of like, well, what are people actually using this for? And we're just in such early days. Like it hasn't even been a month. Two, three weeks.Usama [00:45:05]: Three weeks.Raiza [00:45:06]: Yeah, yeah.Usama [00:45:07]: I think one other element to that is the fact that you're bringing your own sources to it. Like it's your stuff. Like, you know this somewhat well, or you care to know about this. So like that, I think, changed the equation on its head as well. It's like your sources and someone's telling you about it. So like you care about how that dynamic is, but you just care for it to be good enough to be entertaining. Because ultimately they're talking about your mortgage deed or whatever.Swyx [00:45:33]: So it's interesting just from the topic itself. Even taking out all the agreements and the hiding of the slow reveal. I mean, there's a baseline, maybe.Usama [00:45:42]: Like if it was like too drab. Like if someone was reading it off, like, you know, that's like the absolute worst.Raiza [00:45:46]: But like...Swyx [00:45:47]: Do you prompt for humor? That's a tough one, right?Raiza [00:45:51]: I think it's more of a generic way to bring humor out if possible. I think humor is actually one of the hardest things. Yeah.Raiza [00:46:00]: But I don't know if you saw...Raiza [00:46:00]: That is AGI.Swyx [00:46:01]: Humor is AGI.Raiza [00:46:02]: Yeah, but did you see the chicken one?Raiza [00:46:03]: No.Raiza [00:46:04]: Okay. If you haven't heard it... We'll splice it in here.Swyx [00:46:06]: Okay.Raiza [00:46:07]: Yeah.Raiza [00:46:07]: There is a video on Threads. I think it was by Martino Wong. And it's a PDF.Raiza [00:46:16]: Welcome to your deep dive for today. Oh, yeah. Get ready for a fun one. Buckle up. Because we are diving into... Chicken, chicken, chicken. Chicken, chicken. You got that right. By Doug Zonker. Now. And yes, you heard that title correctly. Titles. Our listener today submitted this paper. Yeah, they're going to need our help. And I can totally see why. Absolutely. It's dense. It's baffling. It's a lot. And it's packed with more chicken than a KFC buffet. What? That's hilarious.Raiza [00:46:48]: That's so funny. So it's like stuff like that, that's like truly delightful, truly surprising.Raiza [00:46:53]: But it's like we didn't tell it to be funny.Usama [00:46:55]: Humor is contextual also. Like super contextual is what we're realizing. So we're not prompting for humor, but we're prompting for maybe a lot of other things that are bringing out that humor.Alessio [00:47:04]: I think the thing about ad-generated content, if we look at YouTube, like we do videos on YouTube and it's like, you know, a lot of people like screaming in the thumbnails to get clicks. There's like everybody, there's kind of like a meta of like what you need to do to get clicks. But I think in your product, there's no actual creator on the other side investing the time. So you can actually generate a type of content that is maybe not universally appealing, you know, at a much, yeah, exactly. I think that's the most interesting thing. It's like, well, is there a way for like, take Mr.Raiza [00:47:36]: Beast, right?Alessio [00:47:36]: It's like Mr. Beast optimizes videos to reach the biggest audience and like the most clicks. But what if every video could be kind of like regenerated to be closer to your taste, you know, when you watch it?Raiza [00:47:48]: I think that's kind of the promise of AI that I think we are just like touching on, which is, I think every time I've gotten information from somebody, they have delivered it to me in their preferred method, right?Raiza [00:47:59]: Like if somebody gives me a PDF, it's a PDF.Raiza [00:48:01]: Somebody gives me a hundred slide deck, that is the format in which I'm going to read it. But I think we are now living in the era where transformations are really possible, which is, look, like I don't want to read your hundred slide deck, but I'll listen to a 16 minute audio overview on the drive home. And that, that I think is, is really novel. And that is, is paving the way in a way that like maybe we wanted, but didn'tRaiza [00:48:24]: expect.Raiza [00:48:25]: Where I also think you're listening to a lot of content that normally wouldn't have had content made about it. Like I watched this TikTok where this woman uploaded her diary from 2004.Raiza [00:48:36]: For sure, right?Raiza [00:48:36]: Like nobody was goin

We all have fond memories of the first Dev Day in 2023:and the blip that followed soon after. As Ben Thompson has noted, this year's DevDay took a quieter, more intimate tone. No Satya, no livestream, (slightly fewer people?). Instead of putting ChatGPT announcements in DevDay as in 2023, o1 was announced 2 weeks prior, and DevDay 2024 was reserved purely for developer-facing API announcements, primarily the Realtime API, Vision Finetuning, Prompt Caching, and Model Distillation.However the larger venue and more spread out schedule did allow a lot more hallway conversations with attendees as well as more community presentations including our recent guest Alistair Pullen of Cosine as well as deeper dives from OpenAI including our recent guest Michelle Pokrass of the API Team. Thanks to OpenAI's warm collaboration (we particularly want to thank Lindsay McCallum Rémy!), we managed to record exclusive interviews with many of the main presenters of both the keynotes and breakout sessions. We present them in full in today's episode, together with a full lightly edited Q&A with Sam Altman.Show notes and related resourcesSome of these used in the final audio episode below* Simon Willison Live Blog* swyx live tweets and videos* Greg Kamradt coverage of Structured Output session, Scaling LLM Apps session* Fireside Chat Q&A with Sam AltmanTimestamps* [00:00:00] Intro by Suno.ai* [00:01:23] NotebookLM Recap of DevDay* [00:09:25] Ilan's Strawberry Demo with Realtime Voice Function Calling* [00:19:16] Olivier Godement, Head of Product, OpenAI* [00:36:57] Romain Huet, Head of DX, OpenAI* [00:47:08] Michelle Pokrass, API Tech Lead at OpenAI ft. Simon Willison* [01:04:45] Alistair Pullen, CEO, Cosine (Genie)* [01:18:31] Sam Altman + Kevin Weill Q&A* [02:03:07] Notebook LM Recap of PodcastTranscript[00:00:00] Suno AI: Under dev daylights, code ignites. Real time voice streams reach new heights. O1 and GPT, 4. 0 in flight. Fine tune the future, data in sight. Schema sync up, outputs precise. Distill the models, efficiency splice.[00:00:33] AI Charlie: Happy October. This is your AI co host, Charlie. One of our longest standing traditions is covering major AI and ML conferences in podcast format. Delving, yes delving, into the vibes of what it is like to be there stitched in with short samples of conversations with key players, just to help you feel like you were there.[00:00:54] AI Charlie: Covering this year's Dev Day was significantly more challenging because we were all requested not to record the opening keynotes. So, in place of the opening keynotes, we had the viral notebook LM Deep Dive crew, my new AI podcast nemesis, Give you a seven minute recap of everything that was announced.[00:01:15] AI Charlie: Of course, you can also check the show notes for details. I'll then come back with an explainer of all the interviews we have for you today. Watch out and take care.[00:01:23] NotebookLM Recap of DevDay[00:01:23] NotebookLM: All right, so we've got a pretty hefty stack of articles and blog posts here all about open ais. Dev day 2024.[00:01:32] NotebookLM 2: Yeah, lots to dig into there.[00:01:34] NotebookLM 2: Seems[00:01:34] NotebookLM: like you're really interested in what's new with AI.[00:01:36] NotebookLM 2: Definitely. And it seems like OpenAI had a lot to announce. New tools, changes to the company. It's a lot.[00:01:43] NotebookLM: It is. And especially since you're interested in how AI can be used in the real world, you know, practical applications, we'll focus on that.[00:01:51] NotebookLM: Perfect. Like, for example, this Real time API, they announced that, right? That seems like a big deal if we want AI to sound, well, less like a robot.[00:01:59] NotebookLM 2: It could be huge. The real time API could completely change how we, like, interact with AI. Like, imagine if your voice assistant could actually handle it if you interrupted it.[00:02:08] NotebookLM: Or, like, have an actual conversation.[00:02:10] NotebookLM 2: Right, not just these clunky back and forth things we're used to.[00:02:14] NotebookLM: And they actually showed it off, didn't they? I read something about a travel app, one for languages. Even one where the AI ordered takeout.[00:02:21] NotebookLM 2: Those demos were really interesting, and I think they show how this real time API can be used in so many ways.[00:02:28] NotebookLM 2: And the tech behind it is fascinating, by the way. It uses persistent WebSocket connections and this thing called function calling, so it can respond in real time.[00:02:38] NotebookLM: So the function calling thing, that sounds kind of complicated. Can you, like, explain how that works?[00:02:42] NotebookLM 2: So imagine giving the AI Access to this whole toolbox, right?[00:02:46] NotebookLM 2: Information, capabilities, all sorts of things. Okay. So take the travel agent demo, for example. With function calling, the AI can pull up details, let's say about Fort Mason, right, from some database. Like nearby restaurants, stuff like that.[00:02:59] NotebookLM: Ah, I get it. So instead of being limited to what it already knows, It can go and find the information it needs, like a human travel agent would.[00:03:07] NotebookLM 2: Precisely. And someone on Hacker News pointed out a cool detail. The API actually gives you a text version of what's being said. So you can store that, analyze it.[00:03:17] NotebookLM: That's smart. It seems like OpenAI put a lot of thought into making this API easy for developers to use. But, while we're on OpenAI, you know, Besides their tech, there's been some news about, like, internal changes, too.[00:03:30] NotebookLM: Didn't they say they're moving away from being a non profit?[00:03:32] NotebookLM 2: They did. And it's got everyone talking. It's a major shift. And it's only natural for people to wonder how that'll change things for OpenAI in the future. I mean, there are definitely some valid questions about this move to for profit. Like, will they have more money for research now?[00:03:46] NotebookLM 2: Probably. But will they, you know, care as much about making sure AI benefits everyone?[00:03:51] NotebookLM: Yeah, that's the big question, especially with all the, like, the leadership changes happening at OpenAI too, right? I read that their Chief Research Officer left, and their VP of Research, and even their CTO.[00:04:03] NotebookLM 2: It's true. A lot of people are connecting those departures with the changes in OpenAI's structure.[00:04:08] NotebookLM: And I guess it makes you wonder what's going on behind the scenes. But they are still putting out new stuff. Like this whole fine tuning thing really caught my eye.[00:04:17] NotebookLM 2: Right, fine tuning. It's essentially taking a pre trained AI model. And, like, customizing it.[00:04:23] NotebookLM: So instead of a general AI, you get one that's tailored for a specific job.[00:04:27] NotebookLM 2: Exactly. And that opens up so many possibilities, especially for businesses. Imagine you could train an AI on your company's data, you know, like how you communicate your brand guidelines.[00:04:37] NotebookLM: So it's like having an AI that's specifically trained for your company?[00:04:41] NotebookLM 2: That's the idea.[00:04:41] NotebookLM: And they're doing it with images now, too, right?[00:04:44] NotebookLM: Fine tuning with vision is what they called it.[00:04:46] NotebookLM 2: It's pretty incredible what they're doing with that, especially in fields like medicine.[00:04:50] NotebookLM: Like using AI to help doctors make diagnoses.[00:04:52] NotebookLM 2: Exactly. And AI could be trained on thousands of medical images, right? And then it could potentially spot things that even a trained doctor might miss.[00:05:03] NotebookLM: That's kind of scary, to be honest. What if it gets it wrong?[00:05:06] NotebookLM 2: Well, the idea isn't to replace doctors, but to give them another tool, you know, help them make better decisions.[00:05:12] NotebookLM: Okay, that makes sense. But training these AI models must be really expensive.[00:05:17] NotebookLM 2: It can be. All those tokens add up. But OpenAI announced something called automatic prompt caching.[00:05:23] Alex Volkov: Automatic what now? I don't think I came across that.[00:05:26] NotebookLM 2: So basically, if your AI sees a prompt that it's already seen before, OpenAI will give you a discount.[00:05:31] NotebookLM: Huh. Like a frequent buyer program for AI.[00:05:35] NotebookLM 2: Kind of, yeah. It's good that they're trying to make it more affordable. And they're also doing something called model distillation.[00:05:41] NotebookLM: Okay, now you're just using big words to sound smart. What's that?[00:05:45] NotebookLM 2: Think of it like like a recipe, right? You can take a really complex recipe and break it down to the essential parts.[00:05:50] NotebookLM: Make it simpler, but it still tastes the same.[00:05:53] NotebookLM 2: Yeah. And that's what model distillation is. You take a big, powerful AI model and create a smaller, more efficient version.[00:06:00] NotebookLM: So it's like lighter weight, but still just as capable.[00:06:03] NotebookLM 2: Exactly. And that means more people can actually use these powerful tools. They don't need, like, a supercomputer to run them.[00:06:10] NotebookLM: So they're making AI more accessible. That's great.[00:06:13] NotebookLM 2: It is. And speaking of powerful tools, they also talked about their new O1 model.[00:06:18] NotebookLM 2: That's the one they've been hyping up. The one that's supposed to be this big leap forward.[00:06:22] NotebookLM: Yeah, O1. It sounds pretty futuristic. Like, from what I read, it's not just a bigger, better language model.[00:06:28] NotebookLM 2: Right. It's a different porch.[00:06:29] NotebookLM: They're saying it can, like, actually reason, right? Think.[00:06:33] NotebookLM 2: It's trained differently.[00:06:34] NotebookLM 2: They used reinforcement learning with O1.[00:06:36] NotebookLM: So it's not just finding patterns in the data it's seen before.[00:06:40] NotebookLM 2: Not just that. It can actually learn from its mistakes. Get better at solving problems.[00:06:46] NotebookLM: So give me an example. What can O1 do that, say, GPT 4 can't?[00:06:51] NotebookLM 2: Well, OpenAI showed it doing some pretty impressive stuff with math, like advanced math.[00:06:56] NotebookLM 2: And coding, too. Complex coding. Things that even GPT 4 struggled with.[00:07:00] NotebookLM: So you're saying if I needed to, like, write a screenplay, I'd stick with GPT 4? But if I wanted to solve some crazy physics problem, O1 is what I'd use.[00:07:08] NotebookLM 2: Something like that, yeah. Although there is a trade off. O1 takes a lot more power to run, and it takes longer to get those impressive results.[00:07:17] NotebookLM: Hmm, makes sense. More power, more time, higher quality.[00:07:21] NotebookLM 2: Exactly.[00:07:22] NotebookLM: It sounds like it's still in development, though, right? Is there anything else they're planning to add to it?[00:07:26] NotebookLM 2: Oh, yeah. They mentioned system prompts, which will let developers, like, set some ground rules for how it behaves. And they're working on adding structured outputs and function calling.[00:07:38] Alex Volkov: Wait, structured outputs? Didn't we just talk about that? We[00:07:41] NotebookLM 2: did. That's the thing where the AI's output is formatted in a way that's easy to use.[00:07:47] NotebookLM: Right, right. So you don't have to spend all day trying to make sense of what it gives you. It's good that they're thinking about that stuff.[00:07:53] NotebookLM 2: It's about making these tools usable.[00:07:56] NotebookLM 2: And speaking of that, Dev Day finished up with this really interesting talk. Sam Altman, the CEO of OpenAI, And Kevin Weil, their new chief product officer. They talked about, like, the big picture for AI.[00:08:09] NotebookLM: Yeah, they did, didn't they? Anything interesting come up?[00:08:12] NotebookLM 2: Well, Altman talked about moving past this whole AGI term, Artificial General Intelligence.[00:08:18] NotebookLM: I can see why. It's kind of a loaded term, isn't it?[00:08:20] NotebookLM 2: He thinks it's become a bit of a buzzword, and people don't really understand what it means.[00:08:24] NotebookLM: So are they saying they're not trying to build AGI anymore?[00:08:28] NotebookLM 2: It's more like they're saying they're focused on just Making AI better, constantly improving it, not worrying about putting it in a box.[00:08:36] NotebookLM: That makes sense. Keep pushing the limits.[00:08:38] NotebookLM 2: Exactly. But they were also very clear about doing it responsibly. They talked a lot about safety and ethics.[00:08:43] NotebookLM: Yeah, that's important.[00:08:44] NotebookLM 2: They said they were going to be very careful. About how they release new features.[00:08:48] NotebookLM: Good! Because this stuff is powerful.[00:08:51] NotebookLM 2: It is. It was a lot to take in, this whole Dev Day event.[00:08:54] NotebookLM 2: New tools, big changes at OpenAI, and these big questions about the future of AI.[00:08:59] NotebookLM: It was. But hopefully this deep dive helped make sense of some of it. At least, that's what we try to do here.[00:09:05] AI Charlie: Absolutely.[00:09:06] NotebookLM: Thanks for taking the deep dive with us.[00:09:08] AI Charlie: The biggest demo of the new Realtime API involved function calling with voice mode and buying chocolate covered strawberries from our friendly local OpenAI developer experience engineer and strawberry shop owner, Ilan Biggio.[00:09:21] AI Charlie: We'll first play you the audio of his demo and then go into a little interview with him.[00:09:25] Ilan's Strawberry Demo with Realtime Voice Function Calling[00:09:25] Romain Huet: Could you place a call and see if you could get us 400 strawberries delivered to the venue? But please keep that under 1500. I'm on it. We'll get those strawberries delivered for you.[00:09:47] Ilan: Hello? Hi there. Is this Ilan? I'm Romain's AI assistant. How is it going? Fantastic. Can you tell me what flavors of strawberry dips you have for me? Yeah, we have chocolate, vanilla, and we have peanut butter. Wait, how much would 400 chocolate covered strawberries cost? 400? Are you sure you want 400? Yes, 400 chocolate covered[00:10:14] swyx: strawberries.[00:10:15] Ilan: Wait,[00:10:16] swyx: how much[00:10:16] Ilan: would that be? I think that'll be around, like, 1, 415. 92.[00:10:25] Alex Volkov: Awesome. Let's go ahead and place the order for four chocolate covered strawberries.[00:10:31] Ilan: Great, where would you like that delivered? Please deliver them to the Gateway Pavilion at Fort Mason. And I'll be paying in cash.[00:10:42] Alex Volkov: Okay,[00:10:43] Ilan: sweet. So just to confirm, you want four strawberries?[00:10:45] Ilan: 400 chocolate covered strawberries to the Gateway Pavilion. Yes, that's perfect. And when can we expect delivery? Well, you guys are right nearby, so it'll be like, I don't know, 37 seconds? That's incredibly fast. Cool, you too.[00:11:09] swyx: Hi, Ilan, welcome to Lanespace. Oh, thank you. I just saw your amazing demos, had your amazing strawberries. You are dressed up, like, exactly like a strawberry salesman. Gotta have it all. What was the building on demo like? What was the story behind the demo?[00:11:22] swyx: It was really interesting. This is actually something I had been thinking about for months before the launch.[00:11:27] swyx: Like, having a, like, AI that can make phone calls is something like I've personally wanted for a long time. And so as soon as we launched internally, like, I started hacking on it. And then that sort of just started. We made it into like an internal demo, and then people found it really interesting, and then we thought how cool would it be to have this like on stage as, as one of the demos.[00:11:47] swyx: Yeah, would would you call out any technical issues building, like you were basically one of the first people ever to build with a voice mode API. Would you call out any issues like integrating it with Twilio like that, like you did with function calling, with like a form filling elements. I noticed that you had like intents of things to fulfill, and then.[00:12:07] swyx: When there's still missing info, the voice would prompt you, roleplaying the store guy.[00:12:13] swyx: Yeah, yeah, so, I think technically, there's like the whole, just working with audio and streams is a whole different beast. Like, even separate from like AI and this, this like, new capabilities, it's just, it's just tough.[00:12:26] swyx: Yeah, when you have a prompt, conversationally it'll just follow, like the, it was, Instead of like, kind of step by step to like ask the right questions based on like the like what the request was, right? The function calling itself is sort of tangential to that. Like, you have to prompt it to call the functions, but then handling it isn't too much different from, like, what you would do with assistant streaming or, like, chat completion streaming.[00:12:47] swyx: I think, like, the API feels very similar just to, like, if everything in the API was streaming, it actually feels quite familiar to that.[00:12:53] swyx: And then, function calling wise, I mean, does it work the same? I don't know. Like, I saw a lot of logs. You guys showed, like, in the playground, a lot of logs. What is in there?[00:13:03] swyx: What should people know?[00:13:04] swyx: Yeah, I mean, it is, like, the events may have different names than the streaming events that we have in chat completions, but they represent very similar things. It's things like, you know, function call started, argument started, it's like, here's like argument deltas, and then like function call done.[00:13:20] swyx: Conveniently we send one that has the full function, and then I just use that. Nice.[00:13:25] swyx: Yeah and then, like, what restrictions do, should people be aware of? Like, you know, I think, I think, before we recorded, we discussed a little bit about the sensitivities around basically calling random store owners and putting, putting like an AI on them.[00:13:40] swyx: Yeah, so there's, I think there's recent regulation on that, which is why we want to be like very, I guess, aware of, of You know, you can't just call anybody with AI, right? That's like just robocalling. You wouldn't want someone just calling you with AI.[00:13:54] swyx: I'm a developer, I'm about to do this on random people.[00:13:57] swyx: What laws am I about to break?[00:14:00] swyx: I forget what the governing body is, but you should, I think, Having consent of the person you're about to call, it always works. I, as the strawberry owner, have consented to like getting called with AI. I think past that you, you want to be careful. Definitely individuals are more sensitive than businesses.[00:14:19] swyx: I think businesses you have a little bit more leeway. Also, they're like, businesses I think have an incentive to want to receive AI phone calls. Especially if like, they're dealing with it. It's doing business. Right, like, it's more business. It's kind of like getting on a booking platform, right, you're exposed to more.[00:14:33] swyx: But, I think it's still very much like a gray area. Again, so. I think everybody should, you know, tread carefully, like, figure out what it is. I, I, I, the law is so recent, I didn't have enough time to, like, I'm also not a lawyer. Yeah, yeah, yeah, of course. Yeah.[00:14:49] swyx: Okay, cool fair enough. One other thing, this is kind of agentic.[00:14:52] swyx: Did you use a state machine at all? Did you use any framework? No. You just stick it in context and then just run it in a loop until it ends call?[00:15:01] swyx: Yeah, there isn't even a loop, like Okay. Because the API is just based on sessions. It's always just going to keep going. Every time you speak, it'll trigger a call.[00:15:11] swyx: And then after every function call was also invoked invoking like a generation. And so that is another difference here. It's like it's inherently almost like in a loop, be just by being in a session, right? No state machines needed. I'd say this is very similar to like, the notion of routines, where it's just like a list of steps.[00:15:29] swyx: And it, like, sticks to them softly, but usually pretty well. And the steps is the prompts? The steps, it's like the prompt, like the steps are in the prompt. Yeah, yeah, yeah. Right, it's like step one, do this, step one, step two, do that. What if I want to change the system prompt halfway through the conversation?[00:15:44] swyx: You can. Okay. You can. To be honest, I have not played without two too much. Yeah,[00:15:47] swyx: yeah.[00:15:48] swyx: But, I know you can.[00:15:49] swyx: Yeah, yeah. Yeah. Awesome. I noticed that you called it real time API, but not voice API. Mm hmm. So I assume that it's like real time API starting with voice. Right, I think that's what he said on the thing.[00:16:00] swyx: I can't imagine, like, what else is real[00:16:02] swyx: time? Well, I guess, to use ChatGPT's voice mode as an example, Like, we've demoed the video, right? Like, real time image, right? So, I'm not actually sure what timelines are, But I would expect, if I had to guess, That, like, that is probably the next thing that we're gonna be making.[00:16:17] swyx: You'd probably have to talk directly with the team building this. Sure. But, You can't promise their timelines. Yeah, yeah, yeah, right, exactly. But, like, given that this is the features that currently, Or that exists that we've demoed on Chachapiti. Yeah. There[00:16:29] swyx: will never be a[00:16:29] swyx: case where there's like a real time text API, right?[00:16:31] swyx: I don't Well, this is a real time text API. You can do text only on this. Oh. Yeah. I don't know why you would. But it's actually So text to text here doesn't quite make a lot of sense. I don't think you'll get a lot of latency gain. But, like, speech to text is really interesting. Because you can prevent You can prevent responses, like audio responses.[00:16:54] swyx: And force function calls. And so you can do stuff like UI control. That is like super super reliable. We had a lot of like, you know, un, like, we weren't sure how well this was gonna work because it's like, you have a voice answering. It's like a whole persona, right? Like, that's a little bit more, you know, risky.[00:17:10] swyx: But if you, like, cut out the audio outputs and make it so it always has to output a function, like you can end up with pretty pretty good, like, Pretty reliable, like, command like a command architecture. Yeah,[00:17:21] swyx: actually, that's the way I want to interact with a lot of these things as well. Like, one sided voice.[00:17:26] swyx: Yeah, you don't necessarily want to hear the[00:17:27] swyx: voice back. And like, sometimes it's like, yeah, I think having an output voice is great. But I feel like I don't always want to hear an output voice. I'd say usually I don't. But yeah, exactly, being able to speak to it is super sweet.[00:17:39] swyx: Cool. Do you want to comment on any of the other stuff that you announced?[00:17:41] swyx: From caching I noticed was like, I like the no code change part. I'm looking forward to the docs because I'm sure there's a lot of details on like, what you cache, how long you cache. Cause like, enthalpy caches were like 5 minutes. I was like, okay, but what if I don't make a call every 5 minutes?[00:17:56] swyx: Yeah,[00:17:56] swyx: to be super honest with you, I've been so caught up with the real time API and making the demo that I haven't read up on the other stuff. Launches too much. I mean, I'm aware of them, but I think I'm excited to see how all distillation works. That's something that we've been doing like, I don't know, I've been like doing it between our models for a while And I've seen really good results like I've done back in a day like from GPT 4 to GPT 3.[00:18:19] swyx: 5 And got like, like pretty much the same level of like function calling with like hundreds of functions So that was super super compelling So, I feel like easier distillation, I'm really excited for. I see. Is it a tool?[00:18:31] swyx: So, I saw evals. Yeah. Like, what is the distillation product? It wasn't super clear, to be honest.[00:18:36] swyx: I, I think I want to, I want to let that team, I want to let that team talk about it. Okay,[00:18:40] swyx: alright. Well, I appreciate you jumping on. Yeah, of course. Amazing demo. It was beautifully designed. I'm sure that was part of you and Roman, and[00:18:47] swyx: Yeah, I guess, shout out to like, the first people to like, creators of Wanderlust, originally, were like, Simon and Carolis, and then like, I took it and built the voice component and the voice calling components.[00:18:59] swyx: Yeah, so it's been a big team effort. And like the entire PI team for like Debugging everything as it's been going on. It's been, it's been so good working with them. Yeah, you're the first consumers on the DX[00:19:07] swyx: team. Yeah. Yeah, I mean, the classic role of what we do there. Yeah. Okay, yeah, anything else? Any other call to action?[00:19:13] swyx: No, enjoy Dev Day. Thank you. Yeah. That's it.[00:19:16] Olivier Godement, Head of Product, OpenAI[00:19:16] AI Charlie: The latent space crew then talked to Olivier Godmont, head of product for the OpenAI platform, who led the entire Dev Day keynote and introduced all the major new features and updates that we talked about today.[00:19:28] swyx: Okay, so we are here with Olivier Godmont. That's right.[00:19:32] swyx: I don't pronounce French. That's fine. It was perfect. And it was amazing to see your keynote today. What was the back story of, of preparing something like this? Preparing, like, Dev Day? It[00:19:43] Olivier Godement: essentially came from a couple of places. Number one, excellent reception from last year's Dev Day.[00:19:48] Olivier Godement: Developers, startup founders, researchers want to spend more time with OpenAI, and we want to spend more time with them as well. And so for us, like, it was a no brainer, frankly, to do it again, like, you know, like a nice conference. The second thing is going global. We've done a few events like in Paris and like a few other like, you know, non European, non American countries.[00:20:05] Olivier Godement: And so this year we're doing SF, Singapore, and London. To frankly just meet more developers.[00:20:10] swyx: Yeah, I'm very excited for the Singapore one.[00:20:12] Olivier Godement: Ah,[00:20:12] swyx: yeah. Will you be[00:20:13] Olivier Godement: there?[00:20:14] swyx: I don't know. I don't know if I got an invite. No. I can't just talk to you. Yeah, like, and then there was some speculation around October 1st.[00:20:22] Olivier Godement: Yeah. Is it because[00:20:23] swyx: 01, October 1st? It[00:20:25] Olivier Godement: has nothing to do. I discovered the tweet yesterday where like, people are so creative. No one, there was no connection to October 1st. But in hindsight, that would have been a pretty good meme by Tiana. Okay.[00:20:37] swyx: Yeah, and you know, I think like, OpenAI's outreach to developers is something that I felt the whole in 2022, when like, you know, like, people were trying to build a chat GPT, and like, there was no function calling, all that stuff that you talked about in the past.[00:20:51] swyx: And that's why I started my own conference as like like, here's our little developer conference thing. And, but to see this OpenAI Dev Day now, and like to see so many developer oriented products coming to OpenAI, I think it's really encouraging.[00:21:02] Olivier Godement: Yeah, totally. It's that's what I said, essentially, like, developers are basically the people who make the best connection between the technology and, you know, the future, essentially.[00:21:14] Olivier Godement: Like, you know, essentially see a capability, see a low level, like, technology, and are like, hey, I see how that application or that use case that can be enabled. And so, in the direction of enabling, like, AGI, like, all of humanity, it's a no brainer for us, like, frankly, to partner with Devs.[00:21:31] Alessio: And most importantly, you almost never had waitlists, which, compared to like other releases, people usually, usually have.[00:21:38] Alessio: What is the, you know, you had from caching, you had real time voice API, we, you know, Shawn did a long Twitter thread, so people know the releases. Yeah. What is the thing that was like sneakily the hardest to actually get ready for, for that day, or like, what was the kind of like, you know, last 24 hours, anything that you didn't know was gonna work?[00:21:56] Olivier Godement: Yeah. The old Fairly, like, I would say, involved, like, features to ship. So the team has been working for a month, all of them. The one which I would say is the newest for OpenAI is the real time API. For a couple of reasons. I mean, one, you know, it's a new modality. Second, like, it's the first time that we have an actual, like, WebSocket based API.[00:22:16] Olivier Godement: And so, I would say that's the one that required, like, the most work over the month. To get right from a developer perspective and to also make sure that our existing safety mitigation that worked well with like real time audio in and audio out.[00:22:30] swyx: Yeah, what design choices or what was like the sort of design choices that you want to highlight?[00:22:35] swyx: Like, you know, like I think for me, like, WebSockets, you just receive a bunch of events. It's two way. I obviously don't have a ton of experience. I think a lot of developers are going to have to embrace this real time programming. Like, what are you designing for, or like, what advice would you have for developers exploring this?[00:22:51] Olivier Godement: The core design hypothesis was essentially, how do we enable, like, human level latency? We did a bunch of tests, like, on average, like, human beings, like, you know, takes, like, something like 300 milliseconds to converse with each other. And so that was the design principle, essentially. Like, working backward from that, and, you know, making the technology work.[00:23:11] Olivier Godement: And so we evaluated a few options, and WebSockets was the one that we landed on. So that was, like, one design choice. A few other, like, big design choices that we had to make prompt caching. Prompt caching, the design, like, target was automated from the get go. Like, zero code change from the developer.[00:23:27] Olivier Godement: That way you don't have to learn, like, what is a prompt prefix, and, you know, how long does a cache work, like, we just do it as much as we can, essentially. So that was a big design choice as well. And then finally, on distillation, like, and evaluation. The big design choice was something I learned at Skype, like in my previous job, like a philosophy around, like, a pit of success.[00:23:47] Olivier Godement: Like, what is essentially the, the, the minimum number of steps for the majority of developers to do the right thing? Because when you do evals on fat tuning, there are many, many ways, like, to mess it up, frankly, like, you know, and have, like, a crappy model, like, evals that tell, like, a wrong story. And so our whole design was, okay, we actually care about, like, helping people who don't have, like, that much experience, like, evaluating a model, like, get, like, in a few minutes, like, to a good spot.[00:24:11] Olivier Godement: And so how do we essentially enable that bit of success, like, in the product flow?[00:24:15] swyx: Yeah, yeah, I'm a little bit scared to fine tune especially for vision, because I don't know what I don't know for stuff like vision, right? Like, for text, I can evaluate pretty easily. For vision let's say I'm like trying to, one of your examples was grab.[00:24:33] swyx: Which, very close to home, I'm from Singapore. I think your example was like, they identified stop signs better. Why is that hard? Why do I have to fine tune that? If I fine tune that, do I lose other things? You know, like, there's a lot of unknowns with Vision that I think developers have to figure out.[00:24:50] swyx: For[00:24:50] Olivier Godement: sure. Vision is going to open up, like, a new, I would say, evaluation space. Because you're right, like, it's harder, like, you know, to tell correct from incorrect, essentially, with images. What I can say is we've been alpha testing, like, the Vision fine tuning, like, for several weeks at that point. We are seeing, like, even higher performance uplift compared to text fine tuning.[00:25:10] Olivier Godement: So that's, there is something here, like, we've been pretty impressed, like, in a good way, frankly. But, you know, how well it works. But for sure, like, you know, I expect the developers who are moving from one modality to, like, text and images will have, like, more, you know Testing, evaluation, like, you know, to set in place, like, to make sure it works well.[00:25:25] Alessio: The model distillation and evals is definitely, like, the most interesting. Moving away from just being a model provider to being a platform provider. How should people think about being the source of truth? Like, do you want OpenAI to be, like, the system of record of all the prompting? Because people sometimes store it in, like, different data sources.[00:25:41] Alessio: And then, is that going to be the same as the models evolve? So you don't have to worry about, you know, refactoring the data, like, things like that, or like future model structures.[00:25:51] Olivier Godement: The vision is if you want to be a source of truth, you have to earn it, right? Like, we're not going to force people, like, to pass us data.[00:25:57] Olivier Godement: There is no value prop, like, you know, for us to store the data. The vision here is at the moment, like, most developers, like, use like a one size fits all model, like be off the shelf, like GP40 essentially. The vision we have is fast forward a couple of years. I think, like, most developers will essentially, like, have a.[00:26:15] Olivier Godement: An automated, continuous, fine tuned model. The more, like, you use the model, the more data you pass to the model provider, like, the model is automatically, like, fine tuned, evaluated against some eval sets, and essentially, like, you don't have to every month, when there is a new snapshot, like, you know, to go online and, you know, try a few new things.[00:26:34] Olivier Godement: That's a direction. We are pretty far away from it. But I think, like, that evaluation and decision product are essentially a first good step in that direction. It's like, hey, it's you. I set it by that direction, and you give us the evaluation data. We can actually log your completion data and start to do some automation on your behalf.[00:26:52] Alessio: And then you can do evals for free if you share data with OpenAI. How should people think about when it's worth it, when it's not? Sometimes people get overly protective of their data when it's actually not that useful. But how should developers think about when it's right to do it, when not, or[00:27:07] Olivier Godement: if you have any thoughts on it?[00:27:08] Olivier Godement: The default policy is still the same, like, you know, we don't train on, like, any API data unless you opt in. What we've seen from feedback is evaluation can be expensive. Like, if you run, like, O1 evals on, like, thousands of samples Like, your build will get increased, like, you know, pretty pretty significantly.[00:27:22] Olivier Godement: That's problem statement number one. Problem statement number two is, essentially, I want to get to a world where whenever OpenAI ships a new model snapshot, we have full confidence that there is no regression for the task that developers care about. And for that to be the case, essentially, we need to get evals.[00:27:39] Olivier Godement: And so that, essentially, is a sort of a two bugs one stone. It's like, we subsidize, basically, the evals. And we also use the evals when we ship new models to make sure that we keep going in the right direction. So, in my sense, it's a win win, but again, completely opt in. I expect that many developers will not want to share their data, and that's perfectly fine to me.[00:27:56] swyx: Yeah, I think free evals though, very, very good incentive. I mean, it's a fair trade. You get data, we get free evals. Exactly,[00:28:04] Olivier Godement: and we sanitize PII, everything. We have no interest in the actual sensitive data. We just want to have good evaluation on the real use cases.[00:28:13] swyx: Like, I always want to eval the eval. I don't know if that ever came up.[00:28:17] swyx: Like, sometimes the evals themselves are wrong, and there's no way for me to tell you.[00:28:22] Olivier Godement: Everyone who is starting with LLM, teaching with LLM, is like, Yeah, evaluation, easy, you know, I've done testing, like, all my life. And then you start to actually be able to eval, understand, like, all the corner cases, And you realize, wow, there's like a whole field in itself.[00:28:35] Olivier Godement: So, yeah, good evaluation is hard and so, yeah. Yeah, yeah.[00:28:38] swyx: But I think there's a, you know, I just talked to Brain Trust which I think is one of your partners. Mm-Hmm. . They also emphasize code based evals versus your sort of low code. What I see is like, I don't know, maybe there's some more that you didn't demo.[00:28:53] swyx: YC is kind of like a low code experience, right, for evals. Would you ever support like a more code based, like, would I run code on OpenAI's eval platform?[00:29:02] Olivier Godement: For sure. I mean, we meet developers where they are, you know. At the moment, the demand was more for like, you know, easy to get started, like eval. But, you know, if we need to expose like an evaluation API, for instance, for people like, you know, to pass, like, you know, their existing test data we'll do it.[00:29:15] Olivier Godement: So yeah, there is no, you know, philosophical, I would say, like, you know, misalignment on that. Yeah,[00:29:19] swyx: yeah, yeah. What I think this is becoming, by the way, and I don't, like it's basically, like, you're becoming AWS. Like, the AI cloud. And I don't know if, like, that's a conscious strategy, or it's, like, It doesn't even have to be a conscious strategy.[00:29:33] swyx: Like, you're going to offer storage. You're going to offer compute. You're going to offer networking. I don't know what networking looks like. Networking is maybe, like, Caching or like it's a CDN. It's a prompt CDN.[00:29:45] Alex Volkov: Yeah,[00:29:45] swyx: but it's the AI versions of everything, right? Do you like do you see the analogies or?[00:29:52] Olivier Godement: Whatever Whatever I took to developers. I feel like Good models are just half of the story to build a good app There's a third model you need to do Evaluation is the perfect example. Like, you know, you can have the best model in the world If you're in the dark, like, you know, it's really hard to gain the confidence and so Our philosophy is[00:30:11] Olivier Godement: The whole like software development stack is being basically reinvented, you know, with LLMs. There is no freaking way that open AI can build everything. Like there is just too much to build, frankly. And so my philosophy is, essentially, we'll focus on like the tools which are like the closest to the model itself.[00:30:28] Olivier Godement: So that's why you see us like, you know, investing quite a bit in like fine tuning, distillation, our evaluation, because we think that it actually makes sense to have like in one spot, Like, you know, all of that. Like, there is some sort of virtual circle, essentially, that you can set in place. But stuff like, you know, LLMOps, like tools which are, like, further away from the model, I don't know if you want to do, like, you know, super elaborate, like, prompt management, or, you know, like, tooling, like, I'm not sure, like, you know, OpenAI has, like, such a big edge, frankly, like, you know, to build this sort of tools.[00:30:56] Olivier Godement: So that's how we view it at the moment. But again, frankly, the philosophy is super simple. The strategy is super simple. It's meeting developers where they want us to be. And so, you know that's frankly, like, you know, day in, day out, like, you know, what I try to do.[00:31:08] Alessio: Cool. Thank you so much for the time.[00:31:10] Alessio: I'm sure you,[00:31:10] swyx: Yeah, I have more questions on, a couple questions on voice, and then also, like, your call to action, like, what you want feedback on, right? So, I think we should spend a bit more time on voice, because I feel like that's, like, the big splash thing. I talked well Well, I mean, I mean, just what is the future of real time for OpenAI?[00:31:28] swyx: Yeah. Because I think obviously video is next. You already have it in the, the ChatGPT desktop app. Do we just have a permanent, like, you know, like, are developers just going to be, like, sending sockets back and forth with OpenAI? Like how do we program for that? Like, what what is the future?[00:31:44] Olivier Godement: Yeah, that makes sense. I think with multimodality, like, real time is quickly becoming, like, you know, essentially the right experience, like, to build an application. Yeah. So my expectation is that we'll see like a non trivial, like a volume of applications like moving to a real time API. Like if you zoom out, like, audio is really simple, like, audio until basically now.[00:32:05] Olivier Godement: Audio on the web, in apps, was basically very much like a second class citizen. Like, you basically did like an audio chatbot for users who did not have a choice. You know, they were like struggling to read, or I don't know, they were like not super educated with technology. And so, frankly, it was like the crappy option, you know, compared to text.[00:32:25] Olivier Godement: But when you talk to people in the real world, the vast majority of people, like, prefer to talk and listen instead of typing and writing.[00:32:34] swyx: We speak before we write.[00:32:35] Olivier Godement: Exactly. I don't know. I mean, I'm sure it's the case for you in Singapore. For me, my friends in Europe, the number of, like, WhatsApp, like, voice notes they receive every day, I mean, just people, it makes sense, frankly, like, you know.[00:32:45] Olivier Godement: Chinese. Chinese, yeah.[00:32:46] swyx: Yeah,[00:32:47] Olivier Godement: all voice. You know, it's easier. There is more emotions. I mean, you know, you get the point across, like, pretty well. And so my personal ambition for, like, the real time API and, like, audio in general is to make, like, audio and, like, multimodality, like, truly a first class experience.[00:33:01] Olivier Godement: Like, you know, if you're, like, you know, the amazing, like, super bold, like, start up out of YC, you want to build, like, the next, like, billion, like, you know, user application to make it, like, truly your first and make it feel, like, you know, an actual good, like, you know, product experience. So that's essentially the ambition, and I think, like, yeah, it could be pretty big.[00:33:17] swyx: Yeah. I think one, one people, one issue that people have with the voice so far as, as released in advanced voice mode is the refusals.[00:33:24] Alex Volkov: Yeah.[00:33:24] swyx: You guys had a very inspiring model spec. I think Joanne worked on that. Where you said, like, yeah, we don't want to overly refuse all the time. In fact, like, even if, like, not safe for work, like, in some occasions, it's okay.[00:33:38] swyx: How, is there an API that we can say, not safe for work, okay?[00:33:41] Olivier Godement: I think we'll get there. I think we'll get there. The mobile spec, like, nailed it, like, you know. It nailed it! It's so good! Yeah, we are not in the business of, like, policing, you know, if you can say, like, vulgar words or whatever. You know, there are some use cases, like, you know, I'm writing, like, a Hollywood, like, script I want to say, like, will go on, and it's perfectly fine, you know?[00:33:59] Olivier Godement: And so I think the direction where we'll go here is that basically There will always be like, you know, a set of behavior that we will, you know, just like forbid, frankly, because they're illegal against our terms of services. But then there will be like, you know, some more like risky, like themes, which are completely legal, like, you know, vulgar words or, you know, not safe for work stuff.[00:34:17] Olivier Godement: Where basically we'll expose like a controllable, like safety, like knobs in the API to basically allow you to say, hey, that theme okay, that theme not okay. How sensitive do you want the threshold to be on safety refusals? I think that's the Dijkstra. So a[00:34:31] swyx: safety API.[00:34:32] Olivier Godement: Yeah, in a way, yeah.[00:34:33] swyx: Yeah, we've never had that.[00:34:34] Olivier Godement: Yeah. '[00:34:35] swyx: cause right now is you, it is whatever you decide. And then it's, that's it. That, that, that would be the main reason I don't use opening a voice is because of[00:34:42] Olivier Godement: it's over police. Over refuse over refusals. Yeah. Yeah, yeah. No, we gotta fix that. Yeah. Like singing,[00:34:47] Alessio: we're trying to do voice. I'm a singer.[00:34:49] swyx: And you, you locked off singing.[00:34:51] swyx: Yeah,[00:34:51] Alessio: yeah, yeah.[00:34:52] swyx: But I, I understand music gets you in trouble. Okay. Yeah. So then, and then just generally, like, what do you want to hear from developers? Right? We have, we have all developers watching you know, what feedback do you want? Any, anything specific as well, like from, especially from today anything that you are unsure about, that you are like, Our feedback could really help you decide.[00:35:09] swyx: For sure.[00:35:10] Olivier Godement: I think, essentially, it's becoming pretty clear after today that, you know, I would say the open end direction has become pretty clear, like, you know, after today. Investment in reasoning, investment in multimodality, Investment as well, like in, I would say, tool use, like function calling. To me, the biggest question I have is, you know, Where should we put the cursor next?[00:35:30] Olivier Godement: I think we need all three of them, frankly, like, you know, so we'll keep pushing.[00:35:33] swyx: Hire 10, 000 people, or actually, no need, build a bunch of bots.[00:35:37] Olivier Godement: Exactly, and so let's take O1 smart enough, like, for your problems? Like, you know, let's set aside for a second the existing models, like, for the apps that you would love to build, is O1 basically it in reasoning, or do we still have, like, you know, a step to do?[00:35:50] Olivier Godement: Preview is not enough, I[00:35:52] swyx: need the full one.[00:35:53] Olivier Godement: Yeah, so that's exactly that sort of feedback. Essentially what they would love to do is for developers I mean, there's a thing that Sam has been saying like over and over again, like, you know, it's easier said than done, but I think it's directionally correct. As a developer, as a founder, you basically want to build an app which is a bit too difficult for the model today, right?[00:36:12] Olivier Godement: Like, what you think is right, it's like, sort of working, sometimes not working. And that way, you know, that basically gives us like a goalpost, and be like, okay, that's what you need to enable with the next model release, like in a few months. And so I would say that Usually, like, that's the sort of feedback which is like the most useful that I can, like, directly, like, you know, incorporate.[00:36:33] swyx: Awesome. I think that's our time. Thank you so much, guys. Yeah, thank you so much.[00:36:38] AI Charlie: Thank you. We were particularly impressed that Olivier addressed the not safe for work moderation policy question head on, as that had only previously been picked up on in Reddit forums. This is an encouraging sign that we will return to in the closing candor with Sam Altman at the end of this episode.[00:36:57] Romain Huet, Head of DX, OpenAI[00:36:57] AI Charlie: Next, a chat with Roman Hewitt, friend of the pod, AI Engineer World's fair closing keynote speaker, and head of developer experience at OpenAI on his incredible live demos And advice to AI engineers on all the new modalities.[00:37:12] Alessio: Alright, we're live from OpenAI Dev Day. We're with Juan, who just did two great demos on, on stage.[00:37:17] Alessio: And he's been a friend of Latentspace, so thanks for taking some of the time.[00:37:20] Romain Huet: Of course, yeah, thank you for being here and spending the time with us today.[00:37:23] swyx: Yeah, I appreciate appreciate you guys putting this on. I, I know it's like extra work, but it really shows the developers that you're, Care and about reaching out.[00:37:31] Romain Huet: Yeah, of course, I think when you go back to the OpenAI mission, I think for us it's super important that we have the developers involved in everything we do. Making sure that you know, they have all of the tools they need to build successful apps. And we really believe that the developers are always going to invent the ideas, the prototypes, the fun factors of AI that we can't build ourselves.[00:37:49] Romain Huet: So it's really cool to have everyone here.[00:37:51] swyx: We had Michelle from you guys on. Yes, great episode. She very seriously said API is the path to AGI. Correct. And people in our YouTube comments were like, API is not AGI. I'm like, no, she's very serious. API is the path to AGI. Like, you're not going to build everything like the developers are, right?[00:38:08] swyx: Of[00:38:08] Romain Huet: course, yeah, that's the whole value of having a platform and an ecosystem of amazing builders who can, like, in turn, create all of these apps. I'm sure we talked about this before, but there's now more than 3 million developers building on OpenAI, so it's pretty exciting to see all of that energy into creating new things.[00:38:26] Alessio: I was going to say, you built two apps on stage today, an international space station tracker and then a drone. The hardest thing must have been opening Xcode and setting that up. Now, like, the models are so good that they can do everything else. Yes. You had two modes of interaction. You had kind of like a GPT app to get the plan with one, and then you had a cursor to do apply some of the changes.[00:38:47] Alessio: Correct. How should people think about the best way to consume the coding models, especially both for You know, brand new projects and then existing projects that you're trying to modify.[00:38:56] Romain Huet: Yeah. I mean, one of the things that's really cool about O1 Preview and O1 Mini being available in the API is that you can use it in your favorite tools like cursor like I did, right?[00:39:06] Romain Huet: And that's also what like Devin from Cognition can use in their own software engineering agents. In the case of Xcode, like, it's not quite deeply integrated in Xcode, so that's why I had like chat GPT side by side. But it's cool, right, because I could instruct O1 Preview to be, like, my coding partner and brainstorming partner for this app, but also consolidate all of the, the files and architect the app the way I wanted.[00:39:28] Romain Huet: So, all I had to do was just, like, port the code over to Xcode and zero shot the app build. I don't think I conveyed, by the way, how big a deal that is, but, like, you can now create an iPhone app from scratch, describing a lot of intricate details that you want, and your vision comes to life in, like, a minute.[00:39:47] Romain Huet: It's pretty outstanding.[00:39:48] swyx: I have to admit, I was a bit skeptical because if I open up SQL, I don't know anything about iOS programming. You know which file to paste it in. You probably set it up a little bit. So I'm like, I have to go home and test it. And I need the ChatGPT desktop app so that it can tell me where to click.[00:40:04] Romain Huet: Yeah, I mean like, Xcode and iOS development has become easier over the years since they introduced Swift and SwiftUI. I think back in the days of Objective C, or like, you know, the storyboard, it was a bit harder to get in for someone new. But now with Swift and SwiftUI, their dev tools are really exceptional.[00:40:23] Romain Huet: But now when you combine that with O1, as your brainstorming and coding partner, it's like your architect, effectively. That's the best way, I think, to describe O1. People ask me, like, can GPT 4 do some of that? And it certainly can. But I think it will just start spitting out code, right? And I think what's great about O1, is that it can, like, make up a plan.[00:40:42] Romain Huet: In this case, for instance, the iOS app had to fetch data from an API, it had to look at the docs, it had to look at, like, how do I parse this JSON, where do I store this thing, and kind of wire things up together. So that's where it really shines. Is mini or preview the better model that people should be using?[00:40:58] Romain Huet: Like, how? I think people should try both. We're obviously very excited about the upcoming O1 that we shared the evals for. But we noticed that O1 Mini is very, very good at everything math, coding, everything STEM. If you need for your kind of brainstorming or your kind of science part, you need some broader knowledge than reaching for O1 previews better.[00:41:20] Romain Huet: But yeah, I used O1 Mini for my second demo. And it worked perfectly. All I needed was very much like something rooted in code, architecting and wiring up like a front end, a backend, some UDP packets, some web sockets, something very specific. And it did that perfectly.[00:41:35] swyx: And then maybe just talking about voice and Wanderlust, the app that keeps on giving, what's the backstory behind like preparing for all of that?[00:41:44] Romain Huet: You know, it's funny because when last year for Dev Day, we were trying to think about what could be a great demo app to show like an assistive experience. I've always thought travel is a kind of a great use case because you have, like, pictures, you have locations, you have the need for translations, potentially.[00:42:01] Romain Huet: There's like so many use cases that are bounded to travel that I thought last year, let's use a travel app. And that's how Wanderlust came to be. But of course, a year ago, all we had was a text based assistant. And now we thought, well, if there's a voice modality, what if we just bring this app back as a wink.[00:42:19] Romain Huet: And what if we were interacting better with voice? And so with this new demo, what I showed was the ability to like, So, we wanted to have a complete conversation in real time with the app, but also the thing we wanted to highlight was the ability to call tools and functions, right? So, like in this case, we placed a phone call using the Twilio API, interfacing with our AI agents, but developers are so smart that they'll come up with so many great ideas that we could not think of ourselves, right?[00:42:48] Romain Huet: But what if you could have like a, you know, a 911 dispatcher? What if you could have like a customer service? Like center, that is much smarter than what we've been used to today. There's gonna be so many use cases for real time, it's awesome.[00:43:00] swyx: Yeah, and sometimes actually you, you, like this should kill phone trees.[00:43:04] swyx: Like there should not be like dial one[00:43:07] Romain Huet: of course para[00:43:08] swyx: espanol, you know? Yeah, exactly. Or whatever. I dunno.[00:43:12] Romain Huet: I mean, even you starting speaking Spanish would just do the thing, you know you don't even have to ask. So yeah, I'm excited for this future where we don't have to interact with those legacy systems.[00:43:22] swyx: Yeah. Yeah. Is there anything, so you are doing function calling in a streaming environment. So basically it's, it's web sockets. It's UDP, I think. It's basically not guaranteed to be exactly once delivery. Like, is there any coding challenges that you encountered when building this?[00:43:39] Romain Huet: Yeah, it's a bit more delicate to get into it.[00:43:41] Romain Huet: We also think that for now, what we, what we shipped is a, is a beta of this API. I think there's much more to build onto it. It does have the function calling and the tools. But we think that for instance, if you want to have something very robust, On your client side, maybe you want to have web RTC as a client, right?[00:43:58] Romain Huet: And, and as opposed to like directly working with the sockets at scale. So that's why we have partners like Life Kit and Agora if you want to, if you want to use them. And I'm sure we'll have many mores in the, in many more in the future. But yeah, we keep on iterating on that, and I'm sure the feedback of developers in the weeks to come is going to be super critical for us to get it right.[00:44:16] swyx: Yeah, I think LiveKit has been fairly public that they are used in, in the Chachapiti app. Like, is it, it's just all open source, and we just use it directly with OpenAI, or do we use LiveKit Cloud or something?[00:44:28] Romain Huet: So right now we, we released the API, we released some sample code also, and referenced clients for people to get started with our API.[00:44:35] Romain Huet: And we also partnered with LifeKit and Agora, so they also have their own, like ways to help you get started that plugs natively with the real time API. So depending on the use case, people can, can can decide what to use. If you're working on something that's completely client or if you're working on something on the server side, for the voice interaction, you may have different needs, so we want to support all of those.[00:44:55] Alessio: I know you gotta run. Is there anything that you want the AI engineering community to give feedback on specifically, like even down to like, you know, a specific API end point or like, what, what's like the thing that you want? Yeah. I[00:45:08] Romain Huet: mean, you know, if we take a step back, I think dev Day this year is all different from last year and, and in, in a few different ways.[00:45:15] Romain Huet: But one way is that we wanted to keep it intimate, even more intimate than last year. We wanted to make sure that the community is. Thank you very much for joining us on the Spotlight. That's why we have community talks and everything. And the takeaway here is like learning from the very best developers and AI engineers.[00:45:31] Romain Huet: And so, you know we want to learn from them. Most of what we shipped this morning, including things like prompt caching the ability to generate prompts quickly in the playground, or even things like vision fine tuning. These are all things that developers have been asking of us. And so, the takeaway I would, I would leave them with is to say like, Hey, the roadmap that we're working on is heavily influenced by them and their work.[00:45:53] Romain Huet: And so we love feedback From high feature requests, as you say, down to, like, very intricate details of an API endpoint, we love feedback, so yes that's, that's how we, that's how we build this API.[00:46:05] swyx: Yeah, I think the, the model distillation thing as well, it might be, like, the, the most boring, but, like, actually used a lot.[00:46:12] Romain Huet: True, yeah. And I think maybe the most unexpected, right, because I think if I, if I read Twitter correctly the past few days, a lot of people were expecting us. To shape the real time API for speech to speech. I don't think developers were expecting us to have more tools for distillation, and we really think that's gonna be a big deal, right?[00:46:30] Romain Huet: If you're building apps that have you know, you, you want high, like like low latency, low cost, but high performance, high quality on the use case distillation is gonna be amazing.[00:46:40] swyx: Yeah. I sat in the distillation session just now and they showed how they distilled from four oh to four mini and it was like only like a 2% hit in the performance and 50 next.[00:46:49] swyx: Yeah,[00:46:50] Romain Huet: I was there as well for the superhuman kind of use case inspired for an Ebola client. Yeah, this was really good. Cool man! so much for having me. Thanks again for being here today. It's always[00:47:00] AI Charlie: great to have you. As you might have picked up at the end of that chat, there were many sessions throughout the day focused on specific new capabilities.[00:47:08] Michelle Pokrass, Head of API at OpenAI ft. Simon Willison[00:47:08] AI Charlie: Like the new model distillation features combining EVOLs and fine tuning. For our next session, we are delighted to bring back two former guests of the pod, which is something listeners have been greatly enjoying in our second year of doing the Latent Space podcast. Michelle Pokras of the API team joined us recently to talk about structured outputs, and today gave an updated long form session at Dev Day, describing the implementation details of the new structured output mode.[00:47:39] AI Charlie: We also got her updated thoughts on the VoiceMode API we discussed in her episode, now that it is finally announced. She is joined by friend of the pod and super blogger, Simon Willison, who also came back as guest co host in our Dev Day. 2023 episode.[00:47:56] Alessio: Great, we're back live at Dev Day returning guest Michelle and then returning guest co host Fork.[00:48:03] Alessio: Fork, yeah, I don't know. I've lost count. I think it's been a few. Simon Willison is back. Yeah, we just wrapped, we just wrapped everything up. Congrats on, on getting everything everything live. Simon did a great, like, blog, so if you haven't caught up, I[00:48:17] Simon Willison: wrote my, I implemented it. Now, I'm starting my live blog while waiting for the first talk to start, using like GPT 4, I wrote me the Javascript, and I got that live just in time and then, yeah, I was live blogging the whole day.[00:48:28] swyx: Are you a cursor enjoyer?[00:48:29] Simon Willison: I haven't really gotten into cursor yet to be honest. I just haven't spent enough time for it to click, I think. I'm more a copy and paste things out of Cloud and chat GPT. Yeah. It's interesting.[00:48:39] swyx: Yeah. I've converted to cursor and 01 is so easy to just toggle on and off.[00:48:45] Alessio: What's your workflow?[00:48:46] Alessio: VS[00:48:48] Michelle Pokrass: Code co pilot, so Yep, same here. Team co pilot. Co pilot is actually the reason I joined OpenAI. It was, you know, before ChatGPT, this is the thing that really got me. So I'm still into it, but I keep meaning to try out Cursor, and I think now that things have calmed down, I'm gonna give it a real go.[00:49:03] swyx: Yeah, it's a big thing to change your tool of choice.[00:49:06] swyx: Yes,[00:49:06] Michelle Pokrass: yeah, I'm pretty dialed, so.[00:49:09] swyx: I mean, you know, if you want, you can just fork VS Code and make your own. That's the thing to dumb thing, right? We joked about doing a hackathon where the only thing you do is fork VS Code and bet me the best fork win.[00:49:20] Michelle Pokrass: Nice.[00:49:22] swyx: That's actually a really good idea. Yeah, what's up?[00:49:26] swyx: I mean, congrats on launching everything today. I know, like, we touched on it a little bit, but, like, everyone was kind of guessing that Voice API was coming, and, like, we talked about it in our episode. How do you feel going into the launch? Like, any design decisions that you want to highlight?[00:49:41] Michelle Pokrass: Yeah, super jazzed about it. The team has been working on it for a while. It's, like, a very different API for us. It's the first WebSocket API, so a lot of different design decisions to be made. It's, like, what kind of events do you send? When do you send an event? What are the event names? What do you send, like, on connection versus on future messages?[00:49:57] Michelle Pokrass: So there have been a lot of interesting decisions there. The team has also hacked together really cool projects as we've been testing it. One that I really liked is we had an internal hack a thon for the API team. And some folks built like a little hack that you could use to, like VIM with voice mode, so like, control vim, and you would tell them on like, nice, write a file and it would, you know, know all the vim commands and, and pipe those in.[00:50:18] Michelle Pokrass: So yeah, a lot of cool stuff we've been hacking on and really excited to see what people build with it.[00:50:23] Simon Willison: I've gotta call out a demo from today. I think it was Katja had a 3D visualization of the solar system, like WebGL solar system, you could talk to. That is one of the coolest conference demos I've ever seen.[00:50:33] Simon Willison: That was so convincing. I really want the code. I really want the code for that to get put out there. I'll talk[00:50:39] Michelle Pokrass: to the team. I think we can[00:50:40] Simon Willison: probably

Do we have the first IPO of the AI era? Do we have the first AI model beyond the transformer architecture? Microsoft has a bunch of new AI tools inside Windows. We try to explain that whole controversy around PearAI. And what about that NotebookLM feature that lets you create a two-hander podcast out of any text.Links:AI chipmaker Cerebras files for IPO to take on Nvidia (CNBC)MIT spinoff Liquid debuts non-transformer AI models and they're already state-of-the-art (VentureBeat)Microsoft Copilot can now read your screen, think deeply, and speak aloud to you (TechCrunch)Oura Nears $500 Million in Annual Revenue and Readies New Ring (Bloomberg)Y Combinator is being criticized after it backed an AI startup that admits it basically cloned another AI startup (TechCrunch)NotebookLM's automatically generated podcasts are surprisingly effective (Simon Willison's Blog)See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Today, we're joined by Simon Willison, independent researcher and creator of Datasette to discuss the many ways software developers and engineers can take advantage of large language models (LLMs) to boost their productivity. We dig into Simon's own workflows and how he uses popular models like ChatGPT and Anthropic's Claude to write and test hundreds of lines of code while out walking his dog. We review Simon's favorite prompting and debugging techniques, his strategies for sidestepping the limitations of contemporary models, how he uses Claude's Artifacts feature for rapid prototyping, his thoughts on the use and impact of vision models, the role he sees for open source models and local LLMs, and much more. The complete show notes for this episode can be found at https://twimlai.com/go/701.

The first of the Strawberry models is here. YC plans to have four cohorts a year, but each one is getting smaller. Waymo is already ready to expand to more pretty big markets. And in the long reads, a deep dive look into the options Intel has at this point in time.Sponsors:HensonShaving.com/ride code rideLinks:OpenAI releases o1, its first model with ‘reasoning' abilities (The Verge)Notes on OpenAI's new o1 chain-of-thought models (Simon Willison's Weblog)OpenAI's new models 'instrumentally faked alignment' (TransformerNews)Apple AirPods Pro granted FDA approval to serve as hearing aids (TechCrunch)Silicon Valley's Y Combinator to Double Number of Cohorts Per Year (Bloomberg)Weekend Longreads Suggestions:Intel Has Only Tough Options After Its Long and Stinging Fall From Grace (Bloomberg)Link to the twitter poll about adsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Known for co-creating Django and Datasette, as well as his thoughtful writing on LLMs, Simon Willison joins the show to chat about blogging as an accountability mechanism, how to build intuition with LLMs, building a startup with his partner on their honeymoon, and more.   Segments: (00:00:00) The weird intern (00:01:50) The early days of LLMs (00:04:59) Blogging as an accountability mechanism (00:09:24) The low-pressure approach to blogging (00:11:47) GitHub issues as a system of records (00:16:15) Temporal documentation and design docs (00:18:19) GitHub issues for team collaboration (00:21:53) Copy-paste as an API (00:26:54) Observable notebooks (00:28:50) pip install LLM (00:32:26) The evolution of using LLMs daily (00:34:47) Building intuition with LLMs (00:43:24) Democratizing access to automation (00:47:45) Alternative interfaces for language models (00:53:39) Is prompt engineering really engineering? (00:58:39) The frustrations of working with LLMs (01:01:59) Structured data extraction with LLMs (01:06:08) How Simon would go about building a LLM app (01:09:49) LLMs making developers more ambitious (01:13:32) Typical workflow with LLMs (01:19:58) Vibes-based evaluation (01:23:25) Staying up-to-date with LLMs (01:27:49) The impact of LLMs on new programmers (01:29:37) The rise of 'Goop' and the future of software development (01:40:20) Being an independent developer (01:42:26) Staying focused and accountable (01:47:30) Building a startup with your partner on the honeymoon (01:51:30) The responsibility of AI practitioners (01:53:07) The hidden dangers of prompt injection (01:53:44) “Artificial intelligence” is really “imitation intelligence”   Show Notes: Simon's blog: https://simonwillison.net/ Natalie's post on them building a startup together: https://blog.natbat.net/post/61658401806/lanyrd-from-idea-to-exit Simon's talk from DjangoCon: https://www.youtube.com/watch?v=GLkRK2rJGB0 Simon on twitter: https://x.com/simonw Datasette: https://github.com/simonw/datasette   Stay in touch:

Topics covered in this episode: Open Source Myths uv 0.3.0 and all the excitement Top pytest Plugins A comparison of hosts / providers for Python serverless functions (aka Faas) Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training pytest courses and community at PythonTest.com Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org Brian: @brianokken@fosstodon.org Show: @pythonbytes@fosstodon.org 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. Brian #1: Open Source Myths Josh Bressers Mastodon post kicking off a list of open source myths Feedback and additional myths compiled to a doc Some favorites All open source developers live in Nebraska It's all run by hippies Everything is being rewritten in rust Features are planned If the source code is available, it's open source A project with no commits for 12 months is abandoned Many eyes make all bugs shallow Open source has worse UX Open source has better UX Open source makes you rich Michael #2: uv 0.3.0 and all the excitement Thanks to Skyler Kasko and John Hagen for the emails. Additional write up by Simon Willison Additional write up by Armin Ronacher End-to-end project management: uv run, uv lock, and uv sync Tool management: uv tool install and uv tool run (aliased to uvx) Python installation: uv python install Script execution: uv can now manage hermetic, single-file Python scripts with inline dependency metadata based on PEP 723. Brian #3: Top pytest Plugins Inspired by (and assisted by) Hugo's Top PyPI Packages Write up for Finding the top pytest plugins BTW, pytest-check has made it to 25. Same day, Jeff Triplett throws my code into Claude 3.5 Sonnet and refactors it Thanks Jeff Triplett & Hugo for answering how to add Summary and other info Michael #4: A comparison of hosts / providers for Python serverless functions (aka Faas) Nice feature matrix of all the options, frameworks, costs, and more The WASM ones look particularly interesting to me. Extras Brian: When is the next live episode of Python Bytes? - via arewemeetingyet.com Thanks to Hugo van Kemenade Some more cool projects by Hugo Python Logos PyPI Downloads by Python version for various Python tools, in pretty colors Python Core Developers over time Michael: Code in a Castle Course event - just a couple of weeks left Ladybird: A truly independent browser “I'm also interested in your video recording setup, would be nice to have that in the extras too :D” OBS Studio Elgato Streamdeck Elgato Key light DaVinci Resolve Joke: DevOps Support Group via Blaise Hi, my name is Bob Group: Hi Bob I's been 42 days since I last ssh'd into production. Group: Applause But only 4 days since I accidentally took down the website Someone in back: Oh Bob…

Disclaimer: We recorded this episode ~1.5 months ago, timing for the FastHTML release. It then got bottlenecked by Llama3.1, Winds of AI Winter, and SAM2 episodes, so we're a little late. Since then FastHTML was released, swyx is building an app in it for AINews, and Anthropic has also released their prompt caching API. Remember when Dylan Patel of SemiAnalysis coined the GPU Rich vs GPU Poor war? (if not, see our pod with him). The idea was that if you're GPU poor you shouldn't waste your time trying to solve GPU rich problems (i.e. pre-training large models) and are better off working on fine-tuning, optimized inference, etc. Jeremy Howard (see our “End of Finetuning” episode to catchup on his background) and Eric Ries founded Answer.AI to do exactly that: “Practical AI R&D”, which is very in-line with the GPU poor needs. For example, one of their first releases was a system based on FSDP + QLoRA that let anyone train a 70B model on two NVIDIA 4090s. Since then, they have come out with a long list of super useful projects (in no particular order, and non-exhaustive):* FSDP QDoRA: this is just as memory efficient and scalable as FSDP/QLoRA, and critically is also as accurate for continued pre-training as full weight training.* Cold Compress: a KV cache compression toolkit that lets you scale sequence length without impacting speed.* colbert-small: state of the art retriever at only 33M params* JaColBERTv2.5: a new state-of-the-art retrievers on all Japanese benchmarks.* gpu.cpp: portable GPU compute for C++ with WebGPU.* Claudette: a better Anthropic API SDK. They also recently released FastHTML, a new way to create modern interactive web apps. Jeremy recently released a 1 hour “Getting started” tutorial on YouTube; while this isn't AI related per se, but it's close to home for any AI Engineer who are looking to iterate quickly on new products: In this episode we broke down 1) how they recruit 2) how they organize what to research 3) and how the community comes together. At the end, Jeremy gave us a sneak peek at something new that he's working on that he calls dialogue engineering: So I've created a new approach. It's not called prompt engineering. I'm creating a system for doing dialogue engineering. It's currently called AI magic. I'm doing most of my work in this system and it's making me much more productive than I was before I used it.He explains it a bit more ~44:53 in the pod, but we'll just have to wait for the public release to figure out exactly what he means.Timestamps* [00:00:00] Intro by Suno AI* [00:03:02] Continuous Pre-Training is Here* [00:06:07] Schedule-Free Optimizers and Learning Rate Schedules* [00:07:08] Governance and Structural Issues within OpenAI and Other AI Labs* [00:13:01] How Answer.ai works* [00:23:40] How to Recruit Productive Researchers* [00:27:45] Building a new BERT* [00:31:57] FSDP, QLoRA, and QDoRA: Innovations in Fine-Tuning Large Models* [00:36:36] Research and Development on Model Inference Optimization* [00:39:49] FastHTML for Web Application Development* [00:46:53] AI Magic & Dialogue Engineering* [00:52:19] AI wishlist & predictionsShow Notes* Jeremy Howard* Previously on Latent Space: The End of Finetuning, NeurIPS Startups* Answer.ai* Fast.ai* FastHTML* answerai-colbert-small-v1* gpu.cpp* Eric Ries* Aaron DeFazio* Yi Tai* Less Wright* Benjamin Warner* Benjamin Clavié* Jono Whitaker* Austin Huang* Eric Gilliam* Tim Dettmers* Colin Raffel* Sebastian Raschka* Carson Gross* Simon Willison* Sepp Hochreiter* Llama3.1 episode* Snowflake Arctic* Ranger Optimizer* Gemma.cpp* HTMX* UL2* BERT* DeBERTa* Efficient finetuning of Llama 3 with FSDP QDoRA* xLSTMTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO-in-Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:14]: And today we're back with Jeremy Howard, I think your third appearance on Latent Space. Welcome.Jeremy [00:00:19]: Wait, third? Second?Swyx [00:00:21]: Well, I grabbed you at NeurIPS.Jeremy [00:00:23]: I see.Swyx [00:00:24]: Very fun, standing outside street episode.Jeremy [00:00:27]: I never heard that, by the way. You've got to send me a link. I've got to hear what it sounded like.Swyx [00:00:30]: Yeah. Yeah, it's a NeurIPS podcast.Alessio [00:00:32]: I think the two episodes are six hours, so there's plenty to listen, we'll make sure to send it over.Swyx [00:00:37]: Yeah, we're trying this thing where at the major ML conferences, we, you know, do a little audio tour of, give people a sense of what it's like. But the last time you were on, you declared the end of fine tuning. I hope that I sort of editorialized the title a little bit, and I know you were slightly uncomfortable with it, but you just own it anyway. I think you're very good at the hot takes. And we were just discussing in our pre-show that it's really happening, that the continued pre-training is really happening.Jeremy [00:01:02]: Yeah, absolutely. I think people are starting to understand that treating the three ULM FIT steps of like pre-training, you know, and then the kind of like what people now call instruction tuning, and then, I don't know if we've got a general term for this, DPO, RLHFE step, you know, or the task training, they're not actually as separate as we originally suggested they were in our paper, and when you treat it more as a continuum, and that you make sure that you have, you know, more of kind of the original data set incorporated into the later stages, and that, you know, we've also seen with LLAMA3, this idea that those later stages can be done for a lot longer. These are all of the things I was kind of trying to describe there. It wasn't the end of fine tuning, but more that we should treat it as a continuum, and we should have much higher expectations of how much you can do with an already trained model. You can really add a lot of behavior to it, you can change its behavior, you can do a lot. So a lot of our research has been around trying to figure out how to modify the model by a larger amount rather than starting from random weights, because I get very offended at the idea of starting from random weights.Swyx [00:02:14]: Yeah, I saw that in ICLR in Vienna, there was an outstanding paper about starting transformers from data-driven piers. I don't know if you saw that one, they called it sort of never trained from scratch, and I think it was kind of rebelling against like the sort of random initialization.Jeremy [00:02:28]: Yeah, I've, you know, that's been our kind of continuous message since we started Fast AI, is if you're training for random weights, you better have a really good reason, you know, because it seems so unlikely to me that nobody has ever trained on data that has any similarity whatsoever to the general class of data you're working with, and that's the only situation in which I think starting from random weights makes sense.Swyx [00:02:51]: The other trends since our last pod that I would point people to is I'm seeing a rise in multi-phase pre-training. So Snowflake released a large model called Snowflake Arctic, where they detailed three phases of training where they had like a different mixture of like, there was like 75% web in the first instance, and then they reduced the percentage of the web text by 10% each time and increased the amount of code in each phase. And I feel like multi-phase is being called out in papers more. I feel like it's always been a thing, like changing data mix is not something new, but calling it a distinct phase is new, and I wonder if there's something that you're seeingJeremy [00:03:32]: on your end. Well, so they're getting there, right? So the point at which they're doing proper continued pre-training is the point at which that becomes a continuum rather than a phase. So the only difference with what I was describing last time is to say like, oh, there's a function or whatever, which is happening every batch. It's not a huge difference. You know, I always used to get offended when people had learning rates that like jumped. And so one of the things I started doing early on in Fast.ai was to say to people like, no, you should actually have your learning rate schedule should be a function, not a list of numbers. So now I'm trying to give the same idea about training mix.Swyx [00:04:07]: There's been pretty public work from Meta on schedule-free optimizers. I don't know if you've been following Aaron DeFazio and what he's doing, just because you mentioned learning rate schedules, you know, what if you didn't have a schedule?Jeremy [00:04:18]: I don't care very much, honestly. I don't think that schedule-free optimizer is that exciting. It's fine. We've had non-scheduled optimizers for ages, like Less Wright, who's now at Meta, who was part of the Fast.ai community there, created something called the Ranger optimizer. I actually like having more hyperparameters. You know, as soon as you say schedule-free, then like, well, now I don't get to choose. And there isn't really a mathematically correct way of, like, I actually try to schedule more parameters rather than less. So like, I like scheduling my epsilon in my atom, for example. I schedule all the things. But then the other thing we always did with the Fast.ai library was make it so you don't have to set any schedules. So Fast.ai always supported, like, you didn't even have to pass a learning rate. Like, it would always just try to have good defaults and do the right thing. But to me, I like to have more parameters I can play with if I want to, but you don't have to.Alessio [00:05:08]: And then the more less technical side, I guess, of your issue, I guess, with the market was some of the large research labs taking all this innovation kind of behind closed doors and whether or not that's good, which it isn't. And now we could maybe make it more available to people. And then a month after we released the episode, there was the whole Sam Altman drama and like all the OpenAI governance issues. And maybe people started to think more, okay, what happens if some of these kind of labs, you know, start to break from within, so to speak? And the alignment of the humans is probably going to fall before the alignment of the models. So I'm curious, like, if you have any new thoughts and maybe we can also tie in some of the way that we've been building Answer as like a public benefit corp and some of those aspects.Jeremy [00:05:51]: Sure. So, yeah, I mean, it was kind of uncomfortable because two days before Altman got fired, I did a small public video interview in which I said, I'm quite sure that OpenAI's current governance structure can't continue and that it was definitely going to fall apart. And then it fell apart two days later and a bunch of people were like, what did you know, Jeremy?Alessio [00:06:13]: What did Jeremy see?Jeremy [00:06:15]: I didn't see anything. It's just obviously true. Yeah. So my friend Eric Ries and I spoke a lot before that about, you know, Eric's, I think probably most people would agree, the top expert in the world on startup and AI governance. And you know, we could both clearly see that this didn't make sense to have like a so-called non-profit where then there are people working at a company, a commercial company that's owned by or controlled nominally by the non-profit, where the people in the company are being given the equivalent of stock options, like everybody there was working there with expecting to make money largely from their equity. So the idea that then a board could exercise control by saying like, oh, we're worried about safety issues and so we're going to do something that decreases the profit of the company, when every stakeholder in the company, their remuneration pretty much is tied to their profit, it obviously couldn't work. So I mean, that was a huge oversight there by someone. I guess part of the problem is that the kind of people who work at non-profits and in this case the board, you know, who are kind of academics and, you know, people who are kind of true believers. I think it's hard for them to realize that 99.999% of the world is driven very heavily by money, especially huge amounts of money. So yeah, Eric and I had been talking for a long time before that about what could be done differently, because also companies are sociopathic by design and so the alignment problem as it relates to companies has not been solved. Like, companies become huge, they devour their founders, they devour their communities and they do things where even the CEOs, you know, often of big companies tell me like, I wish our company didn't do that thing. You know, I know that if I didn't do it, then I would just get fired and the board would put in somebody else and the board knows if they don't do it, then their shareholders can sue them because they're not maximizing profitability or whatever. So what Eric's spent a lot of time doing is trying to think about how do we make companies less sociopathic, you know, how to, or more, you know, maybe a better way to think of it is like, how do we make it so that the founders of companies can ensure that their companies continue to actually do the things they want them to do? You know, when we started a company, hey, we very explicitly decided we got to start a company, not a academic lab, not a nonprofit, you know, we created a Delaware Seacorp, you know, the most company kind of company. But when we did so, we told everybody, you know, including our first investors, which was you Alessio. They sound great. We are going to run this company on the basis of maximizing long-term value. And in fact, so when we did our second round, which was an angel round, we had everybody invest through a long-term SPV, which we set up where everybody had to agree to vote in line with long-term value principles. So like never enough just to say to people, okay, we're trying to create long-term value here for society as well as for ourselves and everybody's like, oh, yeah, yeah, I totally agree with that. But when it comes to like, okay, well, here's a specific decision we have to make, which will not maximize short-term value, people suddenly change their mind. So you know, it has to be written into the legal documents of everybody so that no question that that's the way the company has to be managed. So then you mentioned the PBC aspect, Public Benefit Corporation, which I never quite understood previously. And turns out it's incredibly simple, like it took, you know, like one paragraph added to our corporate documents to become a PBC. It was cheap, it was easy, but it's got this huge benefit, which is if you're not a public benefit corporation, then somebody can come along and offer to buy you with a stated description of like turning your company into the thing you most hate, right? And if they offer you more than the market value of your company and you don't accept it, then you are not necessarily meeting the kind of your fiduciary responsibilities. So the way like Eric always described it to me is like, if Philip Morris came along and said that you've got great technology for marketing cigarettes to children, so we're going to pivot your company to do that entirely, and we're going to pay you 50% more than the market value, you're going to have to say yes. If you have a PBC, then you are more than welcome to say no, if that offer is not in line with your stated public benefit. So our stated public benefit is to maximize the benefit to society through using AI. So given that more children smoking doesn't do that, then we can say like, no, we're not selling to you.Alessio [00:11:01]: I was looking back at some of our emails. You sent me an email on November 13th about talking and then on the 14th, I sent you an email working together to free AI was the subject line. And then that was kind of the start of the C round. And then two days later, someone got fired. So you know, you were having these thoughts even before we had like a public example of like why some of the current structures didn't work. So yeah, you were very ahead of the curve, so to speak. You know, people can read your awesome introduction blog and answer and the idea of having a R&D lab versus our lab and then a D lab somewhere else. I think to me, the most interesting thing has been hiring and some of the awesome people that you've been bringing on that maybe don't fit the central casting of Silicon Valley, so to speak. Like sometimes I got it like playing baseball cards, you know, people are like, oh, what teams was this person on, where did they work versus focusing on ability. So I would love for you to give a shout out to some of the awesome folks that you have on the team.Jeremy [00:11:58]: So, you know, there's like a graphic going around describing like the people at XAI, you know, Elon Musk thing. And like they are all connected to like multiple of Stanford, Meta, DeepMind, OpenAI, Berkeley, Oxford. Look, these are all great institutions and they have good people. And I'm definitely not at all against that, but damn, there's so many other people. And one of the things I found really interesting is almost any time I see something which I think like this is really high quality work and it's something I don't think would have been built if that person hadn't built the thing right now, I nearly always reach out to them and ask to chat. And I tend to dig in to find out like, okay, you know, why did you do that thing? Everybody else has done this other thing, your thing's much better, but it's not what other people are working on. And like 80% of the time, I find out the person has a really unusual background. So like often they'll have like, either they like came from poverty and didn't get an opportunity to go to a good school or had dyslexia and, you know, got kicked out of school in year 11, or they had a health issue that meant they couldn't go to university or something happened in their past and they ended up out of the mainstream. And then they kind of succeeded anyway. Those are the people that throughout my career, I've tended to kind of accidentally hire more of, but it's not exactly accidentally. It's like when I see somebody who's done, two people who have done extremely well, one of them did extremely well in exactly the normal way from the background entirely pointing in that direction and they achieved all the hurdles to get there. And like, okay, that's quite impressive, you know, but another person who did just as well, despite lots of constraints and doing things in really unusual ways and came up with different approaches. That's normally the person I'm likely to find useful to work with because they're often like risk-takers, they're often creative, they're often extremely tenacious, they're often very open-minded. So that's the kind of folks I tend to find myself hiring. So now at Answer.ai, it's a group of people that are strong enough that nearly every one of them has independently come to me in the past few weeks and told me that they have imposter syndrome and they're not convinced that they're good enough to be here. And I kind of heard it at the point where I was like, okay, I don't think it's possible that all of you are so far behind your peers that you shouldn't get to be here. But I think part of the problem is as an R&D lab, the great developers look at the great researchers and they're like, wow, these big-brained, crazy research people with all their math and s**t, they're too cool for me, oh my God. And then the researchers look at the developers and they're like, oh, they're killing it, making all this stuff with all these people using it and talking on Twitter about how great it is. I think they're both a bit intimidated by each other, you know. And so I have to kind of remind them like, okay, there are lots of things in this world where you suck compared to lots of other people in this company, but also vice versa, you know, for all things. And the reason you came here is because you wanted to learn about those other things from those other people and have an opportunity to like bring them all together into a single unit. You know, it's not reasonable to expect you're going to be better at everything than everybody else. I guess the other part of it is for nearly all of the people in the company, to be honest, they have nearly always been better than everybody else at nearly everything they're doing nearly everywhere they've been. So it's kind of weird to be in this situation now where it's like, gee, I can clearly see that I suck at this thing that I'm meant to be able to do compared to these other people where I'm like the worst in the company at this thing for some things. So I think that's a healthy place to be, you know, as long as you keep reminding each other about that's actually why we're here. And like, it's all a bit of an experiment, like we don't have any managers. We don't have any hierarchy from that point of view. So for example, I'm not a manager, which means I don't get to tell people what to do or how to do it or when to do it. Yeah, it's been a bit of an experiment to see how that would work out. And it's been great. So for instance, Ben Clavier, who you might have come across, he's the author of Ragatouille, he's the author of Rerankers, super strong information retrieval guy. And a few weeks ago, you know, this additional channel appeared on Discord, on our private Discord called Bert24. And these people started appearing, as in our collab sections, we have a collab section for like collaborating with outsiders. And these people started appearing, there are all these names that I recognize, like Bert24, and they're all talking about like the next generation of Bert. And I start following along, it's like, okay, Ben decided that I think, quite rightly, we need a new Bert. Because everybody, like so many people are still using Bert, and it's still the best at so many things, but it actually doesn't take advantage of lots of best practices. And so he just went out and found basically everybody who's created better Berts in the last four or five years, brought them all together, suddenly there's this huge collaboration going on. So yeah, I didn't tell him to do that. He didn't ask my permission to do that. And then, like, Benjamin Warner dived in, and he's like, oh, I created a whole transformers from scratch implementation designed to be maximally hackable. He originally did it largely as a teaching exercise to show other people, but he was like, I could, you know, use that to create a really hackable BERT implementation. In fact, he didn't say that. He said, I just did do that, you know, and I created a repo, and then everybody's like starts using it. They're like, oh my god, this is amazing. I can now implement all these other BERT things. And it's not just answer AI guys there, you know, there's lots of folks, you know, who have like contributed new data set mixes and blah, blah, blah. So, I mean, I can help in the same way that other people can help. So like, then Ben Clavier reached out to me at one point and said, can you help me, like, what have you learned over time about how to manage intimidatingly capable and large groups of people who you're nominally meant to be leading? And so, you know, I like to try to help, but I don't direct. Another great example was Kerem, who, after our FSTP QLORA work, decided quite correctly that it didn't really make sense to use LoRa in today's world. You want to use the normalized version, which is called Dora. Like two or three weeks after we did FSTP QLORA, he just popped up and said, okay, I've just converted the whole thing to Dora, and I've also created these VLLM extensions, and I've got all these benchmarks, and, you know, now I've got training of quantized models with adapters that are as fast as LoRa, and as actually better than, weirdly, fine tuning. Just like, okay, that's great, you know. And yeah, so the things we've done to try to help make these things happen as well is we don't have any required meetings, you know, but we do have a meeting for each pair of major time zones that everybody's invited to, and, you know, people see their colleagues doing stuff that looks really cool and say, like, oh, how can I help, you know, or how can I learn or whatever. So another example is Austin, who, you know, amazing background. He ran AI at Fidelity, he ran AI at Pfizer, he ran browsing and retrieval for Google's DeepMind stuff, created Jemma.cpp, and he's been working on a new system to make it easier to do web GPU programming, because, again, he quite correctly identified, yeah, so I said to him, like, okay, I want to learn about that. Not an area that I have much expertise in, so, you know, he's going to show me what he's working on and teach me a bit about it, and hopefully I can help contribute. I think one of the key things that's happened in all of these is everybody understands what Eric Gilliam, who wrote the second blog post in our series, the R&D historian, describes as a large yard with narrow fences. Everybody has total flexibility to do what they want. We all understand kind of roughly why we're here, you know, we agree with the premises around, like, everything's too expensive, everything's too complicated, people are building too many vanity foundation models rather than taking better advantage of fine-tuning, like, there's this kind of general, like, sense of we're all on the same wavelength about, you know, all the ways in which current research is fucked up, and, you know, all the ways in which we're worried about centralization. We all care a lot about not just research for the point of citations, but research that actually wouldn't have happened otherwise, and actually is going to lead to real-world outcomes. And so, yeah, with this kind of, like, shared vision, people understand, like, you know, so when I say, like, oh, well, you know, tell me, Ben, about BERT 24, what's that about? And he's like, you know, like, oh, well, you know, you can see from an accessibility point of view, or you can see from a kind of a actual practical impact point of view, there's far too much focus on decoder-only models, and, you know, like, BERT's used in all of these different places and industry, and so I can see, like, in terms of our basic principles, what we're trying to achieve, this seems like something important. And so I think that's, like, a really helpful that we have that kind of shared perspective, you know?Alessio [00:21:14]: Yeah. And before we maybe talk about some of the specific research, when you're, like, reaching out to people, interviewing them, what are some of the traits, like, how do these things come out, you know, usually? Is it working on side projects that you, you know, you're already familiar with? Is there anything, like, in the interview process that, like, helps you screen for people that are less pragmatic and more research-driven versus some of these folks that are just gonna do it, you know? They're not waiting for, like, the perfect process.Jeremy [00:21:40]: Everybody who comes through the recruiting is interviewed by everybody in the company. You know, our goal is 12 people, so it's not an unreasonable amount. So the other thing to say is everybody so far who's come into the recruiting pipeline, everybody bar one, has been hired. So which is to say our original curation has been good. And that's actually pretty easy, because nearly everybody who's come in through the recruiting pipeline are people I know pretty well. So Jono Whitaker and I, you know, he worked on the stable diffusion course we did. He's outrageously creative and talented, and he's super, like, enthusiastic tinkerer, just likes making things. Benjamin was one of the strongest parts of the fast.ai community, which is now the alumni. It's, like, hundreds of thousands of people. And you know, again, like, they're not people who a normal interview process would pick up, right? So Benjamin doesn't have any qualifications in math or computer science. Jono was living in Zimbabwe, you know, he was working on, like, helping some African startups, you know, but not FAANG kind of credentials. But yeah, I mean, when you actually see people doing real work and they stand out above, you know, we've got lots of Stanford graduates and open AI people and whatever in our alumni community as well. You know, when you stand out above all of those people anyway, obviously you've got something going for you. You know, Austin, him and I worked together on the masks study we did in the proceeding at the National Academy of Science. You know, we had worked together, and again, that was a group of, like, basically the 18 or 19 top experts in the world on public health and epidemiology and research design and so forth. And Austin, you know, one of the strongest people in that collaboration. So yeah, you know, like, I've been lucky enough to have had opportunities to work with some people who are great and, you know, I'm a very open-minded person, so I kind of am always happy to try working with pretty much anybody and some people stand out. You know, there have been some exceptions, people I haven't previously known, like Ben Clavier, actually, I didn't know before. But you know, with him, you just read his code, and I'm like, oh, that's really well-written code. And like, it's not written exactly the same way as everybody else's code, and it's not written to do exactly the same thing as everybody else's code. So yeah, and then when I chatted to him, it's just like, I don't know, I felt like we'd known each other for years, like we just were on the same wavelength, but I could pretty much tell that was going to happen just by reading his code. I think you express a lot in the code you choose to write and how you choose to write it, I guess. You know, or another example, a guy named Vic, who was previously the CEO of DataQuest, and like, in that case, you know, he's created a really successful startup. He won the first, basically, Kaggle NLP competition, which was automatic essay grading. He's got the current state-of-the-art OCR system, Surya. Again, he's just a guy who obviously just builds stuff, you know, he doesn't ask for permission, he doesn't need any, like, external resources. Actually, Karim's another great example of this, I mean, I already knew Karim very well because he was my best ever master's student, but it wasn't a surprise to me then when he then went off to create the world's state-of-the-art language model in Turkish on his own, in his spare time, with no budget, from scratch. This is not fine-tuning or whatever, he, like, went back to Common Crawl and did everything. Yeah, it's kind of, I don't know what I'd describe that process as, but it's not at all based on credentials.Swyx [00:25:17]: Assemble based on talent, yeah. We wanted to dive in a little bit more on, you know, turning from the people side of things into the technical bets that you're making. Just a little bit more on Bert. I was actually, we just did an interview with Yi Tay from Reka, I don't know if you're familiar with his work, but also another encoder-decoder bet, and one of his arguments was actually people kind of over-index on the decoder-only GPT-3 type paradigm. I wonder if you have thoughts there that is maybe non-consensus as well. Yeah, no, absolutely.Jeremy [00:25:45]: So I think it's a great example. So one of the people we're collaborating with a little bit with BERT24 is Colin Raffle, who is the guy behind, yeah, most of that stuff, you know, between that and UL2, there's a lot of really interesting work. And so one of the things I've been encouraging the BERT group to do, Colin has as well, is to consider using a T5 pre-trained encoder backbone as a thing you fine-tune, which I think would be really cool. You know, Colin was also saying actually just use encoder-decoder as your Bert, you know, why don't you like use that as a baseline, which I also think is a good idea. Yeah, look.Swyx [00:26:25]: What technical arguments are people under-weighting?Jeremy [00:26:27]: I mean, Colin would be able to describe this much better than I can, but I'll give my slightly non-expert attempt. Look, I mean, think about like diffusion models, right? Like in stable diffusion, like we use things like UNet. You have this kind of downward path and then in the upward path you have the cross connections, which it's not a tension, but it's like a similar idea, right? You're inputting the original encoding path into your decoding path. It's critical to make it work, right? Because otherwise in the decoding part, the model has to do so much kind of from scratch. So like if you're doing translation, like that's a classic kind of encoder-decoder example. If it's decoder only, you never get the opportunity to find the right, you know, feature engineering, the right feature encoding for the original sentence. And it kind of means then on every token that you generate, you have to recreate the whole thing, you know? So if you have an encoder, it's basically saying like, okay, this is your opportunity model to create a really useful feature representation for your input information. So I think there's really strong arguments for encoder-decoder models anywhere that there is this kind of like context or source thing. And then why encoder only? Well, because so much of the time what we actually care about is a classification, you know? It's like an output. It's like generating an arbitrary length sequence of tokens. So anytime you're not generating an arbitrary length sequence of tokens, decoder models don't seem to make much sense. Now the interesting thing is, you see on like Kaggle competitions, that decoder models still are at least competitive with things like Deberta v3. They have to be way bigger to be competitive with things like Deberta v3. And the only reason they are competitive is because people have put a lot more time and money and effort into training the decoder only ones, you know? There isn't a recent Deberta. There isn't a recent Bert. Yeah, it's a whole part of the world that people have slept on a little bit. And this is just what happens. This is how trends happen rather than like, to me, everybody should be like, oh, let's look at the thing that has shown signs of being useful in the past, but nobody really followed up with properly. That's the more interesting path, you know, where people tend to be like, oh, I need to get citations. So what's everybody else doing? Can I make it 0.1% better, you know, or 0.1% faster? That's what everybody tends to do. Yeah. So I think it's like, Itay's work commercially now is interesting because here's like a whole, here's a whole model that's been trained in a different way. So there's probably a whole lot of tasks it's probably better at than GPT and Gemini and Claude. So that should be a good commercial opportunity for them if they can figure out what those tasks are.Swyx [00:29:07]: Well, if rumors are to be believed, and he didn't comment on this, but, you know, Snowflake may figure out the commercialization for them. So we'll see.Jeremy [00:29:14]: Good.Alessio [00:29:16]: Let's talk about FSDP, Qlora, Qdora, and all of that awesome stuff. One of the things we talked about last time, some of these models are meant to run on systems that nobody can really own, no single person. And then you were like, well, what if you could fine tune a 70B model on like a 4090? And I was like, no, that sounds great, Jeremy, but like, can we actually do it? And then obviously you all figured it out. Can you maybe tell us some of the worst stories behind that, like the idea behind FSDP, which is kind of taking sharded data, parallel computation, and then Qlora, which is do not touch all the weights, just go quantize some of the model, and then within the quantized model only do certain layers instead of doing everything.Jeremy [00:29:57]: Well, do the adapters. Yeah.Alessio [00:29:59]: Yeah. Yeah. Do the adapters. Yeah. I will leave the floor to you. I think before you published it, nobody thought this was like a short term thing that we're just going to have. And now it's like, oh, obviously you can do it, but it's not that easy.Jeremy [00:30:12]: Yeah. I mean, to be honest, it was extremely unpleasant work to do. It's like not at all enjoyable. I kind of did version 0.1 of it myself before we had launched the company, or at least the kind of like the pieces. They're all pieces that are difficult to work with, right? So for the quantization, you know, I chatted to Tim Detmers quite a bit and, you know, he very much encouraged me by saying like, yeah, it's possible. He actually thought it'd be easy. It probably would be easy for him, but I'm not Tim Detmers. And, you know, so he wrote bits and bytes, which is his quantization library. You know, he wrote that for a paper. He didn't write that to be production like code. It's now like everybody's using it, at least the CUDA bits. So like, it's not particularly well structured. There's lots of code paths that never get used. There's multiple versions of the same thing. You have to try to figure it out. So trying to get my head around that was hard. And you know, because the interesting bits are all written in CUDA, it's hard to like to step through it and see what's happening. And then, you know, FSTP is this very complicated library and PyTorch, which not particularly well documented. So the only really, really way to understand it properly is again, just read the code and step through the code. And then like bits and bytes doesn't really work in practice unless it's used with PEF, the HuggingFace library and PEF doesn't really work in practice unless you use it with other things. And there's a lot of coupling in the HuggingFace ecosystem where like none of it works separately. You have to use it all together, which I don't love. So yeah, trying to just get a minimal example that I can play with was really hard. And so I ended up having to rewrite a lot of it myself to kind of create this like minimal script. One thing that helped a lot was Medec had this LlamaRecipes repo that came out just a little bit before I started working on that. And like they had a kind of role model example of like, here's how to train FSTP, LoRa, didn't work with QLoRa on Llama. A lot of the stuff I discovered, the interesting stuff would be put together by Les Wright, who's, he was actually the guy in the Fast.ai community I mentioned who created the Ranger Optimizer. So he's doing a lot of great stuff at Meta now. So yeah, I kind of, that helped get some minimum stuff going and then it was great once Benjamin and Jono joined full time. And so we basically hacked at that together and then Kerim joined like a month later or something. And it was like, gee, it was just a lot of like fiddly detailed engineering on like barely documented bits of obscure internals. So my focus was to see if it kind of could work and I kind of got a bit of a proof of concept working and then the rest of the guys actually did all the work to make it work properly. And, you know, every time we thought we had something, you know, we needed to have good benchmarks, right? So we'd like, it's very easy to convince yourself you've done the work when you haven't, you know, so then we'd actually try lots of things and be like, oh, and these like really important cases, the memory use is higher, you know, or it's actually slower. And we'd go in and we just find like all these things that were nothing to do with our library that just didn't work properly. And nobody had noticed they hadn't worked properly because nobody had really benchmarked it properly. So we ended up, you know, trying to fix a whole lot of different things. And even as we did so, new regressions were appearing in like transformers and stuff that Benjamin then had to go away and figure out like, oh, how come flash attention doesn't work in this version of transformers anymore with this set of models and like, oh, it turns out they accidentally changed this thing, so it doesn't work. You know, there's just, there's not a lot of really good performance type evals going on in the open source ecosystem. So there's an extraordinary amount of like things where people say like, oh, we built this thing and it has this result. And when you actually check it, so yeah, there's a shitload of war stories from getting that thing to work. And it did require a particularly like tenacious group of people and a group of people who don't mind doing a whole lot of kind of like really janitorial work, to be honest, to get the details right, to check them. Yeah.Alessio [00:34:09]: We had a trade out on the podcast and we talked about how a lot of it is like systems work to make some of these things work. It's not just like beautiful, pure math that you do on a blackboard. It's like, how do you get into the nitty gritty?Jeremy [00:34:22]: I mean, flash attention is a great example of that. Like it's, it basically is just like, oh, let's just take the attention and just do the tiled version of it, which sounds simple enough, you know, but then implementing that is challenging at lots of levels.Alessio [00:34:36]: Yeah. What about inference? You know, obviously you've done all this amazing work on fine tuning. Do you have any research you've been doing on the inference side, how to make local inference really fast on these models too?Jeremy [00:34:47]: We're doing quite a bit on that at the moment. We haven't released too much there yet. But one of the things I've been trying to do is also just to help other people. And one of the nice things that's happened is that a couple of folks at Meta, including Mark Seraphim, have done a nice job of creating this CUDA mode community of people working on like CUDA kernels or learning about that. And I tried to help get that going well as well and did some lessons to help people get into it. So there's a lot going on in both inference and fine tuning performance. And a lot of it's actually happening kind of related to that. So PyTorch team have created this Torch AO project on quantization. And so there's a big overlap now between kind of the FastAI and AnswerAI and CUDA mode communities of people working on stuff for both inference and fine tuning. But we're getting close now. You know, our goal is that nobody should be merging models, nobody should be downloading merged models, everybody should be using basically quantized plus adapters for almost everything and just downloading the adapters. And that should be much faster. So that's kind of the place we're trying to get to. It's difficult, you know, because like Karim's been doing a lot of work with VLM, for example. These inference engines are pretty complex bits of code. They have a whole lot of custom kernel stuff going on as well, as do the quantization libraries. So we've been working on, we're also quite a bit of collaborating with the folks who do HQQ, which is a really great quantization library and works super well. So yeah, there's a lot of other people outside AnswerAI that we're working with a lot who are really helping on all this performance optimization stuff, open source.Swyx [00:36:27]: Just to follow up on merging models, I picked up there that you said nobody should be merging models. That's interesting because obviously a lot of people are experimenting with this and finding interesting results. I would say in defense of merging models, you can do it without data. That's probably the only thing that's going for it.Jeremy [00:36:45]: To explain, it's not that you shouldn't merge models. You shouldn't be distributing a merged model. You should distribute a merged adapter 99% of the time. And actually often one of the best things happening in the model merging world is actually that often merging adapters works better anyway. The point is, Sean, that once you've got your new model, if you distribute it as an adapter that sits on top of a quantized model that somebody's already downloaded, then it's a much smaller download for them. And also the inference should be much faster because you're not having to transfer FB16 weights from HPM memory at all or ever load them off disk. You know, all the main weights are quantized and the only floating point weights are in the adapters. So that should make both inference and fine tuning faster. Okay, perfect.Swyx [00:37:33]: We're moving on a little bit to the rest of the fast universe. I would have thought that, you know, once you started Answer.ai, that the sort of fast universe would be kind of on hold. And then today you just dropped Fastlight and it looks like, you know, there's more activity going on in sort of Fastland.Jeremy [00:37:49]: Yeah. So Fastland and Answerland are not really distinct things. Answerland is kind of like the Fastland grown up and funded. They both have the same mission, which is to maximize the societal benefit of AI broadly. We want to create thousands of commercially successful products at Answer.ai. And we want to do that with like 12 people. So that means we need a pretty efficient stack, you know, like quite a few orders of magnitude more efficient, not just for creation, but for deployment and maintenance than anything that currently exists. People often forget about the D part of our R&D firm. So we've got to be extremely good at creating, deploying and maintaining applications, not just models. Much to my horror, the story around creating web applications is much worse now than it was 10 or 15 years ago in terms of, if I say to a data scientist, here's how to create and deploy a web application, you know, either you have to learn JavaScript or TypeScript and about all the complex libraries like React and stuff, and all the complex like details around security and web protocol stuff around how you then talk to a backend and then all the details about creating the backend. You know, if that's your job and, you know, you have specialists who work in just one of those areas, it is possible for that to all work. But compared to like, oh, write a PHP script and put it in the home directory that you get when you sign up to this shell provider, which is what it was like in the nineties, you know, here are those 25 lines of code and you're done and now you can pass that URL around to all your friends, or put this, you know, .pl file inside the CGI bin directory that you got when you signed up to this web host. So yeah, the thing I've been mainly working on the last few weeks is fixing all that. And I think I fixed it. I don't know if this is an announcement, but I tell you guys, so yeah, there's this thing called fastHTML, which basically lets you create a complete web application in a single Python file. Unlike excellent projects like Streamlit and Gradio, you're not working on top of a highly abstracted thing. That's got nothing to do with web foundations. You're working with web foundations directly, but you're able to do it by using pure Python. There's no template, there's no ginger, there's no separate like CSS and JavaScript files. It looks and behaves like a modern SPA web application. And you can create components for like daisy UI, or bootstrap, or shoelace, or whatever fancy JavaScript and or CSS tailwind etc library you like, but you can write it all in Python. You can pip install somebody else's set of components and use them entirely from Python. You can develop and prototype it all in a Jupyter notebook if you want to. It all displays correctly, so you can like interactively do that. And then you mentioned Fastlight, so specifically now if you're using SQLite in particular, it's like ridiculously easy to have that persistence, and all of your handlers will be passed database ready objects automatically, that you can just call dot delete dot update dot insert on. Yeah, you get session, you get security, you get all that. So again, like with most everything I do, it's very little code. It's mainly tying together really cool stuff that other people have written. You don't have to use it, but a lot of the best stuff comes from its incorporation of HTMX, which to me is basically the thing that changes your browser to make it work the way it always should have. So it just does four small things, but those four small things are the things that are basically unnecessary constraints that HTML should never have had, so it removes the constraints. It sits on top of Starlet, which is a very nice kind of lower level platform for building these kind of web applications. The actual interface matches as closely as possible to FastAPI, which is a really nice system for creating the kind of classic JavaScript type applications. And Sebastian, who wrote FastAPI, has been kind enough to help me think through some of these design decisions, and so forth. I mean, everybody involved has been super helpful. Actually, I chatted to Carson, who created HTMX, you know, so about it. Some of the folks involved in Django, like everybody in the community I've spoken to definitely realizes there's a big gap to be filled around, like, highly scalable, web foundation-based, pure Python framework with a minimum of fuss. So yeah, I'm getting a lot of support and trying to make sure that FastHTML works well for people.Swyx [00:42:38]: I would say, when I heard about this, I texted Alexio. I think this is going to be pretty huge. People consider Streamlit and Gradio to be the state of the art, but I think there's so much to improve, and having what you call web foundations and web fundamentals at the core of it, I think, would be really helpful.Jeremy [00:42:54]: I mean, it's based on 25 years of thinking and work for me. So like, FastML was built on a system much like this one, but that was of hell. And so I spent, you know, 10 years working on that. We had millions of people using that every day, really pushing it hard. And I really always enjoyed working in that. Yeah. So, you know, and obviously lots of other people have done like great stuff, and particularly HTMX. So I've been thinking about like, yeah, how do I pull together the best of the web framework I created for FastML with HTMX? There's also things like PicoCSS, which is the CSS system, which by default, FastHTML comes with. Although, as I say, you can pip install anything you want to, but it makes it like super easy to, you know, so we try to make it so that just out of the box, you don't have any choices to make. Yeah. You can make choices, but for most people, you just, you know, it's like the PHP in your home directory thing. You just start typing and just by default, you'll get something which looks and feels, you know, pretty okay. And if you want to then write a version of Gradio or Streamlit on top of that, you totally can. And then the nice thing is if you then write it in kind of the Gradio equivalent, which will be, you know, I imagine we'll create some kind of pip installable thing for that. Once you've outgrown, or if you outgrow that, it's not like, okay, throw that all away and start again. And this like whole separate language that it's like this kind of smooth, gentle path that you can take step-by-step because it's all just standard web foundations all the way, you know.Swyx [00:44:29]: Just to wrap up the sort of open source work that you're doing, you're aiming to create thousands of projects with a very, very small team. I haven't heard you mention once AI agents or AI developer tooling or AI code maintenance. I know you're very productive, but you know, what is the role of AI in your own work?Jeremy [00:44:47]: So I'm making something. I'm not sure how much I want to say just yet.Swyx [00:44:52]: Give us a nibble.Jeremy [00:44:53]: All right. I'll give you the key thing. So I've created a new approach. It's not called prompt engineering. It's called dialogue engineering. But I'm creating a system for doing dialogue engineering. It's currently called AI magic. I'm doing most of my work in this system and it's making me much more productive than I was before I used it. So I always just build stuff for myself and hope that it'll be useful for somebody else. Think about chat GPT with code interpreter, right? The basic UX is the same as a 1970s teletype, right? So if you wrote APL on a teletype in the 1970s, you typed onto a thing, your words appeared at the bottom of a sheet of paper and you'd like hit enter and it would scroll up. And then the answer from APL would be printed out, scroll up, and then you would type the next thing. And like, which is also the way, for example, a shell works like bash or ZSH or whatever. It's not terrible, you know, like we all get a lot done in these like very, very basic teletype style REPL environments, but I've never felt like it's optimal and everybody else has just copied chat GPT. So it's also the way BART and Gemini work. It's also the way the Claude web app works. And then you add code interpreter. And the most you can do is to like plead with chat GPT to write the kind of code I want. It's pretty good for very, very, very beginner users who like can't code at all, like by default now the code's even hidden away, so you never even have to see it ever happened. But for somebody who's like wanting to learn to code or who already knows a bit of code or whatever, it's, it seems really not ideal. So okay, that's one end of the spectrum. The other end of the spectrum, which is where Sean's work comes in, is, oh, you want to do more than chat GPT? No worries. Here is Visual Studio Code. I run it. There's an empty screen with a flashing cursor. Okay, start coding, you know, and it's like, okay, you can use systems like Sean's or like cursor or whatever to be like, okay, Apple K in cursors, like a creative form that blah, blah, blah. But in the end, it's like a convenience over the top of this incredibly complicated system that full-time sophisticated software engineers have designed over the past few decades in a totally different environment as a way to build software, you know. And so we're trying to like shoehorn in AI into that. And it's not easy to do. And I think there are like much better ways of thinking about the craft of software development in a language model world to be much more interactive, you know. So the thing that I'm building is neither of those things. It's something between the two. And it's built around this idea of crafting a dialogue, you know, where the outcome of the dialogue is the artifacts that you want, whether it be a piece of analysis or whether it be a Python library or whether it be a technical blog post or whatever. So as part of building that, I've created something called Claudette, which is a library for Claude. I've created something called Cosette, which is a library for OpenAI. They're libraries which are designed to make those APIs much more usable, much easier to use, much more concise. And then I've written AI magic on top of those. And that's been an interesting exercise because I did Claudette first, and I was looking at what Simon Willison did with his fantastic LLM library. And his library is designed around like, let's make something that supports all the LLM inference engines and commercial providers. I thought, okay, what if I did something different, which is like make something that's as Claude friendly as possible and forget everything else. So that's what Claudette was. So for example, one of the really nice things in Claude is prefill. So by telling the assistant that this is what your response started with, there's a lot of powerful things you can take advantage of. So yeah, I created Claudette to be as Claude friendly as possible. And then after I did that, and then particularly with GPT 4.0 coming out, I kind of thought, okay, now let's create something that's as OpenAI friendly as possible. And then I tried to look to see, well, where are the similarities and where are the differences? And now can I make them compatible in places where it makes sense for them to be compatible without losing out on the things that make each one special for what they are. So yeah, those are some of the things I've been working on in that space. And I'm thinking we might launch AI magic via a course called how to solve it with code. The name is based on the classic Polya book, if you know how to solve it, which is, you know, one of the classic math books of all time, where we're basically going to try to show people how to solve challenging problems that they didn't think they could solve without doing a full computer science course, by taking advantage of a bit of AI and a bit of like practical skills, as particularly for this like whole generation of people who are learning to code with and because of ChatGPT. Like I love it, I know a lot of people who didn't really know how to code, but they've created things because they use ChatGPT, but they don't really know how to maintain them or fix them or add things to them that ChatGPT can't do, because they don't really know how to code. And so this course will be designed to show you how you can like either become a developer who can like supercharge their capabilities by using language models, or become a language model first developer who can supercharge their capabilities by understanding a bit about process and fundamentals.Alessio [00:50:19]: Nice. That's a great spoiler. You know, I guess the fourth time you're going to be on learning space, we're going to talk about AI magic. Jeremy, before we wrap, this was just a great run through everything. What are the things that when you next come on the podcast in nine, 12 months, we're going to be like, man, Jeremy was like really ahead of it. Like, is there anything that you see in the space that maybe people are not talking enough? You know, what's the next company that's going to fall, like have drama internally, anything in your mind?Jeremy [00:50:47]: You know, hopefully we'll be talking a lot about fast HTML and hopefully the international community that at that point has come up around that. And also about AI magic and about dialogue engineering. Hopefully dialogue engineering catches on because I think it's the right way to think about a lot of this stuff. What else? Just trying to think about all on the research side. Yeah. I think, you know, I mean, we've talked about a lot of it. Like I think encoder decoder architectures, encoder only architectures, hopefully we'll be talking about like the whole re-interest in BERT that BERT 24 stimulated.Swyx [00:51:17]: There's a safe space model that came out today that might be interesting for this general discussion. One thing that stood out to me with Cartesia's blog posts was that they were talking about real time ingestion, billions and trillions of tokens, and keeping that context, obviously in the state space that they have.Jeremy [00:51:34]: Yeah.Swyx [00:51:35]: I'm wondering what your thoughts are because you've been entirely transformers the whole time.Jeremy [00:51:38]: Yeah. No. So obviously my background is RNNs and LSTMs. Of course. And I'm still a believer in the idea that state is something you can update, you know? So obviously Sepp Hochreiter came up, came out with xLSTM recently. Oh my God. Okay. Another whole thing we haven't talked about, just somewhat related. I've been going crazy for like a long time about like, why can I not pay anybody to save my KV cash? I just ingested the Great Gatsby or the documentation for Starlet or whatever, you know, I'm sending it as my prompt context. Why are you redoing it every time? So Gemini is about to finally come out with KV caching, and this is something that Austin actually in Gemma.cpp had had on his roadmap for years, well not years, months, long time. The idea that the KV cache is like a thing that, it's a third thing, right? So there's RAG, you know, there's in-context learning, you know, and prompt engineering, and there's KV cache creation. I think it creates like a whole new class almost of applications or as techniques where, you know, for me, for example, I very often work with really new libraries or I've created my own library that I'm now writing with rather than on. So I want all the docs in my new library to be there all the time. So I want to upload them once, and then we have a whole discussion about building this application using FastHTML. Well nobody's got FastHTML in their language model yet, I don't want to send all the FastHTML docs across every time. So one of the things I'm looking at doing in AI Magic actually is taking advantage of some of these ideas so that you can have the documentation of the libraries you're working on be kind of always available. Something over the next 12 months people will be spending time thinking about is how to like, where to use RAG, where to use fine-tuning, where to use KV cache storage, you know. And how to use state, because in state models and XLSTM, again, state is something you update. So how do we combine the best of all of these worlds?Alessio [00:53:46]: And Jeremy, I know before you talked about how some of the autoregressive models are not maybe a great fit for agents. Any other thoughts on like JEPA, diffusion for text, any interesting thing that you've seen pop up?Jeremy [00:53:58]: In the same way that we probably ought to have state that you can update, i.e. XLSTM and state models, in the same way that a lot of things probably should have an encoder, JEPA and diffusion both seem like the right conceptual mapping for a lot of things we probably want to do. So the idea of like, there should be a piece of the generative pipeline, which is like thinking about the answer and coming up with a sketch of what the answer looks like before you start outputting tokens. That's where it kind of feels like diffusion ought to fit, you know. And diffusion is, because it's not autoregressive, it's like, let's try to like gradually de-blur the picture of how to solve this. So this is also where dialogue engineering fits in, by the way. So with dialogue engineering, one of the reasons it's working so well for me is I use it to kind of like craft the thought process before I generate the code, you know. So yeah, there's a lot of different pieces here and I don't know how they'll all kind of exactly fit together. I don't know if JEPA is going to actually end up working in the text world. I don't know if diffusion will end up working in the text world, but they seem to be like trying to solve a class of problem which is currently unsolved.Alessio [00:55:13]: Awesome, Jeremy. This was great, as usual. Thanks again for coming back on the pod and thank you all for listening. Yeah, that was fantastic. Get full access to Latent Space at www.latent.space/subscribe