Podcasts about ai engineer

Как очистить резюме от цифрового мусора и привлечь HR-ботов?Действительно ли удаленка умерла и теперь правит гибрид?Превратился ли LinkedIn в Tinder для программистов?Почему $200k стали новыми $100k в IT-зарплатах?Выбирают ли стартапы теперь vibe важнее технических скиллов?Нужно ли фронтендеру знать DevOps или это просто способ сэкономить на зарплате?Маша (Мария) Подоляк (Marsha Podolyak)Автор Телеграм канала "

Hey folks, this is Alex, finally back home! This week was full of crazy AI news, both model related but also shifts in the AI landscape and big companies, with Zuck going all in on scale & execu-hiring Alex Wang for a crazy $14B dollars. OpenAI meanwhile, maybe received a new shipment of GPUs? Otherwise, it's hard to explain how they have dropped the o3 price by 80%, while also shipping o3-pro (in chat and API). Apple was also featured in today's episode, but more so for the lack of AI news, completely delaying the “very personalized private Siri powered by Apple Intelligence” during WWDC25 this week. We had 2 guests on the show this week, Stefania Druga and Eric Provencher (who builds RepoPrompt). Stefania helped me cover the AI Engineer conference we all went to last week, and shared some cool Science CoPilot stuff she's working on, while Eric is the GOTO guy for O3-pro helped us understand what this model is great for! As always, TL;DR and show notes at the bottom, video for those who prefer watching is attached below, let's dive in! Big Companies LLMs & APIsLet's start with big companies, because the landscape has shifted, new top reasoner models dropped and some huge companies didn't deliver this week! Zuck goes all in on SuperIntelligence - Meta's $14B stake in ScaleAI and Alex WangThis may be the most consequential piece of AI news today. Fresh from the dissapointing results of LLama 4, reports of top researchers leaving the Llama team, many have decided to exclude Meta from the AI race. We have a saying at ThursdAI, don't bet against Zuck! Zuck decided to spend a lot of money (nearly 20% of their reported $65B investment in AI infrastructure) to get a 49% stake in Scale AI and bring Alex Wang it's (now former) CEO to lead the new Superintelligence team at Meta. For folks who are not familiar with Scale, it's a massive company in providing human annotated data services to all the big AI labs, Google, OpenAI, Microsoft, Anthropic.. all of them really. Alex Wang, is the youngest self made billionaire because of it, and now Zuck not only has access to all their expertise, but also to a very impressive AI persona, who could help revive the excitement about Meta's AI efforts, help recruit the best researchers, and lead the way inside Meta. Wang is also an outspoken China hawk who spends as much time in congressional hearings as in Slack, so the geopolitics here are … spicy. Meta just stapled itself to the biggest annotation funnel on Earth, hired away Google's Jack Rae (who was on the pod just last week, shipping for Google!) for brainy model alignment, and started waving seven-to-nine-figure comp packages at every researcher with “Transformer” in their citation list. Whatever disappointment you felt over Llama-4's muted debut, Zuck clearly felt it too—and responded like a founder who still controls every voting share. OpenAI's Game-Changer: o3 Price Slash & o3-pro launches to top the intelligence leaderboards!Meanwhile OpenAI dropping not one, but two mind-blowing updates. First, they've slashed the price of o3—their premium reasoning model—by a staggering 80%. We're talking from $40/$10 per million tokens down to just $8/$2. That's right, folks, it's now in the same league as Claude Sonnet cost-wise, making top-tier intelligence dirt cheap. I remember when a price drop of 80% after a year got us excited; now it's 80% in just four months with zero quality loss. They've confirmed it's the full o3 model—no distillation or quantization here. How are they pulling this off? I'm guessing someone got a shipment of shiny new H200s from Jensen!And just when you thought it couldn't get better, OpenAI rolled out o3-pro, their highest intelligence offering yet. Available for pro and team accounts, and via API (87% cheaper than o1-pro, by the way), this model—or consortium of models—is a beast. It's topping charts on Artificial Analysis, barely edging out Gemini 2.5 as the new king. Benchmarks are insane: 93% on AIME 2024 (state-of-the-art territory), 84% on GPQA Diamond, and nearing a 3000 ELO score on competition coding. Human preference tests show 64-66% of folks prefer o3-pro for clarity and comprehensiveness across tasks like scientific analysis and personal writing.I've been playing with it myself, and the way o3-pro handles long context and tough problems is unreal. As my friend Eric Provencher (creator of RepoPrompt) shared on the show, it's surgical—perfect for big refactors and bug diagnosis in coding. It's got all the tools o3 has—web search, image analysis, memory personalization—and you can run it in background mode via API for async tasks. Sure, it's slower due to deep reasoning (no streaming thought tokens), but the consistency and depth? Worth it. Oh, and funny story—I was prepping a talk for Hamel Hussain's evals course, with a slide saying “don't use large reasoning models if budget's tight.” The day before, this price drop hits, and I'm scrambling to update everything. That's AI pace for ya!Apple WWDC: Where's the Smarter Siri? Oh Apple. Sweet, sweet Apple. Remember all those Bella Ramsey ads promising a personalized Siri that knows everything about you? Well, Craig Federighi opened WWDC by basically saying "Yeah, about that smart Siri... she's not coming. Don't wait up."Instead, we got:* AI that can combine emojis (revolutionary!

Find out more about Jashan on Instagram @Jashan_KalekaYou + Happy podcast on Instagram @YouPlusHappy Host @Selena_MarshaeFuture of AI: Job Impact, Career Success, and More with AI Engineer & Comedian Jashan Kaleka

Заменит ли AI всех разработчиков или создаст миллионы новых рабочих мест? Какие навыки программиста станут бесполезными уже через два года? Почему получить диплом в 30 лет стало нормой в IT? Почему junior с тремя языками программирования - это красный флаг? Что важнее в 2025 году - диплом или реальный опыт в IT? Какие AI скилы стоит изучать прямо сейчас, чтобы не остаться за бортом? Что делать продактам, проджектам, маркетологам: QA? Повторится ли история доткомов с AI стартапами или это разные времена?Евгений Волчков, Engineering Manager в iManage (ex-Bank of America и Verizon).LinkedIn: https://www.linkedin.com/in/valchkou/ Видео по теме:- Найм сломан. Тысячи кандидатов, а подходящих нет? Почему так сложно найти программиста в 2025? Юлия Тарасова https://youtube.com/live/6uVCZsF4aQE- Новая эра: AI, работа и профессии будущего. Как ИИ меняет правила игры на рынке труда. Ник Береза. https://youtube.com/live/eO9PghMknOY- Тренды IT 2025: венчур, стартапы, искусственный интеллект. Алексей Моисеенков. https://youtube.com/live/1d7hRZrJTkM- Аутсорсинг в IT. Дешевый код - это новая реальность? Кто кого вытеснит с рынка разработки? Валерий Широков и Евгений Волчков https://youtube.com/live/LVrEzC3zai4 ***Записаться на карьерную консультацию (резюме, LinkedIn, карьерная стратегия, поиск работы в США) https://annanaumova.comКоучинг (синдром самозванца, прокрастинация, неуверенность в себе, страхи, лень) https://annanaumova.notion.site/3f6ea5ce89694c93afb1156df3c903abВидео курс по составлению резюме для международных компаний "Идеальное американское резюме": https://go.mbastrategy.com/resumecoursemainГайд "Идеальное американское резюме" https://go.mbastrategy.com/usresumeПодписывайтесь на мой Телеграм канал: https://t.me/prodcastUSAПодписывайтесь на мой Инстаграм https://www.instagram.com/prodcast.us Гайд "Как оформить профиль в LinkedIn, чтобы рекрутеры не смогли пройти мимо" https://go.mbastrategy.com/linkedinguide⏰ Timecodes ⏰00:00 Начало9:10 Что изменилось на рынке найма в США?24:20 Вопросы из чата31:12 Что нужно учить в AI сейчас?1:12:56 Кого заменит AI?

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Hey folks, this is Alex, coming to you LIVE from the AI Engineer Worlds Fair! What an incredible episode this week, we recorded live from floor 30th at the Marriott in SF, while Yam was doing live correspondence from the floor of the AI Engineer event, all while Swyx, the cohost of Latent Space podcast, and the creator of AI Engineer (both the conference and the concept itself) joined us for the whole stream - here's the edited version, please take a look. We've had around 6500 people tune in, and at some point we got 2 surprise guests, straight from the keynote stage, Logan Kilpatrick (PM for AI Studio and lead cheerleader for Gemini) and Jack Rae (principal scientist working on reasoning) joined us for a great chat about Gemini! Mind was absolutely blown! They have just launched the new Gemini 2.5 Pro and I though it would only be fitting to let their new model cover this podcast this week (so below is fully AI generated ... non slop I hope). The show notes and TL;DR is as always in the end. Okay, enough preamble… let's dive into the madness!

 It's been a while since I've released an episode. I'm heading to the AI Engineer World's Fair tomorrowv If you're going to be there, I am going to be wearing a Superman shirt, so come say hi! I would love to talk to listeners and hear how your prompting journey has been going.I'm also planning on restarting the podcast, with one of a couple different directions: agents, vibe coding, or using reasoning models. Which one would you find most useful? Poll here: https://forms.gle/fLqiKeouDPazuU3s5Stay in touch on:Youtube: youtube.com/@PromptEngineeringPodcastTelegram: https://t.me/PromptEngineeringMastermindLinkedIn: https://www.linkedin.com/groups/14231334/Support the show

One of the new tracks at next week's AI Engineer conference in SF is a new focus on LLMs + Robotics, ft. household names like Waymo and Physical Intelligence. However there are many other companies applying LLMs and VLMs in the real world! CloudChef, the first industrial-scale kitchen robotics company with one-shot demonstration learning and an incredibly simple business model, will be serving tasty treats all day with Zippy (https://www.cloudchef.co/zippy ) their AI Chef platform. This is a lightning pod with CEO Nikhil Abraham to preview what Zippy is capable of! https://www.cloudchef.co/platform See a real chef comparison: https://www.youtube.com/watch?v=INDhZ7LwSeo&t=64s See it in the AI Engineer Expo at SF next week: https://ai.engineer Chapters 00:00 Welcome and Introductions 00:58 What is Cloud Chef? 01:36 How the Robots Work: Culinary Intelligence 05:57 Commercial Applications and Early Success 07:02 The Software-First Approach 10:09 Business Model and Pricing 13:10 Demonstration Learning: Training the Robots 16:03 Call to Action and Engineering Opportunities 18:45 Final Thoughts and Technical Details

Hey everyone, Alex here

Hey folks, Alex here, welcome back to ThursdAI! And folks, after the last week was the calm before the storm, "The storm came, y'all" – that's an understatement. This wasn't just a storm; it was an AI hurricane, a category 5 of announcements that left us all reeling (in the best way possible!). From being on the ground at Google I/O to live-watching Anthropic drop Claude 4 during our show, it's been an absolute whirlwind.This week was so packed, it felt like AI Christmas, with tech giants and open-source heroes alike showering us with gifts. We saw OpenAI play their classic pre-and-post-Google I/O chess game, Microsoft make some serious open-source moves, Google unleash an avalanche of updates, and Anthropic crash the party with Claude 4 Opus and Sonnet live stream in the middle of ThursdAI!So buckle up, because we're about to try and unpack this glorious chaos. As always, we're here to help you collectively know, learn, and stay up to date, so you don't have to. Let's dive in! (TL;DR and links in the end) Open Source LLMs Kicking Things OffEven with the titans battling, the open-source community dropped some serious heat this week. It wasn't the main headline grabber, but the releases were significant!Gemma 3n: Tiny But Mighty MatryoshkaFirst up, Google's Gemma 3n. This isn't just another small model; it's a "Nano-plus" preview, a 4-billion parameter MatFormer (Matryoshka Transformer – how cool is that name?) model designed for mobile-first multimodal applications. The really slick part? It has a nested 2-billion parameter sub-model that can run entirely on phones or Chromebooks.Yam was particularly excited about this one, pointing out the innovative "model inside another model" design. The idea is you can use half the model, not depth-wise, but throughout the layers, for a smaller footprint without sacrificing too much. It accepts interleaved text, image, audio, and video, supports ASR and speech translation, and even ships with RAG and function-calling libraries for edge apps. With a 128K token window and responsible AI features baked in, Gemma 3n is looking like a powerful tool for on-device AI. Google claims it beats prior 4B mobile models on MMLU-Lite and MMMU-Mini. It's an early preview in Google AI Studio, but it definitely flies on mobile devices.Mistral & AllHands Unleash Devstral 24BThen we got a collaboration from Mistral and AllHands: Devstral, a 24-billion parameter, state-of-the-art open model focused on code. We've been waiting for Mistral to drop some open-source goodness, and this one didn't disappoint.Nisten was super hyped, noting it beats o3-Mini on SWE-bench verified – a tough benchmark! He called it "the first proper vibe coder that you can run on a 3090," which is a big deal for coders who want local power and privacy. This is a fantastic development for the open-source coding community.The Pre-I/O Tremors: OpenAI & Microsoft Set the StageAs we predicted, OpenAI couldn't resist dropping some news right before Google I/O.OpenAI's Codex Returns as an AgentOpenAI launched Codex – yes, that Codex, but reborn as an asynchronous coding agent. This isn't just a CLI tool anymore; it connects to GitHub, does pull requests, fixes bugs, and navigates your codebase. It's powered by a new coding model fine-tuned for large codebases and was SOTA on SWE Agent when it dropped. Funnily, the model is also called Codex, this time, Codex-1. And this gives us a perfect opportunity to talk about the emerging categories I'm seeing among Code Generator agents and tools:* IDE-based (Cursor, Windsurf): Live pair programming in your editor* Vibe coding (Lovable, Bolt, v0): "Build me a UI" style tools for non-coders* CLI tools (Claude Code, Codex-cli): Terminal-based assistants* Async agents (Claude Code, Jules, Codex, GitHub Copilot agent, Devin): Work on your repos while you sleep, open pull requests for you to review, asyncCodex (this new one) falls into category number 4, and with today's release, Cursor seems to also strive to get to category number 4 with background processing. Microsoft BUILD: Open Source Copilot and Copilot Agent ModeThen came Microsoft Build, their huge developer conference, with a flurry of announcements.The biggest one for me? GitHub Copilot's front-end code is now open source! The VS Code editor part was already open, but the Copilot integration itself wasn't. This is a massive move, likely a direct answer to the insane valuations of VS Code clones like Cursor. Now, you can theoretically clone GitHub Copilot with VS Code and swing for the fences.GitHub Copilot also launched as an asynchronous coding assistant, very similar in function to OpenAI's Codex, allowing it to be assigned tasks and create/update PRs. This puts Copilot right into category 4 of code assistants, and with the native Github Integration, they may actually have a leg up in this race!And if that wasn't enough, Microsoft is adding MCP (Model Context Protocol) support directly into the Windows OS. The implications of having the world's biggest operating system natively support this agentic protocol are huge.Google I/O: An "Ultra" Event Indeed!Then came Tuesday, and Google I/O. I was there in the thick of it, and folks, it was an absolute barrage. Google is shipping. The theme could have been "Ultra" for many reasons, as we'll see.First off, the scale: Google reported a 49x increase in AI usage since last year's I/O, jumping from 9 trillion tokens processed to a mind-boggling 480 trillion tokens. That's a testament to their generous free tiers and the explosion of AI adoption.Gemini 2.5 Pro & Flash: #1 and #2 LLMs on ArenaGemini 2.5 Flash got an update and is now #2 on the LMArena leaderboard (with Gemini 2.5 Pro still holding #1). Both Pro and Flash gained some serious new capabilities:* Deep Think mode: This enhanced reasoning mode is pushing Gemini's scores to new heights, hitting 84% on MMMU and topping LiveCodeBench. It's about giving the model more "time" to work through complex problems.* Native Audio I/O: We're talking real-time TTS in 24 languages with two voices, and affective dialogue capabilities. This is the advanced voice mode we've been waiting for, now built-in.* Project Mariner: Computer-use actions are being exposed via the Gemini API & Vertex AI for RPA partners. This started as a Chrome extension to control your browser and now seems to be a cloud-based API, allowing Gemini to use the web, not just browse it. This feels like Google teaching its AI to interact with the JavaScript-heavy web, much like they taught their crawlers years ago.* Thought Summaries: Okay, here's one update I'm not a fan of. They've switched from raw thinking traces to "thought summaries" in the API. We want the actual traces! That's how we learn and debug.* Thinking Budgets: Previously a Flash-only feature, token ceilings for controlling latency/cost now extend to Pro.* Flash Upgrade: 20-30% fewer tokens, better reasoning/multimodal scores, and GA in early June.Gemini Diffusion: Speed Demon for Code and MathThis one got Yam Peleg incredibly excited. Gemini Diffusion is a new approach, different from transformers, for super-speed editing of code and math tasks. We saw demos hitting 2000 tokens per second! While there might be limitations at longer contexts, its speed and infilling capabilities are seriously impressive for a research preview. This is the first diffusion model for text we've seen from the frontier labs, and it looks sick. Funny note, they had to slow down the demo video to actually show the diffusion process, because at 2000t/s - apps appear as though out of thin air!The "Ultra" Tier and Jules, Google's Coding AgentRemember the "Ultra event" jokes? Well, Google announced a Gemini Ultra tier for $250/month. This tops OpenAI's Pro plan and includes DeepThink access, a generous amount of VEO3 generation, YouTube Premium, and a whopping 30TB of storage. It feels geared towards creators and developers.And speaking of developers, Google launched Jules (jules.google)! This is their asynchronous coding assistant (Category 4!). Like Codex and GitHub Copilot Agent, it connects to your GitHub, opens PRs, fixes bugs, and more. The big differentiator? It's currently free, which might make it the default for many. Another powerful agent joins the fray!AI Mode in Search: GA and EnhancedAI Mode in Google Search, which we've discussed on the show before with Robby Stein, is now in General Availability in the US. This is Google's answer to Perplexity and chat-based search.But they didn't stop there:* Personalization: AI Mode can now connect to your Gmail and Docs (if you opt-in) for more personalized results.* Deep Search: While AI Mode is fast, Deep Search offers more comprehensive research capabilities, digging through hundreds of sources, similar to other "deep research" tools. This will eventually be integrated, allowing you to escalate an AI Mode query for a deeper dive.* Project Mariner Integration: AI Mode will be able to click into websites, check availability for tickets, etc., bridging the gap to an "agentic web."I've had a chat with Robby during I/O and you can listen to that interview at the end of the podcast.Veo3: The Undisputed Star of Google I/OFor me, and many others I spoke to, Veo3 was the highlight. This is Google's flagship video generation model, and it's on another level. (the video above, including sounds is completely one shot generated from VEO3, no processing or editing)* Realism and Physics: The visual quality and understanding of physics are astounding.* Natively Multimodal: This is huge. Veo3 generates native audio, including coherent speech, conversations, and sound effects, all synced perfectly. It can even generate text within videos.* Coherent Characters: Characters remain consistent across scenes and have situational awareness, who speaks when, where characters look.* Image Upload & Reference Ability: While image upload was closed for the demo, it has reference capabilities.* Flow: An editor for video creation using Veo3 and Imagen4 which also launched, allowing for stiching and continuous creation.I got access and created videos where Veo3 generated a comedian telling jokes (and the jokes were decent!), characters speaking with specific accents (Indian, Russian – and they nailed it!), and lip-syncing that was flawless. The situational awareness, the laugh tracks kicking in at the right moment... it's beyond just video generation. This feels like a world simulator. It blew through the uncanny valley for me. More on Veo3 later, because it deserves its own spotlight.Imagen4, Virtual Try-On, and XR Glasses* Imagen4: Google's image generation model also got an upgrade, with extra textual ability.* Virtual Try-On: In Google Shopping, you can now virtually try on clothes. I tried it; it's pretty cool and models different body types well.* XR AI Glasses from Google: Perhaps the coolest, but most futuristic, announcement. AI-powered glasses with an actual screen, memory, and Gemini built-in. You can talk to it, it remembers things for you, and interacts with your environment. This is agentic AI in a very tangible form.Big Company LLMs + APIs: The Beat Goes OnThe news didn't stop with Google.OpenAI (acqui)Hires Jony Ive, Launches "IO" for HardwareThe day after I/O, Sam Altman confirmed that Jony Ive, the legendary designer behind Apple's iconic products, is joining OpenAI. He and his company, LoveFrom, have jointly created a new company called "IO" (yes, IO, just like the conference) which is joining OpenAI in a stock deal reportedly worth $6.5 billion. They're working on a hardware device, unannounced for now, but expected next year. This is a massive statement of intent from OpenAI in the hardware space.Legendary iPhone analyst Ming-Chi Kuo shed some light on the possible device, it won't have a screen, as Jony wants to "wean people off screens"... funny right? They are targeting 2027 for mass production, which is really interesting as 2027 is when most big companies expect AGI to be here. "The current prototype is slightly larger than AI Pin, with a form factor comparable to iPod Shuffle, with one intended use cases is to wear it around your neck, with microphones and cameras for environmental detection" LMArena Raises $100M Seed from a16zThis one raised some eyebrows. LMArena, the go-to place for vibe-checking LLMs, raised a $100 million seed round from Andreessen Horowitz. That's a huge number for a seed, reminiscent of Stability AI's early funding. It also brings up questions about how a VC-backed startup maintains impartiality as a model evaluation platform. Interesting times ahead for leaderboards, how they intent to make 100x that amount to return to investors. Very curious.

This podcast discuss Deep Research, defining it as a comprehensive engagement with information beyond superficial inquiry. It contrasts this with surface learning and simple information gathering, emphasizing the need for rigor and critical analysis. The emergence of AI-powered deep research tools, such as Grok, ChatGPT, and Gemini, is explored as a new dimension, capable of automating and enhancing research processes with unprecedented speed and scale, although they introduce challenges related to accuracy, bias, and ethical considerations. Ultimately, the text argues that while AI can significantly augment human capabilities, the core principles of deep understanding and ethical conduct remain fundamentally reliant on human intellect and oversight, essential for advancing knowledge and tackling complex global issues across various domains.

This episode from the week of 11-18 May 2025 cover a range of AI developments, highlighting major model releases and updates from companies like OpenAI, Google, and Anthropic, as well as the strategic deployment of AI in various sectors, including healthcare, law, education, and content creation. They also touch upon significant ethical and regulatory considerations, such as data privacy concerns raised by international partnerships, debates over copyright protection for artists, the persistent issue of AI "hallucinations," and discussions around government approaches to AI regulation. The reports also reflect on the evolving capabilities of AI agents in tasks from software engineering to web research and customer service, alongside breakthroughs in AI-assisted scientific discovery.

Theis episode and sources collectively offer a snapshot of the AI landscape on a May 16th 2025 highlighting diverse developments. One major theme is advancements in AI model capabilities and applications, with Windsurf launching specialised models for software development, OpenAI introducing a coding agent in ChatGPT, and a new AI model, YingLong, focusing on rapid, high-resolution local weather forecasts. Simultaneously, the texts reveal challenges in AI reliability and deployment, including Anthropic's Claude hallucinating a legal citation, Meta delaying a major model release due to insufficient improvements, and research indicating current LLMs struggle with coherence in multi-turn conversations. Finally, the articles touch on practical AI integrations and evaluations, such as Zapier automating legal document analysis, a pilot for an "AI doctor" clinic in Saudi Arabia, and OpenAI releasing a benchmark specifically for healthcare AI, demonstrating the ongoing effort to apply and rigorously assess AI in various fields.

These excerpts highlight a busy day in the field of artificial intelligence, showcasing major developments from leading companies and broader industry trends. Key updates include Anthropic reportedly preparing a new advanced "Claude Neptune" model and enhancing existing ones with increased autonomy, while OpenAI integrated its new GPT-4.1 models into ChatGPT, improving capabilities for users, especially in coding. Google DeepMind's AlphaEvolve AI demonstrated the ability to autonomously discover novel mathematical advancements, solving long-standing problems and optimising algorithms. Other notable points cover new AI-powered tools for document creation, OpenAI's launch of a Safety Evaluations Hub for transparency, and a US legislative proposal for a 10-year ban on state-level AI regulation. Additionally, Google Cloud introduced a generative AI certification program for business leaders, Databricks made a significant acquisition to bolster its AI agent platform, and a report questioned the immediate replacement of human radiologists by AI, underscoring AI's role as an assistive tool rather than a full substitute in complex medical fields.

This podcast and sources discuss the Google Cloud Generative AI Leader certification, a first-of-its-kind credential designed for professionals who aim to strategically implement Generative AI within their organizations. The material outlines the exam's structure and logistics, including its four key domains covering fundamentals, Google's offerings, output improvement techniques, and business strategies. It also details the official learning path, exam preparation strategies, and the importance of responsible and secure AI adoption for successful Generative AI leadership.Get the eBook at: https://play.google.com/store/books/details?id=bgZeEQAAQBAJDjamgatech: https://djamgatech.com/product/ace-the-google-cloud-generative-ai-leader-certification-ebook-audiobook/Shopify: https://djamgatech.myshopify.com/products/%F0%9F%93%9Aace-the-google-cloud-generative-ai-leader-certification-comprehensive-guide-to-strategic-ai-leadership?utm_source=copyToPasteBoard&utm_medium=product-links&utm_content=webGoogle Play: https://play.google.com/store/books/details?id=bgZeEQAAQBAJApple iBook: https://books.apple.com/us/book/id6745973508

This podcast examines how Artificial Intelligence (AI) can significantly improve our understanding and conservation of biodiversity. It identifies seven major knowledge gaps, known as "shortfalls," which impede effective conservation efforts. The source highlights a review that suggests AI can help bridge five of these shortfalls, although its current application is limited primarily to mapping species distribution and detecting traits. Overcoming the barriers to widespread AI adoption in this field requires addressing issues with data availability and standardization, technological complexities, resource limitations, and fostering better interdisciplinary collaboration. The text also stresses the critical importance of ensuring equity and addressing biases, particularly concerning data from less studied regions and respecting Indigenous knowledge, advocating for responsible AI development through transparency and accountability.

This AI Unraveled podcast episode and sources chronicle significant advancements and strategic moves in the field of artificial intelligence on a specific day, May 14th, 2025. They highlight major hardware deals, such as Nvidia supplying advanced chips to Saudi Arabia, alongside shifts in how major tech companies like Google are integrating AI, evidenced by testing "AI Mode" in search and expanding Gemini across numerous devices. Furthermore, the sources reveal emerging trends in AI adoption, including non-programmers using "vibe coding," and new AI capabilities in creative tools, like TikTok's AI Alive feature for animating photos, and in content production, as seen with Audible's AI narration tools. Finally, they touch upon AI's increasing role in scientific research and changes in international AI chip export policies.

AI, Marketing, and Human Decision Making // MLOps Podcast #313 with Fausto Albers, AI Engineer & Community Lead at AI Builders Club.Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractDemetrios and Fausto Albers explore how generative AI transforms creative work, decision-making, and human connection, highlighting both the promise of automation and the risks of losing critical thinking and social nuance.// BioFausto Albers is a relentless explorer of the unconventional—a techno-optimist with a foundation in sociology and behavioral economics, always connecting seemingly absurd ideas that, upon closer inspection, turn out to be the missing pieces of a bigger puzzle. He thrives in paradox: he overcomplicates the simple, oversimplifies the complex, and yet somehow lands on solutions that feel inevitable in hindsight. He believes that true innovation exists in the tension between chaos and structure—too much of either, and you're stuck.His career has been anything but linear. He's owned and operated successful restaurants, served high-stakes cocktails while juggling bottles on London's bar tops, and later traded spirits for code—designing digital waiters, recommender systems, and AI-driven accounting tools. Now, he leads the AI Builders Club Amsterdam, a fast-growing community where AI engineers, researchers, and founders push the boundaries of intelligent systems.Ask him about RAG, and he'll insist on specificity—because, as he puts it, discussing retrieval-augmented generation without clear definitions is as useful as declaring that “AI will have an impact on the world.” An engaging communicator, a sharp systems thinker, and a builder of both technology and communities, Fausto is here to challenge perspectives, deconstruct assumptions, and remix the future of AI.// Related LinksWebsite: aibuilders.clubMoravec's paradox: https://en.wikipedia.org/wiki/Moravec%27s_paradox?utm_source=chatgpt.comBehavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // Devansh Devansh // #311: https://youtu.be/jJXee5rMtHI~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Fausto on LinkedIn: /stepintoliquidTimestamps:[00:00] Fausto's preferred coffee[00:26] Takeaways[01:18] Automated Ad Creative Generation[07:14] AI in Marketing Workflows[13:23] MCP and System Bottlenecks[21:45] Forward Compatibility vs Optimization[29:57] Unlocking Workflow Speed[33:48] AI Dependency vs Critical Thinking[37:44] AI Realism and Paradoxes[42:30] Outsourcing Decision-Making Risks[46:22] Human Value in Automation[49:02] Wrap up

This episode offers a snapshot of the rapidly evolving artificial intelligence landscape on a May 13th 2025, highlighting advancements and strategic shifts across diverse sectors. We see insights into the projected capabilities of AI in fields like software engineering and healthcare, with discussions on AI reaching junior engineer levels and the development of benchmarks to assess its reliability in medical scenarios. Furthermore, the text covers hardware and infrastructure developments, noting Saudi Arabia's moves to secure AI chip supplies from multiple vendors. Innovative applications such as AI-powered battery management and the use of AI to analyse facial photos for biological age and health prediction are also presented. Finally, the sources touch on changes in user interaction, including Apple exploring brain-computer interfaces and Google experimenting with incorporating AI directly into its search interface.

There are reports of significant negotiations between OpenAI and Microsoft regarding their partnership terms, possibly influenced by OpenAI's future IPO plans. The texts also highlight the Vatican's view on AI as a critical challenge to humanity, with Pope Leo XIV emphasizing ethical guidance. From a technological perspective, breakthroughs are noted in AI training methods, such as the "Absolute Zero" system enabling models to teach themselves, and in new silicon-free transistor technology developed in China. Finally, the articles touch on the practical applications of AI, covering the use of AI tools for personalised avatar creation and Klarna's decision to reintroduce human staff after an AI-only customer service approach negatively impacted quality.

AI, Marketing, and Human Decision Making // MLOps Podcast #313 with Fausto Albers, AI Engineer & Community Lead at AI Builders Club.Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractDemetrios and Fausto Albers explore how generative AI transforms creative work, decision-making, and human connection, highlighting both the promise of automation and the risks of losing critical thinking and social nuance.// BioFausto Albers is a relentless explorer of the unconventional—a techno-optimist with a foundation in sociology and behavioral economics, always connecting seemingly absurd ideas that, upon closer inspection, turn out to be the missing pieces of a bigger puzzle. He thrives in paradox: he overcomplicates the simple, oversimplifies the complex, and yet somehow lands on solutions that feel inevitable in hindsight. He believes that true innovation exists in the tension between chaos and structure—too much of either, and you're stuck.His career has been anything but linear. He's owned and operated successful restaurants, served high-stakes cocktails while juggling bottles on London's bar tops, and later traded spirits for code—designing digital waiters, recommender systems, and AI-driven accounting tools. Now, he leads the AI Builders Club Amsterdam, a fast-growing community where AI engineers, researchers, and founders push the boundaries of intelligent systems.Ask him about RAG, and he'll insist on specificity—because, as he puts it, discussing retrieval-augmented generation without clear definitions is as useful as declaring that “AI will have an impact on the world.” An engaging communicator, a sharp systems thinker, and a builder of both technology and communities, Fausto is here to challenge perspectives, deconstruct assumptions, and remix the future of AI.// Related LinksWebsite: aibuilders.clubMoravec's paradox: https://en.wikipedia.org/wiki/Moravec%27s_paradox?utm_source=chatgpt.comBehavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // Devansh Devansh // #311: https://youtu.be/jJXee5rMtHI~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Fausto on LinkedIn: /stepintoliquid

Scott Wu is the co-founder and CEO of Cognition, the company behind Devin—the world's first autonomous AI software engineer. Unlike other AI coding tools, Devin works like an autonomous engineer that you can interact with through Slack, Linear, and GitHub, just like with a remote engineer. With Scott's background in competitive programming and a previous AI-powered startup, Lunchclub, teaching AI to code has become his ultimate passion.What you'll learn:1. How a team of “Devins” are already producing 25% of Cognition's pull requests, and they are on track to hit 50% by year's end2. How each engineer on Cognition's 15-person engineering team works with about five Devins each3. How Devin has evolved from a “high school CS student” to a “junior engineer” over the past year4. Why engineering will shift from “bricklayers” to “architects”5. Why AI tools will lead to more engineering jobs rather than fewer6. How Devin creates its own wiki to understand and document complex codebases7. The eight pivots Cognition went through before landing on their current approach8. The cultural shifts required to successfully adopt AI engineers—Brought to you by:Enterpret—Transform customer feedback into product growthParagon—Ship every SaaS integration your customers wantAttio—The powerful, flexible CRM for fast-growing startups—Where to find Scott Wu:• X: https://x.com/scottwu46• LinkedIn: https://www.linkedin.com/in/scott-wu-8b94ab96/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Scott Wu and Devin(09:13) Scaling and future prospects(10:23) Devin's origin story(17:26) The idea of Devin as a person(22:19) How a team of “Devins” are already producing 25% of Cognition's pull requests(25:17) Important skills in the AI era(30:21) How Cognition's engineering team works with Devin's(34:37) Live demo(42:20) Devin's codebase integration(44:50) Automation with Linear(46:53) What Devin does best(52:56) The future of AI in software engineering(57:13) Moats and stickiness in AI(01:01:57) The tech that enables Devin(01:04:14) AI will be the biggest technology shift of our lives(01:07:25) Adopting Devin in your company(01:15:13) Startup wisdom and hiring practices(01:22:32) Lightning round and final thoughts—Referenced:• Devin: https://devin.ai/• GitHub: https://github.com/• Linear: https://linear.app/• Waymo: https://waymo.com/• GitHub Copilot: https://github.com/features/copilot• Cursor: https://www.cursor.com/• Anysphere: https://anysphere.inc/• Bolt: https://bolt.new/• StackBlitz: https://stackblitz.com/• Cognition: https://cognition.ai/• v0: https://v0.dev/• Vercel: https://vercel.com/• Everyone's an engineer now: Inside v0's mission to create a hundred million builders | Guillermo Rauch (founder and CEO of Vercel, creators of v0 and Next.js): https://www.lennysnewsletter.com/p/everyones-an-engineer-now-guillermo-rauch• Inside Bolt: From near-death to ~$40m ARR in 5 months—one of the fastest-growing products in history | Eric Simons (founder and CEO of StackBlitz): https://www.lennysnewsletter.com/p/inside-bolt-eric-simons• Assembly: https://en.wikipedia.org/wiki/Assembly_language• Pascal: https://en.wikipedia.org/wiki/Pascal_(programming_language)• Python: https://www.python.org/• Jevons paradox: https://en.wikipedia.org/wiki/Jevons_paradox• Datadog: https://www.datadoghq.com/• Bending the universe in your favor | Claire Vo (LaunchDarkly, Color, Optimizely, ChatPRD): https://www.lennysnewsletter.com/p/bending-the-universe-in-your-favor• OpenAI's CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai• Behind the product: Replit | Amjad Masad (co-founder and CEO): https://www.lennysnewsletter.com/p/behind-the-product-replit-amjad-masad• Windsurf: https://windsurf.com/• COBOL: https://en.wikipedia.org/wiki/COBOL• Fortran: https://en.wikipedia.org/wiki/Fortran• Magic the Gathering: https://magic.wizards.com/en• Aura frames: https://auraframes.com/• AirPods: https://www.apple.com/airpods/• Steven Hao on LinkedIn: https://www.linkedin.com/in/steven-hao-160b9638/• Walden Yan on LinkedIn: https://www.linkedin.com/in/waldenyan/—Recommended books:• How to Win Friends & Influence People: https://www.amazon.com/How-Win-Friends-Influence-People/dp/0671027034• The Power Law: Venture Capital and the Making of the New Future: https://www.amazon.com/Power-Law-Venture-Capital-Making/dp/052555999X• The Great Gatsby: https://www.amazon.com/Great-Gatsby-F-Scott-Fitzgerald/dp/0743273567—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

If you're keen to share your story, please reach out to us!Guest:https://www.linkedin.com/in/nad-chishtie/https://lovable.dev/careers/Powered by Artifeks!https://www.linkedin.com/company/artifeksrecruitmenthttps://www.artifeks.co.ukhttps://www.linkedin.com/in/agilerecruiterLinkedIn: https://www.linkedin.com/company/enginearsioTwitter: https://x.com/EnginearsioAll Podcast Platforms: https://smartlink.ausha.co/enginears00:00 - Enginears Intro.01:14 - Lovable Intro.04:38 - Nad Intro & Lovable origin.08:42 - Multiple iterations of Lovable.11:13 - Importance of crafting the right team in an AI native environment.12:52 - Driving factor behind early stage decisions.16:32 - Technical challenges at Lovable.19:17 - Design perspective at Lovable.20:31 - Next 12 months of AI.24:24 - What is Lovable's AI tech stack?26:11 - How is Nad assessing technical ability in interviews?28:01 - Hiring at Lovable.31:40 - Nad & Lovable Outro.32:54 - Enginears Outro.Hosted by Ausha. See ausha.co/privacy-policy for more information.

Beyond the Matrix: AI and the Future of Human Creativity // MLOps Podcast #300 with Fausto Albers, AI Engineer & Community Lead at AI Builders Club.Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractFausto Albers discusses the intersection of AI and human creativity. He explores AI's role in job interviews, personalized AI assistants, and the evolving nature of human-computer interaction. Key topics include AI-driven self-analysis, context-aware AI systems, and the impact of AI on optimizing human decision-making. The conversation highlights how AI can enhance creativity, collaboration, and efficiency by reducing cognitive load and making intelligent suggestions in real time.// BioFausto Albers is a relentless explorer of the unconventional—a techno-optimist with a foundation in sociology and behavioral economics, always connecting seemingly absurd ideas that, upon closer inspection, turn out to be the missing pieces of a bigger puzzle. He thrives in paradox: he overcomplicates the simple, oversimplifies the complex, and yet somehow lands on solutions that feel inevitable in hindsight. He believes that true innovation exists in the tension between chaos and structure—too much of either, and you're stuck.His career has been anything but linear. He's owned and operated successful restaurants, served high-stakes cocktails while juggling bottles on London's bar tops, and later traded spirits for code—designing digital waiters, recommender systems, and AI-driven accounting tools. Now, he leads the AI Builders Club Amsterdam, a fast-growing community where AI engineers, researchers, and founders push the boundaries of intelligent systems.Ask him about RAG, and he'll insist on specificity—because, as he puts it, discussing retrieval-augmented generation without clear definitions is as useful as declaring that “AI will have an impact on the world.” An engaging communicator, a sharp systems thinker, and a builder of both technology and communities, Fausto is here to challenge perspectives, deconstruct assumptions, and remix the future of AI.// Related LinksWebsite: aibuilders.club~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Fausto on LinkedIn: /stepintoliquid

ChatGPT vs Qwen vs DeepSeek.A comprehensive study compares the performance of ChatGPT, Qwen, and DeepSeek across various real-world AI applications, including language understanding, data analysis, and complex problem-solving.This article benchmarks three AI models—ChatGPT, Qwen, and DeepSeek—across various tasks, including physics simulations, problem-solving, and creative writing. DeepSeek excels in precision and complex calculations, making it ideal for scientific and engineering applications. Qwendemonstrates strong problem-solving speed and multilingual capabilities, suitable for business and legal tasks. ChatGPT, while proficient in creative writing, struggles with complex problems, requiring multiple attempts for solutions. The comparison highlights the unique strengths and weaknesses of each model, guiding users towards the most appropriate AI tool based on their specific needs. Ultimately, the article advocates for choosing AI models based on task-specific requirements rather than solely focusing on general performance.Which AI Model Outperforms in Coding, Mechanics, and Algorithmic Precision— Which Model Delivers Real-World Precision?Reference: https://decodebuzzing.medium.com/qbenchmarking-chatgpt-qwen-and-deepseek-on-real-world-ai-tasks-75b4d7040742

AI Weekly Rundown Feb 02 to Feb 09 2025: Latest Updates From

This podcast episode discusses the transformative impact of Vision AI, a type of artificial intelligence, on food and agriculture. Vision AI agents analyse visual data from various sources to improve crop yields, enhance food safety, and optimise supply chains. The episode highlights numerous companies utilising this technology, showcasing applications in precision farming, automated harvesting, food sorting, livestock monitoring, and quality control. Future applications, such as autonomous harvesting and AI-driven supply chain optimisation, are also explored, along with the potential of platforms like Landing AI to simplify Vision AI development. The episode concludes with a call to action for listeners in the agricultural and food industries to explore the benefits of this technology.

This podcast episode previews Super Bowl 2025, exploring the game's history and the significant role of artificial intelligence. AI's impact is examined across injury prevention, game strategy, and the fan experience, with predictions for the game's outcome offered. The episode also speculates on future applications of AI in coaching, training, officiating, and fan engagement. Despite AI's growing influence, the podcast emphasises the enduring importance of the human element in football. Finally, the podcast concludes by celebrating the blend of tradition and innovation in the modern game.

Why Subscribe?Imagine having a dedicated AI Engineer at your fingertips—without the overhead of a full-time hire. For $999.99 per month, you get 10 hours of expert AI support and solutions. Whether you need a custom chatbot installed, automated data cleanup, or specialized workflows, I can help you transform your day-to-day operations and keep your core business running smoothly.Subscribe at https://buy.stripe.com/14k7sE411gQq6EE3chWhat I OfferAI Chatbot Installation & Maintenance Automated Data Entry & Cleanup AI-Driven Insights & Reporting Lead Gathering & Management Workflow Automation GIS + AI Integration (Extra)My BackgroundAI Unraveled Podcast I'm the creator of AI Unraveled, a daily podcast delivering the latest AI news and insights. Listen on Apple Podcasts: AI Unraveled PodcastProfessional Credentials Check out my LinkedIn resume to see my achievements, skills, and the projects I've led. With years of AI integration experience, I ensure smooth, reliable solutions tailored to your needs.Proven AI IntegrationsDjamgatech AI Chatbot Witness a live example of a custom chatbot in action: View Djamgatech AI ChatbotGeovision AI A robust geospatial intelligence system for real-time data capture and analysis: Explore Geovision AIEmail Lead Workflow Automated system sending hundreds of customized outreach emails daily—effective and efficient lead generation.Why Subscribe?Cost-Effective Expertise: Avoid the overhead of a full-time AI engineer. My $999.99/month plan is flexible and predictable.10 Hours of Dedicated Support: Accelerated Innovation: Ongoing Collaboration: We'll adapt the AI approach as your organization evolves, ensuring continuous value.Ready to Commit?If you're confident this subscription is right for you and your business, subscribe here and let's get started.Have Questions or Want to Discuss Custom Needs? Let's Connect – Book a quick call to see how we can tailor AI solutions for you.

Zara Hajihashemi, AI Engineer and Founder of Cybele Health, joins the podcast to share her journey from Apple tech lead to femtech entrepreneur, driven by a mission to revolutionize women's health with AI-driven insights. With a PhD in machine learning, Zara spent six years at Apple leading cross-functional AI projects before founding Cybele Health to address the inefficiencies in healthcare for professional women and working mothers.In this episode, you'll discover:The Evolution from AI Engineer to Founder: Learn how Zara's experience at Apple, coupled with her PhD research, shaped her vision for Cybele Health and the need for AI-powered, personalized healthcare solutions.Bridging the Healthcare Gap with AI: Zara discusses how Cybele Health is leveraging AI to provide 360-degree visibility into women's health, improving communication between patients and providers to create personalized wellness strategies.The Importance of Personalized Health: Discover how diet, mental health, and physical activity should be aligned with a woman's biological cycle to optimize well-being and productivity.The Role of Functional Medicine and Preventative Care: Zara explains why being proactive rather than reactive in healthcare is crucial, and how AI can assist in creating sustainable, individualized health plans.The Future of AI in Femtech: Explore how AI is revolutionizing the health industry by acting as a 24/7 health assistant, providing predictive insights, and closing gaps in traditional medical care.Building a Health-Tech Startup: Zara shares her journey of founding Cybele Health, securing early users, and the marketing strategies she is employing to drive adoption among both providers and consumers.Zara's Top Health and Wellness Tips:Read labels and avoid processed foods with unrecognizable ingredients.Sync your diet, workouts, and daily habits with your biological cycle for optimal results.Prioritize functional medicine approaches for proactive rather than reactive health management.Connect with Zara and Learn More:Website join the waitlist: Cybele Health LinkedIn: Zara Hajihashemi

AI Job Boom, Microsoft's OneDrive Update, and NVIDIA's Future Vision | Hashtag Trending In today's episode, AI jobs take the spotlight on LinkedIn's fastest growing careers list with roles like AI Engineer and AI Consultant on the rise. Microsoft is closing a loophole in OneDrive that could affect unlicensed accounts starting in 2025. NVIDIA's CEO presents groundbreaking advancements in 'physical AI' at CES 2025, introducing Project Digits—a personal AI supercomputer. Meanwhile, Meta removes third-party fact-checking, shifting to community-driven notes. Join Jim Love for these stories and more on Hashtag Trending. 00:00 Introduction and Host Welcome 00:26 AI Jobs on the Rise 01:43 Microsoft Closes OneDrive Loophole 03:05 NVIDIA's Vision for the Future 05:37 Meta's Shift in Fact-Checking Policy 07:44 Conclusion and Sign-Off

Welcome to another episode of The Elite Expert Insider! Today, we're turning the tables as Melanie Johnson, our usual host, steps into the spotlight as our guest, interviewed by her co-owner Jenn Foster. Learn the power of AI in business, especially focusing on practical applications and debunking the fears surrounding artificial intelligence.

Singapore's GovTech is hosting an AI CTF challenge with ~$15,000 in prizes, starting October 26th, open to both local and virtual hackers. It will be hosted on Dreadnode's Crucible platform; signup here!It is common to say if you want to work in AI, you should come to San Francisco. Not everyone can. Not everyone should. If you can only do meaningful AI work in one city, then AI has failed to generalize meaningfully.As non-Americans working in the US, we know what it's like to see AI progress so rapidly here, and yet be at a loss for what our home countries can do. Through Latent Space we've tried to tell the story of AI outside of the Bay Area bubble; we talked to Notion in New York and Humanloop and Wondercraft in London and HuggingFace in Paris and ICLR in Vienna, and the Reka, RWKV, and Winds of AI Winter episodes were taped in Singapore (the World's Fair also had Latin America representation and we intend to at least add China, Japan, and India next year).The Role of Government with AIAs an intentionally technical resource, we've mostly steered clear of regulation and safety debates on the podcast; whether it is safety bills or technoalarmism, often at the cost of our engagement numbers or ability to book big name guests with a political agenda. When SOTA shifts 3x faster than it takes to pass a law, when nobody agrees on definitions of important things, when you can elicit never-before-seen behavior by slightly different prompting or sampling, it is hard enough to simply keep up to speed, so we are happy limiting our role to that. The story of AI progress has more often been achieved in the private sector, usually in spite of, rather than with thanks to, government intervention.But industrial policy is inextricably linked to the business of AI, which we do very much care about, has an explicitly accelerationist intent if not impact, and has a track record of success in correcting for legitimate market failures in private sector investment, particularly outside of the US. It is with this lens we approach today's episode and special guest, our first with a sitting Cabinet member.Singapore's National AI StrategyIt is well understood that much of Singapore's economic success is attributable to industrial policy, from direct efforts like the Jurong Town Corporation industrialization to indirect ones like going all in on English as national first language. Singapore's National AI Strategy grew out of its 2014 Smart Nation initiative, first launched in 2019 and then refreshed in 2023 by Minister Josephine Teo, our guest today.While Singapore is not often thought of as an AI leader, the National University ranks in the top 10 in publications (above Oxford/Harvard!), and many overseas Singaporeans work at the leading AI companies and institutions in the US (and some of us even run leading AI Substacks?). OpenAI has often publicly named the Singapore government as their model example of government collaborator and is opening an office in Singapore in time for DevDay 2024.AI Engineer NationsSwyx first pitched the AI Engineer Nation concept at a private Sovereign AI summit featuring Dr. He Ruimin, Chief AI Officer of Singapore, which eventually led to an invitation to discuss the concept with Minister Teo, the country's de-facto minister for tech (she calls it Digital Development, for good reasons she explains in the pod).This chat happened (with thanks to Jing Long, Joyce, and other folks from MDDI)!The central pitch for any country, not just Singapore, to emphasize and concentrate bets on AI Engineers, compared with other valuable efforts like training more researchers, releasing more government-approved data, or offering more AI funding, is a calculated one, based on the fact that: * GPU clusters and researchers have massive returns to scale and colocation, mostly concentrated in the US, that are irresponsibly expensive to replicate* Even if research stopped today and there was no progress for the next 30 years, there are far more capabilities to unlock and productize from existing foundation models and we

We are in

Shawn Wang (aka swyx) is the founder of smol.ai (AI news curation), and the cohost of Latent Space (popular AI Engineer podcast). Plus, Shawn started the AI Engineer movement with his essay Rise of the AI Engineer and organized two incredible AI engineer conferences in the past twelve months - AI Engineer World's Fair and AI Engineer SummitAnd Shawn has angel invested in DevTools like Airbyte, Railway, Supabase, Replay.io, Stackblitz, Flutterflow, Fireworks.ai while running the DevTools angels community. Besides this, Shawn curates DX.tips (DevTools magazine) and in a past life wrote the Coding Career handbook, championed learn in public, cofounded Svelte Society and was previously Head of Developer Experience at Temporal, and a Developer Advocate at AWS and Netlify.Also, before this, Shawn had a very successful career in investment banking, trading, building data pipelines and performing quantitate portfolio management. I think this brings him a very unique perspective - I've always admired his ability to zoom out and see the big picture and the trends. Even though Shawn is now all-in on AI, he's still one of the go-to authorities on DevTools go-to-market.As you can tell, Shawn is someone I deeply admire. So I'm glad he came back.What we discuss:Organizing the AI Engineer ConferencesRise of the AI EngineerIntentionality and principles (yes we even talk about Alcoholics Anonymous)The AI CEOInvisible deadlinesIlya believing in AGI more than most people at OpenAIAre developers going to be obsolete? Thor convinced swyx to invest in SupabaseBuilding DevTools that work well with LLMsAngel investing in DevTools - why and howIs DevRel dead?How to hire DevRelWhy DX.tips existsLinks:Rise of the AI Engineer https://www.latent.space/p/ai-engineerLatent Space Podcast https://www.latent.space/swyx's Twitter https://x.com/swyxswyx's website https://www.swyx.io/swyx's LinkedIn https://www.linkedin.com/in/shawnswyxwang/smol.ai https://smol.ai/DevTools Angels https://github.com/sw-yx/devtools-angelsDX.tips https://dx.tips/DevRel's Death as Zero Interest Rate Phenomenon https://dx.tips/zirp AI Engineer Summit https://www.ai.engineer/summit/2023AI Engineer World's Fair https://www.ai.engineer/worldsfairCoding Career Handbook https://www.learninpublic.org/Shawn's previous appearance on Scaling DevTools https://podcast.scalingdevtools.com/episodes/swyx Eisenhower Matrix https://asana.com/resources/eisenhower-matrixThor from Supabase https://x.com/thorwebdevSolaris AI coworking space in SF https://www.solarissf.com/Browserbase https://www.browserbase.com/Indent https://indent.com/ and Fouad https://x.com/fouadmatinHow to do hackathons https://dx.tips/hackathonsHow to do conferences https://dx.tips/conf-guideHow to hire DevRel https://dx.tips/mailbox-first-devrel-hiringClimbing the ladder of abstraction with Amelia Wattenberger https://www.youtube.com/watch?v=PAy_GHUAICwCheck out the Enterprise Ready Conf from WorkOS https://enterprise-ready.com/

Today we have Tejas Kumar on the show. Tejas is part of the Developer Relations team at Datastax. He's really good at frontend, got a great podcast and he has written a book called Fluent React. He spoke recently at the Shift Conference in Croatia, where he talked about AI engineering and what that means. So we talked about AI Engineering, we talked about React, content creation, education, and much more. This episode is full of value and we think you'd love this one.

Congrats to Damien on successfully running AI Engineer London! See our community page and the Latent Space Discord for all upcoming events.This podcast came together in a far more convoluted way than usual, but happens to result in a tight 2 hours covering the ENTIRE OpenAI product suite across ChatGPT-latest, GPT-4o and the new o1 models, and how they are delivered to AI Engineers in the API via the new Structured Output mode, Assistants API, client SDKs, upcoming Voice Mode API, Finetuning/Vision/Whisper/Batch/Admin/Audit APIs, and everything else you need to know to be up to speed in September 2024.This podcast has two parts: the first hour is a regular, well edited, podcast on 4o, Structured Outputs, and the rest of the OpenAI API platform. The second was a rushed, noisy, hastily cobbled together recap of the top takeaways from the o1 model release from yesterday and today.Building AGI with Structured Outputs — Michelle Pokrass of OpenAI API teamMichelle Pokrass built massively scalable platforms at Google, Stripe, Coinbase and Clubhouse, and now leads the API Platform at Open AI. She joins us today to talk about why structured output is such an important modality for AI Engineers that Open AI has now trained and engineered a Structured Output mode with 100% reliable JSON schema adherence. To understand why this is important, a bit of history is important:* June 2023 when OpenAI first added a "function calling" capability to GPT-4-0613 and GPT 3.5 Turbo 0613 (our podcast/writeup here)* November 2023's OpenAI Dev Day (our podcast/writeup here) where the team shipped JSON Mode, a simpler schema-less JSON output mode that nevertheless became more popular because function calling often failed to match the JSON schema given by developers. * Meanwhile, in open source, many solutions arose, including * Instructor (our pod with Jason here) * LangChain (our pod with Harrison here, and he is returning next as a guest co-host)* Outlines (Remi Louf's talk at AI Engineer here)* Llama.cpp's constrained grammar sampling using GGML-BNF* April 2024: OpenAI started implementing constrained sampling with a new `tool_choice: required` parameter in the API* August 2024: the new Structured Output mode, co-led by Michelle* Sept 2024: Gemini shipped Structured Outputs as wellWe sat down with Michelle to talk through every part of the process, as well as quizzing her for updates on everything else the API team has shipped in the past year, from the Assistants API, to Prompt Caching, GPT4 Vision, Whisper, the upcoming Advanced Voice Mode API, OpenAI Enterprise features, and why every Waterloo grad seems to be a cracked engineer.Part 1 Timestamps and TranscriptTranscript here.* [00:00:42] Episode Intro from Suno* [00:03:34] Michelle's Path to OpenAI* [00:12:20] Scaling ChatGPT* [00:13:20] Releasing Structured Output* [00:16:17] Structured Outputs vs Function Calling* [00:19:42] JSON Schema and Constrained Grammar* [00:20:45] OpenAI API team* [00:21:32] Structured Output Refusal Field* [00:24:23] ChatML issues* [00:26:20] Function Calling Evals* [00:28:34] Parallel Function Calling* [00:29:30] Increased Latency* [00:30:28] Prompt/Schema Caching* [00:30:50] Building Agents with Structured Outputs: from API to AGI* [00:31:52] Assistants API* [00:34:00] Use cases for Structured Output* [00:37:45] Prompting Structured Output* [00:39:44] Benchmarking Prompting for Structured Outputs* [00:41:50] Structured Outputs Roadmap* [00:43:37] Model Selection vs GPT4 Finetuning* [00:46:56] Is Prompt Engineering Dead?* [00:47:29] 2 models: ChatGPT Latest vs GPT 4o August* [00:50:24] Why API => AGI* [00:52:40] Dev Day* [00:54:20] Assistants API Roadmap* [00:56:14] Model Reproducibility/Determinism issues* [00:57:53] Tiering and Rate Limiting* [00:59:26] OpenAI vs Ops Startups* [01:01:06] Batch API* [01:02:54] Vision* [01:04:42] Whisper* [01:07:21] Voice Mode API* [01:08:10] Enterprise: Admin/Audit Log APIs* [01:09:02] Waterloo grads* [01:10:49] Books* [01:11:57] Cognitive Biases* [01:13:25] Are LLMs Econs?* [01:13:49] Hiring at OpenAIEmergency O1 Meetup — OpenAI DevRel + Strawberry teamthe following is our writeup from AINews, which so far stands the test of time.o1, aka Strawberry, aka Q*, is finally out! There are two models we can use today: o1-preview (the bigger one priced at $15 in / $60 out) and o1-mini (the STEM-reasoning focused distillation priced at $3 in/$12 out) - and the main o1 model is still in training. This caused a little bit of confusion.There are a raft of relevant links, so don't miss:* the o1 Hub* the o1-preview blogpost* the o1-mini blogpost* the technical research blogpost* the o1 system card* the platform docs* the o1 team video and contributors list (twitter)Inline with the many, many leaks leading up to today, the core story is longer “test-time inference” aka longer step by step responses - in the ChatGPT app this shows up as a new “thinking” step that you can click to expand for reasoning traces, even though, controversially, they are hidden from you (interesting conflict of interest…):Under the hood, o1 is trained for adding new reasoning tokens - which you pay for, and OpenAI has accordingly extended the output token limit to >30k tokens (incidentally this is also why a number of API parameters from the other models like temperature and role and tool calling and streaming, but especially max_tokens is no longer supported).The evals are exceptional. OpenAI o1:* ranks in the 89th percentile on competitive programming questions (Codeforces),* places among the top 500 students in the US in a qualifier for the USA Math Olympiad (AIME),* and exceeds human PhD-level accuracy on a benchmark of physics, biology, and chemistry problems (GPQA).You are used to new models showing flattering charts, but there is one of note that you don't see in many model announcements, that is probably the most important chart of all. Dr Jim Fan gets it right: we now have scaling laws for test time compute, and it looks like they scale loglinearly.We unfortunately may never know the drivers of the reasoning improvements, but Jason Wei shared some hints:Usually the big model gets all the accolades, but notably many are calling out the performance of o1-mini for its size (smaller than gpt 4o), so do not miss that.Part 2 Timestamps* [01:15:01] O1 transition* [01:16:07] O1 Meetup Recording* [01:38:38] OpenAI Friday AMA recap* [01:44:47] Q&A Part 2* [01:50:28] O1 DemosDemo Videos to be posted shortly Get full access to Latent Space at www.latent.space/subscribe

In episode 18 of Generationship, Rachel Chalmers sits down with Shawn "swyx" Wang to delve into AI Engineering. Shawn shares his journey from popularizing the term "AI Engineer" to navigating the rapid advancements in AI technology. Together, they explore the evolving demands and opportunities in AI, offering unparalleled insights into the future of this transformative field.

In episode 18 of Generationship, Rachel Chalmers sits down with Shawn "swyx" Wang to delve into AI Engineering. Shawn shares his journey from popularizing the term "AI Engineer" to navigating the rapid advancements in AI technology. Together, they explore the evolving demands and opportunities in AI, offering unparalleled insights into the future of this transformative field.

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

Dino Scheidt is an AI Engineer, former CTO and Founder who works with Fortune 50s, Start-Ups, and Governments on Data Intelligence and AI Architectures. In this conversation, we explore the evolving landscape of AI with a particular focus on generative AI and its applications. Dino criticizes the concept of 'AI strategies,' arguing that AI should be seen as a tool rather than a strategy. Despite his initial skepticism towards generative AI, Dino acknowledges its potential, especially in transforming traditional value chains through AI-enabled communications. We also delve into the challenges and opportunities posed by non-deterministic systems, the concept of Data P&L, and the complexity of integrating generative AI into existing business operations. Dino wraps up the discussion by emphasizing the importance of lateral thinking and digital representation of business processes to leverage future AI innovations effectively. EPISODE LINKS: Dino Scheidt LinkedIn: https://linkedin.com/in/dinoscheidt Dino Scheidt Website: https://din.ooo The Gartner Hype Cycle: https://en.wikipedia.org/wiki/Gartner_hype_cycle TIMESTAMPS: 00:00:12 Introduction and background 00:00:45 AI and Generative Models 00:04:36 Deterministic vs Non-Deterministic Systems 00:08:19 Unpredictability, Value Chain, and AI Integration 00:13:26 Hype Cycle: Adjective to Noun 00:19:13 Strategic Integration of Generative AI 00:28:36 Value of Generative AI: Consumer vs. Enterprise Perspectives 00:32:12 Event Storming and Tactical AI 00:39:51 Future of AI and Final Thoughts 00:42:36 Closing CONNECT: Website: https://hoo.be/elijahmurray YouTube: https://www.youtube.com/@elijahmurray Twitter: https://twitter.com/elijahmurray Instagram: https://www.instagram.com/elijahmurray LinkedIn: https://www.linkedin.com/in/elijahmurray/ Apple Podcasts: https://podcasts.apple.com/us/podcast/the-long-game-w-elijah-murray/ Spotify: https://podcasters.spotify.com/pod/show/elijahmurray RSS: https://anchor.fm/s/3e31c0c/podcast/rss

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Join us in this enlightening episode of the Limitless Podcast as we sit down with Joel Dean to discuss groundbreaking innovations and strategic approaches in the business world. Joel, a seasoned entrepreneur and strategist, shares his insights on navigating the challenges of today's market and leveraging new growth opportunities.Highlights:

Dive into the latest insights from the AI Engineer World's Fair in San Francisco. This event, touted as the biggest technical AI conference in the city, brought together over 100 speakers and countless developers. Discover seven key observations that highlight the current state and future of AI development, from the focus on practical, production-specific solutions to the emergence of AI engineers as a distinct category. Learn about the innovative conversations happening around AI agents and the unique dynamics of this rapidly evolving field. Learn how to use AI with the world's biggest library of fun and useful tutorials: https://besuper.ai/ Use code 'youtube' for 50% off your first month. The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614 Subscribe to the newsletter: https://aidailybrief.beehiiv.com/ Join our Discord: https://bit.ly/aibreakdown

The World's Fair is officially sold out! Thanks for all the support and stay tuned for recaps of all the great goings on in this very special celebration of the AI Engineer!Longtime listeners will remember the fan favorite Raza Habib, CEO of HumanLoop, on the pod:Well, he's caught the podcasting bug and is now flipping the tables on swyx! Subscribe to High Agency wherever the finest Artificial Intelligence podcast are sold.High Agency Pod DescriptionIn this episode, I chatted with Shawn Wang about his upcoming AI engineering conference and what an AI engineer really is. It's been a year since he penned the viral essay "Rise of the AI Engineer' and we discuss if this new role will be enduring, the make up of the optimal AI team and trends in machine learning.Timestamps00:00 - Introduction and background on Shawn Wang (Swyx)03:45 - Reflecting on the "Rise of the AI Engineer" essay07:30 - Skills and characteristics of AI Engineers12:15 - Team composition for AI products16:30 - Vertical vs. horizontal AI startups23:00 - Advice for AI product creators and leaders28:15 - Tools and buying vs. building for AI products33:30 - Key trends in AI research and development41:00 - Closing thoughts and information on the AI Engineer World Fair SummitVideo Get full access to Latent Space at www.latent.space/subscribe

In this episode, Akshay, who transitioned from a data science intern to an applied AI engineer, highlights the importance of setting actionable goals, systematized planning, and the role of personal background and family support in choosing his career path in machine learning over IoT. His story underlines the significance of understanding one's 'why' to drive ambition and maintain a balanced life. You can connect with Akshay on: Twitter: https://twitter.com/Gautam_A_k LinkedIn: https://www.linkedin.com/in/ak-gautam/ Git: https://github.com/Ak-Gautam/ Website: https://www.akshaygautam.me/#contact --- Support this podcast: https://podcasters.spotify.com/pod/show/vinodsharma/support

Meet Chris Immel, AI Engineer and Digital Artist at Luminifera Projects. Chris shares how he works to create a symbiosis between software development and art and why he remains optimistic when it comes to the AI revolution. Show Links Partner with Dev & CodeNewbie! (sponsor) Chris' Instagram Chris' Website Chris' GitHub Chris' LinkedIn

Aaron Burciaga is a renowned Data Scientist, AI Engineer, author, Advisor along with being a competitive bodybuilder. As a seasoned technology and business leader in both startup and enterprise settings, he has focused his career on delivering efficiency and value through automation, data science, machine learning, Artificial Intelligence, blockchain, quantum computing, and emerging concepts and innovative technologies. His development and implementation of programs and initiatives have enhanced multi-billion dollar programs and operational efficiencies across industries in commercial, federal, and defense. Aaron's roles have included CEO and co-founder of Prime.AI Senior Practice Manager for Artificial Intelligence System Integration at Amazon Web Services (AWS) Vice President Data & Artificial Intelligence at ECS Technology, Global Operation Director Analytics & Artificial Intelligence at HCL Technologies CTO at Analytics2Go Vice President Data Science & AI at Booz Allen Hamilton Global Analytics Platform Lead at AccentureSenior Research Scientist at Elder Research.Before turning his attention to commercial and public sectors, Aaron was a Marine Corps officer and Iraq war veteran and was the head operations research analyst and director of an operations analysis activity at the Pentagon, supporting the Marine Corps Headquarters. He is also a Marine Reservist, where he supports the Chief Information Officer at Headquarters Marine Corps-Pentagon as Lead Data Technologist. Aaron is a Forbes contributor, frequently invited keynote and speaker, and Certified Analytics Professional (CAP). He is an appointed member of the U.S. Department of Commerce's National Technology Information Service advisory board. Aaron received his M.S. Operations Research from the Naval Postgraduate School and his B.S. from the US Naval Academy.An Eagle Scout, graduate of the US Naval Academy, a Marine Combat Veteran, and proud father to 3 dauSupport the showFollow me on Facebook ⬇️https://www.facebook.com/manuj.aggarwal❤️ ID - Manuj Aggarwal■ LinkedIn: https://www.linkedin.com/in/manujaggarwal/ ■ Facebook: https://www.facebook.com/realmanuj■ Instagram: ...