Vanishing Gradients

 This is continual learning, right? Everyone has been talking about continual learning as the next challenge in AI. Actually, it’s solved. Just tell it to keep some notes somewhere. Sure, it’s not, it’s not machine learning, but in some ways it is because when it will load this text file again, it will influence what it does … And it works so well: it’s easy to understand. It’s easy to inspect, it’s easy to evolve and modify!Eleanor Berger and Isaac Flaath, the minds behind Elite AI Assisted Coding, join Hugo to talk about how to redefine software development through effective AI-assisted coding, leveraging “specification-first” approaches and advanced agentic workflows.We Discuss:* Markdown learning loops: Use simple agents.md files for agents to self-update rules and persist context, creating inspectable, low-cost learning;* Intent-first development: As AI commoditizes syntax, defining clear specs and what makes a result “good” becomes the core, durable developer skill;* Effortless documentation: Leverage LLMs to distill messy “brain dumps” or walks-and-talks into structured project specifications, offloading context faster;* Modular agent skills: Transition from MCP servers to simple markdown-based “skills” with YAML and scripts, allowing progressive disclosure of tool details;* Scheduled async agents: Break the chat-based productivity ceiling by using GitHub Actions or Cron jobs for agents to work on issues, shifting humans to reviewers;* Automated tech debt audits: Deploy background agents to identify duplicate code, architectural drift, or missing test coverage, leveraging AI to police AI-induced messiness;* Explicit knowledge culture: AI agents eliminate “cafeteria chat” by forcing explicit, machine-readable documentation, solving the perennial problem of lost institutional knowledge;* Tiered model strategy: Optimize token spend by using high-tier “reasoning” models (e.g., Opus) for planning and low-cost, high-speed models (e.g., Flash) for execution;* Ephemeral software specs: With near-zero generation costs, software shifts from static products to dynamic, regenerated code based on a permanent, underlying specification.You can also find the full episode on Spotify, Apple Podcasts, and YouTube.You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!👉 Eleanor & Isaac are teaching their next cohort of their Elite AI Assisted Coding course starting this week. They’re kindly giving readers of Vanishing Gradients 25% off. Use this link.👈👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Here is a discount code for readers. 👈Show Notes* Elite AI Assisted Coding Substack* Eleanor Berger on LinkedIn* Isaac Flaath on LinkedIn* Elite AI Assisted Coding Course (Use the code HUGO for 25% off)* How to Build an AI Agent with AI-Assisted Coding* Eleanor/Isaac’s blog post “The SpecFlow Process for AI Coding”* Eleanor’s growing list of (free) tutorials on Agent Skills* Eleanor’s YouTube playlist on agent skills* Eleanor’s blog post “Are (Agent) Skills the New Apps”* Simon Willison’s blog post on skills/general computer automation/data journalism agents* Eleanor/Isaac’s blog post about asynchronous client agents in GitHub actions* Eleanor/Isaac’s blog post on agentic coding workflows with Hang Yu, Product Lead for Qoder @ Alibaba* Upcoming Events on Luma* Vanishing Gradients on YouTube* Watch the podcast video on YouTube* Join the final cohort of our Building AI Applications course in Q1, 2026 (25% off for listeners)Timestamps (for YouTube livestream)00:00 Introduction to Elite AI Assisted Coding02:24 Starting a New AI Project: Best Practices03:19 The Importance of Context in AI Projects07:19 Specification-First Planning12:01 Sharing Intent and Documentation18:27 Living Documentation and Continual Learning24:36 Choosing the Right Tools and Models29:18 Managing Costs and Token Usage40:16 Using Different Models for Different Tasks43:41 Mastering One Model for Better Results44:54 The Rise of Agent Skills in 202645:34 Understanding the Importance of Skills47:18 Practical Applications of Agent Skills01:11:43 Security Concerns with AI Agents01:15:02 Collaborative AI-Assisted Coding01:18:59 Future of AI-Assisted Coding01:22:27 Key Takeaways for Effective AI-Assisted CodingLive workshop with Eleanor, Isaac, & HugoWe also recently did a 90-minute workshop on How to Build an AI Agent with AI-Assisted Coding.We wrote a blog post on it for those who don’t have 90 minutes right now. Check it out here.I then made a 4 min video about it all for those who don’t have time to read the blog post.👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Here is a discount code for readers. 👈https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vg-ei This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

Surprise. We don’t have agents. I actually went in and did an audit of all the LLM applications that we’ve developed internally. And if you were to take Anthropic’s definition of workflow versus agent, we don’t have agents. I would not classify any of our applications as agents. xEric Ma, who leads Research Data Science in the Data Science and AI group at Moderna, joins Hugo on moving past the hype of autonomous agents to build reliable, high-value workflows.We discuss:* Reliable Workflows: Prioritize rigid workflows over dynamic AI agents to ensure reliability and minimize stochasticity in production environments;* Permission Mapping: The true challenge in regulated environments is security, specifically mapping permissions across source documents, vector stores, and model weights;* Trace Log Risk: LLM execution traces pose a regulatory risk, inadvertently leaking restricted data like trade secrets or personal information;* High-Value Data Work: LLMs excel at transforming archived documents and freeform forms into required formats, offloading significant “janitorial” work from scientists;* “Non-LLM” First: Solve problems with simpler tools like Python or ML models before LLMs to ensure robustness and eliminate generative AI stochasticity;* Contextual Evaluation: Tailor evaluation rigor to consequences; low-stakes tools can be “vibe-checked,” while patient safety outputs demand exhaustive error characterization;* Serverless Biotech Backbone: Serverless infrastructure like Modal and reactive notebooks such as Marimo empowers biotech data scientists for rapid deployment without heavy infrastructure overhead.You can also find the full episode on Spotify, Apple Podcasts, and YouTube.You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Our final cohort is in Q1, 2206. Here is a 35% discount code for readers. 👈https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgch👉 Eric & Hugo have a free upcoming livestream workshop: Building Tools for Thinking with AI (register to join live or get the recording afterwards) 👈Show notes* Eric’s website* Eric Ma on LinkedIn* Eric’s blog* Eric’s data science newsletter* Building Effective AI Agents by the Anthropic team* Wow, Marimo from Eric’s blog* Wow, Modal from Eric’s blog* Upcoming Events on Luma* Watch the podcast video on YouTube* Join the final cohort of our Building AI Applications course in Q1, 2026 (35% off for listeners)Timestamps00:00 Defining Agents and Workflows02:04 Challenges in Regulated Environments04:24 Eric Ma's Role at Moderna, Leading Research Data Science in the Data Science and AI Group12:37 Document Reformatting and Automation15:42 Data Security and Permission Mapping20:05 Choosing the Right Model for Production20:41 Evaluating Model Changes with Benchmarks23:10 Vibe-Based Evaluation vs. Formal Testing27:22 Security and Fine-Tuning in LLMs28:45 Challenges and Future of Fine-Tuning34:00 Security Layers and Information Leakage37:48 Wrap-Up and Final Remarks👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Our final cohort is in Q1, 2026. Here is a 35% discount code for readers. 👈https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgch This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

We’re really moving from a world where humans are authoring search queries and humans are executing those queries and humans are digesting the results to a world where AI is doing that for us.Jeff Huber, CEO and co-founder of Chroma, joins Hugo to talk about how agentic search and retrieval are changing the very nature of search and software for builders and users alike.We Discuss:* “Context engineering”, the strategic design and engineering of what context gets fed to the LLM (data, tools, memory, and more), which is now essential for building reliable, agentic AI systems;* Why simply stuffing large context windows is no longer feasible due to “context rot” as AI applications become more goal-oriented and capable of multi-step tasks* A framework for precisely curating and providing only the most relevant, high-precision information to ensure accurate and dependable AI systems;* The “agent harness”, the collection of tools and capabilities an agent can access, and how to construct these advanced systems;* Emerging best practices for builders, including hybrid search as a robust default, creating “golden datasets” for evaluation, and leveraging sub-agents to break down complex tasks* The major unsolved challenge of agent evaluation, emphasizing a shift towards iterative, data-centric approaches.You can also find the full episode on Spotify, Apple Podcasts, and YouTube.You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Our final cohort is in Q1, 2206. Here is a 35% discount code for readers. 👈Oh! One more thing: we’ve just announced a Vanishing Gradients livestream for January 21 that you may dig:* A Builder’s Guide to Agentic Search & Retrieval with Doug Turnbull and John Berryman (register to join live or get the recording afterwards.Show notes* Jeff Huber on Twitter* Jeff Huber on LinkedIn* Try Chroma!* Context Rot: How Increasing Input Tokens Impacts LLM Performance by The Chroma Team* AI Agent Harness, 3 Principles for Context Engineering, and the Bitter Lesson Revisited* From Context Engineering to AI Agent Harnesses: The New Software Discipline* Generative Benchmarking by The Chroma Team* Effective context engineering for AI agents by The Anthropic Team* Making Sense of Millions of Conversations for AI Agents by Ivan Leo (Manus) and Hugo* How we built our multi-agent research system by The Anthropic Team* Upcoming Events on Luma* Watch the podcast video on YouTube👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Our final cohort is in Q1, 2206. Here is a 35% discount code for readers. 👈https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgch This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

We have been sold a story of complexity. Michael Kennedy (Talk Python) argues we can escape this by relentlessly focusing on the problem at hand, reducing costs by orders of magnitude in software, data, and AI.In this episode, Michael joins Hugo to dig into the practical side of running Python systems at scale. They connect these ideas to the data science workflow, exploring which software engineering practices allow AI teams to ship faster and with more confidence. They also detail how to deploy systems without unnecessary complexity and how Agentic AI is fundamentally reshaping development workflows.We talk through:- Escaping complexity hell to reduce costs and gain autonomy- The specific software practices, like the "Docker Barrier", that matter most for data scientists- How to replace complex cloud services with a simple, robust $30/month stack- The shift from writing code to "systems thinking" in the age of Agentic AI- How to manage the people-pleasing psychology of AI agents to prevent broken code- Why struggle is still essential for learning, even when AI can do the work for youLINKSTalk Python In Production, the Book! (https://talkpython.fm/books/python-in-production)Just Enough Python for Data Scientists Course (https://training.talkpython.fm/courses/just-enough-python-for-data-scientists)Agentic AI Programming for Python Course (https://training.talkpython.fm/courses/agentic-ai-programming-for-python)Talk Python To Me (https://talkpython.fm/) and a recent episode with Hugo as guest: Building Data Science with Foundation LLM Models (https://talkpython.fm/episodes/show/526/building-data-science-with-foundation-llm-models)Python Bytes podcast (https://pythonbytes.fm/)Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)Watch the podcast video on YouTube (https://youtube.com/live/jfSRxxO3aRo?feature=share)Join the final cohort of our Building AI Applications course starting Jan 12, 2026 (35% off for listeners) (https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgrav): https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgrav This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

Gemini 3 is a few days old and the massive leap in performance and model reasoning has big implications for builders: as models begin to self-heal, builders are literally tearing out the functionality they built just months ago... ripping out the defensive coding and reshipping their agent harnesses entirely.Ravin Kumar (Google DeepMind) joins Hugo to breaks down exactly why the rapid evolution of models like Gemini 3 is changing how we build software. They detail the shift from simple tool calling to building reliable "Agent Harnesses", explore the architectural tradeoffs between deterministic workflows and high-agency systems, the nuance of preventing context rot in massive windows, and why proper evaluation infrastructure is the only way to manage the chaos of autonomous loops.They talk through:- The implications of models that can "self-heal" and fix their own code- The two cultures of agents: LLM workflows with a few tools versus when you should unleash high-agency, autonomous systems.- Inside NotebookLM: moving from prototypes to viral production features like Audio Overviews- Why Needle in a Haystack benchmarks often fail to predict real-world performance- How to build agent harnesses that turn model capabilities into product velocity- The shift from measuring latency to managing time-to-compute for reasoning tasksLINKSFrom Context Engineering to AI Agent Harnesses: The New Software Discipline, a podcast Hugo did with Lance Martin, LangChain (https://high-signal.delphina.ai/episode/context-engineering-to-ai-agent-harnesses-the-new-software-discipline)Context Rot: How Increasing Input Tokens Impacts LLM Performance (https://research.trychroma.com/context-rot)Effective context engineering for AI agents by Anthropic (https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents)Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)Watch the podcast video on YouTube (https://youtu.be/CloimQsQuJM)Join the final cohort of our Building AI Applications course starting Jan 12, 2026 (https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgrav): https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgrav This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

Many leaders are trapped between chasing ambitious, ill-defined AI projects and the paralysis of not knowing where to start. Dr. Randall Olson argues that the real opportunity isn't in moonshots, but in the "trillions of dollars of business value" available right now. As co-founder of Wyrd Studios, he bridges the gap between data science, AI engineering, and executive strategy to deliver a practical framework for execution.In this episode, Randy and Hugo lay out how to find and solve what might be considered "boring but valuable" problems, like an EdTech company automating 20% of its support tickets with a simple retrieval bot instead of a complex AI tutor. They discuss how to move incrementally along the "agentic spectrum" and why treating AI evaluation with the same rigor as software engineering is non-negotiable for building a disciplined, high-impact AI strategy.They talk through:How a non-technical leader can prototype a complex insurance claim classifier using just photos and a ChatGPT subscription.The agentic spectrum: Why you should start by automating meeting summaries before attempting to build fully autonomous agents.The practical first step for any executive: Building a personal knowledge base with meeting transcripts and strategy docs to get tailored AI advice.Why treating AI evaluation with the same rigor as unit testing is essential for shipping reliable products.The organizational shift required to unlock long-term AI gains, even if it means a short-term productivity dip.LINKSRandy on LinkedIn (https://www.zenml.io/llmops-database)Wyrd Studios (https://thewyrdstudios.com/)Stop Building AI Agents (https://www.decodingai.com/p/stop-building-ai-agents)Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)Watch the podcast video on YouTube (https://youtu.be/-YQjKH3wRvc)🎓 Learn more:In Hugo's course: Building AI Applications for Data Scientists and Software Engineers (https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?promoCode=AI20) — https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?promoCode=AI20 Next cohort starts November 3: come build with us! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

Most AI teams find their multi-agent systems devolving into chaos, but ML Engineer Alex Strick van Linschoten argues they are ignoring the production reality. In this episode, he draws on insights from the LLM Ops Database (750+ real-world deployments then; now nearly 1,000!) to systematically measure and engineer constraint, turning unreliable prototypes into robust, enterprise-ready AI.Drawing from his work at Zen ML, Alex details why success requires scaling down and enforcing MLOps discipline to navigate the unpredictable "Agent Reliability Cliff". He provides the essential architectural shifts, evaluation hygiene techniques, and practical steps needed to move beyond guesswork and build scalable, trustworthy AI products.We talk through:- Why "shoving a thousand agents" into an app is the fastest route to unmanageable chaos- The essential MLOps hygiene (tracing and continuous evals) that most teams skip- The optimal (and very low) limit for the number of tools an agent can reliably use- How to use human-in-the-loop strategies to manage the risk of autonomous failure in high-sensitivity domains- The principle of using simple Python/RegEx before resorting to costly LLM judgesLINKSThe LLMOps Database: 925 entries as of today....submit a use case to help it get to 1K! (https://www.zenml.io/llmops-database)Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)Watch the podcast video on YouTube (https://youtu.be/-YQjKH3wRvc)🎓 Learn more:-This was a guest Q&A from Building LLM Applications for Data Scientists and Software Engineers (https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?promoCode=AI20) — https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?promoCode=AI20 Next cohort starts November 3: come build with us! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

Most AI teams find "evals" frustrating, but ML Engineer Hamel Husain argues they’re just using the wrong playbook. In this episode, he lays out a data-centric approach to systematically measure and improve AI, turning unreliable prototypes into robust, production-ready systems.Drawing from his experience getting countless teams unstuck, Hamel explains why the solution requires a "revenge of the data scientists." He details the essential mindset shifts, error analysis techniques, and practical steps needed to move beyond guesswork and build AI products you can actually trust.We talk through: The 10(+1) critical mistakes that cause teams to waste time on evals Why "hallucination scores" are a waste of time (and what to measure instead) The manual review process that finds major issues in hours, not weeks A step-by-step method for building LLM judges you can actually trust How to use domain experts without getting stuck in endless review committees Guest Bryan Bischof's "Failure as a Funnel" for debugging complex AI agentsIf you're tired of ambiguous "vibe checks" and want a clear process that delivers real improvement, this episode provides the definitive roadmap.LINKSHamel's website and blog (https://hamel.dev/)Hugo speaks with Philip Carter (Honeycomb) about aligning your LLM-as-a-judge with your domain expertise (https://vanishinggradients.fireside.fm/51)Hamel Husain on Lenny's pocast, which includes a live demo of error analysis (https://www.lennysnewsletter.com/p/why-ai-evals-are-the-hottest-new-skill)The episode of VG in which Hamel and Hugo talk about Hamel's "data consulting in Vegas" era (https://vanishinggradients.fireside.fm/9)Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)Watch the podcast video on YouTube (https://youtube.com/live/QEk-XwrkqhI?feature=share)Hamel's AI evals course, which he teaches with Shreya Shankar (UC Berkeley): starts Oct 6 and this link gives 35% off! (https://maven.com/parlance-labs/evals?promoCode=GOHUGORGOHOME) https://maven.com/parlance-labs/evals?promoCode=GOHUGORGOHOME🎓 Learn more:Hugo's course: Building LLM Applications for Data Scientists and Software Engineers (https://maven.com/s/course/d56067f338) — https://maven.com/s/course/d56067f338 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

John Berryman (Arcturus Labs; early GitHub Copilot engineer; co-author of Relevant Search and Prompt Engineering for LLMs) has spent years figuring out what makes AI applications actually work in production. In this episode, he shares the “seven deadly sins” of LLM development — and the practical fixes that keep projects from stalling. From context management to retrieval debugging, John explains the patterns he’s seen succeed, the mistakes to avoid, and why it helps to think of an LLM as an “AI intern” rather than an all-knowing oracle. We talk through: - Why chasing perfect accuracy is a dead end - How to use agents without losing control - Context engineering: fitting the right information in the window - Starting simple instead of over-orchestrating - Separating retrieval from generation in RAG - Splitting complex extractions into smaller checks - Knowing when frameworks help — and when they slow you down A practical guide to avoiding the common traps of LLM development and building systems that actually hold up in production.LINKS:Context Engineering for AI Agents, a free, upcoming lightning lesson from John and Hugo (https://maven.com/p/4485aa/context-engineering-for-ai-agents)The Hidden Simplicity of GenAI Systems, a previous lightning lesson from John and Hugo (https://maven.com/p/a8195d/the-hidden-simplicity-of-gen-ai-systems)Roaming RAG – RAG without the Vector Database, by John (https://arcturus-labs.com/blog/2024/11/21/roaming-rag--rag-without-the-vector-database/)Cut the Chit-Chat with Artifacts, by John (https://arcturus-labs.com/blog/2024/11/11/cut-the-chit-chat-with-artifacts/)Prompt Engineering for LLMs by John and Albert Ziegler (https://amzn.to/4gChsFf)Relevant Search by John and Doug Turnbull (https://amzn.to/3TXmDHk)Arcturus Labs (https://arcturus-labs.com/)Watch the podcast on YouTube (https://youtu.be/mKTQGKIUq8M)Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)🎓 Learn more:Hugo's course (this episode was a guest Q&A from the course): Building LLM Applications for Data Scientists and Software Engineers (https://maven.com/s/course/d56067f338) — https://maven.com/s/course/d56067f338 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

While most conversations about generative AI focus on chatbots, Thomas Wiecki (PyMC Labs, PyMC) has been building systems that help companies make actual business decisions. In this episode, he shares how Bayesian modeling and synthetic consumers can be combined with LLMs to simulate customer reactions, guide marketing spend, and support strategy. Drawing from his work with Colgate and others, Thomas explains how to scale survey methods with AI, where agents fit into analytics workflows, and what it takes to make these systems reliable. We talk through: Using LLMs as “synthetic consumers” to simulate surveys and test product ideas How Bayesian modeling and causal graphs enable transparent, trustworthy decision-making Building closed-loop systems where AI generates and critiques ideas Guardrails for multi-agent workflows in marketing mix modeling Where generative AI breaks (and how to detect failure modes) The balance between useful models and “correct” models If you’ve ever wondered how to move from flashy prototypes to AI systems that actually inform business strategy, this episode shows what it takes. LINKS:The AI MMM Agent, An AI-Powered Shortcut to Bayesian Marketing Mix Insights (https://www.pymc-labs.com/blog-posts/the-ai-mmm-agent)AI-Powered Decision Making Under Uncertainty Workshop w/ Allen Downey & Chris Fonnesbeck (PyMC Labs) (https://youtube.com/live/2Auc57lxgeU)The Podcast livestream on YouTube (https://youtube.com/live/so4AzEbgSjw?feature=share)Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)🎓 Learn more:Hugo's course: Building LLM Applications for Data Scientists and Software Engineers (https://maven.com/s/course/d56067f338) — https://maven.com/s/course/d56067f338 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

While many people talk about “agents,” Shreya Shankar (UC Berkeley) has been building the systems that make them reliable. In this episode, she shares how AI agents and LLM judges can be used to process millions of documents accurately and cheaply. Drawing from work on projects ranging from databases of police misconduct reports to large-scale customer transcripts, Shreya explains the frameworks, error analysis, and guardrails needed to turn flaky LLM outputs into trustworthy pipelines. We talk through: - Treating LLM workflows as ETL pipelines for unstructured text - Error analysis: why you need humans reviewing the first 50–100 traces - Guardrails like retries, validators, and “gleaning” - How LLM judges work — rubrics, pairwise comparisons, and cost trade-offs - Cheap vs. expensive models: when to swap for savings - Where agents fit in (and where they don’t) If you’ve ever wondered how to move beyond unreliable demos, this episode shows how to scale LLMs to millions of documents — without breaking the bank.LINKSShreya's website (https://www.sh-reya.com/)DocETL, A system for LLM-powered data processing (https://www.docetl.org/)Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)Watch the podcast video on YouTube (https://youtu.be/3r_Hsjy85nk)Shreya's AI evals course, which she teaches with Hamel "Evals" Husain (https://maven.com/parlance-labs/evals?promoCode=GOHUGORGOHOME)🎓 Learn more:Hugo's course: Building LLM Applications for Data Scientists and Software Engineers (https://maven.com/s/course/d56067f338) — https://maven.com/s/course/d56067f338 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

While much of the AI world chases ever-larger models, Ravin Kumar (Google DeepMind) and his team build across the size spectrum, from billions of parameters down to this week’s release: Gemma 270M, the smallest member yet of the Gemma 3 open-weight family. At just 270 million parameters, a quarter the size of Gemma 1B, it’s designed for speed, efficiency, and fine-tuning. We explore what makes 270M special, where it fits alongside its billion-parameter siblings, and why you might reach for it in production even if you think “small” means “just for experiments.” We talk through: - Where 270M fits into the Gemma 3 lineup — and why it exists - On-device use cases where latency, privacy, and efficiency matter - How smaller models open up rapid, targeted fine-tuning - Running multiple models in parallel without heavyweight hardware - Why “small” models might drive the next big wave of AI adoption If you’ve ever wondered what you’d do with a model this size (or how to squeeze the most out of it) this episode will show you how small can punch far above its weight.LINKSIntroducing Gemma 3 270M: The compact model for hyper-efficient AI (Google Developer Blog) (https://developers.googleblog.com/en/introducing-gemma-3-270m/)Full Model Fine-Tune Guide using Hugging Face Transformers (https://ai.google.dev/gemma/docs/core/huggingface_text_full_finetune)The Gemma 270M model on HuggingFace (https://huggingface.co/google/gemma-3-270m)The Gemma 270M model on Ollama (https://ollama.com/library/gemma3:270m)Building AI Agents with Gemma 3, a workshop with Ravin and Hugo (https://www.youtube.com/live/-IWstEStqok) (Code here (https://github.com/canyon289/ai_agent_basics))From Images to Agents: Building and Evaluating Multimodal AI Workflows, a workshop with Ravin and Hugo (https://www.youtube.com/live/FNlM7lSt8Uk)(Code here (https://github.com/canyon289/ai_image_agent))Evaluating AI Agents: From Demos to Dependability, an upcoming workshop with Ravin and Hugo (https://lu.ma/ezgny3dl)Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)Watch the podcast video on YouTube (https://youtu.be/VZDw6C2A_8E)🎓 Learn more:Hugo's course: Building LLM Applications for Data Scientists and Software Engineers (https://maven.com/s/course/d56067f338) — https://maven.com/s/course/d56067f338 ($600 off early bird discount for November cohort availiable until August 16) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

Traditional software expects 100% passing tests. In LLM-powered systems, that’s not just unrealistic — it’s a feature, not a bug. Eric Ma leads research data science in Moderna’s data science and AI group, and over breakfast at SciPy we explored why AI products break the old rules, what skills different personas bring (and miss), and how to keep systems alive after the launch hype fades. You’ll hear the clink of coffee cups, the murmur of SciPy in the background, and the occasional bite of frittata as we talk (hopefully also a feature, not a bug!)We talk through: • The three personas — and the blind spots each has when shipping AI systems • Why “perfect” tests can be a sign you’re testing the wrong thing • Development vs. production observability loops — and why you need both • How curiosity about failing data separates good builders from great ones • Ways large organizations can create space for experimentation without losing delivery focus If you want to build AI products that thrive in the messy real world, this episode will help you embrace the chaos — and make it work for you.LINKSEric' Website (https://ericmjl.github.io/)More about the workshops Eric and Hugo taught at SciPy (https://hugobowne.substack.com/p/stress-testing-llms-evaluation-frameworks)Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)🎓 Learn more:Hugo's course: Building LLM Applications for Data Scientists and Software Engineers (https://maven.com/s/course/d56067f338) — https://maven.com/s/course/d56067f338 ($600 off early bird discount for November cohort availiable until August 16) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

Colab is cozy. But production won’t fit on a single GPU.Zach Mueller leads Accelerate at Hugging Face and spends his days helping people go from solo scripts to scalable systems. In this episode, he joins me to demystify distributed training and inference — not just for research labs, but for any ML engineer trying to ship real software.We talk through: • From Colab to clusters: why scaling isn’t just about training massive models, but serving agents, handling load, and speeding up iteration • Zero-to-two GPUs: how to get started without Kubernetes, Slurm, or a PhD in networking • Scaling tradeoffs: when to care about interconnects, which infra bottlenecks actually matter, and how to avoid chasing performance ghosts • The GPU middle class: strategies for training and serving on a shoestring, with just a few cards or modest credits • Local experiments, global impact: why learning distributed systems—even just a little—can set you apart as an engineerIf you’ve ever stared at a Hugging Face training script and wondered how to run it on something more than your laptop: this one’s for you.LINKSZach on LinkedIn (https://www.linkedin.com/in/zachary-mueller-135257118/)Hugo's blog post on Stop Buliding AI Agents (https://www.linkedin.com/posts/hugo-bowne-anderson-045939a5_yesterday-i-posted-about-stop-building-ai-activity-7346942036752613376-b8-t/)Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)Hugo's recent newsletter about upcoming events and more! (https://hugobowne.substack.com/p/stop-building-agents)🎓 Learn more:Hugo's course: Building LLM Applications for Data Scientists and Software Engineers (https://maven.com/s/course/d56067f338) — https://maven.com/s/course/d56067f338Zach's course (45% off for VG listeners!): Scratch to Scale: Large-Scale Training in the Modern World (https://maven.com/walk-with-code/scratch-to-scale?promoCode=hugo39) -- https://maven.com/walk-with-code/scratch-to-scale?promoCode=hugo39📺 Watch the video version on YouTube: YouTube link (https://youtube.com/live/76NAtzWZ25s?feature=share) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

Demos are easy; durability is hard. Samuel Colvin has spent a decade building guardrails in Python (first with Pydantic, now with Logfire), and he’s convinced most LLM failures have nothing to do with the model itself. They appear where the data is fuzzy, the prompts drift, or no one bothered to measure real-world behavior. Samuel joins me to show how a sprinkle of engineering discipline keeps those failures from ever reaching users.We talk through: • Tiny labels, big leverage: how five thumbs-ups/thumbs-downs are enough for Logfire to build a rubric that scores every call in real time • Drift alarms, not dashboards: catching the moment your prompt or data shifts instead of reading charts after the fact • Prompt self-repair: a prototype agent that rewrites its own system prompt—and tells you when it still doesn’t have what it needs • The hidden cost curve: why the last 15 percent of reliability costs far more than the flashy 85 percent demo • Business-first metrics: shipping features that meet real goals instead of chasing another decimal point of “accuracy”If you’re past the proof-of-concept stage and staring down the “now it has to work” cliff, this episode is your climbing guide.LINKSPydantic (https://pydantic.dev/)Logfire (https://pydantic.dev/logfire)Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)Hugo's recent newsletter about upcoming events and more! (https://hugobowne.substack.com/p/stop-building-agents)🎓 Learn more:Hugo's course: Building LLM Applications for Data Scientists and Software Engineers (https://maven.com/s/course/d56067f338) — next cohort starts July 8: https://maven.com/s/course/d56067f338📺 Watch the video version on YouTube: YouTube link (https://youtube.com/live/wk6rPZ6qJSY?feature=share) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

Most LLM-powered features do not break at the model. They break at the context. So how do you retrieve the right information to get useful results, even under vague or messy user queries?In this episode, we hear from Eric Ma, who leads data science research in the Data Science and AI group at Moderna. He shares what it takes to move beyond toy demos and ship LLM features that actually help people do their jobs.We cover:• How to align retrieval with user intent and why cosine similarity is not the answer• How a dumb YAML-based system outperformed so-called smart retrieval pipelines• Why vague queries like “what is this all about” expose real weaknesses in most systems• When vibe checks are enough and when formal evaluation is worth the effort• How retrieval workflows can evolve alongside your product and user needsIf you are building LLM-powered systems and care about how they work, not just whether they work, this one is for you.LINKSEric's website (https://ericmjl.github.io/)Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)Hugo's recent newsletter about upcoming events and more! (https://hugobowne.substack.com/p/stop-building-agents)🎓 Learn more:Hugo's course: Building LLM Applications for Data Scientists and Software Engineers (https://maven.com/s/course/d56067f338) — next cohort starts July 8: https://maven.com/s/course/d56067f338📺 Watch the video version on YouTube: YouTube link (https://youtu.be/d-FaR5Ywd5k) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

What does it take to actually ship LLM-powered features, and what breaks when you connect them to real production data?In this episode, we hear from Philip Carter — then a Principal PM at Honeycomb and now a Product Management Director at Salesforce. In early 2023, he helped build one of the first LLM-powered SaaS features to ship to real users. More recently, he and his team built a production-ready MCP server.We cover: • How to evaluate LLM systems using human-aligned judges • The spreadsheet-driven process behind shipping Honeycomb’s first LLM feature • The challenges of tool usage, prompt templates, and flaky model behavior • Where MCP shows promise, and where it breaks in the real worldIf you’re working on LLMs in production, this one’s for you!LINKSSo We Shipped an AI Product: Did it Work? by Philip Carter (https://www.honeycomb.io/blog/we-shipped-ai-product)Vanishing Gradients YouTube Channel (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)Hugo's recent newsletter about upcoming events and more! (https://hugobowne.substack.com/p/ai-as-a-civilizational-technology)🎓 Learn more:Hugo's course: Building LLM Applications for Data Scientists and Software Engineers (https://maven.com/s/course/d56067f338) — next cohort starts July 8: https://maven.com/s/course/d56067f338📺 Watch the video version on YouTube: YouTube link (https://youtu.be/JDMzdaZh9Ig) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

If we want AI systems that actually work, we need to get much better at evaluating them, not just building more pipelines, agents, and frameworks.In this episode, Hugo talks with Hamel Hussain (ex-Airbnb, GitHub, DataRobot) about how teams can improve AI products by focusing on error analysis, data inspection, and systematic iteration. The conversation is based on Hamel’s blog post A Field Guide to Rapidly Improving AI Products, which he joined Hugo’s class to discuss.They cover:🔍 Why most teams struggle to measure whether their systems are actually improving 📊 How error analysis helps you prioritize what to fix (and when to write evals) 🧮 Why evaluation isn’t just a metric — but a full development process ⚠️ Common mistakes when debugging LLM and agent systems 🛠️ How to think about the tradeoffs in adding more evals vs. fixing obvious issues 👥 Why enabling domain experts — not just engineers — can accelerate iterationIf you’ve ever built an AI system and found yourself unsure how to make it better, this conversation is for you.LINKS* A Field Guide to Rapidly Improving AI Products by Hamel Husain (https://hamel.dev/blog/posts/field-guide/)* Vanishing Gradients YouTube Channel (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA) * Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)* Hugo's recent newsletter about upcoming events and more! (https://hugobowne.substack.com/p/ai-as-a-civilizational-technology)🎓 Learn more:Hugo's course: Building LLM Applications for Data Scientists and Software Engineers (https://maven.com/s/course/d56067f338) — next cohort starts July 8: https://maven.com/s/course/d56067f338Hamel & Shreya's course: AI Evals For Engineers & PMs (https://maven.com/parlance-labs/evals?promoCode=GOHUGORGOHOME) — use code GOHUGORGOHOME for $800 off📺 Watch the video version on YouTube: YouTube link (https://youtu.be/rWToRi2_SeY) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

If we want AI systems that actually work in production, we need better infrastructure—not just better models.In this episode, Hugo talks with Akshay Agrawal (Marimo, ex-Google Brain, Netflix, Stanford) about why data and AI pipelines still break down at scale, and how we can fix the fundamentals: reproducibility, composability, and reliable execution.They discuss:🔁 Why reactive execution matters—and how current tools fall short🛠️ The design goals behind Marimo, a new kind of Python notebook⚙️ The hidden costs of traditional workflows (and what breaks at scale)📦 What it takes to build modular, maintainable AI apps🧪 Why debugging LLM systems is so hard—and what better tooling looks like🌍 What we can learn from decades of tools built for and by data practitionersToward the end of the episode, Hugo and Akshay walk through two live demos: Hugo shares how he’s been using Marimo to prototype an app that extracts structured data from world leader bios, and Akshay shows how Marimo handles agentic workflows with memory and tool use—built entirely in a notebook.This episode is about tools, but it’s also about culture. If you’ve ever hit a wall with your current stack—or felt like your tools were working against you—this one’s for you.LINKS* marimo | a next-generation Python notebook (https://marimo.io/)* SciPy conference, 2025 (https://www.scipy2025.scipy.org/)* Hugo's face Marimo World Leader Face Embedding demo (https://www.youtube.com/watch?v=DO21QEcLOxM)* Vanishing Gradients YouTube Channel (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA) * Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)* Hugo's recent newsletter about upcoming events and more! (https://hugobowne.substack.com/p/ai-as-a-civilizational-technology)* Watch the podcast here on YouTube! (https://youtube.com/live/WVxAz19tgZY?feature=share)🎓 Want to go deeper?Check out Hugo's course: Building LLM Applications for Data Scientists and Software Engineers.Learn how to design, test, and deploy production-grade LLM systems — with observability, feedback loops, and structure built in.This isn’t about vibes or fragile agents. It’s about making LLMs reliable, testable, and actually useful.Includes over $800 in compute credits and guest lectures from experts at DeepMind, Moderna, and more.Cohort starts July 8 — Use this link for a 10% discount (https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?promoCode=LLM10) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

If we want to make progress toward AGI, we need a clear definition of intelligence—and a way to measure it.In this episode, Hugo talks with Greg Kamradt, President of the ARC Prize Foundation, about ARC-AGI: a benchmark built on Francois Chollet’s definition of intelligence as “the efficiency at which you learn new things.” Unlike most evals that focus on memorization or task completion, ARC is designed to measure generalization—and expose where today’s top models fall short.They discuss:🧠 Why we still lack a shared definition of intelligence🧪 How ARC tasks force models to learn novel skills at test time📉 Why GPT-4-class models still underperform on ARC🔎 The limits of traditional benchmarks like MMLU and Big-Bench⚙️ What the OpenAI O₃ results reveal—and what they don’t💡 Why generalization and efficiency, not raw capability, are key to AGIGreg also shares what he’s seeing in the wild: how startups and independent researchers are using ARC as a North Star, how benchmarks shape the frontier, and why the ARC team believes we’ll know we’ve reached AGI when humans can no longer write tasks that models can’t solve.This conversation is about evaluation—not hype. If you care about where AI is really headed, this one’s worth your time.LINKS* ARC Prize -- What is ARC-AGI? (https://arcprize.org/arc-agi)* On the Measure of Intelligence by François Chollet (https://arxiv.org/abs/1911.01547)* Greg Kamradt on Twitter (https://x.com/GregKamradt)* Hugo's High Signal Podcast with Fei-Fei Li (https://high-signal.delphina.ai/episode/fei-fei-on-how-human-centered-ai-actually-gets-built)* Vanishing Gradients YouTube Channel (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA) * Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)* Hugo's recent newsletter about upcoming events and more! (https://hugobowne.substack.com/p/ai-as-a-civilizational-technology)* Watch the podcast here on YouTube! (https://youtu.be/wU82fz4iRfo)🎓 Want to go deeper?Check out Hugo's course: Building LLM Applications for Data Scientists and Software Engineers.Learn how to design, test, and deploy production-grade LLM systems — with observability, feedback loops, and structure built in.This isn’t about vibes or fragile agents. It’s about making LLMs reliable, testable, and actually useful.Includes over $800 in compute credits and guest lectures from experts at DeepMind, Moderna, and more.Cohort starts July 8 — Use this link for a 10% discount (https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?promoCode=LLM10) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com