Vanishing Gradients

Nicolay Gerold works all day and night on AMP, one of the most interesting coding-agent harnesses out there. If you’re building with coding agents, this conversation will help you understand: * when to trust the model, * when to build harnesses around it, * which model is worth paying for, * which programming languages gives the agent better feedback, and * when to take the keyboard back. Coding-a...

So I think we’re really at a historical moment, and the opportunity is massive. Almost 15 years ago, we were promised that data science was going to be this incredible thing and create all this value for people. And I think nowadays it’s mostly viewed as a cost center in most companies. I think we can really now fulfill that original promise with agentic data science. Thomas Wiecki , Co-creator of...

One thing that I don’t like about Claude is that you get into this weird mental state: oh, I think I trust the model. Let’s do the slot machine. Hit click, which puts you in an inactive mode of thinking.  Maybe it’s better to use a worse model…. Vincent Warmerdam , senior data professional and prolific open-source maintainer (some packages with over a million downloads), now Engineer at marimo , j...

> I almost don’t read code now . My approach with Roborev is it’s like my code reader. The mantra is: Roborev reads every line of code that is generated . It gets read multiple times. And so, whenever I push up a pull request, the branch gets re-reviewed. And so by the time I’m merging a pull request into a repository, the code has all been read by agents four or five times minimum. I look at the...

There are a lot of reasons why we should do AI evals. For many companies doing AI evals is the way to build the feedback loop into the product development lifecycle. So it is like your compass. We’re using AI evals as a compass to guide product development and also product iteration. And also, many times we need evals to function as the pass or fail gate in release decisions. Whether this product...

Katharine Jarmul , Privacy in ML/AI Expert & Author of Practical Data Privacy , joins Hugo to unpack why most AI privacy advice is theater: and what technical privacy actually looks like when you’re shipping LLMs, agents, and multimodal systems into the real world. In this episode, we dig into how to build defensible systems in an era of AI agents and multimodal models : why system prompts (and yo...

If you take a model release as an anchor point , let’s say Nemotron 3 or Qwen 3.5, you can go in both directions : You can either plug them into an agent and play around with that, or you can look, okay, what does the model look like under the hood ? What are the ingredients? What type of attention mechanism do they use? What are currently research techniques that could make that even better in th...

I often see what I would consider to be b******t evals , especially in data, like write this dumb SQL . Almost every one of these dumb SQL questions that I’ve seen for benchmarks are just so either obviously easy or overwhelmingly adversarial. They just, they don’t feel valuable as a data scientist , it’s something that you probably would never ask a real data scientist to do. So I went out my way...

Our thesis is that AI is still just engineering … those people who tell us for fun and profit, that somehow AI is so, so profound, so new, so different from anything that’s gone before that it somehow eclipses the need for good engineering practice are wrong. We need that good engineering practice still, and for the most part, most things are not new. But there are some things that have become mor...

There’s a company who spent almost $50,000 because an agent went into an infinite loop and they forgot about it for a month. It had no failures and I guess no one was monitoring these costs. It’s nice that people do write about that in the database as well. After it happened, they said: watch out for infinite loops. Watch out for cascading tool failures. Watch out for silent failures where the age...

> It’s the agent writing the code. And it’s the development loop of writing the code, building testing, write the code, build test and iterating. And so I do think we’ll see for many types of software, a shift away from Python towards other programming languages . I think Go is probably the best language for those like other types of software projects. And like I said, I haven’t written a line of...

The best way to build a horrible search product? Don’t ever measure anything against what a user wants. Search veterans Doug Turnbull (Led Search at Reddit + Shopify; Wrote Relevant Search + AI Powered Search ) and John Berryman (Early Engineer on Github Copilot; Author of Relevant Search + Prompt Engineering for LLMs ), join Hugo to talk about how to build Agentic Search Applications. We Discuss:...

  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...

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. x Eric Ma , who leads Research Data Science in the Data Science and AI group at Moderna , joins Hugo on moving past th...

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...

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 engine...

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 o...

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 practic...

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...

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 d...

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...

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, Tho...

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,...

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 270...

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 he...