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PODCAST

Vanishing Gradients

Hugo Bowne-Anderson

A podcast about all things data & AI, brought to you by AI builder, consultant, and educator Hugo Bowne-Anderson. It"s time for more critical conversations about the challenges in our industry in order to build better compasses for the solution space! To this end, this podcast will consist of long-format conversations between Hugo and other people who work broadly in the data science, machine learning, and AI spaces. We"ll dive deep into all the moving parts of the data world, so if you"re new to

technology

All Episodes

88:42

Episode 68: A Builder’s Guide to Agentic Search &...

Vanishing Gradients ·

2026/01/23

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:* The evolution of information retrieval as it moves from traditional keyword search toward “agentic search“

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78:22

Episode 67: Saving Hundreds of Hours of Dev Time with...

Vanishing Gradients ·

2026/01/14

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,

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42:58

Episode 66: The Agent Paradox - Why Moderna's Most...

Vanishing Gradients ·

2026/01/08

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:

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51:53

The Rise of Agentic Search

Vanishing Gradients ·

2025/12/19

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,

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1:02:56

Episode 64: Data Science Meets Agentic AI with...

Vanishing Gradients ·

2025/12/03

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

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1:00:12

Episode 63: Why Gemini 3 Will Change How You Build AI...

Vanishing Gradients ·

2025/11/22

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

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59:04

Episode 62: Practical AI at Work: How Execs and...

Vanishing Gradients ·

2025/10/31

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

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28:04

Episode 61: The AI Agent Reliability Cliff: What...

Vanishing Gradients ·

2025/10/16

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

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1:13:15

Episode 60: 10 Things I Hate About AI Evals with...

Vanishing Gradients ·

2025/09/30

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

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47:37

Episode 59: Patterns and Anti-Patterns For Building...

Vanishing Gradients ·

2025/09/23

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

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1:00:45

Episode 58: Building GenAI Systems That Make Business...

Vanishing Gradients ·

2025/09/09

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

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41:27

Episode 57: AI Agents and LLM Judges at Scale:...

Vanishing Gradients ·

2025/08/29

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

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45:40

Episode 56: DeepMind Just Dropped Gemma 270M... And...

Vanishing Gradients ·

2025/08/14

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

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38:08

Episode 55: From Frittatas to Production LLMs:...

Vanishing Gradients ·

2025/08/12

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

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41:17

Episode 54: Scaling AI: From Colab to Clusters — A...

Vanishing Gradients ·

2025/07/18

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

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44:49

Episode 53: Human-Seeded Evals & Self-Tuning Agents:...

Vanishing Gradients ·

2025/07/08

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

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28:38

Episode 52: Why Most LLM Products Break at Retrieval...

Vanishing Gradients ·

2025/07/02

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

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47:41

Episode 51: Why We Built an MCP Server and What Broke...

Vanishing Gradients ·

2025/06/26

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

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27:42

Episode 50: A Field Guide to Rapidly Improving AI...

Vanishing Gradients ·

2025/06/17

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

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1:21:45

Episode 49: Why Data and AI Still Break at Scale (and...

Vanishing Gradients ·

2025/06/05

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 ⚙️

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68 results

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