Vanishing Gradients

Episode 62: Practical AI at Work: How Execs and Developers Can Actually Use LLMs

October 31st, 2025  |  59 mins 4 secs

agents, ai, data science, machine learning

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.

Episode 60: 10 Things I Hate About AI Evals with Hamel Husain

September 30th, 2025  |  1 hr 13 mins

ai, data science, evals, genai, llms, machine learning

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.

Episode 59: Patterns and Anti-Patterns For Building with AI

September 24th, 2025  |  47 mins 37 secs

ai, data science, machine learning

John Berryman has seen what works — and what breaks — when building AI applications. In this episode, he shares the “seven deadly sins” of LLM development and the fixes that keep projects from falling apart. 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.

Episode 55: From Frittatas to Production LLMs: Breakfast at SciPy

August 13th, 2025  |  38 mins 8 secs

data science, generative ai, llm, machine learning, scipy

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.

Episode 52: Why Most LLM Products Break at Retrieval (And How to Fix Them)

July 3rd, 2025  |  Season 1  |  28 mins 38 secs

ai, data science, genai, llms, machine learning

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.

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

June 27th, 2025  |  Season 1  |  47 mins 41 secs

ai, data science, llms, machine learning

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.

Episode 50: A Field Guide to Rapidly Improving AI Products -- With Hamel Husain

June 17th, 2025  |  Season 1  |  27 mins 42 secs

ai, data science, evals, llms, machine learning

Hugo talks with Hamel Hussain (ex-Airbnb, GitHub, DataRobot) about how to improve AI products through evaluation, error analysis, and iteration. They discuss why most teams overlook debugging LLM systems, how to prioritize what to fix, and why evals are not just metrics—but a full development process.

Episode 49: Why Data and AI Still Break at Scale (and What to Do About It)

June 5th, 2025  |  Season 1  |  1 hr 21 mins

ai, data science, llms, machine learning

Hugo talks with Akshay Agrawal (Marimo, ex-Google Brain, Netflix, Stanford) about why data and AI systems still break at scale—and what it takes to fix them. They dive into the limits of existing workflows, the importance of reproducibility and reactive execution, and how Marimo reimagines notebooks for modern software development.

Episode 48: HOW TO BENCHMARK AGI WITH GREG KAMRADT

May 23rd, 2025  |  Season 1  |  1 hr 4 mins

agi, ai, data science, machine learning

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.

Episode 47: The Great Pacific Garbage Patch of Code Slop with Joe Reis

April 7th, 2025  |  Season 1  |  1 hr 19 mins

ai, data science, genai, llms, machine learning, vibe coding

What if the cost of writing code dropped to zero — but the cost of understanding it skyrocketed?

In this episode, Hugo sits down with Joe Reis to unpack how AI tooling is reshaping the software development lifecycle — from experimentation and prototyping to deployment, maintainability, and everything in between.