Vanishing Gradients

Episode 54: Scaling AI: From Colab to Clusters — A Practitioner’s Guide to Distributed Training and Inference

July 19th, 2025  |  Season 1  |  41 mins 17 secs

ai, compute, genai, llm

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.

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.

Episode 46: Software Composition Is the New Vibe Coding

April 3rd, 2025  |  Season 1  |  1 hr 8 mins

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

What if building software felt more like composing than coding?

In this episode, Hugo and Greg explore how LLMs are reshaping the way we think about software development—from deterministic programming to a more flexible, prompt-driven, and collaborative style of building. It’s not just hype or grift—it’s a real shift in how we express intent, reason about systems, and collaborate across roles.

Hugo speaks with Greg Ceccarelli—co-founder of SpecStory, former CPO at Pluralsight, and Director of Data Science at GitHub—about the rise of software composition and how it changes the way individuals and teams create with LLMs.

Episode 44: The Future of AI Coding Assistants: Who’s Really in Control?

February 4th, 2025  |  Season 1  |  1 hr 34 mins

ai, data science, llms, machine learning

AI coding assistants are reshaping how developers write, debug, and maintain code—but who’s really in control? In this episode, Hugo speaks with Tyler Dunn, CEO and co-founder of Continue, an open-source AI-powered code assistant that gives developers more customization and flexibility in their workflows.

Episode 43: Tales from 400+ LLM Deployments: Building Reliable AI Agents in Production

January 17th, 2025  |  Season 1  |  1 hr 1 min

ai, data science, llms, machine learning

Hugo speaks with Alex Strick van Linschoten, Machine Learning Engineer at ZenML and creator of a comprehensive LLMOps database documenting over 400 deployments. Alex's extensive research into real-world LLM implementations gives him unique insight into what actually works—and what doesn't—when deploying AI agents in production.