Vanishing Gradients

Episode 63: Why Gemini 3 Will Change How You Build AI Agents with Ravin Kumar (Google DeepMind)

November 22nd, 2025  |  1 hr 12 secs

ai, genai, machine learning

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 their 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.

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 56: DeepMind Just Dropped Gemma 270M... And Here’s Why It Matters

August 15th, 2025  |  45 mins 40 secs

genai, llms, machine learning

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.”

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 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 42: Learning, Teaching, and Building in the Age of AI

January 4th, 2025  |  Season 1  |  1 hr 20 mins

ai, data science, genai, llms, machine learning

The tables turn as Hugo sits down with Alex Andorra, host of Learning Bayesian Statistics. Hugo shares his journey from mathematics to AI, reflecting on how Bayesian inference shapes his approach to data science, teaching, and building AI-powered applications.

Episode 41: Beyond Prompt Engineering: Can AI Learn to Set Its Own Goals?

December 31st, 2024  |  Season 1  |  43 mins 51 secs

data science, genai, llms, machine learning

Hugo Bowne-Anderson hosts a panel discussion from the MLOps World and Generative AI Summit in Austin, exploring the long-term growth of AI by distinguishing real problem-solving from trend-based solutions. If you're navigating the evolving landscape of generative AI, productionizing models, or questioning the hype, this episode dives into the tough questions shaping the field.

Episode 38: The Art of Freelance AI Consulting and Products: Data, Dollars, and Deliverables

November 5th, 2024  |  Season 1  |  1 hr 23 mins

consulting, data science, genai, llms, machine learning

Hugo speaks with Jason Liu, an independent AI consultant with experience at Meta and Stitch Fix. At Stitch Fix, Jason developed impactful AI systems, like a $50 million product similarity search and the widely adopted Flight recommendation framework. Now, he helps startups and enterprises design and deploy production-level AI applications, with a focus on retrieval-augmented generation (RAG) and scalable solutions.