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PODCAST

Vanishing Gradients

Hugo Bowne-Anderson

A podcast for people who build with AI. Long-format conversations with people shaping the field about agents, evals, multimodal systems, data infrastructure, and the tools behind them. Guests include Jeremy Howard (fast.ai), Hamel Husain (Parlance Labs), Shreya Shankar (UC Berkeley), Wes McKinney (creator of pandas), Samuel Colvin (Pydantic) and more. hugobowne.substack.com

technology

All Episodes

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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1:04:25

Episode 48: HOW TO BENCHMARK AGI WITH GREG KAMRADT

Vanishing Gradients ·

2025/05/23

If we want to make progress toward AGI, we need a clear definition of intelligence—and a way to measure it. In this episode, 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. They discuss: 🧠 Why

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1:19:12

Episode 47: The Great Pacific Garbage Patch of Code...

Vanishing Gradients ·

2025/04/07

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. Joe is the co-author of Fundamentals of Data Engineering and a longtime voice on the systems side of modern software. He’s also one of the sharpest critics of “vibe coding” — the emerging

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1:08:57

Episode 46: Software Composition Is the New Vibe Coding

Vanishing Gradients ·

2025/04/03

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

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1:17:30

Episode 45: Your AI application is broken. Here’s...

Vanishing Gradients ·

2025/02/20

Too many teams are building AI applications without truly understanding why their models fail. Instead of jumping straight to LLM evaluations, dashboards, or vibe checks, how do you actually fix a broken AI app? In this episode, Hugo speaks with Hamel Husain, longtime ML engineer, open-source contributor, and consultant, about why debugging generative AI systems starts with looking at your data. In this episode, we dive into: Why “look at your data” is the best debugging advice no one follows

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1:34:11

Episode 44: The Future of AI Coding Assistants: Who’s...

Vanishing Gradients ·

2025/02/04

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. In this episode, we dive into: - The trade-offs between proprietary vs. open-source AI coding assistants—why open-source might be the future. - How structured workflows, modular AI, and

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1:01:03

Episode 43: Tales from 400+ LLM Deployments: Building...

Vanishing Gradients ·

2025/01/16

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. In this episode, we dive into: - The current state of AI agents in production, from successes to common failure modes - Practical lessons learned from analyzing hundreds of

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1:20:03

Episode 42: Learning, Teaching, and Building in the...

Vanishing Gradients ·

2025/01/04

In this episode of Vanishing Gradients, 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. They dive into the realities of deploying LLM applications, overcoming “proof-of-concept purgatory,” and why first principles and iteration are critical for success in AI. Whether you’re an educator,

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43:51

Episode 41: Beyond Prompt Engineering: Can AI Learn...

Vanishing Gradients ·

2024/12/30

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. The panel features: - Ben Taylor (Jepson) (https://www.linkedin.com/in/jepsontaylor/) – CEO and Founder at VEOX Inc., with

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1:43:34

Episode 40: What Every LLM Developer Needs to Know...

Vanishing Gradients ·

2024/12/24

Hugo speaks with Charles Frye, Developer Advocate at Modal and someone who really knows GPUs inside and out. If you’re a data scientist, machine learning engineer, AI researcher, or just someone trying to make sense of hardware for LLMs and AI workflows, this episode is for you. Charles and Hugo dive into the practical side of GPUs—from running inference on large models, to fine-tuning and even training from scratch. They unpack the real pain points developers face, like figuring out: - How much

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1:43:28

Episode 39: From Models to Products: Bridging...

Vanishing Gradients ·

2024/11/25

Hugo speaks with Ravin Kumar, Senior Research Data Scientist at Google Labs. Ravin’s career has taken him from building rockets at SpaceX to driving data science and technology at Sweetgreen, and now to advancing generative AI research and applications at Google Labs and DeepMind. His multidisciplinary experience gives him a rare perspective on building AI systems that combine technical rigor with practical utility. In this episode, we dive into: • Ravin’s fascinating career path, including the

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1:23:47

Episode 38: The Art of Freelance AI Consulting and...

Vanishing Gradients ·

2024/11/04

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. This episode is a bit of an experiment. Instead of our usual technical deep dives,

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50:36

Episode 37: Prompt Engineering, Security in...

Vanishing Gradients ·

2024/10/08

Hugo speaks with three leading figures from the world of AI research: Sander Schulhoff, a recent University of Maryland graduate and lead contributor to the Learn Prompting initiative; Philip Resnik, professor at the University of Maryland, known for his pioneering work in computational linguistics; and Dennis Peskoff, a researcher from Princeton specializing in prompt engineering and its applications in the social sciences. This is Part 2 of a special two-part episode, prompted—no pun intended—by

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1:03:46

Episode 36: Prompt Engineering, Security in...

Vanishing Gradients ·

2024/09/30

Hugo speaks with three leading figures from the world of AI research: Sander Schulhoff, a recent University of Maryland graduate and lead contributor to the Learn Prompting initiative; Philip Resnik, professor at the University of Maryland, known for his pioneering work in computational linguistics; and Dennis Peskoff, a researcher from Princeton specializing in prompt engineering and its applications in the social sciences. This is Part 1 of a special two-part episode, prompted—no pun intended—by

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58:13

Episode 35: Open Science at NASA -- Measuring Impact...

Vanishing Gradients ·

2024/09/19

Hugo speaks with Dr. Chelle Gentemann, Open Science Program Scientist for NASA’s Office of the Chief Science Data Officer, about NASA’s ambitious efforts to integrate AI across the research lifecycle. In this episode, we’ll dive deeper into how AI is transforming NASA’s approach to science, making data more accessible and advancing open science practices. We explore Measuring the Impact of Open Science: How NASA is developing new metrics to evaluate the effectiveness of open science, moving beyond

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1:42:51

Episode 34: The AI Revolution Will Not Be Monopolized

Vanishing Gradients ·

2024/08/22

Hugo speaks with Ines Montani and Matthew Honnibal, the creators of spaCy and founders of Explosion AI. Collectively, they've had a huge impact on the fields of industrial natural language processing (NLP), ML, and AI through their widely-used open-source library spaCy and their innovative annotation tool Prodigy. These tools have become essential for many data scientists and NLP practitioners in industry and academia alike. In this wide-ranging discussion, we dive into: • The evolution of applied

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1:25:10

Episode 33: What We Learned Teaching LLMs to 1,000s...

Vanishing Gradients ·

2024/08/12

Hugo speaks with Dan Becker and Hamel Husain, two veterans in the world of data science, machine learning, and AI education. Collectively, they’ve worked at Google, DataRobot, Airbnb, Github (where Hamel built out the precursor to copilot and more) and they both currently work as independent LLM and Generative AI consultants. Dan and Hamel recently taught a course on fine-tuning large language models that evolved into a full-fledged conference, attracting over 2,000 participants. This experience

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72 results

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