Vanishing Gradients

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 45: Your AI application is broken. Here’s what to do about it.

February 21st, 2025  |  Season 1  |  1 hr 17 mins

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.

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.

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 40: What Every LLM Developer Needs to Know About GPUs

December 24th, 2024  |  Season 1  |  1 hr 43 mins

ai, data science, llms, machine learning

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.

Episode 39: From Models to Products: Bridging Research and Practice in Generative AI at Google Labs

November 26th, 2024  |  Season 1  |  1 hr 43 mins

ai, data science, llms, machine learning

From building rockets at SpaceX to advancing generative AI at Google Labs, Ravin Kumar has carved a unique path through the world of technology. In this episode, we explore how to build scalable, reliable AI systems, the skills needed to work across the AI/ML pipeline, and the real-world impact of tools like open-weight models such as Gemma. Ravin also shares insights into designing AI tools like Notebook LM with the user journey at the forefront.

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.

Episode 37: Prompt Engineering, Security in Generative AI, and the Future of AI Research Part 2

October 8th, 2024  |  Season 1  |  50 mins 36 secs

data science, genai, llms, machine learning, nlp, prompt engineering

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.

Episode 36: Prompt Engineering, Security in Generative AI, and the Future of AI Research Part 1

September 30th, 2024  |  Season 1  |  1 hr 3 mins

data science, genai, llms, machine learning, nlp, prompt engineering

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.

Episode 35: Open Science at NASA -- Measuring Impact and the Future of AI

September 19th, 2024  |  Season 1  |  58 mins 13 secs

data science, genai, llms, machine learning

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.

Episode 34: The AI Revolution Will Not Be Monopolized

August 22nd, 2024  |  Season 1  |  1 hr 42 mins

data science, genai, llms, machine learning, nlp

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.

Episode 33: What We Learned Teaching LLMs to 1,000s of Data Scientists

August 12th, 2024  |  Season 1  |  1 hr 25 mins

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 pre-cursor 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.

In this episode, we dive deep into their experience and the unique insights it gave them into the current state and future of AI education and application.

Episode 32: Building Reliable and Robust ML/AI Pipelines

July 27th, 2024  |  Season 1  |  1 hr 15 mins

data science, genai, llms, machine learning

Hugo speaks with Shreya Shankar, a researcher at UC Berkeley focusing on data management systems with a human-centered approach. Shreya's work is at the cutting edge of human-computer interaction (HCI) and AI, particularly in the realm of large language models (LLMs). Her impressive background includes being the first ML engineer at Viaduct, doing research engineering at Google Brain, and software engineering at Facebook.