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Vanishing Gradients

Hugo Bowne-Anderson
Machine Learning
Large Language Models
Data Science
Prompt Engineering
Generative AI
Artificial Intelligence
AI Agents
Context Engineering
Google Deepmind
AI Applications
Gemini 3
Software Development
Artificial General Intelligence
Natural Language Processing
Machine Learning Benchmarks
Structured Workflows
Error Analysis
Software Engineering
Multimodal AI
Multimodal Applications

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

PublishesTwice monthlyEpisodes75Founded4 years ago
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Latest Episodes

There are a lot of reasons why we should do AI evals. For many companies doing AI evals is the way to build the feedback loop into the product development lifecycle. So it is like your compass. We’re using AI evals as a compass to guide product devel... more

Katharine Jarmul, Privacy in ML/AI Expert & Author of Practical Data Privacy, joins Hugo to unpack why most AI privacy advice is theater: and what technical privacy actually looks like when you’re shipping LLMs, agents, and multimodal systems into th... more

If you take a model release as an anchor point, let’s say Nemotron 3 or Qwen 3.5, you can go in both directions: You can either plug them into an agent and play around with that, or you can look, okay, what does the model look like under the hood? Wh... more

I often see what I would consider to be b******t evals, especially in data, like write this dumb SQL. Almost every one of these dumb SQL questions that I’ve seen for benchmarks are just so either obviously easy or overwhelmingly adversarial. They jus... more

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Recent Guests

Stella Liu
AI evals practitioner and researcher, head of applied science at ASU, co-creator of the AI Evals and Analytics Playbooks course on Maven
Arizona State University
Episode: Next Level AI Evals for 2026
Eddie Landesberg
Staff data scientist at Google and causal AI expert
Google
Episode: Next Level AI Evals for 2026
Katharine Jarmul
Data and AI privacy expert, author of Practical Data Privacy
ThoughtWorks (former principal data scientist) / Cape Privacy (former)
Episode: Privacy Theater Is Not Privacy Engineering: What It Actually Takes to Ship Safe AI
Eleanor Berger
Founder of Elite AI Assisted Coding
Elite AI Assisted Coding
Episode: Episode 67: Saving Hundreds of Hours of Dev Time with AI Agents That Learn
Isaac Flaath
Founder of Elite AI Assisted Coding
Elite AI Assisted Coding
Episode: Episode 67: Saving Hundreds of Hours of Dev Time with AI Agents That Learn
Jeff Huber
CEO and co-founder of Chroma, building modern search infrastructure for AI
Chroma
Episode: Episode 65: The Rise of Agentic Search
Ravin Kumar
Researcher at Google DeepMind focused on AI and generative models
Google DeepMind
Episode: Episode 63: Why Gemini 3 Will Change How You Build AI Agents with Ravin Kumar (Google DeepMind)
Dr. Randall Olson
Co-founder of Wyrd Studios, an expert in AI applications and executive strategy
Wyrd Studios
Episode: Episode 62: Practical AI at Work: How Execs and Developers Can Actually Use LLMs
Bryan Bischof
Guest caller from Theory Ventures discussing failure analysis in AI.
Theory Ventures
Episode: Episode 60: 10 Things I Hate About AI Evals with Hamel Husain

Host

Hugo Bowne-Anderson
Host of Vanishing Gradients, AI and data tooling writer/educator; focuses on durable agents, agentic search, and practical AI deployments.

Reviews

4.8 out of 5 stars from 45 ratings
  • Host may please consider to say more in less; the sweet spot is under 40 minutes.

    Podcast Addict
    3
    MrDaggubati
    a month ago
  • Substantive!

    Practical, smart content from an industry veteran

    Apple Podcasts
    5
    eddielandes
    United States4 months ago
  • Best data science podcast to come out in a while

    [see title]

    Apple Podcasts
    5
    vishalthatsme
    United States4 years ago

Listeners Say

Key themes from listener reviews, highlighting what works and what could be improved about the show.

Best data science podcast to come out in a while.
Substantive! Insightful and practical content from an industry veteran.
The depth and breadth of guests keep every episode valuable for practitioners.

Chart Rankings

How this podcast ranks in the Apple Podcasts, Spotify and YouTube charts.

Apple Podcasts
#116
Netherlands/Technology

Talking Points

Recent interactions between the hosts and their guests.

Next Level AI Evals for 2026
Q: What is the future of AI evals in terms of standards and regulation?
There is a push toward industry standards for AI evals, with regulatory guidance that is currently high level; practitioners want concrete standards to ensure compliance and measurable impact in procurement and governance contexts.
Next Level AI Evals for 2026
Q: How should we think about offline vs online evals, and when to use each?
Offline evals are used to align with expectations and establish a baseline before costly experiments, while online evals and AB testing validate real-world impact; both are needed for a robust evaluation program, depending on stage and risk.
Episode 71: Durable Agents - How to Build AI Systems That Survive a Crash with Samuel Colvin
Q: Could you just give us a bit of your background?
Pydantic predates Gen.AI by a few years and started as a project in 2017 out of a need to validate data more efficiently.
Episode 70: 1,400 Production AI Deployments
Q: What are your thoughts on context engineering and its importance in AI development?
Context engineering has emerged as a discipline to manage the limited context of LLMs, and it's essential to find ways to optimize context usage to ensure systems operate effectively without overwhelming token limits.
Episode 70: 1,400 Production AI Deployments
Q: What has changed in the development of AI since the last time we talked?
There has been a step function improvement in model performance, making more reliable agentic systems possible and leading to greater adoption in enterprise environments.

Audience Metrics

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Frequently Asked Questions About Vanishing Gradients

What is Vanishing Gradients about and what kind of topics does it cover?

This show focuses on AI engineering, production systems, and the practical realities of building with large language models, agents, and data infrastructure. Episodes frequently explore durable and agentic AI, robust evaluation, context engineering, and the evolving tooling around deployment, observability, and multi-tool agent systems. Guests come from academia and industry and include researchers, startup founders, and practitioners who share hands-on lessons, case studies, and strategies for making AI systems reliable, scalable, and cost-effective. The format tends to center on concrete problems, evolving best practices, and the trade-offs of production AI, making it a strong resource for technical leaders, engineers, and data scientists... more

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2. Latent Space: The AI Engineer Podcast
3. Python Bytes
4. Super Data Science: ML & AI Podcast with Jon Krohn
5. Practical AI

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Vanishing Gradients launched 4 years ago and published 75 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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What guests have appeared on Vanishing Gradients?

Recent guests on Vanishing Gradients include:

1. Stella Liu
2. Eddie Landesberg
3. Katharine Jarmul
4. Eleanor Berger
5. Isaac Flaath
6. Jeff Huber
7. Ravin Kumar
8. Dr. Randall Olson

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