
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
| Publishes | Twice monthly | Episodes | 75 | Founded | 4 years ago |
|---|---|---|---|---|---|
| Number of Listeners | Categories | TechnologyScience | |||

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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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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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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