Vanishing Gradients
Hugo Bowne-Anderson
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hugobowne.substack.com
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The Future of Agentic Data Science
So I think we’re really at a historical moment, and the opportunity is massive. Almost 15 years ago, we were promised that data science was going to be this incredible thing and create all this value…
Agent-Harness.ipynb*
One thing that I don’t like about Claude is that you get into this weird mental state: oh, I think I trust the model. Let’s do the slot machine. Hit click, which puts you in an inactive mode of…
Agentic Engineering and the Lost Art of Verification
> I almost don’t read code now. My approach with Roborev is it’s like my code reader. The mantra is: Roborev reads every line of code that is generated. It gets read multiple times. And so,…
Next Level AI Evals for 2026
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…
Privacy Theater Is Not Privacy Engineering: What It Actually Takes to Ship Safe AI
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…
LLM Architecture in 2026: What You Need to Know with Sebastian Raschka
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,…
Episode 72: Why Agents Solve the Wrong Problem (and What Data Scientists Do Instead)
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…
Episode 71: Durable Agents - How to Build AI Systems That Survive a Crash with Samuel Colvin
Our thesis is that AI is still just engineering… those people who tell us for fun and profit, that somehow AI is so, so profound, so new, so different from anything that’s gone before that it somehow…
Episode 70: 1,400 Production AI Deployments
There’s a company who spent almost $50,000 because an agent went into an infinite loop and they forgot about it for a month.It had no failures and I guess no one was monitoring these costs. It’s nice…
Episode 69: Python is Dead. Long Live Python! With the Creators of pandas & Parquet
> It’s the agent writing the code. And it’s the development loop of writing the code, building testing, write the code, build test and iterating. And so I do think we’ll see for many types of…
Episode 68: A Builder’s Guide to Agentic Search & Retrieval with Doug Turnbull & John Berryman
The best way to build a horrible search product? Don’t ever measure anything against what a user wants.Search veterans Doug Turnbull (Led Search at Reddit + Shopify; Wrote Relevant Search + AI…
Episode 67: Saving Hundreds of Hours of Dev Time with AI Agents That Learn
This is continual learning, right? Everyone has been talking about continual learning as the next challenge in AI. Actually, it’s solved. Just tell it to keep some notes somewhere. Sure, it’s not,…
Episode 66: The Agent Paradox - Why Moderna's Most Productive AI Systems Aren't Agents
Surprise. We don’t have agents. I actually went in and did an audit of all the LLM applications that we’ve developed internally. And if you were to take Anthropic’s definition of workflow versus…
Episode 65: The Rise of Agentic Search
We’re really moving from a world where humans are authoring search queries and humans are executing those queries and humans are digesting the results to a world where AI is doing that for us.Jeff…
Episode 64: Data Science Meets Agentic AI with Michael Kennedy (Talk Python)
We have been sold a story of complexity. Michael Kennedy (Talk Python) argues we can escape this by relentlessly focusing on the problem at hand, reducing costs by orders of magnitude in software,…
Episode 63: Why Gemini 3 Will Change How You Build AI Agents with Ravin Kumar (Google DeepMind)
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…
Episode 62: Practical AI at Work: How Execs and Developers Can Actually Use LLMs
Many leaders are trapped between chasing ambitious, ill-defined AI projects and the paralysis of not knowing where to start. Dr. Randall Olson argues that the real opportunity isn't in moonshots, but…
Episode 61: The AI Agent Reliability Cliff: What Happens When Tools Fail in Production
Most AI teams find their multi-agent systems devolving into chaos, but ML Engineer Alex Strick van Linschoten argues they are ignoring the production reality. In this episode, he draws on insights…
Episode 60: 10 Things I Hate About AI Evals with Hamel Husain
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…
Episode 59: Patterns and Anti-Patterns For Building with AI
John Berryman (Arcturus Labs; early GitHub Copilot engineer; co-author of Relevant Search and Prompt Engineering for LLMs) has spent years figuring out what makes AI applications actually work in…
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Vanishing Gradients has published 78 episodes since February 2022, covering topics in Science, Technology.
Vanishing Gradients is currently highly active with new episodes every 2 weeks. Average episode length is 1h 12m.
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