Episode

Agent-Harness.ipynb*

Podcast
Vanishing Gradients
Published
May 20, 2026
Duration seconds
4786
Processing state
not_requested
Canonical source
https://hugobowne.substack.com/p/agent-harnessipynb
Audio
https://api.substack.com/feed/podcast/198498721/e6049a6fb95927a5e661d0f48abe9687.mp3
JSON
/v1/public/podcasts/vanishing-gradients-4989163/episodes/agent-harness-ipynb
Markdown
/podcast/vanishing-gradients-4989163/agent-harness-ipynb.md

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Summary

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 thinking. Maybe it’s better to use a worse model…. Vincent Warmerdam , senior data professional and prolific open-source maintainer (some packages with over a million downloads), now Engineer at marimo , joins Hugo to talk about how the Python notebook is evolving from a static scratchpad into a working agent harness, and what it takes to stay in the loop as a developer when agents are writing most of the code. This episode was originally a livestream Q&A with the Vanishing Gradients audience. We Discuss: * Shared Notebook Canvas : Notebooks act as a shared memory space where agents and humans co-exist, enabling real-time visual feedback by direct manipulation of global state and UI elements ; * Speed-of-Thought Models : Faster, open-weight models like Kimi K2 enhance exploratory flow by keeping humans more alert to the code, unlike frontier models that can induce passive thinking; * Pi as a Harness : Vincent favors an agent harness where agents extend themselves rather than reach for MCP , and where hooks can rigidly constrain which files an agent is allowed to read or touch; * Why PRDs Don’t Fit Notebooks : Notebook work is fundamentally exploratory, so the discipline that works for shipping web apps does not transfer cleanly; the one exception is reproducing a paper; * Interactive Code Review : Interactive UIs (e.g., dragging integers) transform code into a physical object , incentivizing developers to actively review and understand agent logic; * Modular “Lego” Components : Provide agents with high-level, well-tested components (”Lego” code) instead of raw boilerplate, c…