Michael Ryan

Diyi Yang Verified account @Diyi_Yang · Sep 2

This fall @michaelryan207 @jyangballin and I are teaching a new course CS329Z "Engineering AI Agents" on how to build AI Agents from scratch. Come join us and learn how to build them 🤖

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Michael Ryan Verified account @michaelryan207 · Sep 2

🚨 New Course Announcement!

This Fall @Diyi_Yang @jyangballin and I are teaching a BRAND NEW Stanford course on how to build AI Agents from scratch!

Millions of users interact with AI Agents every day.  Come join us and learn how to build them!

🧵 (1/5)

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Augmented Mind Podcast Verified account @augmind_fm · 5d

“I should have my choice. How much automation do I want helping me? But also, how much control do I want?”

“You have to struggle whether it’s lifting weights or whether it’s mental exercise. You’ve got to actually do the work yourself.”

For EP8, “After Data and Compute, What’s the Next Frontier in AI,” we welcome Peter Norvig, co-author of Artificial Intelligence: A Modern Approach and former director of research at Google. Peter is now a Distinguished Education Fellow at Stanford HAI and a researcher at Recursive, working on self-improving AI.

As AI takes on more of our work, how do we make sure it makes us more capable? Peter explores what’s still worth learning in AI and programming, why learning still requires struggle, how more automation can coexist with more human control, and why getting AI’s objectives right remains one of the field’s biggest challenges.

Outline:
0:00 - Introduction
2:32 - What Has (and Hasn’t) Changed Throughout Peter’s Career?
7:03 - How Peter Makes Career Pivots
11:17 - Is This AI Moment Different From Previous Cycles?
15:32 - What’s Still Worth Learning in AI and Programming?
19:55 - How Will AI Transform Higher Education?
24:10 - Main Bottlenecks to Building Human-Centered AI
35:10 - Reward Hacking vs. Creativity
41:51 - Continual Learning Remains an Unsolved Challenge
44:16 - Open Source AI Models
46:32 - AI, Inequality, and Winner-Take-All Markets

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Rikiya Takehi @rikiyatakehi · Nov 2

We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.

We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵
Blog: rikiyat.github.io/blog/weight-sp…

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