`CLAUDE.md` is a high-leverage configuration point for Claude Code. Learning how to write a good `CLAUDE.md` (or `AGENTS.md`) is a key skill for agent-enabled software engineering.
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai
November 11, 2025
This lecture covers agents, prompts, and RAG.
To learn more about enrolling in this course, visit: https://online.stanford.edu/courses/cs230-deep-learning
Please follow along with the course schedule and syllabus: https://cs230.stanford.edu/syllabus/
More lectures will be published regularly.
Andrew Ng
Founder of DeepLearning.AI
Adjunct Professor, Stanford University’s Computer Science Department
Kian Katanforoosh
CEO and Founder of Workera
Adjunct Lecturer, Stanford University’s Computer Science Department
A friend made me aware of a reading list from A16Z containg recommendations for books, weighted towards science fiction since that’s mostly what people there read.
MIT study finds AI can already replace 11.7% of U.S. workforce
Artificial intelligence can already replace 11.7% of the U.S. labor market, across finance, health care and professional services, according to MIT's study.
Here's a delightful project by Tom Gally, inspired by my pelican SVG benchmark. He asked Claude to help create more prompts of the form Generate an SVG of [A] [doing] …
Olmo 3: Charting a path through the model flow to lead open-source AI | Ai2
Our new flagship Olmo 3 model family empowers the open source community with not only state-of-the-art open models, but the entire model flow and full traceability back to training data.
Spec-Driven Development: The Waterfall Strikes Back
Kiro, Spec-kit, Bmad, Tessl, and other SDD frameworks turn business analysts into Markdown reviewers. Isn't there a more agile way to use Coding Agents?
Has Google Quietly Solved Two of AI’s Oldest Problems?
A mysterious new model currently in testing on Google’s AI Studio is nearly perfect on automated handwriting recognition but it is also showing signs of spontaneous, abstract, symbolic reasoning.
Setup n8n in 5 min with https://hostinger.com/ncn8n + use code NETWORKCHUCK for a discount.
In this video, I take n8n to the NEXT LEVEL by building an AI agent. Meet my new IT employee “Terry” who can monitor, troubleshoot, and even fix my homelab. From Docker containers to Plex, Proxmox, and UniFi, Terry learns how to keep everything running smoothly. This is not just automation; this is AI running my ENTIRE homelab.
GUIDE / RESOURCES:
📃 Guide / Documentation: https://github.com/theNetworkChuck/n8n-terry-guide
📺 Part 1 (Baby Terry – first n8n video): https://youtu.be/ONgECvZNI3o?si=xCwIMQLjIjug-hNP
TIMESTAMPS:
0:00 - Intro: Meet Terminator Terry (The AI IT Employee)
0:58 - Employee Onboarding: Teaching Terry the Basics
2:22 - Setting Up n8n in the Cloud (Hostinger Setup)
2:58 - Connecting Terry to Your Homelab with Twingate
3:15 - Building Terry's First Workflow in n8n
4:01 - Creating a Test Website (Docker Container Setup)
5:00 - Terry's First Tool: HTTP Request (Website Monitoring)
6:11 - Teaching Terry to Monitor Like a Human
7:43 - Terry's Second Tool: SSH Access (Docker Commands)
10:00 - Converting SSH to a Workflow Tool
11:06 - Terry Learns to Troubleshoot (Docker PS & Inspect)
13:05 - Giving Terry More Freedom (Dynamic Commands)
14:16 - Making Terry Autonomous (Schedule Trigger Setup)
17:54 - Telegram Notifications: Terry Reports Back
19:24 - Structured Output: Teaching Terry Clean Data
22:33 - Smart Filtering: Only Alert on Problems
23:09 - Level 2 Engineer: Teaching Terry to Fix Things
24:49 - The Port Conflict Challenge (Terry's First Real Test)
26:42 - Upgrading Terry's Brain (GPT-4 vs Mini)
28:18 - Human-in-the-Loop: Taking Back Control
30:27 - Setting Up Approval Workflows
32:02 - Terry Asks Permission Before System Changes
35:06 - Testing the Complete Workflow (Monitor → Troubleshoot → Approve → Fix)
37:02 - Connecting Terry to Your Real Homelab
37:40 - Unifi Network Control (API Integration)
38:55 - Proxmox Integration (CLI & API Access)
🔥🔥Join the NetworkChuck Academy!: https://ntck.co/NCAcademy
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how to use n8n to automate your homelab
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Code execution with MCP: building more efficient AI agents \ Anthropic
Learn how code execution with the Model Context Protocol enables agents to handle more tools while using fewer tokens, reducing context overhead by up to 98.7%.
Nano Banana can be prompt engineered for extremely nuanced AI image generation
Max Woolf provides an exceptional deep dive into Google's Nano Banana aka Gemini 2.5 Flash Image model, still the best available image manipulation LLM tool three months after its initial …
Code research projects with async coding agents like Claude Code and Codex
I’ve been experimenting with a pattern for LLM usage recently that’s working out really well: asynchronous code research tasks. Pick a research question, spin up an asynchronous coding agent and …
In this video, I look at Kimi K2 Thinking from Moonshot AI, the most recent fully open reasoning model that scores higher than GPT-5 and Anthropic for multiple benchmarks.
Blog: https://moonshotai.github.io/Kimi-K2/thinking.html
Model Weights: https://huggingface.co/moonshotai/Kimi-K2-Thinking
For more tutorials on using LLMs and building agents, check out my Patreon
Patreon: https://www.patreon.com/SamWitteveen
Twitter: https://x.com/Sam_Witteveen
🕵️ Interested in building LLM Agents? Fill out the form below
Building LLM Agents Form: https://drp.li/dIMes
👨💻Github:
https://github.com/samwit/llm-tutorials