Stanford CS336: Language Models from Scratch! This is a comprehensive course on language models covers the full process of building one from scratch. It includes data collection, pretraining, transformer architecture, training, evaluation, and deployment.
LLMS
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Post-training LLMs: New Course by Banghua Zhou
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New Course: Post-training of LLMs
— Andrew Ng (@AndrewYNg) 9 juillet 2025
Learn to post-train and customize an LLM in this short course, taught by @BanghuaZ, Assistant Professor at the University of Washington @UW, and co-founder of @NexusflowX.
Training an LLM to follow instructions or answer questions has two key… pic.twitter.com/ssqCqYqK4KNew Course: Post-training of LLMs Learn to post-train and customize an LLM in this short course, taught by @BanghuaZ
, Assistant Professor at the University of Washington @UW
, and co-founder of @NexusflowX. Training an LLM to follow instructions or answer questions has two key -
MemAgent: Long-Context LLM Memory Architecture with RL
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MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent Yu et al.: https://
arxiv.org/abs/2507.02259 #ArtificialIntelligence #DeepLearning #MachineLearning -
Build a Visual Web-Browser Agent with LangGraph
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Explore LangGraph: Build a Visual Web‑Browser Agent Enhance your AI automation stack with a visual web‑browser agent, leveraging LangGraph, Playwright, Gemini (or GPT‑4o), and vision‑enabled LLMs. In this comprehensive LearnOpenCV guide, you will learn about: • The
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LLMs Self-Awareness: Temperature Inference as Genuine Introspection
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iulia Comsa and Murray Shanahan suggest that LLMs being able to infer their own temperature should be considered a valid example of genuine introspection of LLMs into their actual states (temperature is not a parameter in human minds, so the LLM is not just imitating self report)
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Claude Desktop App May Add MCP Connectors Directory
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Claude desktop app may get a Connectors Directory, a curated list of remote and desktop-specific MCPs. Lowering an entry barrier into MCPs
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Build a Local AI Coding Agent Guide
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Let's do it! https://
open.substack.com/pub/louisbouch
ard/p/build-a-local-ai-coding-agent?r=25qlky&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
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Local LLM Agent System with Tool Chaining and Extensibility
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It will:
– Use Ollama to run a local LLM (we’ll use qwen3:32b)
– Handle tool calls for reading/writing files, listing directories, and running shell commands
– Chain multiple tools to solve multi-step tasks
– Be easily extendable with things like web search or formatters -
Building Local Cursor: Offline AI Coding Agent with Open-Source Models
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In this iteration, we’ll build a local version of Cursor — a terminal-based AI coding agent that runs entirely offline using open-source models. Note: The term “local” is often misused. Here, it means everything — from model inference to file access — runs entirely on your
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Open Local Cursor: Offline Terminal AI Coding Agent
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Let's build an open Local Cursor — a terminal-based AI coding agent that runs entirely offline using open-source models.