🚨 We’re LIVE! Join us for a deep dive into SHAP, LIME, Grad-CAM & DL-Backtrace. Introducing xai_evals — the open-source benchmark for explainability methods. 🔴 Tune in here: hubs.la/Q03wsDFp0 #XAI #AIExplainability
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Stanford CS336: Comprehensive Course on Building Language Models from Scratch
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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.
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GitHub Pages Streamlit Deployment Options
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Haven't tried that! Do github pages allow the streamlit run command to deploy the app?
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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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XAI Methods Evaluation Session and xai_evals Framework Launch
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🔴 Just 2 Hours to Go! Join our closed-door session on evaluating XAI methods like SHAP, LIME, Grad-CAM & AryaXAI’s DL-Backtrace. Introducing xai_evals — an open-source framework to quantify & benchmark explainability tools. 🔗hubs.la/Q03wqGTZ0 #AIExplainability #XAI
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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