They're like riding a bike: easy, and you don't get it until you try.

Visualizing the internal state of a Transformer model

Really useful official guide to running the OpenAI gpt-oss models using llama-server from llama.cpp - which provides an OpenAI-compatible localhost API and a neat web interface for interacting with the …

LLMs have unlocked powerful new capabilities, but the dominant pattern for using them, writing increasingly desperate prompts, is fragile, unscalable, and of...

This app explains how language models transform text into meaningful representations through embeddings. It provides a visual guide to help you understand traditional and modern language model tech...

Build, debug, and optimize AI agents using DSPy and MLflow.

Complete introduction to the simplest, most efficient, and yet most powerful way I’ve found to create AI agents, AI workflows, and AI programs in Python. Instead of manual prompting, we use automatic prompt optimization with DSPy and its concept of signatures. Timestamps / Outline: 00:00 How to Call LLMs from Python, the Simple Way 0:21 Declare Your First AI Program (in 1 LOC) 2:24 Setting Up Your Large Language Model Backend 6:10 Program 2: Processing Images 9:14 Deeper Dive into Signatures 14:01 Program 3: Processing Entities from Paragraphs 19:19 Fetching text from wikipedia with Attachments 20:39 Setting Up a DataFrame 22:22 Apply Gemini Flash lite to each paragraph 23:02 Creating a Synthetic Gold Set 24:35 Quick Baseline Evaluation 25:11 Creating DSPy Examples 25:55 Evaluation Metric 26:10 Prompt Optimization with DSPy 29:10 Final Evaluation Follow Max: Twitter: [https://x.com/MaximeRivest](https://x.com/MaximeRivest) GitHub: [https://github.com/MaximeRivest](https://github.com/MaximeRivest) Links to Relevant Repositories: Attachments: [https://github.com/MaximeRivest/attachments](https://github.com/MaximeRivest/attachments) DSPy: [https://github.com/stanfordnlp/dspy](https://github.com/stanfordnlp/dspy) FunnyDSPy: [https://github.com/MaximeRivest/funnydspy](https://github.com/MaximeRivest/funnydspy) Docs: [https://dspy.ai/](https://dspy.ai/) [https://maximerivest.github.io/attachments/](https://maximerivest.github.io/attachments/) If you’re new to my channel, my name is Maxime Rivest. I’m an Applied AI Engineer and Data Engineer. I like to educate people on the best tools in Data Analytics and AI Engineering. Max

Full guide on claude tips and tricks and how you can optimise your claude code the best & strive to find every command possible even hidden ones! - zebbern/claude-code-guide

Christopher Smith ran a mini hackathon in Albany New York at the weekend around uses of my LLM - the first in-person event I'm aware of dedicated to that project! …

In this video, I break down Proximal Policy Optimization (PPO) from first principles, without assuming prior knowledge of Reinforcement Learning. By the end, you’ll understand the core RL building blocks that led to PPO, including: 🔵 Policy Gradient 🔵 Actor-Critic Models 🔵 The Value Function 🔵 The Generalized Advantage Estimate In the LLM world, PPO was used to train reasoning models like OpenAI's o1/o3, and presumably Claude 3.7, Grok 3, etc. It’s the backbone of Reinforcement Learning with Human Feedback (RLHF) -- which helps align AI models with human preferences and Reinforcement Learning with Verifiable Rewards (RLVR), which gives LLMs reasoning abilities. Papers: - PPO paper: https://arxiv.org/pdf/1707.06347 - GAE paper: https://arxiv.org/pdf/1506.02438 - TRPO paper: https://arxiv.org/pdf/1502.05477 Well-written blogposts: - https://danieltakeshi.github.io/2017/04/02/notes-on-the-generalized-advantage-estimation-paper/ - https://huggingface.co/blog/NormalUhr/rlhf-pipeline - https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/ Implementations: - (Original) OpenAI Baseslines: https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/ppo2 - Hugging Face: https://github.com/huggingface/trl/blob/main/trl/trainer/ppo_trainer.py - Hugging Face docs: https://huggingface.co/docs/trl/main/en/ppo_trainer Mother of all RL books (Barto & Sutton): http://incompleteideas.net/book/RLbook2020.pdf 00:00 Intro 01:21 RL for LLMs 05:53 Policy Gradient 09:23 The Value Function 12:14 Generalized Advantage Estimate 17:17 End-to-end Training Algorithm 18:23 Importance Sampling 20:02 PPO Clipping 21:36 Outro Special thanks to Anish Tondwalkar for discussing some of these concepts with me. Note: At 21:10, A_t should have been inside the min. Thanks @t.w.7065 for catching this.

VS Code AI Customization: Learn to use custom instructions, prompt files, and custom chat modes to personalize AI code generation, reviews, and chat responses.

Introduction to GraphRAG

Get started with 10Web and their AI Website Builder API: https://10web.io/website-builder-api/?utm_source=YouTube&utm_medium=Influencer&utm_campaign=TechWithTim Today, you'll learn how to fine-tune LLMs in Python for use in Ollama. I'll walk you through it step by step, give you all the code and show you how to test it out. DevLaunch is my mentorship program where I personally help developers go beyond tutorials, build real-world projects, and actually land jobs. No fluff. Just real accountability, proven strategies, and hands-on guidance. Learn more here - https://training.devlaunch.us/tim ⏳ Timestamps ⏳ 00:00 | What is Fine-Tuning? 02:25 | Gathering Data 05:52 | Google Collab Setup 09:17 | Fine-Tuning with Unsloth 16:58 | Model Setup in Ollama 🎞 Video Resources 🎞 Code in this video: https://drive.google.com/drive/folders/1p4ZilsJsdxB5lH6ZBMdIEJBt0WVUMsDq?usp=sharing Notebook Google Collab: https://colab.research.google.com/drive/1NsRGmHVupulRzsq9iUTx8V8WgTSpO_04?usp=sharing Hashtags #Python #Ollama #LLM

Learn how to fine‑tune Qwen‑3‑14B on your own data—with LoRA adapters, Unsloth’s 4‑bit quantization, and just 12 GB of VRAM—while preserving its chain‑of‑thought reasoning. I’ll walk you through dataset prep, the key hyper‑parameters that prevent catastrophic forgetting, and the exact Colab notebook to get you running in minutes. Build a lightweight, reasoning‑ready Qwen‑3 model tailored to your project today! LINKS: https://qwenlm.github.io/blog/qwen3/ https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs https://huggingface.co/datasets/unsloth/OpenMathReasoning-mini https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune https://huggingface.co/datasets/mlabonne/FineTome-100k https://docs.unsloth.ai/get-started/fine-tuning-guide https://arxiv.org/html/2308.08747v5 https://heidloff.net/article/efficient-fine-tuning-lora/ NOTEBOOK: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(14B)-Reasoning-Conversational.ipynb Fine-tuning Playlist: https://www.youtube.com/playlist?list=PLVEEucA9MYhPjLFhcIoNxw8FkN28-ixAn Website: https://engineerprompt.ai/ RAG Beyond Basics Course: https://prompt-s-site.thinkific.com/courses/rag Let's Connect: 🦾 Discord: https://discord.com/invite/t4eYQRUcXB ☕ Buy me a Coffee: https://ko-fi.com/promptengineering |🔴 Patreon: https://www.patreon.com/PromptEngineering 💼Consulting: https://calendly.com/engineerprompt/consulting-call 📧 Business Contact: engineerprompt@gmail.com Become Member: http://tinyurl.com/y5h28s6h 💻 Pre-configured localGPT VM: https://bit.ly/localGPT (use Code: PromptEngineering for 50% off). Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 Fine-Tuning Qwen-3 Models: Step-by-Step Guide 00:00 Introduction to Fine-Tuning Qwen-3 01:24 Understanding Catastrophic Forgetting and LoRa Adapters 03:06 Installing and Using unsloth for Fine-Tuning 04:19 Code Walkthrough: Preparing Your Dataset 07:14 Combining Reasoning and Non-Reasoning Datasets 09:48 Prompt Templates and Fine-Tuning 16:13 Inference and Hyperparameter Settings 18:11 Saving and Loading LoRa Adapters

Which AIs to use, and how to use them

Working with GitHub Copilot in VS Code amps out your efficiency as a programmer - but did you know that adding a simple markdown file can boost this efficiency even more, while *also* decreasing the size of your prompt? Custom Instructions can help you and your team do so much more with GitHub Copilot, and @rconery will show you how in this video. 🔎 Chapters: 00:12 Simple, automatic instructions 02:07 Custom Git commit messages 03:26 Customizing Copilot functionality in VS Code 05:00 Going all in with markdown files as instructions 🔗 Links: Get Copilot: https://aka.ms/get-copilot Instruction Snippets for JSONC: https://gist.github.com/robconery/f93d016ace16feb7156f9b7905f3f499 🎙️ Featuring:‪ @rconery‬ #vscode #copilot #githubcopilot

✅ Learn how to build robust and scalable software architecture: https://arjan.codes/checklist. Want your AI tools to actually *do* something? In this video, I’ll show you how to integrate external tools with language models using **MCP (Model Context Protocol)**. You’ll learn two common architecture patterns, see real code examples, and get tips on keeping your setup clean and scalable. Whether you’re building for Claude, ChatGPT, or any other LLM—this is how you connect your backend to AI. 🔥 GitHub Repository: https://git.arjan.codes/2025/mcp-server. 🎓 ArjanCodes Courses: https://www.arjancodes.com/courses. 🔖 Chapters: 0:00 Intro 0:46 What is MCP? 3:14 YouTube MCP Version 1 7:58 YouTube MCP Version 2 12:18 Final Thoughts #arjancodes #softwaredesign #python

MCP Servers using Local LLMs tutorial

In my previous article , we explored Model Context Protocol (MCP) — a standardized way for LLMs to invoke tools or APIs with structured…

Learn how to use vector search and embeddings to easily combine your data with large language models like GPT-4. You will first learn the concepts and then create three projects. ✏️ Course developed by Beau Carnes. 💻 Code: https://github.com/beaucarnes/vector-search-tutorial 🔗 Access MongoDB Atlas: https://cloud.mongodb.com/ 🏗️ MongoDB provided a grant to make this course possible. ⭐️ Contents ⭐️ ⌨️ (00:00) Introduction ⌨️ (01:18) What are vector embeddings? ⌨️ (02:39) What is vector search? ⌨️ (03:40) MongoDB Atlas vector search ⌨️ (04:30) Project 1: Semantic search for movie database ⌨️ (32:55) Project 2: RAG with Atlas Vector Search, LangChain, OpenAI ⌨️ (54:36) Project 3: Chatbot connected to your documentation 🎉 Thanks to our Champion and Sponsor supporters: 👾 davthecoder 👾 jedi-or-sith 👾 南宮千影 👾 Agustín Kussrow 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Justin Hual 👾 Otis Morgan 👾 Oscar Rahnama -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news ❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp

That gameboy blender animation...took 6 hours to render 😅. Anyway, had a ton of fun coding this up and finally getting back to some proper ML. I've been thi...

Anthropic's Interactive Prompt Engineering Tutorial - anthropics/prompt-eng-interactive-tutorial

In this article, we carry out handwritten text recognition using OCR. We fine tune the TrOCR model on the GNHK dataset.

Learn how `sqlite-vec` turns SQLite into a fast, embedded vector search engine. With support for float32, int8, and bit vectors, optimized distance metrics, and native SQL integration, it's ideal for offline AI, semantic search, and lightweight ML apps. This post walks through how it works and why it's surprisingly powerful.

Looking for the best open-source embedding model for your RAG app? We share a comparison workflow so you can stop paying the OpenAI tax.

🎬 Ever wondered how AI could turn your life into a documentary? Watch as I create a seemingly professional documentary about myself in minutes using Gemma 3, Ollama, and ElevenLabs - no film crew needed! 🎯 In this video, you'll learn: • How to use Gemma 3's multimodal capabilities with multiple images • Building a simple CLI app with Deno/TypeScript for image processing • Working with n8n workflows for AI integration • Creating convincing AI-generated narratives with Ollama • Complete workflow from capture to final video production ⏱️ Timestamps: 00:00 - Start 00:28 - I'm in a Documentary 01:56 - Gemma3 02:13 - Whats new in Gemma3 with Ollama 02:26 - Tell a story with many images 03:54 - Creating the app with Windsurf 04:49 - It's not in x language 05:19 - Let's look at the code 08:32 - The backend in n8n 🛠️ Tools & Resources Mentioned: • Gemma 3 27b • Ollama (https://ollama.com) • ElevenLabs (https://try.elevenlabs.io/tvlst) • n8n • Deno/TypeScript Want to create your own AI-powered content? Drop a comment below with your ideas or questions! #AIContent #TechTutorial #AIDocumentary My Links 🔗 👉🏻 Subscribe (free): https://www.youtube.com/technovangelist 👉🏻 Join and Support: https://www.youtube.com/channel/UCHaF9kM2wn8C3CLRwLkC2GQ/join 👉🏻 Newsletter: https://technovangelist.substack.com/subscribe 👉🏻 Twitter: https://www.twitter.com/technovangelist 👉🏻 Discord: https://discord.gg/uS4gJMCRH2 👉🏻 Patreon: https://patreon.com/technovangelist 👉🏻 Instagram: https://www.instagram.com/technovangelist/ 👉🏻 Threads: https://www.threads.net/@technovangelist?xmt=AQGzoMzVWwEq8qrkEGV8xEpbZ1FIcTl8Dhx9VpF1bkSBQp4 👉🏻 LinkedIn: https://www.linkedin.com/in/technovangelist/ 👉🏻 All Source Code: https://github.com/technovangelist/videoprojects Want to sponsor this channel? Let me know what your plans are here: https://www.technovangelist.com/sponsor