AI/ML

I started writing a post called "how to write AI apps" but it was over-reach so I scaled it back to this. Who am I to tell you how to write anything? But here's what I'll be applying to my own writing of AI-powered apps, specifically LLM applications. A battle I've already lost is that we shouldn't call LLMs "AI" at all; they are machine learning and not the general intelligence that is implied to the layman by the name. It is an even less helpful name than "serverless", my previous can

Get your documents ready for gen AI. Contribute to docling-project/docling development by creating an account on GitHub.

Turns Codebase into Easy Tutorial with AI. Contribute to The-Pocket/Tutorial-Codebase-Knowledge development by creating an account on GitHub.

📄 A curated list of awesome .cursorrules files. Contribute to PatrickJS/awesome-cursorrules development by creating an account on GitHub.

Agent Framework / shim to use Pydantic with LLMs

Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals.

The first generation of AI-powered products (often called “AI Wrapper” apps, because they “just” are wrapped around an LLM API) were quickly brought to market by small teams of engineers, …

An LLM Query Understanding Service Doug Turnbull recently wrote about how all search is structured now: “Many times, even a small open source LLM will be able to turn a search query into reasonable structure at relatively low cost.”

As more people start hacking around with implementations of MCP (the Model Context Protocol, a new standard for making tools available to LLM-powered systems) the security implications of tools built on that protocol are starting to come into focus.

Reinforcement Learning has helped train neural networks to win games, drive cars and even get ChatGPT to sound more human when it responds to your prompt. Th...

What happens when you minimize the chance of data leakage?

New evidence that confirms so much of what I have been saying

As Large Language Models improve, the tokens they predict form ever more complicated and nuanced outcomes. Rob Miles and Ryan Greenblatt discuss "Alignment F...

Artificial intelligence foundation for therapeutic science

Get the full code: https://github.com/riza-io/examples/tree/main/demos/mcp_and_pydanticai Try out Riza's MCP server: https://docs.riza.io/getting-started/mcp-servers PydanticAI is one of the most popular frameworks for building AI agents in Python, and it recently launched MCP support. If you've been wanting to learn MCP, this is a great place to start. In this tutorial you'll build a simple agent with PydanticAI, and then add an MCP server called fetch, which enables web browsing. Then we'll use the Postgres MCP server to add database querying to our agent. Then we'll use Riza's remote MCP server to add a code interpreter to our agent.

Pydantic Evals Brand new package from the Pydantic AI team which directly tackles what I consider to be the single hardest problem in AI engineering: building evals to determine if your LLM-based system is working correctly and getting better over time.

Google's Gemma 3 model (the 27B variant is particularly capable, I've been trying it out [via Ollama](https://ollama.com/library/gemma3)) supports function calling exclusively through prompt engineering. The official documentation describes two recommended …

The Playwright team at Microsoft have released an MCP ([Model Context Protocol](https://github.com/microsoft/playwright-mcp)) server wrapping Playwright, and it's pretty fascinating. They implemented it on top of the Chrome accessibility tree, so …

Advanced RAG 101 - build agentic RAG with llama3Get free HubSpot report of how AI is redefining startup GTM strategy: https://clickhubspot.com/4hx🔗 Links- J...

Shortform link: https://shortform.com/artem In this video we will talk about backpropagation – an algorithm powering the entire field of machine learning and try to derive it from first principles. OUTLINE: 00:00 Introduction 01:28 Historical background 02:50 Curve Fitting problem 06:26 Random vs guided adjustments 09:43 Derivatives 14:34 Gradient Descent 16:23 Higher dimensions 21:36 Chain Rule Intuition 27:01 Computational Graph and Autodiff 36:24 Summary 38:16 Shortform 39:20 Outro USEFUL RESOURCES: Andrej Karpathy's playlist: https://youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ&si=zBUZW5kufVPLVy9E Jürgen Schmidhuber's blog on the history of backprop: https://people.idsia.ch/~juergen/who-invented-backpropagation.html CREDITS: Icons by https://www.freepik.com/

Discover Artificial Intelligence, 3rd Edition, David L. Poole, HB ISBN: 9781009258197 on Higher Education from Cambridge

A CLI that helps you easily install and manage Model Context Protocol Servers. Simple package management with comprehensive analytics and GitHub integration.

A collection of MCP servers. Contribute to punkpeye/awesome-mcp-servers development by creating an account on GitHub.

Everyone is starting to realize how big of a deal Claude’s Model Context Protocol (MCP) is - it’s the first ever “standard” for connecting LLMs with services like your database, Slack, GitHub, web search, etc. It’s VERY powerful and not well understood by many, so in this video I break down everything you need to know about MCP at a high level. I go quick here unlike my usual videos, but I call out a bunch of different resources you can use to dive into anything deeper that you’re curious about - MCP architecture, building your own MCP server, integrating your custom AI agent with MCP, etc. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Check out Stagehand, an incredible tool to crawl and scrape websites with natural language which I used in this video: https://github.com/browserbase/stagehand And here is the Stagehand MCP server that I showcased (you will need a Browserbase API key which is free to start!): https://github.com/browserbase/mcp-server-browserbase/blob/main/stagehand/README.md ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Documentation for Claude’s MCP: https://modelcontextprotocol.io/introduction List of MCP Servers on GitHub: https://github.com/modelcontextprotocol/servers Example n8n MCP Agent: https://github.com/coleam00/ottomator-agents/tree/main/n8n-mcp-agent n8n Community Node for MCP: https://github.com/nerding-io/n8n-nodes-mcp Example Pydantic AI MCP Agent: https://github.com/coleam00/ottomator-agents/tree/main/pydantic-ai-mcp-agent Dive deep into the architecture of MCP: https://modelcontextprotocol.io/docs/concepts/architecture ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 00:00 - MCP is Blowing Up 01:55 - What is MCP? 03:12 - Making MCP "Click" (Deep Dive with Diagrams) 05:33 - How Agents Work with MCP 07:16 - Word of Caution - What MCP Isn't 08:17 - Where You Can Use MCP 09:47 - MCP Servers You Can Use NOW 11:18 - How to Set Up MCP Servers 12:08 - Using MCP Servers in Claude Desktop 13:11 - MCP Demo in Claude Desktop (Brave + Stagehand) 14:09 - Building with MCP (Servers and Clients) 15:22 - Building Your Own MCP Server 18:09 - MCP with n8n AI Agents 20:10 - MCP with Python AI Agents 21:56 - The Future of MCP 23:51 - Outro ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Join me as I push the limits of what is possible with AI. I'll be uploading videos at least two times a week - Sundays and Wednesdays at 7:00 PM CDT!

Create a Python MCP server. Contribute to modelcontextprotocol/create-python-server development by creating an account on GitHub.

In this comprehensive guide, the author discusses MCP, it's components, and takes a deep dive on how to build servers from scratch.

Nicholas Carlini, previously deeply skeptical about the utility of LLMs, discusses at length his thoughts on where the technology might go. He presents compelling, detailed arguments for both ends of …

Thanks to KiwiCo for sponsoring today’s video! Go to https://www.kiwico.com/welchlabs and use code WELCHLABS for 50% off your first monthly club crate or for 20% off your first Panda Crate! MLA/DeepSeek Poster at 17:12 (Free shipping for a limited time with code DEEPSEEK): https://www.welchlabs.com/resources/mladeepseek-attention-poster-13x19 Limited edition MLA Poster and Signed Book: https://www.welchlabs.com/resources/deepseek-bundle-mla-poster-and-signed-book-limited-run Imaginary Numbers book is back in stock! https://www.welchlabs.com/resources/imaginary-numbers-book Special Thanks to Patrons https://www.patreon.com/c/welchlabs Juan Benet, Ross Hanson, Yan Babitski, AJ Englehardt, Alvin Khaled, Eduardo Barraza, Hitoshi Yamauchi, Jaewon Jung, Mrgoodlight, Shinichi Hayashi, Sid Sarasvati, Dominic Beaumont, Shannon Prater, Ubiquity Ventures, Matias Forti, Brian Henry, Tim Palade, Petar Vecutin, Nicolas baumann, Jason Singh, Robert Riley, vornska, Barry Silverman, Jake Ehrlich References DeepSeek-V2 paper: https://arxiv.org/pdf/2405.04434 DeepSeek-R1 paper: https://arxiv.org/abs/2501.12948 Great Article by Ege Erdil: https://epoch.ai/gradient-updates/how-has-deepseek-improved-the-transformer-architecture GPT-2 Visualizaiton: https://github.com/TransformerLensOrg/TransformerLens Manim Animations: https://github.com/stephencwelch/manim_videos Technical Notes 1. Note that DeepSeek-V2 paper claims a KV cache size reduction of 93.3%. They don’t exactly publish their methodology, but as far as I can tell it’s something likes this: start with Deepseek-v2 hyperparameters here: https://huggingface.co/deepseek-ai/DeepSeek-V2/blob/main/configuration_deepseek.py. num_hidden_layers=30, num_attention_heads=32, v_head_dim = 128. If DeepSeek-v2 was implemented with traditional MHA, then KV cache size would be 2*32*128*30*2=491,520 B/token. With MLA with a KV cache size of 576, we get a total cache size of 576*30=34,560 B/token. The percent reduction in KV cache size is then equal to (491,520-34,560)/492,520=92.8%. The numbers I present in this video follow the same approach but are for DeepSeek-v3/R1 architecture: https://huggingface.co/deepseek-ai/DeepSeek-V3/blob/main/config.json. num_hidden_layers=61, num_attention_heads=128, v_head_dim = 128. So traditional MHA cache would be 2*128*128*61*2 = 3,997,696 B/token. MLA reduces this to 576*61*2=70,272 B/token. Tor the DeepSeek-V3/R1 architecture, MLA reduces the KV cache size by a factor of 3,997,696/70,272 =56.9X. 2. I claim a couple times that MLA allows DeepSeek to generate tokens more than 6x faster than a vanilla transformer. The DeepSeek-V2 paper claims a slightly less than 6x throughput improvement with MLA, but since the V3/R1 architecture is heavier, we expect a larger lift, which is why i claim “more than 6x faster than a vanilla transformer” - in reality it’s probably significantly more than 6x for the V3/R1 architecture. 3. In all attention patterns and walkthroughs, we’re ignoring the |beginning of sentence| token. “The American flag is red, white, and” actually maps to 10 tokens if we include this starting token, and may attention patterns do assign high values to this token. 4. We’re ignoring bias terms matrix equations. 5. We’re ignoring positional embeddings. These are fascinating. See DeepSeek papers and ROPE.