AI/ML

AI/ML

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Teaching LLMs how to solid model
Teaching LLMs how to solid model
It turns out that LLMs can make CAD models for simple 3D mechanical parts. And, I think they’ll be extremely good at it soon.
·willpatrick.xyz·
Teaching LLMs how to solid model
What I've learned about writing AI apps so far | Seldo.com
What I've learned about writing AI apps so far | Seldo.com
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
·seldo.com·
What I've learned about writing AI apps so far | Seldo.com
Prompt Engineering | Kaggle
Prompt Engineering | Kaggle
Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals.
·kaggle.com·
Prompt Engineering | Kaggle
A quote from Drew Breunig
A quote from Drew Breunig
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, …
·simonwillison.net·
A quote from Drew Breunig
An LLM Query Understanding Service
An LLM Query Understanding Service
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.”
·simonwillison.net·
An LLM Query Understanding Service
Model Context Protocol has prompt injection security problems
Model Context Protocol has prompt injection security problems
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.
·simonwillison.net·
Model Context Protocol has prompt injection security problems
MCP + PydanticAI - Build powerful AI agents
MCP + PydanticAI - Build powerful AI agents
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.
·youtube.com·
MCP + PydanticAI - Build powerful AI agents
Pydantic Evals
Pydantic Evals
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.
·simonwillison.net·
Pydantic Evals
Function calling with Gemma
Function calling with Gemma
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 …
·simonwillison.net·
Function calling with Gemma
microsoft/playwright-mcp
microsoft/playwright-mcp
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 …
·simonwillison.net·
microsoft/playwright-mcp
The Most Important Algorithm in Machine Learning
The Most Important Algorithm in Machine Learning
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/
·youtube.com·
The Most Important Algorithm in Machine Learning