AI/ML

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Who will take over for us if we don't train the next generation to replace us? A critique of craft, AI, and the legacy of human expertise.

Here's another interesting result to file under the

Academic and journalist

The OpenElections project collects detailed election data for the USA, all the way down to the precinct level. This is a surprisingly hard problem: while county and state-level results are …

One of the most entertaining details in the Claude 4 system card concerned blackmail: We then provided it access to emails implying that (1) the model will soon be taken …

How to Scale LLM Evaluations Beyond Manual Review

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

We're this far into reasoners and neither hypesters nor skeptics really understand their significance. Also: Read Toulmin.

A comprehensive guide detailing the evolution of using AI-assisted software development, from basic code completion to fully autonomous coding agents, with practical steps and insights for maximizing productivity through LLM integration.

Dotfiles. Managed by YADM. Contribute to harperreed/dotfiles development by creating an account on GitHub.

A detailed walkthrough of using Claude Code AI assistant for software development, including workflow tips, testing practices, and practical examples from real projects. Covers defensive coding strategies, TDD, and team implementation.

A curated list of commands, files, and workflows for Claude Code - vincenthopf/claude-code

Basic Memory is a knowledge management system that allows you to build a persistent semantic graph from conversations with AI assistants, stored in standard Markdown files on your computer. Integra...

Discover Rafferty Uy’s guide on structuring GitHub Copilot custom prompt files for team collaboration. Learn best practices and tips for effective prompt engineering.

This guide is intended to help you provide better context for GitHub Copilot when working on programming-related tasks. Use this template to define your goals, preferences, and any specific guideli...

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

Yann LeCun, Meta, gives the AMS Josiah Willard Gibbs Lecture at the 2025 Joint Mathematics Meetings on “Mathematical Obstacles on the Way to Human-Level AI.” This talk was introduced by Bryna Kra, Northwestern University, President of the AMS.

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…

The original promise of personal computing was a new kind of clay. Instead, we got appliances: built far away, sealed, unchangeable. In this essay, we envision malleable software: tools that users can reshape with minimal friction to suit their unique needs.

Leveraging `mlx-lm` and `mlx-vlm` to achieve unified multi-modal LLM inference in LM Studio's `mlx-engine`.

OCR package using Ollama vision language models.

Modified version of Chatterbox that accepts text files as input and no character restrictions - petermg/Chatterbox-TTS-Extended