An guide how to activate Windows 11 Pro for free
Because you will get some more features like an Bitlocker and host your device as an External Desktop which can be accessed through the internet
The answer is yes! You can switch from almost any edition to Pro completely for free!
People which already have Pro, but not activated, can skip to this step.
What you first need to do is open CMD (Command Prompt) as Administrator using this keyboard key:
A reusable GitHub Actions workflow that runs npm audit fix on a
schedule, bumps the patch version, and opens a pull request with the
resulting dependency updates. Drop it into any Node.js repository to
keep vulnerabilities patched automatically.
A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.
| #!/bin/bash | |
| # | |
| # mac-clean-v3.2.sh | |
| # macOS developer disk scanner + conservative interactive cleaner | |
| # Compatible with macOS Bash 3.2. | |
| # | |
| # Main ideas: | |
| # 1) Scan first. Delete nothing during discovery. | |
| # 2) Auto-detect common development roots (~/Dev, ~/Code, ~/Projects, ...). | |
| # 3) Identify projects by marker files before considering build/cache folders. |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
A hands-on one semester course where students build their own compiler from scratch, starting from elementwise programs and ending with training SOTA LLMs on GPUs. This course aggressively builds on the previous week, and is an exercise in slop management. If you let any slop in early, it will compound and you will not finish the class.
Course description: This course covers the design and implementation of a modern machine learning compiler, and examines the interaction between IR design, hardware capabilities, and the structure of machine learning programs. Topics covered include term rewriting, code generation, movement operators, kernel fusion, memory hierarchies, GPU architecture, automatic differentiation, and flash attention. It is a project course, providing experience with performance-oriented programming, managing a codebase that grows all semester, and working in 1 or 2 person teams, culminating in a compiler capable of training modern LLMs.
Prerequisites: This c
| # Яндекс Браузер в Ubuntu 26.04 не воспроизводит HLS-видео: Ffmpeg not found / libffmpeg.so — решение | |
| Короткая памятка для Ubuntu 26.04. | |
| Ситуация: Яндекс Браузер открывает страницу с HLS-видео, плеер отображается, поток вроде пытается загрузиться, но вместо картинки появляется ошибка про отсутствующий видеокодек. | |
| Например: | |
| ```text | |
| Отсутствует видеокодек. |