| // 1. Even or odd | |
| function isEven(value){ | |
| if (value % 2 == 0){ | |
| return true; | |
| } | |
| else | |
| return false; | |
| } |
This a curate guide to continue your preparation for technical coding interviews, with resources, tips, and practice problems organized by topic. Enjoyed 🙌
List of freely available resources to study computer graphics programming.
| { config, pkgs, modulesPath, lib, system, ... }: | |
| { | |
| imports = [ | |
| (modulesPath + "/profiles/qemu-guest.nix") | |
| ]; | |
| config = { | |
| #Provide a default hostname | |
| networking.hostName = lib.mkDefault "base"; |
This is a compiled list of falsehoods programmers tend to believe about working with time.
Don't re-invent a date time library yourself. If you think you understand everything about time, you're probably doing it wrong.
A guide how to get and activate Windows 8, 8.1, 10 and 11 Pro for free!
If you see the Windows keyboard button 
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.
| claude --model claude-opus-4-8 --permission-mode dontAsk |
