(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
| # Backup | |
| docker exec CONTAINER /usr/bin/mysqldump -u root --password=root DATABASE > backup.sql | |
| # Restore | |
| cat backup.sql | docker exec -i CONTAINER /usr/bin/mysql -u root --password=root DATABASE | |
This is a story about how I tried to use Go for scripting. In this story, I’ll discuss the need for a Go script, how we would expect it to behave and the possible implementations; During the discussion I’ll deep dive to scripts, shells, and shebangs. Finally, we’ll discuss solutions that will make Go scripts work.
While python and bash are popular scripting languages, C, C++ and Java are not used for scripts at all, and some languages are somewhere in between.
| # Default config for sway | |
| # | |
| # Copy this to ~/.config/sway/config and edit it to your liking. | |
| # | |
| # Read `man 5 sway` for a complete reference. | |
| ### Variables | |
| # | |
| # Logo key. Use Mod1 for Alt. | |
| set $mod Mod4 |
| #include <iostream> | |
| #include <vector> | |
| #include <fstream> | |
| #include <sstream> | |
| #include <faiss/IndexBinaryIVF.h> | |
| #include <faiss/AutoTune.h> | |
| #include <faiss/IndexBinaryFlat.h> | |
| using namespace std; |
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.