Help with SQL commands to interact with a MySQL database
- Mac /usr/local/mysql/bin
- Windows /Program Files/MySQL/MySQL version/bin
- Xampp /xampp/mysql/bin
When connecting to a remote host with Ghostty, commands like
clear, tmux, or less may fail with:
'xterm-ghostty': unknown terminal type.
This happens because the remote host doesn't have Ghostty's terminfo entry. Here are
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.
How to build Claude Code mods: plugins that hook the engine, add commands and tools, and draw their own UI inside Claude Code, from status lines to animated panes.
By ruvnet. Written from building and shipping a real mod, with every pitfall we hit included.
Early access. Mods (function hooks) are an early-access Claude Code feature, and the API moves between releases. Your build writes its own type definitions next to every mod you load (see Setup), and those types are the authority. Grep them before you trust any guide, this one included.
Throughout this guide:
- 🧪 Field-tested means we did it in a shipped mod and saw it work (or break) for real.
| { | |
| "version": "3.6.0", | |
| "config": { | |
| "catalogs": [ | |
| { | |
| "id": "mdblist.official.justwatch-streaming-charts", | |
| "type": "movie", | |
| "name": "Top 100 Today", | |
| "enabled": true, | |
| "showInHome": true, |
| """ | |
| 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 |