qoomon

Conventional Commit Messages

See how a minor change to your commit message style can make a difference.

git commit -m"<type>(<optional scope>): <description>" \
 -m"<optional body>" \

jtrv

{
    "input": {
        "blocklist": [],
        "compressor#0": {
            "attack": 15.0,
            "boost-amount": 0.0,
            "boost-threshold": -72.0,
            "bypass": false,
            "dry": -80.01,
            "hpf-frequency": 10.0,

itachi-re

🧠 The Ultimate Neovim + LazyVim Plugin Stack (2026)

A ranked, no-fluff list of the plugins and setups actually worth knowing about on top of LazyVim, Neovim's most popular pre-configured distro, built on lazy.nvim. Get LazyVim running first, then layer these in via lua/plugins/ or toggle them with :LazyExtras.

mv ~/.config/nvim ~/.config/nvim.bak
git clone https://github.com/LazyVim/starter ~/.config/nvim
nvim

Klerith

Instalaciones - Curso de React Native

Esta es la lista de instalaciones recomendadas para el curso de React Native, si encuentran enlaces adicionales o cambios en esta hoja, pueden hacerlos.

milmazz

The Imposter's Handbook

This a collection of interesting links found in The Imposter's Handbook by Rob Conery.

Content:

• Introduction
• A Little History
• The Tools
• Customizing The Terminal

ketsuban

zsh users are advised to use the terminfo database as the most portable way to assign commands to particular keychords, but I haven't been able to find a database of which terminfo sigils correspond to which keys. Fortunately, I found enough information written in ancient Sumerian that I could translate it into something modern humans can comprehend.

Key Chord	Terminfo Name
Backspace	kbs
Ctrl-Backspace	cub1
Insert	kich1
Shift-Insert	kIC
Alt-Insert	kIC3
Shift-Alt-Insert	kIC4

synchronic1

Medina 14B: Multi-GPU LoRA Hot-Swap Inference Benchmark

A Specialized Model Architecture for Dynamic Persona Adaptation

Date: 2026-05-05
Author: Peter (Synchronic1) / Carlsson Creative
Host: VM104 (192.168.1.243), Proxmox VM on Dell PowerEdge T630
llama.cpp Version: 8240 (d088d5b74)

---

karpathy

"""
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

JJTech0130

import base64
import json
import logging
import plistlib
import random
import uuid
from datetime import datetime

import requests
import urllib3

karpathy

LLM Wiki

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.

The core idea

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.