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>" \
  -m"<optional footer>"

vHeemstra

Using Font Awesome 6 as Custom Icon set in Elementor

As many already know, Elementor is very reluctant to stay up-to-date with their CMS. It still uses Font Awesome version 5 and if you don't have a Pro license for Font Awesome, it is very difficult to integrate FA6 as icons.

So to help fellow developers, here is how you can do it!

In Elementor you can import icon sets that are exported by Fontello (as ZIP).

Because Fontello does not support FA6, you have to generate this ZIP file yourself.

vedovelli

# Prompt Para Replicar A Arquitetura Da SPA

Você é um arquiteto sênior de frontend. Sua tarefa é criar uma nova single page application com uma arquitetura **praticamente idêntica** à arquitetura descrita abaixo, mas **sem copiar nenhuma regra de negócio**, nomenclatura de domínio, fluxos específicos do produto original ou qualquer texto/fonte do projeto de origem.

O objetivo é reproduzir somente o **blueprint técnico e organizacional**:

- stack
- estrutura de pastas
- camadas
- convenções

jauderho

### WARNING: READ CAREFULLY BEFORE ATTEMPTING ###
#
# Officially, this is not recommended. YMMV
# https://www.raspberrypi.com/news/bookworm-the-new-version-of-raspberry-pi-os/
#
# This mostly works if you are on 64bit. You are on your own if you are on 32bit or mixed 64/32bit
#
# Credit to anfractuosity and fgimenezm for figuring out additional details for kernels
#

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.

nicolasalarconrapela

export ok='\e[1;32m \u2714'
export ko='\e[1;31m \u2715'
export ot='\e[1;30m'
export res='\033[0m'
whibla='\033[47m\033[1;30m'
#.gitconfig
[color]
	ui = true
[alias]
	glog = log --all --graph --decorate --oneline

Blackshome

blueprint:
  name: Holiday & Away Lighting
  description: >
    # ✈️ Holiday & Away Lighting

    **Version: 1.3**


    Make your home glow, no matter where you go! 🚗 💨