Brajesh2022

🚀 Recommended Installation (Auto-Installer)

The easiest and community-recommended way to install Antigravity CLI on Termux is using the standalone auto-installer.

curl -fsSL https://raw.githubusercontent.com/wallentx/antigravity-cli-termux/dev/install.sh | bash

How it works: This script instantly installs the latest release directly from the wallentx/antigravity-cli-termux repository. A huge thanks to the original repository owner, @wallentx, for packaging these releases! The repository automatically runs a GitHub Action every six hours that fetches the official binary and patches it using the exact methods outlined in this guide below.

dschep

CLASSPATH=$(pkg=$(pm path com.koushikdutta.backup);echo ${pkg#*:}) app_process /system/bin com.koushikdutta.shellproxy.ShellRunner2

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>"

citizen428

CC ?= clang
CFLAGS = -std=c23 -Wall -Werror

run: clean abuse
	@./abuse

abuse:
	@$(CC) $(CFLAGS) -I. -o abuse abuse.c

clean:

mattezell

Antigravity / Antigravity IDE — Linux tarball installer

Antigravity 2.0 and the Antigravity IDE currently ship for Linux as raw .tar.gz archives — no .deb, no .AppImage, no Flatpak, no installer. This gist is a small set of shell scripts that turn one of those extracted tarballs into a proper user-local install: a desktop launcher in your app menu, a CLI symlink on your $PATH, an icon, and (on Ubuntu 24.04+) a working Chromium sandbox via an AppArmor profile.

Status: community workaround. Replace with whatever Google ships

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.

skarllot

# Each distribution default Bash prompts


# Gentoo (/etc/bash/bashrc)
if [[ ${EUID} == 0 ]] ; then
    PS1='\[\033[01;31m\]\h\[\033[01;34m\] \W \$\[\033[00m\] '
else
    PS1='\[\033[01;32m\]\u@\h\[\033[01;34m\] \w \$\[\033[00m\] '
fi

liamappelbe

import random
import sys

# Install PIL first: pip install pillow
from PIL import Image

def clamp(x, lo, hi):
  return lo if x <= lo else hi if x >= hi else x

def dizzyDither(pix, size):

dmitry-osin

Полная шпаргалка по HTML

Содержание

1. Введение в HTML
2. Структура HTML-документа
3. Элементы и теги HTML
4. Атрибуты HTML
5. Текстовые элементы
6. Списки
7. Ссылки