aquaductape

#!/usr/bin/env bash

PWD=`pwd`
ZULU_URL='https://cdn.azul.com/zulu/bin/zulu7.38.0.11-ca-jdk7.0.262-linux_x64.tar.gz'
ZULU='zulu-jdk7'
ZULU_ARCHIVE="/opt/${ZULU}.tar.gz"
USER_DATA="${PWD}/data/user.data"

JAVA7="/opt/${ZULU}/bin/java"

szhu

macOS Keyboard Remapper

A command-line tool to view, set, and clear custom keyboard mappings on macOS using hidutil.

Usage

# View current mapping
./macos_keys.py

derv82

Full Circle (Story time)

In 2005 I wanted to get online and couldn't afford internet. I was working full time and going to school, and some classes needed a connection the school computer lab didn't always have.

Enter: Sandman

My friend Sandman came over with a laptop running Slackware, and after a few hours of guides, man pages, and troubleshooting, we were on my neighbor's WEP network. The connection was spotty and I never downloaded anything on it, just did schoolwork.

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.

imikerussell

#!/usr/bin/env bash
# buzz-untag — make ONE Buzz agent answer you without an @-mention.
#
#   buzz-untag.sh <AgentName>          wire it up
#   buzz-untag.sh --undo <AgentName>   put everything back
#
# Every change is per-agent. No other agent is touched, and no file is shared.
set -euo pipefail

UNDO=0

andrisgauracs

#!/usr/bin/env bash
# esp32-ai: test a custom prompt against an already-trained, already-flashed model.
# Does NOT retrain and does NOT rewrite the 15MB model partition.
#
# Usage:
#   ./run_custom_prompt.sh "Once there was a robot"
set -euo pipefail

if [ "$#" -lt 1 ]; then
  echo "Usage: $0 \"Your custom prompt\""

andrisgauracs

#!/usr/bin/env bash
# esp32-ai: clone, train, export, build, and flash — end to end
# Reproduces github.com/slvDev/esp32-ai from a bare board.
# Requires: ESP32-S3 N16R8 (16MB flash, 8MB PSRAM).
#
# Usage:
#   ./build_and_flash.sh              # full run: data prep, train, export, build, flash
#   ./build_and_flash.sh --skip-train # reuse an existing firmware/model/model.bin, just build + flash
set -euo pipefail

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

k16shikano

name	japanese-tech-writing
description	日本語の技術文書・書籍原稿の文章規範。整形（一文一行、引用ブロック、脚注、コラム記法）、段落と論証の構成（パラグラフライティング）、論証の厳密さ（ツッコミどころの除去）、読み手の負荷の管理、視点と語り、演出の抑制、LLM っぽい空句の禁止、冗長の排除を定める。日本語で技術書の章、草稿、記事、解説文を書くとき、または推敲・リライトするときに使用する。

日本語技術文書の文章規範

日本語で技術的な原稿（書籍の章、記事、解説文）を書く・推敲するときは、以下の規範に従う。

整形

jamct

Einrichtung eines Servers mit Nvidia-Hardware als LLM-Server

Treiber und Voraussetzungen

Für Ubuntu 24.04:

sudo apt update
sudo apt upgrade
sudo apt install nvidia-utils-580 nvidia-driver-580