Scott J. Walter ScottJWalter

ZSH on Termux!

Spice up termux with beautiful themes and productivity plugins to make your life easier!

Install ZSH, GIT & LSD

pkg install zsh git lsd vim

Install Oh-My-Zsh

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.

Cherry picking commits across forks

When working with forks in Git, you may need to pull specific commits from one fork to another. This can be useful when you want to include a specific change from one fork to another without merging the entire branch.

From another fork to your fork

From your fork to another fork

From another fork to your fork

Identify the commit hash(es) of the commit(s) you want to cherry-pick. You can find this hash on GitHub or by using git log after fetching.

Building a react native app in WSL2

Install, build and debug a react native app in WSL2 (Windows Subsystem for Linux) and Ubuntu.

Install tools in Windows
Enable mirrored networking mode in WSL2
Install tools in WSL2
Connect to android hardware device from Windows
Connect to android hardware device from WSL2
Connect to android virtual device in Windows

	#!/bin/bash
	#
	# fs-reset.bash -- reset a directory tree's permissions

	# Set directories to 755
	find . -type d -exec chmod 755 {} +

	# Set files to 644
	find . -type f -exec chmod 644 {} +

	"""
	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

	alias oc="openclaw"
	alias occonfig="$EDITOR ~/.openclaw/openclaw.json"

	alias occ="openclaw channels list"
	alias occs="openclaw channels status"
	alias occl="openclaw channels logs"

	alias ocf="openclaw config"
	alias ocfg="openclaw config get"
	alias ocfs="openclaw config set"

	@echo off
	setlocal enabledelayedexpansion

	set "SCRIPT_DIR=%~dp0"
	set "BEST_MAJOR=-1"
	set "BEST_MINOR=-1"
	set "BEST_PATCH=-1"
	set "BEST_NAME="

	for /d %%D in ("%SCRIPT_DIR%scrcpy-win64-v*") do (

	#!/bin/sh
	set -fuC

	# --- USER CONFIG STARTS ---
	IN_KEYS="ingress\|input\|incoming\|ollama"
	OUT_KEYS="egress\|output\|outgoing"
	SEC_KEYS="drop\|deny\|reject\|block"
	# --- USER CONFIG ENDS ---

	show_help() {