Adam Kaminski thimslugga

Random wolfi and tools.

get-archive-info - get a tar tvf output and the .APKINFO for every file in the archive.
build-stage - throw a bunch of files and see which build. they do not depend on each other (each only builds with the wolfi repo)

I used this to help create batches of things when changing lots of files.
test-installable - its like the c-i test that checks that all packages

Flutter CI/CD & Code Signing — Complete Reference Guide

Purpose: Reusable reference for setting up automated builds, code signing, and deployment for Flutter apps across iOS and Android.

Last Updated: July 2025

Audience: Solo developers & small teams

Linux Distro GCC GLIBC C++-Standard versions

GLIBC versions: https://launchpad.net/ubuntu/+source/glibc https://code.launchpad.net/ubuntu/+source/glibc

Build from source

apt-get install gawk bison gcc make wget tar -y

HyperShift

Set environment variables

CLUSTER_NAME="rosa-hcp-rcs"
PREFIX_NAME="hcp-rcs"
REGION="us-east-1"
VERSION="4.14.9"
USER=rcarrata

How to install MS Core fonts on Fedora

install dependencies

$ sudo dnf install -y rpm-build ttmkfdir

prep the work space

Agent Skills: References, Recipes & Token Loading Behavior

Overview

This document captures learnings about how Agent Skills handle references/, recipes/, and services/ folders, including token budget implications and best practices based on the AgentSkills.io specification and GitHub Copilot implementation behavior.

Progressive Disclosure Model

Agent Skills use a three-tier loading model to efficiently manage LLM context windows:

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.

	bind-key C-b send-prefix
	bind-key C-o rotate-window
	bind-key C-z suspend-client
	bind-key Space next-layout
	bind-key ! break-pane
	bind-key " split-window
	bind-key # list-buffers
	bind-key $ command-prompt -I #S "rename-session '%%'"
	bind-key % split-window -h
	bind-key & confirm-before -p "kill-window #W? (y/n)" kill-window

	#!/usr/bin/python

	import argparse
	import os
	import shlex
	import sys
	import tempfile
	import yaml

	config = yaml.full_load(open(os.path.expanduser("~/.config/sandbox.yml")))

	#!/usr/bin/env python3
	"""
	Antigravity to Antigravity IDE Migration Tool
	==============================================
	A self-contained script to automatically migrate extensions, custom settings, keybindings,
	snippets, workspace states, and entire conversation histories from Antigravity v1
	to the new Antigravity IDE.

	Author: Antigravity AI Coding Assistant (pair-programmed with USER)
	License: MIT