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Zig 0.15.x → 0.16.0 Migration — Quickstart Kit

This file is a turn-key quickstart for pointing an AI (or yourself) at a Zig codebase that needs a 0.15.x → 0.16.0 migration. It's small on purpose. The actual reference material lives in ZIG-0.16.0.md (1,800+ lines of changelog, patterns, decoder tables, and workflow playbook distilled from a real end-to-end port).

What this kit is and isn't

It IS: A field-tested migration playbook that took a ~7,300-line Zig 0.15.2 parser generator to 0.16.0 in one session. Tests passed 40/40 afterwards. Performance went up (187s → 1.73s test runtime). It captures both the API changes and the surprises that aren't obvious from release notes alone — most importantly, std.heap.DebugAllocator's up-to-1400× slowdown on allocator-heavy workloads in Debug builds.

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.

PowerShell Setup for Oh-My-Posh with Autocompletion, History, and Theme Selector

1. Install `Oh-My-Posh` Module

To install Oh-My-Posh for PowerShell, run the following command:

Install-Module oh-my-posh -Scope CurrentUser -Force -SkipPublisherCheck

	<!DOCTYPE html>
	<html>
	<head>
	<title></title>
	<meta charset="utf-8" />
	<script src="Scripts/jquery-1.9.1.min.js"></script>
	<link href="Content/bootstrap.min.css" rel="stylesheet" />
	<script src="Scripts/isRockFx.js"></script>
	<script>
	$(function () {

	"""
	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

	#!/bin/zsh

	:<<'ABOUT_THIS_SCRIPT'
	-----------------------------------------------------------------------

	Written by:William Smith
	Partner Program Manager
	Jamf
	bill@talkingmoose.net
	https://gist.github.com/b6637160b65b751824943ede022daa17

	pcm.inferno_spotify {
	type inferno
	@args.DEVICE_ID { type string }
	@args.BIND_IP { type string }
	@args.PROCESS_ID { type string }
	@args.CLOCK_PATH { type string }
	@args.RX_LATENCY_NS { type string }
	@args.TX_LATENCY_NS { type string }

	# note: too long ALSA device string may not work in some or all apps (noticed in PipeWire)

	#define WIN32_LEAN_AND_MEAN
	#include <windows.h>
	#include <tchar.h>
	#include <msi.h>
	#include <stdint.h>

	#define MSICALL __declspec(dllexport) __cdecl

	COLORREF rgbText = 0xffffff;
	LPTSTR pchFontName = _T("Times New Roman");