Reconstructed from Danny Postma's talk How I Built My Own AgentOS on Claude's Agent SDK (So You Can Too) (2026). This document is both a product spec for a human and an implementation prompt for an AI coding agent. Build exactly this system. Do not invent features that are not specified here.
Role contracts and prompts in this file are reconstructed from the talk, not his verbatim files. Mark every reconstructed prompt in code comments and docs as such.
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.
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.
Find original instructions for your linux flavor here: (https://snapcraft.io/docs/installing-snapd)[https://snapcraft.io/docs/installing-snapd].
For ubuntu 18.04:
sudo apt install snapd
Audit this entire codebase for materially useful simplifications in its data structures, state representation, control flow, algorithms, and ownership.
This is an audit-only exercise. Do not edit files, run tests, implement recommendations, commit, or push. Read-only inspection commands are allowed.
You are the coordinator. Continue until the complete codebase has been reviewed and the final audit is validated.
Inspect the repository and inventory every identifiable subsystem.
| # Licensed under a 3-clause BSD style license - see LICENSE.rst | |
| """ | |
| This module relies on the exoplanet database at exoplanets.org for information | |
| about exoplanets and their host stars, by Wright, J.T., Fakhouri, O., Marcy, | |
| G.W., et al. 2011, PASP, 123, 412 [1]_. | |
| .. [1] http://arxiv.org/abs/1409.7709 | |
| """ | |
| from __future__ import (absolute_import, division, print_function, |
| <script> | |
| const original_querySelectorAll = document.querySelectorAll; | |
| document.querySelectorAll = function () { | |
| if (arguments?.[0] == "[class]" && (new Error().stack).split("\n").filter(e=>e!="Error")?.[1]?.includes?.("tailwindcss/browser")) | |
| return [...original_querySelectorAll.apply(document,["*"])].map(e=>({classList:[...e.classList,...[...e.attributes].map(x=>x.name.replace(/⁄/g,'/'))]})); | |
| return original_querySelectorAll.apply(document,arguments); | |
| } | |
| const original_push = Array.prototype.push; | |
| Array.prototype.push = function () { | |
| if ((s=(new Error().stack).split("\n").filter(e=>e!="Error")?.[1],s)?.includes?.("enter") |
Markdown-only workbook for Exam GH-600: Developing in Agentic AI Systems.
Last updated: May 24, 2026.
This version is organized around the official GH-600 domains. Each domain explains the concepts, shows the GitHub implementation artifacts, and includes examples you should be able to read in YAML, Markdown, CLI output, PR timelines, and audit logs.
Public sharing note: this guide is not an exam dump and does not contain real exam questions or answer choices. It is a structured study workbook built from official Microsoft and GitHub documentation, with practical examples written for learning and review.
