This open-source template provides a structured framework for building an LLM-powered second brain using Markdown, Git, and coding agents like Codex or Claude Code. It utilizes a Karpathy-style architecture designed to keep raw source materials immutable while allowing AI agents to synthesize that information into a maintained wiki layer. The system is built for durability and readability, making it ideal for use with tools like Obsidian.
Key features:
- Dual-layer structure separating raw data from synthesized wiki content
- Automated ingestion workflows using coding agents to update indexes and logs
- Git-based version control for reviewing and rolling back AI-generated changes
- Highly compatible with Obsidian and mobile capture workflows
Key features:
- Dual-layer structure separating raw data from synthesized wiki content
- Automated ingestion workflows using coding agents to update indexes and logs
- Git-based version control for reviewing and rolling back AI-generated changes
- Highly compatible with Obsidian and mobile capture workflows
Dominik Kundel demonstrated the versatility of the Codex app server by having theCodex AI agent autonomously integrate itself into the game DOOM.
- Codex modified the DOOM engine and game maps to create a functional, in-game terminal. This allows users to interact with the AI agent directly within the game world to perform coding or game-related tasks.
- The setup uses an Electron app with a fork of `doom-wasm`. The agent patched the game data (Freedoom) and implemented a custom C file (`codex_terminal.c`) so the terminal renders natively within the engine rather than as a simple UI overlay.
- Codex handled the entire end-to-end process—coding, testing, and verification—with minimal human intervention. It even used Playwright to "play" the game itself to verify that textures and logos rendered correctly from different angles.
The project serves as a "demo-driven" proof of concept to show that the Codex app server can be embedded into any software environment or workflow, from IDEs to video games.
- Codex modified the DOOM engine and game maps to create a functional, in-game terminal. This allows users to interact with the AI agent directly within the game world to perform coding or game-related tasks.
- The setup uses an Electron app with a fork of `doom-wasm`. The agent patched the game data (Freedoom) and implemented a custom C file (`codex_terminal.c`) so the terminal renders natively within the engine rather than as a simple UI overlay.
- Codex handled the entire end-to-end process—coding, testing, and verification—with minimal human intervention. It even used Playwright to "play" the game itself to verify that textures and logos rendered correctly from different angles.
The project serves as a "demo-driven" proof of concept to show that the Codex app server can be embedded into any software environment or workflow, from IDEs to video games.
This article by Sebastian Raschka explores the fundamental architecture of coding agents and agent harnesses. Rather than focusing solely on the raw capabilities of Large Language Models, the author delves into the surrounding software layers—the "harness"—that enable effective software engineering tasks. The piece identifies six critical components: providing live repository context, optimizing prompt shapes for cache reuse, implementing structured tool access, managing context bloat through clipping and summarization, maintaining structured session memory, and utilizing bounded subagents for task delegation. By examining these building blocks, the article illustrates how a well-designed system can significantly enhance the practical utility of both standard and reasoning models in complex coding environments.
Unusually detailed post explains how OpenAI handles the Codex agent loop. The article dives into the technical aspects of OpenAI's Codex CLI coding agent, including the agent loop, prompt construction, caching, and context window management.
The article details how their Codex CLI coding agent functions. OpenAI engineer Michael Bolin explains the "agent loop" – the process by which the AI receives user input, generates code, runs tests, and iterates with human supervision.
* **Agent Loop Mechanics:** The agent builds prompts with prioritized components (system, developer, user, assistant) and sends them to OpenAI’s Responses API.
* **Prompt Management:** The system handles growing prompt lengths (quadratic growth) through caching, compaction, and a stateless API design (allowing for "Zero Data Retention"). Cache misses can significantly impact performance.
* **Context Window:** Codex automatically compacts conversations to stay within the AI model's context window.
* **Open Source Focus:** OpenAI open-sources the CLI client for Codex, unlike ChatGPT, suggesting a different approach to development and transparency for coding tools.
* **Challenges Acknowledged:** The article doesn't shy away from the engineering challenges, like performance issues and bugs encountered during development.
* **Future Coverage:** Bolin plans to release further posts detailing the CLI’s architecture, tool implementation, and sandboxing model.
The article details how their Codex CLI coding agent functions. OpenAI engineer Michael Bolin explains the "agent loop" – the process by which the AI receives user input, generates code, runs tests, and iterates with human supervision.
* **Agent Loop Mechanics:** The agent builds prompts with prioritized components (system, developer, user, assistant) and sends them to OpenAI’s Responses API.
* **Prompt Management:** The system handles growing prompt lengths (quadratic growth) through caching, compaction, and a stateless API design (allowing for "Zero Data Retention"). Cache misses can significantly impact performance.
* **Context Window:** Codex automatically compacts conversations to stay within the AI model's context window.
* **Open Source Focus:** OpenAI open-sources the CLI client for Codex, unlike ChatGPT, suggesting a different approach to development and transparency for coding tools.
* **Challenges Acknowledged:** The article doesn't shy away from the engineering challenges, like performance issues and bugs encountered during development.
* **Future Coverage:** Bolin plans to release further posts detailing the CLI’s architecture, tool implementation, and sandboxing model.
One source of truth for all your AI CLI skills. Install once, sync everywhere — Claude, Cursor, Codex, Gemini, and 10+ more.
An extensible Model Context Protocol (MCP) server that provides intelligent semantic code search for AI assistants. Built with local AI models using Matryoshka Representation Learning (MRL) for flexible embedding dimensions.
This article details how the author successfully ran OpenAI's Codex CLI against a gpt-oss:120b model hosted on an NVIDIA DGX Spark, accessed through a Tailscale network. It covers the setup of Tailscale, Ollama configuration, and the process of running the Codex CLI with the remote model, including building a Space Invaders game.
