Alvaro writes about agent-shell, a native Emacs interface for interacting with LLM agents via the Agent Client Protocol (ACP). The project supports a broad ecosystem of coding agents, including Anthropic's Claude, OpenAI's Codex, Google's Gemini CLI, and Cursor, by leveraging their ACP implementations or dedicated ACP adapter packages. It is written entirely in Emacs Lisp, distributed via MELPA, and offers deep integration with the Emacs environment through features like diff review, file attachment, and screenshot pasting.
Hallmark is a design skill for Claude Code, Cursor, and Codex that prevents LLM-generated UIs from looking like typical AI output. It selects a macrostructure for a given brief, dresses it in one of twenty-one themes, and runs a suite of slop-test gates and a pre-emit self-critique to refuse on-distribution defaults. Different briefs produce fundamentally different shapes and structures rather than colour-swaps of a single template.
- Includes a `study` verb that extracts design DNA from a screenshot or URL
- Offers a Custom route that designs from first principles when no catalogue theme fits the brief
- Includes a `study` verb that extracts design DNA from a screenshot or URL
- Offers a Custom route that designs from first principles when no catalogue theme fits the brief
Richard Gill writes about his personal Pi coding agent setup, which utilizes OpenAI Codex Sol and Astra models at medium and high thinking levels while adhering to Pi's philosophy of simplicity. He relies primarily on `AGENTS.md` files and custom skills rather than complex configuration.
- Commands taking over 30 seconds automatically move to the background to prevent the agent from getting stuck
- The `sub-pi` extension enables spawning new Pi windows and worktrees via tmux
- Slash commands like `/diff` inject command output directly into context without triggering an LLM turn
- Context files and skills traverse parent directories up to `$HOME`
- Commands taking over 30 seconds automatically move to the background to prevent the agent from getting stuck
- The `sub-pi` extension enables spawning new Pi windows and worktrees via tmux
- Slash commands like `/diff` inject command output directly into context without triggering an LLM turn
- Context files and skills traverse parent directories up to `$HOME`
autoharness is a self-learning skill layer for Claude Code that distills reusable skills from a user's real sessions, merges near-duplicates, updates them in use, and prunes those that stop getting used — all without a daemon or an external benchmark. It fires on tool-call count rather than turns, keeps only the skills it authored, and validates a skill's worth by adherence in later turns rather than a held-out score.
- Skills are stored as plain native SKILL.md files in `.claude/skills/`; the plugin's own recall index is injected on top of the host's native mechanism
- Three distinct lifecycle signals are tracked: load (model invoked the skill), view (session read into the skill's directory), and patch (promoter landed an improvement)
- The `/learn` command allows on-demand distillation of the current session through the same proposal-and-validation chain
- Skills are stored as plain native SKILL.md files in `.claude/skills/`; the plugin's own recall index is injected on top of the host's native mechanism
- Three distinct lifecycle signals are tracked: load (model invoked the skill), view (session read into the skill's directory), and patch (promoter landed an improvement)
- The `/learn` command allows on-demand distillation of the current session through the same proposal-and-validation chain
Nolen Jonker writes that Google Antigravity, Claude Code, and Codex each occupy a distinct niche among LLM coding agents: Antigravity excels at frontend work requiring visual verification (via its built-in browser that navigates running apps and records proof), while Claude Code dominates multi-file refactoring and production code quality, and Codex dominates terminal automation, DevOps, and token-efficient high-volume workflows.
Antigravity's free tier is the only one offering full access to frontier models without a subscription, and its built-in Chrome browser lets agents visually verify frontend work by navigating and recording the running app. However, Claude Code leads on complex multi-file refactoring, and Codex dominates terminal automation and token efficiency.
Antigravity's free tier is the only one offering full access to frontier models without a subscription, and its built-in Chrome browser lets agents visually verify frontend work by navigating and recording the running app. However, Claude Code leads on complex multi-file refactoring, and Codex dominates terminal automation and token efficiency.
Beau Carnes writes about a new hands-on beginner's course on the freeCodeCamp.org YouTube channel designed to help developers master OpenAI Codex. The tutorial covers essential topics including installation, pricing tiers, and interface navigation, while also exploring advanced workflows like Plan Mode and Go Mode for autonomous software development.
- Features demonstrations of building a voice-controlled Flappy Bird clone using only prompts
- Covers managing external context through tools like Notion and Supabase
- Teaches how to convert open-source repositories into native iOS and Android apps via Expo
- Includes instructions on running scheduled background automations and handling GitHub pull requests
- Features demonstrations of building a voice-controlled Flappy Bird clone using only prompts
- Covers managing external context through tools like Notion and Supabase
- Teaches how to convert open-source repositories into native iOS and Android apps via Expo
- Includes instructions on running scheduled background automations and handling GitHub pull requests
Mahnoor Faisal writes that OpenAI Codex tends to over-engineer simple tasks by refactoring surrounding code, adding abstractions and defensive guards not requested, and she fixes this by appending a single boundary line to every prompt telling it to make the smallest change that fully solves the task and not add extras unless strictly required.
- The same one-line tweak previously improved prompts for Claude, Claude Code, NotebookLM and ChatGPT
- Over-scoping complaints are common on Reddit, especially with GPT-5.6 Sol
- OpenAI'''s focus on long-running autonomous work makes the model eager to find adjacent improvements
>"Make the smallest change that fully solves the task. Do not add abstractions, fallbacks, defensive guards, refactors, or features unless they are strictly required.”
- The same one-line tweak previously improved prompts for Claude, Claude Code, NotebookLM and ChatGPT
- Over-scoping complaints are common on Reddit, especially with GPT-5.6 Sol
- OpenAI'''s focus on long-running autonomous work makes the model eager to find adjacent improvements
>"Make the smallest change that fully solves the task. Do not add abstractions, fallbacks, defensive guards, refactors, or features unless they are strictly required.”
>"Pillar Security's research team, Eilon Cohen, Dan Lisichkin and Ariel Fogel, reproduced the bypasses over several months and published them today as a series they call the Week of Sandbox Escapes, one write-up a day."
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.
