Writing

Warblize is an AI audiobook maker that turns EPUB, PDF, TXT, and DOCX files into narrated audiobooks. The page highlights chapter detection, automatic formatting cleanup, 47 languages, voice styles, and export-ready MP3s for publishing platforms.

An interactive guide and demo for prompt engineering that shows how analogies, examples, and format-matching can improve LLM outputs.

AI-native workspace for writers, bloggers, and lifelong learners. It combines source collection, an idea map, AI-assisted exploration, and a focused writing editor designed to support long-form thinking.

Extend AI turns a spoken or rough written idea into posts that match the user’s own voice across LinkedIn, X, Instagram, and more.

An interactive choose-your-own-adventure that explores 28 possible AI futures through branching questions and personalized vignettes.

An open-source Claude Code skill set for academic research, covering research, writing, review, revision, and finalization. It includes guided workflows, installation steps, and pipeline documentation for researchers using AI in a human-in-the-loop process.

AI assistant for chatting with PDFs, helping users edit, fill, and understand forms step by step. The demo page shows a loaded IRS W-9 and supports guided form-filling workflows.

An AI email assistant that lets people send a question or long email to [email protected] and get back a summary, answer, actions, dates, or draft response by email, with no signup and a free tier.

A font project and keyboard firmware concept that turns typed text into a noise-like character stream while rendering readable output only through the font itself.

AI-powered book recommendation site that suggests nonfiction and other meaningful reads based on what you want to learn or match your vibe.

A technical blog post about using devcontainer + Nix + Docker, plus small helper tools, to keep one reproducible dev environment working for humans, CI, and AI agents.

Enough is a beta personal language system for planning, writing, reviewing, and translation. It supports local models and OpenRouter, and is aimed at users who want more control over their data while building a flexible personal knowledge workflow.

An opinionated write-up on where multi-agent systems have and have not delivered value, with concrete comparisons across coding, images, CAD, and BIM, plus a description of Blade’s evidence-first architecture.

An essay arguing that second-person system prompts create attentional overhead and identity fragmentation in agent design, and proposing first-person initialization as an alternative.

An open-source probabilistic spreadsheet that lets cells hold distributions, confidence intervals, and ranges instead of single values, with formulas that propagate uncertainty.

A MotherDuck blog interview with Wes McKinney on practical AI coding workflows, including spec-driven development, continuous AI code review, and how to keep agent-generated code maintainable.

A detailed tutorial that explains what agent tools are and walks through a basic set of implementation patterns for an AI agent in Python, including bash, file, search, edit, and web fetch tools.

A short policy file for the ripgrep project that explains how contributors may use AI tools while contributing, and sets rules for issues, pull requests, and comments.

A small web app for taking a break from AI and recording a short session away from LLMs.

An educational article explaining world models, latent states, dynamics learning, and planning for agents, with examples from gridworld, Dreamer, and MuZero.

AImpostor is an Electron-based ChatGPT wrapper for macOS that mimics Claude’s visual style and color scheme. The repo includes an install script for placing the app in ~/Applications and a screenshot asset.

An independent white paper proposing a local-deployment path for frontier AI weights, with a PDF, generator script, and a detailed README outlining the argument, sections, and contribution process.

A GitHub-hosted guidelines file for AI coding assistants used in Stanford CS336. It tells assistants how to help students with debugging, explanations, and feedback while avoiding direct solutions or code generation.

An article on applying pace-layer thinking to AI-assisted software development. It explains where generative AI fits well in fast-changing layers, where it is risky in slower layers, and how architecture should separate the two.