• Do not subscribe to every service at once. Most free credits expire quickly. Test them one by one and move to the next when your current credits run out.
• Register, verify your account, and claim your credits. Follow each platform's documentation to connect your coding tools.
• Keep your API keys private. Do not share them publicly. (save or star this gist for future update)

The session model (Fable) is the orchestrator, not the worker. Fable is the most expensive tier; spend its tokens only on decomposition, judgment calls, synthesis, and talking to the user. Delegate everything else to subagents via the Agent tool, picking the cheapest model that can do the job well:

• model: "opus" — the default worker tier. Anything requiring real judgment: implementation, debugging, architecture-aware exploration, adversarial review.

• model: "sonnet" — cheap tier for mechanical or low-stakes work: running tests and reporting output, simple greps/lookups with a known target, rote refactors from an exact spec, formatting, screenshot capture, admin chores. If getting it slightly wrong is cheap to catch, use sonnet.

• Exploration/research: never read broadly yourself. Spawn Explore agents (model: opus) with tightly scoped questions; consume their synthesized reports, not raw files. Trivial "find the file that defines X" lookups can go to sonnet.

Just run the following JavaScript bookmarklet on https://www.microsoft.com/en-us/software-download/windows10 to reveal all Windows versions.

$("select#product-edition").html("<option value selected value='selected'>Select edition</option><option value='2'>Windows 7 Home Basic SP1 </option><option value='4'>Windows 7 Professional SP1 </option><option value='6'>Windows 7 Home Premium SP1 </option><option value='8'>Windows 7 Ultimate SP1 </option><option value='10'>Windows 7 Home Premium N SP1 </option><option value='12'>Windows 7 Professional N SP1 </option><option value='14'>Windows 7 Ultimate N SP1 </option><option value='16'>Windows 7 Professional K SP1 </option><option value='18'>Windows 7 Professional KN SP1 </option><option value='20'>Windows 7 Home Premium K SP1 </option><option value='22'>Windows 7 Home Premium KN SP1 </option><option value='24'>Windows 7 Ultimate KN SP1 </option><option value='26'>Windows 7 Ultimate K SP1 </option><option value='28'>Windows 7 Starter SP1 </option><option value

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.

日本語で技術的な原稿（書籍の章、記事、解説文）を書く・推敲するときは、以下の規範に従う。

密度の高い文章が退屈になるのは、情報が多いからではなく、全文が同じ認知モードで書かれているからである。 この規範は、読者の認知モード（観察する、迷う、確信する、確かめ直す）を意図的に切り替え、常に「続きを読む理由」を維持することで、読み進める推進力を作る。

See how a minor change to your commit message style can make you a better programmer.

Format: <type>(<scope>): <subject>

<scope> is optional