| [CONST_PARAM] | |
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| name | cognitive-rhythm-writing |
|---|---|
| description | 説明的な文章に緩急を設計するための規範。緩急を装飾ではなく認知モードの切替（観察→逡巡→断定→再観察）と未回収の緊張の管理として扱い、文の拍、段落の密度波形、節の入り方、緩みと駄文の判別、執筆後の機械的な点検手順を定める。読み物として読ませたい章・記事・解説文を生成するとき、または「密度はあるが平坦でおもしろくない」文章を診断・修正するときに使用する。 |
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。
This year, we've seen some remarkable leaps in the world of Large Language Models (LLMs). Models like O1, GPT-4o, and Claude Sonnet 3.5 have shown how far LLM capabilities have come, pushing the boundaries of coding, reasoning, and self-reflection. O1, in particular, is one of the best models on the market, known for its self-reflection capabilities, which allows it to iteratively improve its reasoning over time. GPT-4o offers a wide range of capabilities, making it incredibly versatile across tasks, while Claude Sonnet 3.5 excels at coding, solving complex problems with higher efficiency.
What many people don’t realize is that these high-performing models are essentially fine-tuned versions of underlying models. Fine-tuning allows these models to be optimized for specific tasks, making them more useful for things like analysis, coding, and decision-making
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| #define WIN32_LEAN_AND_MEAN | |
| #include <tchar.h> | |
| #include <stdint.h> | |
| #include <windows.h> | |
| #include <shellapi.h> | |
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| LPTSTR pchFontName = _T("Times New Roman"); | |
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Lecture 1: Introduction to Research — [📝Lecture Notebooks] [
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[
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| T3ZWQ-P2738-3FJWS-YE7HT-6NA3K | |
| KFZUS-F3JGV-T95Y7-BXGAS-5NHHP | |
| 65Z2L-P36BY-YWJYC-TMJZL-YDZ2S | |
| SFZHH-2Y246-Z483L-EU92B-LNYUA | |
| GSZVS-5W4WA-T9F2E-L3XUX-68473 | |
| FTZ8A-R3CP8-AVHYW-KKRMQ-SYDLS | |
| Q3ZWN-QWLZG-32G22-SCJXZ-9B5S4 | |
| DAZPH-G39D3-R4QY7-9PVAY-VQ6BU | |
| KLZ5G-X37YY-65ZYN-EUSV7-WPPBS |
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.
