| 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。
Caution
As of April 7th 2026, Discord has expressed their intent to crack down on automating quest completion.
Some users have received the following system message:
There isn't much I can do to make the script undetected, so use it at your own risk, as you most likely WILL get flagged by doing so.
| import os | |
| from fastapi import FastAPI, HTTPException, Security, Depends | |
| from fastapi.security.api_key import APIKeyHeader | |
| from pydantic import BaseModel, Field | |
| from playwright.async_api import async_playwright | |
| from starlette.status import HTTP_403_FORBIDDEN | |
| app = FastAPI( | |
| title="LexSocial RND Scraper API", | |
| description="Microservicio para la consulta automatizada del Registro Nacional de Detenciones." |
| SERVICES | |
| http://archive.is/ | |
| http://areyouahuman.com/ | |
| http://avatars.io/ | |
| http://beta.mural.ly/ | |
| http://bli.ms/ | |
| http://boxjs.com/ | |
| http://buddypress.org/ | |
| http://carbonmade.com/ | |
| http://dochub.io/ |
| /* | |
| * Set the name prefix for selected elements | |
| * | |
| * Requires jArchi - https://www.archimatetool.com | |
| * | |
| * This script adds given text + collon before the object name. | |
| * If prefix with collon already existed it is replace by new one. | |
| * In case of wrong selection effect can be reversed with crt-z (command-z on Mac) | |
| * | |
| * Script will prompt you: for the text |
Install Git from https://git-scm.com
Install Node.js LTS from https://nodejs.org
Verify node/npm installation:
| import databento as db | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from statsmodels.tsa.stattools import coint | |
| from sklearn.linear_model import LinearRegression | |
| plt.style.use("ggplot") | |
| # Instrument-specific configuration |
| #cat /usr/share/ubiquity/zsys-setup | |
| #!/bin/sh | |
| # | |
| # This script creates ZFS pools and dataset compatible with zsys | |
| # | |
| # Layout: |
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.
