| [ | |
| { | |
| "id": "collection-UGED6TEZ", | |
| "title": "Trending / New", | |
| "folders": [ | |
| { | |
| "id": "folder-9D1C5A0D", | |
| "title": "New Movies", | |
| "sources": [ | |
| { |
Generalized versions of all root .md files used by OpenClaw. These files are loaded into the agent's system prompt on every request (except MEMORY.md which is conditional).
Copy these as starting points and customize for your own setup. Replace <placeholders> with your values.
For best results, all forms mentioned below must be printed out, filled out and signed in ink, and mailed directly to OPM at the following address:
U.S. Office of Personnel Management
Attn: Federal Employee Retirement System
Post Office Box 45
Boyers, PA 16017-0045
In order to send directly to OPM, you must wait 30 days after your last day at GSA. However, because part of the process requires you to mail some forms to your financial institution and wait for them to send them back, you can start the process about 15 days after your last day.
| # OpenStruct has caveats and security problems and even leads to a rubocop offense: | |
| # https://ruby-doc.org/stdlib-3.1.0/libdoc/ostruct/rdoc/OpenStruct.html#class-OpenStruct-label-Caveats | |
| # https://msp-greg.github.io/rubocop/RuboCop/Cop/Style/OpenStructUse.html | |
| # | |
| # It's time to replace it. The feature I loved most about OpenStruct was being able to retrieve | |
| # values by calling a method instead of having to use hash access syntax. | |
| # The recommended way to go is Struct, but it is much more cumbersome than OpenStruct was. | |
| # So here's MethodAccessibleHash that allows to do that, but without OpenStruct's security problems. | |
| # | |
| # This revision adds writers, direct creation from Hash, as well as enhanced merging. |
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.
| --- | |
| version: "2" | |
| services: | |
| qbittorrent: | |
| image: linuxserver/qbittorrent:4.5.0 | |
| container_name: qbittorrent | |
| mem_limit: 3000m | |
| mem_reservation: 128M | |
| depends_on: | |
| - openvpn |
| // | |
| // dotdot.m | |
| // dotdot | |
| // | |
| // Created by roooot on 03.08.26. | |
| // Copyright (C) 2026 roooot | |
| // | |
| // This program is free software: you can redistribute it and/or modify | |
| // it under the terms of the GNU Affero General Public License as published | |
| // by the Free Software Foundation, either version 3 of the License, or |
