Para empezar el curso, les dejo una lista de las instalaciones generales que les recomiendo. Recuerden que más adelante tendremos una sección dedicada a la configuración y generalidades de ClaudeCode, estas son configuraciones generales del equipo.
Discover gists
| Home/Core TX9XD-98N7V-6WMQ6-BX7FG-H8Q99 | |
| Home/Core (Country Specific) PVMJN-6DFY6-9CCP6-7BKTT-D3WVR | |
| Home/Core (Single Language) 7HNRX-D7KGG-3K4RQ-4WPJ4-YTDFH | |
| Home/Core N 3KHY7-WNT83-DGQKR-F7HPR-844BM | |
| Professional W269N-WFGWX-YVC9B-4J6C9-T83GX | |
| Professional N MH37W-N47XK-V7XM9-C7227-GCQG9 | |
| Professional Enterprise | |
| Professional Workstation | |
| Enterprise NPPR9-FWDCX-D2C8J-H872K-2YT43 | |
| Enterprise N DPH2V-TTNVB-4X9Q3-TJR4H-KHJW4 |
These are only examples, for a few very common actions. You are expected to write your own rules for the rest. The syntax is regular JavaScript, but see the polkit(8) manpage for the object structure and available API. These examples are for polkit versions 106 and later, with the JS interpreter. They won't work with Debian's polkit v105.
-
If you don't know the action name, either run
pkactionand look for anything similar:pkaction | grep cups...or try to perform the actual action, cancel it, then look in your system logs:
journalctl -t polkitd -n 10 | grep action
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
| -------------------------------------------- | |
| Version: 1.45.1 | |
| Commit: 5763d909d5f12fe19f215cbfdd29a91c0fa9208a | |
| Date: 2020-05-14T08:33:47.663Z | |
| Electron: 7.2.4 | |
| Chrome: 78.0.3904.130 | |
| Node.js: 12.8.1 | |
| V8: 7.8.279.23-electron.0 | |
| OS: Darwin x64 18.5.0 | |
| ------------------------------------------- |
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.
Compiled from awesome-lists (restyler/awesome-sandbox, webcoyote/awesome-AI-sandbox, bureado/awesome-agent-runtime-security) and a survey of vendor blogs / field guides published through 2026. Grouped by isolation primitive and then by deployment model.
These rely on kernel/userland features to constrain a normal host process. Lowest overhead, weakest boundary.
- macOS Seatbelt /
sandbox-exec— Apple's TrustedBSD-based MAC framework. Used directly by Codex CLI, Gemini CLI, and underneath Anthropic'ssrt. - Linux Landlock — Unprivileged filesystem/network LSM; default backend for Codex CLI on Linux.
| // the cursor itself glides between cells instead of jumping. | |
| // Unlike a trail shader, this one is the focused cursor, so it | |
| // requires ghostty's own cursor to be hidden: | |
| // | |
| // cursor-opacity = 0 | |
| // custom-shader = "./shaders/cursor_glide.glsl" | |
| // --- CONFIGURATION --- | |
| const float DURATION = 0.14; // seconds for one glide |