Before we look at some common commands, I just want to note a few keyboard commands that are very helpful:
Up Arrow: Will show your last commandDown Arrow: Will show your next commandTab: Will auto-complete your commandCtrl + L: Will clear the screen
| [ | |
| { | |
| "ProviderGUID": "72d164bf-fd64-4b2b-87a0-62dbcec9ae2a", | |
| "ProviderName": "AccEventTool", | |
| "ProviderGroupGUID": "4f50731a-89cf-4782-b3e0-dce8c90476ba", | |
| "AssociatedFilenames": [ | |
| "accevent.exe", | |
| "inspect.exe", | |
| "narrator.exe", | |
| "srh.dll" |
Latest News: http://site.api.espn.com/apis/site/v2/sports/football/college-football/news
Latest Scores: http://site.api.espn.com/apis/site/v2/sports/football/college-football/scoreboard
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.
name: tufte-viz description: | Ideate and critique data visualizations using Edward Tufte's principles from "The Visual Display of Quantitative Information." Use this skill when: (1) Designing new data visualizations or charts (2) Critiquing or improving existing visualizations (3) Reviewing dashboards or reports for graphical integrity (4) Deciding between visualization approaches (5) Reducing chartjunk or improving data-ink ratio (6) Planning small multiples or high-density displays
| """ | |
| 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 |
| // ==UserScript== | |
| // @name pixiv ID表示 | |
| // @namespace http://tampermonkey.net/ | |
| // @version 2026-01-19 | |
| // @description pixivのブックマークページで非公開・削除された作品のIDを表示する | |
| // @author m4gnett | |
| // @match https://www.pixiv.net/users/*/bookmarks/artworks* | |
| // @icon https://www.google.com/s2/favicons?sz=64&domain=pixiv.net | |
| // @grant none | |
| // ==/UserScript== |
Context: Fedora/Bazzite/Kinoite/universal-blue/KDE/Flatpacks
When using KDE applications (like Okular or Gwenview) installed as Flatpaks in Fedora KDE Bazzite or other containerized Linux distributions, they may fail to open remote files (like those on Samba shares) when launched from Dolphin File Explorer.
Example error from Okular:
Could not open smb://... Reason: Unable to create KIO worker. Unknown protocol 'smb'.
| local states = {} | |
| local timer = nil | |
| local DVD_W, DVD_H = 300, 300 | |
| -- dvd | |
| local function bounce(s) | |
| s.x = s.x + s.vx | |
| s.y = s.y + s.vy | |
| if s.x <= s.mx or s.x + s.w >= s.mx + s.mw then |
