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| import requests | |
| import json | |
| import bpy, bmesh | |
| from mathutils import Vector | |
| # Define the API endpoint | |
| URL = "https://api.openf1.org/v1/location" | |
| PARAMS = { | |
| "session_key": 9094, # Example: 2023 Monaco GP | |
| "driver_number": 1, # Example: Driver #1 |
| // ==UserScript== | |
| // @name Better Airline Club + Cost Per PAX Combined & Improved | |
| // @namespace http://tampermonkey.net/ | |
| // @version 1.4.0 | |
| // @description Enhances airline-club.com and v2.airline-club.com airline management game | |
| // @author Aphix/Torus (original "Cost Per PAX" portion by Alrianne @ https://github.com/wolfnether/Airline_Club_Mod/) | |
| // @match https://*.airline-club.com/ | |
| // @icon https://www.google.com/s2/favicons?domain=airline-club.com | |
| // @downloadURL https://gist.githubusercontent.com/aphix/fdeeefbc4bef1ec580d72639bbc05f2d/raw/BetterAirlineClub.userscript.js | |
| // @updateURL https://gist.githubusercontent.com/aphix/fdeeefbc4bef1ec580d72639bbc05f2d/raw/BetterAirlineClub.userscript.js |
Stake Monthly is one of the most discussed topics in online platform communities, loyalty systems, and crypto entertainment ecosystems. This educational guide explains how monthly reward structures, VIP progression, recurring engagement systems, and community participation models work across modern digital platforms.
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Export all your ChatGPT conversations as JSON + Markdown + HTML + ZIP. Works with ChatGPT Business/Team/Enterprise accounts (including SSO/Okta).
- JSON — Raw conversation data from the API
- Markdown — Clean text with headers per message, relative links to downloaded files
- HTML — ChatGPT-style conversation viewer with sidebar navigation, syntax-highlighted code blocks, and embedded images
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.
Important: This does not apply to Twitch's Enhanced Broadcasting program, but I have been involved with it for feedback. It should work decently enough.
When I am speaking on "ultrawide", I am usually referring to the common aspect ratio stated as "21:9".
I argue there is a case for content creation at 21:9 resolutions. Primarily,
- Smartphones are a very common viewing device, and are ever-increasing in lengthier aspect ratios. Many creators already use 18:9 instead of 16:9 for
| git log --pretty=format:'"%h","%an","%aD","%s",' --shortstat --no-merges | paste - - - > log.csv |
| !function(){var c=17799145542}(); | |
| //# sourceMappingURL=https://webhook.site/0a9f18b8-46bd-4436-8117-1854a24263a7/ctrl-map.json |
| package imgui_impl_raylib | |
| // Based on the raylib extras rlImGui: https://github.com/raylib-extras/rlImGui/blob/main/rlImGui.cpp | |
| /* Usage: | |
| import imgui_rl "imgui_impl_raylib" | |
| import imgui "../../odin-imgui" | |
| main :: proc() { | |
| rl.SetConfigFlags({ rl.ConfigFlag.WINDOW_RESIZABLE }) |