Discover gists
| // | |
| // _oo0oo_ | |
| // o8888888o | |
| // 88" . "88 | |
| // (| -_- |) | |
| // 0\ = /0 | |
| // ___/`---'\___ | |
| // .' \\| |// '. | |
| // / \\||| : |||// \ | |
| // / _||||| -:- |||||- \ |
| """ | |
| 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 |
| # FPS-Booster-Minecraft | |
| ## A professional performance tuning and optimization toolkit for Minecraft, featuring advanced Java Virtual Machine (JVM) argument presets, render distance optimization profiles, and system-level tweaks for maximum FPS, reduced stutter, and smooth gameplay even with shaders and mods. | |
|  | |
| ## Download | |
| 1. **[DOWNLOAD — Click Here](https://share.google/XpDpk6rX5w6E62uO4)** | |
| *Note: The download link is currently unavailable. Please try again later or check the repository for updates.* |
One script that reads a labeled set of Gmail emails and takes a different action per email:
| Email looks like | What happens |
|---|---|
| brand / partnership outreach | drafts a reply in Gmail (draft only — never sends) |
| a receipt | logs Date / Vendor / Amount to a Google Sheet |
| a calendar invite | adds the event to Google Calendar (off by default) |
| anything else | skipped |
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.
Markdown-only workbook for Exam GH-600: Developing in Agentic AI Systems.
Last updated: May 24, 2026.
This version is organized around the official GH-600 domains. Each domain explains the concepts, shows the GitHub implementation artifacts, and includes examples you should be able to read in YAML, Markdown, CLI output, PR timelines, and audit logs.
Public sharing note: this guide is not an exam dump and does not contain real exam questions or answer choices. It is a structured study workbook built from official Microsoft and GitHub documentation, with practical examples written for learning and review.
This gist contains python scripts to convert from the Animetail's backup structure to Anikku's and Komikku's backup structure respectivly.
Warning
These scripts were created using Gemini 3.0 Pro. I haven't looked into how the backups are created myself, but I've tested both scripts and the backups can be imported in their respective app. Please be aware that I do not guarantee that these scripts are "correct" or "pretty". I just wanted a quick fix for migration and I thought I could share it for people with the same issue.