Discover gists
| #!/usr/bin/env python3 | |
| # /// script | |
| # requires-python = ">=3.10" | |
| # dependencies = [] | |
| # /// | |
| """Prune uv-managed Python installs, keeping only the newest patch per minor version. | |
| Why: uv installs a fresh patch release whenever one lands and never removes the old | |
| one, so `uv python list` slowly fills with 3.13.12, 3.13.13, 3.13.14, 3.13.15, and so | |
| on. Each of those is a complete standalone copy of Python sitting in |
| # Files for google Dorking | |
| # Resources | |
| [Bug Bounty Helper] (https://dorks.faisalahmed.me/) | |
| [Google Dorks] (https://www.boxpiper.com/posts/google-dork-list) | |
| https://www.uedbox.com/shdb/type/files-containing-juicy-info/ | |
| https://github.com/0xPugazh/Awesome-Dorks/blob/master/google-dorks.txt |
| sudo profiles show -type enrollment | |
| systemextensionsctl list | |
| ls -la /Library/LaunchAgents /Library/LaunchDaemons ~/Library/LaunchAgents | |
| plutil -p /Library/Receipts/InstallHistory.plist | |
| ls -la /Library/LaunchAgents | |
| ls -la /Library/LaunchDaemons | |
| ls -la ~/Library/LaunchAgents |
| Hi All! | |
| I've recently launched a tool that wraps many of the commands here with a user interface. This desktop application is currently available for macOS. There's a roadmap outlining planned features for the near future. | |
| Feel free to request any features you'd like to see, and I'll prioritize them accordingly. | |
| One of the most important aspects of this application is that every command executed behind the scenes is displayed in a special log section. This allows you to see exactly what’s happening and learn from it. | |
| Here's the link to the repository: https://github.com/Pulimet/ADBugger | |
| App Description: | |
| ADBugger is a desktop tool designed for debugging and QA of Android devices and emulators. It simplifies testing, debugging, and performance analysis by offering device management, automated testing, log analysis, and remote control capabilities. This ensures smooth app performance across various setups. |
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.
| #!/bin/bash | |
| #set -e | |
| # ============ 平台检测 ============= | |
| detect_platform() { | |
| case "$(uname -s)" in | |
| Darwin) | |
| OS="macOS" | |
| SHA_TOOL="shasum" | |
| OPEN_CMD="open" |
Adaptive bed mesh is merged into klipper master branch. You can use this feature without this custom macro. Official klipper adaptive bed mesh
- This macro will dynamically changing the bed mesh area based on the size of the parts will be printed. The fw will only probe on the area that the part will be printed (plus mesh_area_offset value)
