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** 由于此文年事已久,可能某些 URL Schemes 已失效,可在评论区留言指出!(最后更新于 2024.10.28)
由于苹果的各应用都是在沙盒中,不能够互相之间访问或共享数据。但是苹果还是给出了一个可以在 APP 之间跳转的方法:URL Scheme。简单的说,URL Scheme 就是一个可以让 APP 相互之间可以跳转的协议。每个 APP 的 URL Scheme 都是不一样的,如果存在一样的 URL Scheme,那么系统就会响应先安装那个 APP 的 URL Scheme,因为后安装的 APP 的 URL Scheme 被覆盖掉了,是不能被调用的。
| Windows Registry Editor Version 5.00 | |
| [HKEY_CLASSES_ROOT\CLSID\{0E270DAA-1BE6-48F2-AC49-4E2781B29975}] | |
| @="Creative Cloud Files" | |
| "System.IsPinnedToNameSpaceTree"=dword:00000000 | |
| [HKEY_CLASSES_ROOT\Wow6432Node\CLSID\{0E270DAA-1BE6-48F2-AC49-4E2781B29975}] | |
| @="Creative Cloud Files" | |
| "System.IsPinnedToNameSpaceTree"=dword:00000000 |
| import type { CustomToolFactory } from '@oh-my-pi/pi-coding-agent'; | |
| const factory: CustomToolFactory = (pi) => { | |
| const z = pi.zod; | |
| return { | |
| name: 'agy_web_search', | |
| label: 'AGY Web Search', | |
| description: | |
| 'Search and retrieve public web evidence through Antigravity CLI. MUST be used for unknown-URL web research instead of native OMP web search providers. Returns source URLs and page excerpts for the calling model to synthesize; use read for known URLs and browser for interactive pages.', |
| // Path to store all incoming requests | |
| define('LOG_OUTPUT', __DIR__ . '/requests.log'); | |
| $data = sprintf( | |
| "[%s]\n%s %s %s\n\nHTTP HEADERS:\n", | |
| date('c'), | |
| $_SERVER['REQUEST_METHOD'], | |
| $_SERVER['REQUEST_URI'], | |
| $_SERVER['SERVER_PROTOCOL'] | |
| ); |
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.
| """Fusion-style delegation harness built with the OpenHands SDK. | |
| Install: | |
| uv pip install openhands-sdk openhands-tools | |
| Run: | |
| export LLM_API_KEY="..." # or export OPENHANDS_API_KEY="..." | |
| export MAIN_MODEL="openhands/gpt-5.5" | |
| export SIDEKICK_MODEL="openhands/minimax-m2.7" | |
| uv run python fusion_harness_example.py "Find and fix the failing tests in this repo." |
Each developer gets a stable HTTPS URL on your domain (foo.dev.example.com) pointing at his laptop while his tunnel is up — without paying for ngrok reserved domains or handing everyone a random *.ngrok-free.* URL.
Browser → https://foo.dev.example.com
nginx (TLS, shared host)
proxy → 127.0.0.1:5001 (foo's port on server)
SSH reverse tunnel
→ 127.0.0.1:8080 (foo's laptop)
Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000