import sys
import os
NOP = 0x90
offsets_and_values = {
0x00030170: 0x00,
0x000A94D0: NOP, 0x000A94D1: NOP, 0x000A94D2: NOP, 0x000A94D3: NOP, 0x000A94D4: NOP, 0x000A94D5: NOP, 0x000A94D6: NOP, 0x000A94D7: NOP, 0x000A94D8: NOP, 0x000A94D9: NOP, 0x000A94DA: NOP, 0x000A94DB: NOP, 0x000A94DC: NOP, 0x000A94DD: NOP, 0x000A94DE: NOP, 0x000A94DF: NOP, 0x000A94E0: NOP, 0x000A94E1: NOP, 0x000A94E2: NOP, 0x000A94E3: NOP, 0x000A94E4: NOP, 0x000A94E5: NOP, 0x000A94E6: NOP, 0x000A94E7: NOP, 0x000A94E8: NOP, 0x000A94E9: NOP, 0x000A94EA: NOP, 0x000A94EB: NOP, 0x000A94EC: NOP, 0x000A94ED: NOP, 0x000A94EE: NOP, 0x000A94EF: NOP, 0x000A94F0: NOP, 0x000A94F1: NOP, 0x000A94F2: NOP, 0x000A94F3: NOP, 0x000A94F4: NOP, 0x000A94F5: NOP, 0x000A94F6: NOP, 0x000A94F7: NOP, 0x000A94F8: NOP, 0x000A94F9: NOP, 0x000A94FA: NOP, 0x000A94FB: NOP, 0x000A94FC: NOP, 0x000A94FD: NOP, 0x000A94FE: NOP, 0x000A94FF: NOP, 0x000A9500: NOP, 0x000A9501: NOP, 0x000A9502: NOP, 0x000A9503: NOP, 0x000A9504: NOP, 0x000A9505: NOP, 0x000A9506: NODiscover gists
| git init # 初始化本地git仓库(创建新仓库) | |
| git config --global user.name "xxx" # 配置用户名 | |
| git config --global user.email "xxx@xxx.com" # 配置邮件 | |
| git config --global color.ui true # git status等命令自动着色 | |
| git config --global color.status auto | |
| git config --global color.diff auto | |
| git config --global color.branch auto | |
| git config --global color.interactive auto | |
| git config --global --unset http.proxy # remove proxy configuration on git | |
| git clone git+ssh://git@192.168.53.168/VT.git # clone远程仓库 |
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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.
by Flamerix AI · MIT License
Tested on Linux, Claude Code 2.1.257–2.1.312; macOS reported working by others (see Tested versions). The binary is not modified (
BUN_OPTIONS=--preload). An update can silently undo it, so see Verify.
Every ~30 seconds, Claude Code re-sends each background agent's full transcript to the model just
to fill that agent's status line in the agents panel. no-agent-summary.js is a Bun --preload
| import requests | |
| url = "https://openlibrary.org/search.json?q=test" | |
| headers = { | |
| "User-Agent": "MyAppName/1.0 (myemail@example.com)" | |
| } | |
| response = requests.get(url, headers=headers) |
| name | speed-run |
|---|---|
| description | Coordinate a rolling stream of project fixes and improvements through parallel worktree implementers, clarification, independent adversarial review, pushed integration, and delivery to the user's selected app, browser, or other feedback target. Use for /speed-run or an explicitly requested ongoing speed run. Editing this skill does not start the workflow. |
The user names a project, then reports problems or requests improvements while trying it. Own every report through a reviewed, pushed integration and delivery to the selected feedback target. New messages add work or clarify existing work; they do not replace unfinished tasks. Creating or editing this skill does not start a speed run.
| """ | |
| 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 |