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@bdsqqq
bdsqqq / 2026-01-06 ensure your work is a meaningful contribution
Created August 24, 2026 23:45
2026-01-06 ensure your work is a meaningful contribution
# meaningful contribution
Based on embarassing feedback on a bad PR for AXM-10598, and written from my beliefs; with only a little bit of copy pasting, because to be human is to copy from a shitload of places until it becomes your own.
## A contribution is not code, it's proven working code
> "your job is to deliver code you have proven to work." — [simon willison](https://simonwillison.net/2025/Dec/18/code-proven-to-work/)
agent-generated code is cheap. anyone can prompt an LLM to produce a thousand-line patch. that's not valuable. what's valuable is contributing code that demonstrably works, has been tested, and doesn't shift burden to reviewers.
@ry
ry / llm-wiki.md
Last active August 25, 2026 06:15 — forked from karpathy/llm-wiki.md
llm-wiki

LLM Wiki

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.

The core idea

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.

LLM Wiki

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.

The core idea

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.

@Liub0myr
Liub0myr / Shatterline-vulnerabilities.md
Last active August 25, 2026 06:12
An article about how I bypassed the EAC and gained significant access to the client, as well as the concept of a cloud cheat that could do almost anything to the game.
@GrayXu
GrayXu / OpenAI_GPT_API.md
Last active August 25, 2026 06:05
OpenAI GPT API for Zotero-PDF-Translate

This gist is for zotero-pdf-translate.
Please ask your question at repo issues for faster responses
请在仓库issue进行提问以获得更快的回复


  1. Register an account on the OpenAI website.
  2. After successful registration, go to API keys and create an key, which looks like sk-XXXXXX.
  3. Fill in the url with the complete URL, such as https://api.openai.com/v1/chat/completions.
  4. For the model, enter a model name such as gpt-4o.
@k16shikano
k16shikano / SKILL.md
Last active August 25, 2026 06:04
japanese-tech-writing/SKILL
name japanese-tech-writing
description 日本語の技術文書・書籍原稿の文章規範。整形(一文一行、引用ブロック、脚注、コラム記法)、段落と論証の構成(パラグラフライティング)、論証の厳密さ(ツッコミどころの除去)、読み手の負荷の管理、視点と語り、演出の抑制、LLM っぽい空句の禁止、冗長の排除を定める。日本語で技術書の章、草稿、記事、解説文を書くとき、または推敲・リライトするときに使用する。

日本語技術文書の文章規範

日本語で技術的な原稿(書籍の章、記事、解説文)を書く・推敲するときは、以下の規範に従う。

整形

@bmsimons
bmsimons / ALLWINDOWSONTECHBENCH.md
Last active August 25, 2026 06:03
Download all Windows ISOs directly from Microsoft with a trick

Just run the following JavaScript bookmarklet on https://www.microsoft.com/en-us/software-download/windows10 to reveal all Windows versions.

$("select#product-edition").html("<option value selected value='selected'>Select edition</option><option value='2'>Windows 7 Home Basic SP1 </option><option value='4'>Windows 7 Professional SP1 </option><option value='6'>Windows 7 Home Premium SP1 </option><option value='8'>Windows 7 Ultimate SP1 </option><option value='10'>Windows 7 Home Premium N SP1 </option><option value='12'>Windows 7 Professional N SP1 </option><option value='14'>Windows 7 Ultimate N SP1 </option><option value='16'>Windows 7 Professional K SP1 </option><option value='18'>Windows 7 Professional KN SP1 </option><option value='20'>Windows 7 Home Premium K SP1 </option><option value='22'>Windows 7 Home Premium KN SP1 </option><option value='24'>Windows 7 Ultimate KN SP1 </option><option value='26'>Windows 7 Ultimate K SP1 </option><option value='28'>Windows 7 Starter SP1 </option><option value
@jefferycn
jefferycn / setup-koreader.sh
Created August 23, 2026 23:36
KOReader Upstart configuration
#!/bin/bash
# ============================================
# KOReader Kindle 启动脚本
# ============================================
# 用于在 Kindle 设备上安装并配置 KOReader 服务
# 使用 Upstart 管理 KOReader 进程
set -e
echo "Installing KOReader Upstart configuration..."