Deploy a highly available, anti-censorship private DNS server using AdGuard Home via Docker, secure it against public abuse, configure local DNS rewrites, and force both OS and Docker environments on client servers to route traffic through this DNS.
By default, Ubuntu uses systemd-resolved which occupies port 53. You must disable its stub listener on the server that will host AdGuard Home.
WARNING: Article moved to separate repo to allow users contributions: https://github.com/raysan5/custom_game_engines
A couple of weeks ago I played (and finished) A Plague Tale, a game by Asobo Studio. I was really captivated by the game, not only by the beautiful graphics but also by the story and the locations in the game. I decided to investigate a bit about the game tech and I was surprised to see it was developed with a custom engine by a relatively small studio. I know there are some companies using custom engines but it's very difficult to find a detailed market study with that kind of information curated and updated. So this article.
Nowadays lots of companies choose engines like [Unreal](https:
Copying text from a remote shell to your local clipboard can be tricky. Luckily, many terminals support OSC52, an escape sequence that lets you send data straight to the clipboard. Here’s a handy Bash function you can drop into your ~/.bashrc:
osc52() {
local data
data=$(openssl base64 | tr -d '\n')
printf "\e]52;c;%s\a" "$data"
}
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.
| name | japanese-tech-writing |
|---|---|
| description | 日本語の技術文書・書籍原稿の文章規範。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、翻訳調の比喩と擬人化の禁止（「運ぶ」「効く」「開かれた問い」など）、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。 |
| license | Unlicense（https://gist.github.com/k16shikano/67625f2a7d96e3bbdfae8d571a936063） |
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。