# 技術執筆

技術執筆 のナレッジハブ記事一覧。

URL: https://dylanengelbrecht.dev/ja/insights/category-authorship.html

- [UnityマルチプレイヤーにMirrorを選ぶ理由](https://dylanengelbrecht.dev/ja/insights/mirror-multiplayer-unity.html): Mirror is a pragmatic default when Unity teams need a complete networking architecture.
- [学習におけるプログレッシブディスクロージャー：暗記ではなくマップ](https://dylanengelbrecht.dev/ja/insights/progressive-disclosure-learning-with-agents.html): Orchestrate agents across a navigable map — learn foundations deeply, index everything else, verify with the environment.
- [Cursor入門：ループを自動化する](https://dylanengelbrecht.dev/ja/insights/getting-started-with-cursor.html): Treat Cursor as an automation surface — rules, tests, MCP, and agents run the boring parts so you keep one thread for judgment.
- [AIコーディングエージェント向けAGENTS.md標準](https://dylanengelbrecht.dev/ja/insights/agents-md-standard.html): One open Markdown file per repo — closest AGENTS.md wins; user prompts override.
- [AIエージェント向けナレッジの整理](https://dylanengelbrecht.dev/ja/insights/organizing-knowledge-for-ai-agents.html): Pair a public site with a private brain — progressive disclosure, lifespans, verification metadata.
- [search と execute で MCP サーバーを構築する](https://dylanengelbrecht.dev/ja/insights/mcp-search-execute-codemode.html): Two MCP tools — search and execute — beat hundreds of per-operation tool definitions.
- [エージェントモデルにおけるツール呼び出しの強化学習](https://dylanengelbrecht.dev/ja/insights/rl-tool-calling-composer.html): RL specializes models for the tool loop — Composer is the public case study.
- [Keep the thread: why plan–critique–build pipelines fail modern coding agents](https://dylanengelbrecht.dev/ja/insights/single-agent-coding-loops.html): One agent, one thread, small units — verify with tests, not lossy handoffs.