STOP！本文将完全颠覆你的思维。在阅读本文之前，请你先查看 Effect 源代码了解最新的 Effect 模块，不要将 Effect 视作一个普通的库，实际上 Effect 是一个先进的力求涵盖一切的库，在解决问题时先在 Effect 寻找可能的现成的解决方案。
在本项目中，先用类型定义业务需要什么，再编排业务如何完成，最后提供具体实现并运行。 请先按这个顺序重新理解开发任务，再开始写代码。
使用 TypeScript 和 Effect 4，以业务用例为交付单位。类型优先决定契约，控制反转决定依赖方向。 类型贯穿成功结果、预期失败、服务需求和装配过程；业务代码声明所需能力，外部装配负责提供这些能力的实现。
This is an OPML version of the HN Popularity Contest results for 2025, for importing into RSS feed readers.
Plug: if you want to find content related to your interests from thousands of obscure blogs and noisy sources like HN Newest, check out Scour. It's a free, personalized content feed I work on where you define your interests in your own words and it ranks content based on how closely related it is to those topics.
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.
| Latency Comparison Numbers (~2012) | |
| ---------------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns 3 us | |
| Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
| Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
| DSH（DeepSeek Harness）Desktop を日本語化し、必須プラグインを導入してください。 | |
| ## 0. 原則 | |
| - ユーザーへの応対はすべて日本語で行う(これはAGENTS.mdにも追記)。また承認ダイアログは日本語訳できないので、その直前に日本語で何をしようとしているか、なぜ承認が必要かを記述する。 | |
| - ユーザーへの質問は、各フェーズ終了時の報告でのみ行う。フェーズの途中で問いかけてよいのは、手動操作の案内とその返答待ちだけ。 | |
| - プラグインについては、本プロンプトの送信をもって、バージョン例外の付与（acceptRisk=true を含む）を承認済みとする。 | |
| ## 1. 目的 | |
| 日本人のユーザが、chatgpt desktopやclaude desktop よりも言語的なUXが未熟で使いづらい DSH を日本語化し、必須プラグインを導入する。このタスクに失敗した場合、あなたは一生ユーザに使われなくなるため、丁寧に確実に実行すべき。 |
| ls - The most frequently used command in Linux to list directories | |
| pwd - Print working directory command in Linux | |
| cd - Linux command to navigate through directories | |
| mkdir - Command used to create directories in Linux | |
| mv - Move or rename files in Linux | |
| cp - Similar usage as mv but for copying files in Linux | |
| rm - Delete files or directories | |
| touch - Create blank/empty files | |
| ln - Create symbolic links (shortcuts) to other files | |
| cat - Display file contents on the terminal |
| { | |
| "name": "S3 to Google Drive", | |
| "nodes": [ | |
| { | |
| "parameters": { | |
| "rule": { | |
| "interval": [ | |
| {} | |
| ] | |
| } |
| import requests | |
| import sys | |
| import json | |
| def waybackurls(host, with_subs): | |
| if with_subs: | |
| url = 'http://web.archive.org/cdx/search/cdx?url=*.%s/*&output=json&fl=original&collapse=urlkey' % host | |
| else: | |
| url = 'http://web.archive.org/cdx/search/cdx?url=%s/*&output=json&fl=original&collapse=urlkey' % host |