排名数字越高越好。Cost 反映实际支付成本而非标价(OpenAI 额度非常宽松);Intelligence 指可以无监督托付的问题难度;Taste 涵盖 UI/UX、代码质量、API 设计和文案。
| model | cost | intelligence | taste |
|---|---|---|---|
| gpt-5.5 | 9 | 8 | 5 |
| sonnet-5 | 5 | 5 | 7 |
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.
| // ==UserScript== | |
| // @name Fuck X | |
| // @namespace http://tampermonkey.net/ | |
| // @version 0.1 | |
| // @description Save your eyes while using X (formerly Twitter) | |
| // @author Cyandev <unixzii@gmail.com> | |
| // @match https://twitter.com/* | |
| // @grant none | |
| // ==/UserScript== |
| module demo/mysqlperf | |
| go 1.20 | |
| require github.com/go-sql-driver/mysql v1.7.1 |