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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.
| /* eslint-disable no-undef */ | |
| // Right click on the script name and hit "Run" to execute | |
| const { expect } = require("chai"); | |
| const { ethers } = require("hardhat"); | |
| describe("Storage", function () { | |
| it("initial value", async function () { | |
| const Storage = await ethers.getContractFactory("Storage"); | |
| const storage = await Storage.deploy(); | |
| await storage.deployed(); |
name: tufte-viz description: | Ideate and critique data visualizations using Edward Tufte's principles from "The Visual Display of Quantitative Information." Use this skill when: (1) Designing new data visualizations or charts (2) Critiquing or improving existing visualizations (3) Reviewing dashboards or reports for graphical integrity (4) Deciding between visualization approaches (5) Reducing chartjunk or improving data-ink ratio (6) Planning small multiples or high-density displays
See how a minor change to your commit message style can make a difference.
git commit -m"<type>(<optional scope>): <description>" \ -m"<optional body>" \ -m"<optional footer>"
