| name | explain-diff-html |
|---|---|
| description | Use when the user asks for a rich explanation of a code change, diff, branch, or PR. Produces HTML output. |
Please make me a rich, interactive explanation of the specified code change.
It should have these sections:
| FROM ubuntu | |
| RUN apt-get update | |
| RUN apt-get install -y curl | |
| RUN curl -sL https://deb.nodesource.com/setup_18.x | bash - | |
| RUN apt-get upgrade -y | |
| RUN apt-get install -y nodejs | |
| COPY package.json package.json | |
| COPY package-lock.json package-lock.json |
(The below text is licensed with CC0, which means that if you want to use or translate it, that is OK by me.)
Ok, I geeked out, and this is probably more information than you need. But it completely answers the question. Sorry. ☺
Locally, I'm at this commit:
$ git show
commit d6cd1e2bd19e03a81132a23b2025920577f84e37
Author: jnthn <jnthn@jnthn.net>
Date: Sun Apr 15 16:35:03 2012 +0200
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| """PyRSS2Gen - A Python library for generating RSS 2.0 feeds.""" | |
| """ Distributed under the BSD license here.""" | |
| __name__ = "PyRSS2Gen" | |
| __version__ = (1, 1, 0) | |
| __author__ = "Andrew Dalke <dalke@dalkescientific.com>" | |
| _generator_name = __name__ + "-" + ".".join(map(str, __version__)) | |
| import datetime |
| # Minecraft PE v0.6.1 alpha Protocol #9 | |
| # 49 identified Packets | |
| [C ==> S] 0x82 LoginPacket (String, int, int) | |
| [C <== S] 0x83 LoginStatusPacket (int) | |
| [C ==> S] 0x84 ReadyPacket (bits[8]) | |
| [C <== S] 0x85 MessagePacket (String) | |
| [C <== S] 0x86 SetTimePacket (long) | |
| [C <== S] 0x87 StartGamePacket (long, int, int, int, float, float, float) | |
| [C <== S] 0x88 AddMobPacket (int, int, float, float, float, byte, byte, Metadata) |
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.
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| /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */ | |
| /* SHA-3 (FIPS 202) ‘Keccak’ reference implementation in JavaScript (c) Chris Veness 2016-2017 */ | |
| /* MIT Licence */ | |
| /* www.movable-type.co.uk/scripts/sha3.html */ | |
| /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */ | |
| 'use strict'; | |
| /** |