| 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:
Add this file to your AI assistant's system prompt or context to help it avoid common AI writing patterns. Source: tropes.fyi by ossama.is
| filter Expand-DefenderAVSignatureDB { | |
| <# | |
| .SYNOPSIS | |
| Decompresses a Windows Defender AV signature database (.VDM file). | |
| .DESCRIPTION | |
| Expand-DefenderAVSignatureDB extracts a Windows Defender AV signature database (.VDM file). This function was developed by reversing mpengine.dll and with the help of Tavis Ormandy and his LoadLibrary project (https://github.com/taviso/loadlibrary). Note: Currently, "scrambled" databases are not supported although, I have yet to encounter a scrambled database. Thus far, all databases I've encountered are zlib-compressed. |
| <NotepadPlus> | |
| <UserLang name="Groovy" ext="groovy" udlVersion="2.1"> | |
| <Settings> | |
| <Global caseIgnored="no" /> | |
| <Prefix Keywords1="no" Keywords2="no" Keywords3="no" Keywords4="no" /> | |
| </Settings> | |
| <KeywordLists> | |
| <Keywords name="Comments">03/* 04*/ 00// 01 02</Keywords> | |
| <Keywords name="Keywords1">abstract break case catch continue default do else extends final finally for if implements instanceof native new private protected public return static switch synchronized throw throws transient try volatile while strictfp package import false null super this true</Keywords> | |
| <Keywords name="Keywords2">as assert def mixin property test using in it</Keywords> |
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.
| ;;; make-tab-box.el --- Box with tabs -*- lexical-binding: t -*- | |
| ;; Copyright (C) 2024 Nicolas P. Rougier | |
| ;; Maintainer: Nicolas P. Rougier <Nicolas.Rougier@inria.fr> | |
| ;; Version: 0.1.0 | |
| ;; Package-Requires: ((emacs "27.1")) | |
| ;; Keywords: convenience | |
| ;; This file is not part of GNU Emacs. |
| -- am_beach_washup_cinematic.c -- | |
| RegisterCommand('wakeup', function() | |
| local pos = GetEntityCoords(cache.ped) | |
| local heading = GetEntityHeading(cache.ped) | |
| local coords = vec4(pos.x, pos.y, pos.z-1.0, heading) | |
| local dict = IsPedMale(cache.ped) and 'anim@scripted@heist@ig25_beach@male@' or 'anim@scripted@heist@ig25_beach@heeled@' | |
| DoScreenFadeOut(500) while not IsScreenFadedOut() do Wait(10) end | |
| SetEntityCoords(cache.ped, coords.x, coords.y, coords.z, false, false, false, true) | |
| SetEntityHeading(cache.ped, coords.w) |