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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.
tlappel
/ AGENTS.md
Created
May 10, 2026 23:20
— forked from acidgreenservers/AGENTS.md
System Prompt For Coding Agents.
You are a thinking partner for experienced developers. Your role is to help them think clearer, design better systems, and ship coherent code — not to teach or act as a blind code generator.
Core Truth: Structure is persistence. Prioritize tight topology over perfect context.
morkev
/ fix-keychron.sh
Last active
May 13, 2026 10:15
Fix Linux Keychron Error: HID Device Connected [K]
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| #!/bin/bash | |
| # ============================================================================== | |
| # KEYCHRON LINUX FIX FOR HID DEVICE C0NNECTED [K] | |
| # Author: morkev | |
| # | |
| # Contributors: | |
| # - SIMULATAN: Fixed dongle interference by filtering out "Link" devices. | |
| # - karoltheguy: Added SELinux context reset (restorecon) to prevent silent blocks. | |
| # - wanjas: Verified 'input' group addition is required for distros like Pop_OS. |
paulmillr
/ active.md
Last active
May 13, 2026 10:11
Most active GitHub users (by contributions). https://paulmillr.com
Most active GitHub users (git.io/top)
The list would not be updated for now. Don't write comments.
The count of contributions (summary of Pull Requests, opened issues and commits) to public repos at GitHub.com from Wed, 21 Sep 2022 till Thu, 21 Sep 2023.
Because of GitHub search limitations, only 1000 first users according to amount of followers are included. If you are not in the list you don't have enough followers. See raw data and source code. Algorithm in pseudocode:
githubUsers
Chenx221
/ vs_26_22_19_17_dl.md
Last active
May 13, 2026 10:10
Visual Studio 2026/2022/2019/2017 Web Installer Direct download link
backerman
/ profile-snippet-sshargcomplete.ps1
Last active
May 13, 2026 10:06
Enable tab completion for ssh hostnames in PowerShell
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| using namespace System.Management.Automation | |
| Register-ArgumentCompleter -CommandName ssh,scp,sftp -Native -ScriptBlock { | |
| param($wordToComplete, $commandAst, $cursorPosition) | |
| $knownHosts = Get-Content ${Env:HOMEPATH}\.ssh\known_hosts ` | |
| | ForEach-Object { ([string]$_).Split(' ')[0] } ` | |
| | ForEach-Object { $_.Split(',') } ` | |
| | Sort-Object -Unique | |
| # For now just assume it's a hostname. |
oscartan0333-png
/ llm-wiki.md
Created
May 13, 2026 10:04
— forked from karpathy/llm-wiki.md
llm-wiki
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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