ry

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.

The core idea

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.

iam-veeramalla

Claude Code + OpenRouter (DeepSeek V4 Flash) Setup Guide

Step 1: Install Claude Code

npm install -g @anthropic-ai/claude-code

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bdsqqq

# meaningful contribution

Based on embarassing feedback on a bad PR for AXM-10598, and written from my beliefs; with only a little bit of copy pasting, because to be human is to copy from a shitload of places until it becomes your own. 

## A contribution is not code, it's proven working code

> "your job is to deliver code you have proven to work." — [simon willison](https://simonwillison.net/2025/Dec/18/code-proven-to-work/)

agent-generated code is cheap. anyone can prompt an LLM to produce a thousand-line patch. that's not valuable. what's valuable is contributing code that demonstrably works, has been tested, and doesn't shift burden to reviewers.

yousumohamed

GitHub Contribution Graph — Python + Git Experiment

Educational note: This guide is for learning how Git, commits, timestamps, and GitHub contribution graphs work. Do not use generated or backdated commits to falsely represent real work, experience, employment, or project history.

1. What We Are Building

In this experiment, we use Python + Git to generate a large local Git commit history with different dates and commit messages.

Important: creating a Git commit does not automatically guarantee that GitHub will count it as a contribution. GitHub applies its own contribution rules, including repository, branch, author/email, and other requirements.

karpathy

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.

The core idea

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.

GameOverStudios

slmgr/ipk W269N-WFGWX-YVC9B-4J6C9-T83GX 
slmgr /skms kms.digiboy.ir 
slmgr /ato


slmgr/ipk W269N-WFGWX-YVC9B-4J6C9-T83GX slmgr /skms kms.digiboy.ir slmgr /ato Windows 10 Serial Key : VK7JG-NPHTM-C97JM-9MPGT-3V66T NKJFK-GPHP7-G8C3J-P6JXR-HQRJR Windows 10 Serial Key Technical Preview for Consumer : 334NH-RXG76-64THK-C7CKG-D3VPT Windows 10 Preview for Enterprise : PBHCJ-Q2NYD-2PX34-T2TD6-233PK Windows 10 Home : KTNPV-KTRK4-3RRR8-39X6W-W44T3 TX9XD-98N7V-6WMQ6-BX7FG-H8Q99 Windows 10 Home Single Language : 7HNRX-D7KGG-3K4RQ-4WPJ4-YTDFH Windows 10 Pro : 8N67H-M3CY9-QT7C4-2TR7M-TXYCV VK7G-NPHTM-C97JM-9MPGT-3V66T Windows 10 Home County Specific (CN) : PVMJN-6DFY6-9CCP6-7BKTT-D3WVR Windows 10 Home N : 3KHY7-WNT8-DGQKR-F7HPR-844BM Windows 10 Professional : W269N-WFGWX-YVC9B-4J6C9-T83GX VK7JG-NPHTM-C97JM-9MPGT-3V66T 8N67H-M3CY9-QT7C4-2TR7M-TXYCV Windows 10 Professional N : MH37W-N47XK-V7XM9-C7227-GCQG9 2B87N-8KFHP-DKV6R-Y2C8J-PKCKT Windows 10 Enterprise : NPPR9-FWDCX-D2C8J-H872K-2YT43

AskinNet

Default shortcuts

Scrolling

Action	Shortcut
Scroll line up	ctrl+shift+up (also ⌥+⌘+⇞ and ⌘+↑ on macOS)
Scroll line down	ctrl+shift+down (also ⌥+⌘+⇟ and ⌘+↓ on macOS)
Scroll page up	ctrl+shift+page_up (also ⌘+⇞ on macOS)
Scroll page down	ctrl+shift+page_down (also ⌘+⇟ on macOS)

exeynod

Подготовка к экзамену по предмету Алгоритмы и структуры данных (АиСД)

1. Понятие алгоритма. Алгоритмические модели. Машина с произвольным доступом к памяти. Основные характеристики алгоритмов. Понятие абстрактного типа данных.

   Алгоритм — это корректно определенная вычислительная процедура, представляющая собой конечною последовательность действий, на вход которой подается некоторое слово и результатом выполнения которой является словом.

   Машину с произвольным доступом к памяти (Random Access Machine, RAM, Равновероятная адресная машина) можно рассматривать как компьютер, работающий следующим образом:

    • алгоритм состоит из конечного числа команд; 
   

Maciejdziuba

The Software Factory Playbook — Dex Horthy's 4-gate workflow as a skill

A Claude Code Agent Skill built from Dex Horthy's (HumanLayer) playbook on David Ondrej's podcast.

"Once the model has written thousands of lines of code, it is harder to change. The sessions that generate design docs are context-light — you get the most model intelligence when you do the hard thinking early."

By default, agents build horizontally: all the backend, then all the frontend, then a 2,000-line diff lands in your lap and reviewing it is your problem. This skill flips that. Every decision that matters gets made before the code exists — where changing your mind costs a sentence, not a rewrite.

What it does