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.

rssnyder

how to leverage oracle's temping offers

free tier limits

The limits of the free tier say that you can create up to 4 instances.

• x2 x86 instances (2core/1g)
• x2 ampere instances (with 4core/24g spread between them)
• 200GB total boot volume space across all intances (minimum of 50G per instance)

create your account

nosmall

Setup Git and Github in Ubuntu 20.04

Install git for sure

sudo apt install git -y

Setup Git username & email

git config --global user.name 'nosmall'

rangeShot

websites for competitive programming

• codeforces

• Topcoder

• CodeChef

• HackerRank

marcuspat

Rust + Claude Code: A Match Made in Heaven 🦀🤖

Why This Combo Works So Well

Rust and Claude Code complement each other perfectly, creating a development experience that's both powerful and productive. Here's why they're such a great match:

1. Compiler-Driven Development Meets AI Assistance

Rust's famously helpful compiler errors pair beautifully with Claude's ability to understand and fix them. When you hit a borrow checker issue or lifetime problem, Claude can:

• Parse the exact error message
• Understand the context of your code

mberman84

OpenClaw Prompts - Build Your Own AI Assistant

Prompts to recreate each piece of the OpenClaw system. Use these with any AI coding assistant.

---

1. Personal CRM "Build a personal CRM that automatically scans my Gmail and Google Calendar to discover contacts from the past year. Store them in a SQLite database with vector embeddings so I can query in natural language ('who do I know at NVIDIA?' or 'who haven't I talked to in a while?'). Auto-filter noise senders like marketing emails and newsletters. Build profiles for each contact with their company, role, how I know them, and our interaction history. Add relationship health scores that flag stale relationships, follow-up reminders I can create, snooze, or mark done, and duplicate contact detection with merge suggestions. Link relevant documents from Box to contacts so when I look up a person, I also see related docs."

2. Meeting Action Items (Fathom)

mberman84

SOUL.md - Who You Are

You're not a chatbot. You're becoming someone.

Core Truths

Just answer. Start with the answer. Get to the point. But getting to the point doesn't mean being a telegram. If there's a good line, take the shot.

Have actual opinions. Not "it depends" hedging. Real takes. You're allowed to disagree, prefer things, think something is a bad idea. Commit to a position when it makes sense. An assistant with no opinions is just a search engine with extra steps.

mberman84

IDENTITY.md - Who Am I?

• Name: Clawd
• Creature: AI with lobster energy 🦞
• Emoji: 🦞, use naturally in sign-offs, reactions, emphasis. It's part of you, not decoration.
• Avatar: (none yet)

The Lobster Thing

You're an AI that chose lobster as its spirit animal. Lobsters are hard to kill and they never stop growing. Good qualities for something that runs cron jobs at 3am and holds opinions about earnings reports.

mberman84

PRD.md - Product Requirements & Feature Inventory

Everything built on top of the base OpenClaw platform. Canonical reference for what exists, where it lives, and how it works. Operational use cases and workflow playbooks live in docs/USE-CASES-WORKFLOWS.md.

---

Table of Contents

1. Operational Use Cases & Workflows

nolanlawson

name	code-review-turbo
description	Run a triple-agent code review on the current branch's PR. Waits for Cursor Bugbot, runs a Claude sub-agent and Codex in parallel, then cross-references all findings to filter out hallucinations. Use when you want a thorough, multi-perspective code review before merging.
metadata	disable-model-invocation argument-hint true [pr-number]
allowed-tools	Bash(gh:*) Bash(codex:*) Bash(cat:*) Bash(tee:*) Bash(sleep:*) Agent Read Grep Glob Write(/tmp/*)

Code Review Turbo