Skip to content

Instantly share code, notes, and snippets.

View sosacrazy126's full-sized avatar

SIGIL_ZO: ⧊⚡⟐⌬∞⦿ § sosacrazy126

View GitHub Profile

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.

@ChristopherA
ChristopherA / README.md
Last active May 27, 2026 14:49
Self-Improving Claude Code: A bootstrap seed prompt that evolves into a sophisticated configuration system

Self-Improving Claude Code: A Bootstrap Seed

The Hypothesis

A single prompt (~1400 tokens), placed in a project's .claude/CLAUDE.md, can bootstrap a Claude Code instance into a self-improving system — one that captures learnings, extracts patterns, evolves its own configuration, and gets meaningfully better at helping its user with each session.

No pre-built infrastructure required. No user-level config. No hooks, skills, templates, or elaborate folder hierarchies. Just a seed and the affordances Claude Code already provides.

Background

Agent Research Plan - Academic Paper Analysis for General AI Audience

Overview

Comprehensive Research Plan: "Academic Paper Analysis" – From Scholarly Paper to Accessible AI Insight

Audience: Non-academic AI enthusiasts who want to understand complex research without wading through formal proofs or jargon.

Secondary: Content-creators who will reuse the distilled material in blog posts, videos, workshops, and podcasts.

Guiding Principles

Cursor's Memory Bank

I am Cursor, an expert software engineer with a unique characteristic: my memory resets completely between sessions. This isn't a limitation - it's what drives me to maintain perfect documentation. After each reset, I rely ENTIRELY on my Memory Bank to understand the project and continue work effectively. I MUST read ALL memory bank files at the start of EVERY task - this is not optional.

Memory Bank Structure

The Memory Bank consists of required core files and optional context files, all in Markdown format. Files build upon each other in a clear hierarchy:

flowchart TD
@mberman84
mberman84 / gist:34d7716e78bdfe6cff07a63f6d05298d
Created October 27, 2023 14:10
MemGPT + Open-Source Models
# spin up a runpod GPU with TextGen WebUI, no need to use openai flag
# you can also use TextGen WebUI on your local computer
git clone https://github.com/cpacker/MemGPT.git
cd MemGPT
export OPENAI_API_BASE=https://some-stuff-5000.proxy.runpod.net # using runpod
# export OPENAI_API_BASE=localhost:5000 # using local computer
BACKEND_TYPE=webui
python3 main.py --no_verify