Miguel Rodriguez so77id

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.

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

	from math import sqrt

	def put_kernels_on_grid (kernel, pad = 1):

	'''Visualize conv. filters as an image (mostly for the 1st layer).
	Arranges filters into a grid, with some paddings between adjacent filters.

	Args:
	kernel: tensor of shape [Y, X, NumChannels, NumKernels]
	pad: number of black pixels around each filter (between them)

	sudo apt-get update
	sudo apt-get install language-pack-es
	sudo apt-get install git-core curl zlib1g-dev build-essential libssl-dev libreadline-dev libyaml-dev libsqlite3-dev sqlite3 libxml2-dev libxslt1-dev libcurl4-openssl-dev python-software-properties libffi-dev zsh libmysqlclient-dev redis-server imagemagick

	sh -c "$(curl -fsSL https://raw.github.com/robbyrussell/oh-my-zsh/master/tools/install.sh)"

	cd
	git clone git://github.com/sstephenson/rbenv.git .rbenv
	echo 'export PATH="$HOME/.rbenv/bin:$PATH"' >> ~/.bashrc
	echo 'export PATH="$HOME/.rbenv/bin:$PATH"' >> ~/.zshrc

	from PIL import Image
	import sys
	import os
	import math
	import numpy as np

	###########################################################################################
	# script to generate moving mnist video dataset (frame by frame) as described in
	# [1] arXiv:1502.04681 - Unsupervised Learning of Video Representations Using LSTMs
	# Srivastava et al

	#include <assert.h>
	#include <cuda.h>
	#include <stdio.h>
	#include <stdlib.h>
	#include <stddef.h>

	/* Example from "Introduction to CUDA C" from NVIDIA website:
	https://developer.nvidia.com/cuda-education

	Compile with:

	packages:
	yum:
	gcc-c++: []
	make: []
	sources:
	/home/ec2-user: http://download.redis.io/releases/redis-2.8.4.tar.gz
	commands:
	redis_build:
	command: make
	cwd: /home/ec2-user/redis-2.8.4

	// MP 2: Due Sunday, Dec 16, 2012 at 11:59 p.m. PST
	#include <wb.h>

	#define wbCheck(stmt) do { \
	cudaError_t err = stmt; \
	if (err != cudaSuccess) { \
	wbLog(ERROR, "Failed to run stmt ", #stmt); \
	return -1; \
	} \
	} while(0)