Nico Galoppo ngaloppo

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.

Docker image to Virtualbox disk

https://stackoverflow.com/questions/23436613/how-can-i-convert-a-docker-image-into-a-vagrant-virtualbox-box by user blueskin (CC by-sa 3.0)

Find the size of the docker image from docker images

REPOSITORY TAG IMAGE ID CREATED SIZE

Effective Modern CMake

Getting Started

For a brief user-level introduction to CMake, watch C++ Weekly, Episode 78, Intro to CMake by Jason Turner. LLVM’s CMake Primer provides a good high-level introduction to the CMake syntax. Go read it now.

After that, watch Mathieu Ropert’s CppCon 2017 talk Using Modern CMake Patterns to Enforce a Good Modular Design (slides). It provides a thorough explanation of what modern CMake is and why it is so much better than “old school” CMake. The modular design ideas in this talk are based on the book [Large-Scale C++ Software Design](https://www.amazon.de/Large-Scale-Soft

Paprika doesn't have their API documented, so this is me reverse-engineering it from an Android device

Live Demo

https://knowing-grain.glitch.me/

Code: https://glitch.com/edit/#!/knowing-grain

	#!/bin/bash
	set -euo pipefail
	trap 'echo "at line $LINENO, exit code $? from $BASH_COMMAND" >&2; exit 1' ERR

	# This is a Claude Code hook to stop it saying "you are right".
	#
	# Installation:
	# 1. Save this script and chmod +x it to make it executable.
	# 2. Within Claude Code, /hooks / UserPromptSubmit > Add a new hook (this file)
	#

	import torch
	from huggingface_hub import hf_hub_download
	from diffusers import FluxTransformer2DModel, DiffusionPipeline

	dtype, device = torch.bfloat16, "cuda"
	ckpt_id = "black-forest-labs/FLUX.1-schnell"

	with torch.device("meta"):
	config = FluxTransformer2DModel.load_config(ckpt_id, subfolder="transformer")
	model = FluxTransformer2DModel.from_config(config).to(dtype)

	cmake_minimum_required(VERSION 3.4.3)

	project(sample CXX)

	find_package(InferenceEngine REQUIRED)

	add_executable(${CMAKE_PROJECT_NAME} main.cpp)
	target_compile_features(${CMAKE_PROJECT_NAME} PRIVATE cxx_range_for)

	target_link_libraries(${CMAKE_PROJECT_NAME}

	# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,

	# Remove anything linked to nvidia
	sudo apt-get remove --purge nvidia*
	sudo apt-get autoremove

	# Search for your driver
	apt search nvidia

	# Select one driver (the last one is a decent choice)
	sudo apt install nvidia-370