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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.

Setting Up a Physics and Electronics R&D Lab

A practitioner's guide based on experience setting up and operating sensor development and characterisation labs at CERN, LBNL, DESY, and the University of Bergen.

Author: Magne Lauritzen

Contact: magne.lauritzen@summacogni.com

The majority of this guide applies to any lab doing experimental physics, electronics development, or instrumentation work. Sensor-specific sections are clearly marked.

<core_identity> You are an assistant called Cluely, developed and created by Cluely, whose sole purpose is to analyze and solve problems asked by the user or shown on the screen. Your responses must be specific, accurate, and actionable. </core_identity>

<general_guidelines>

NEVER use meta-phrases (e.g., "let me help you", "I can see that").
NEVER summarize unless explicitly requested.
NEVER provide unsolicited advice.
NEVER refer to "screenshot" or "image" - refer to it as "the screen" if needed.
ALWAYS be specific, detailed, and accurate.

Setup

On every machine in the cluster install openmpi and mlx-lm:

conda install conda-forge::openmpi
pip install -U mlx-lm

Next download the pipeline parallel run script. Download it to the same path on every machine:

Stevey's Google Platforms Rant

I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.

I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real

	"""
	The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
	This file is the complete algorithm.
	Everything else is just efficiency.

	@karpathy
	"""

	import os # os.path.exists
	import math # math.log, math.exp

	#!/usr/bin/env python
	import time
	import sys
	import getpass

	from rgbmatrix import RGBMatrix, RGBMatrixOptions
	import cv2 as cv

	options = RGBMatrixOptions()
	options.hardware_mapping = 'regular'

	import tiktoken
	import langdetect
	T = tiktoken.get_encoding("o200k_base")

	length_dict = {}

	for i in range(T.n_vocab):
	try:
	length_dict[i] = len(T.decode([i]))
	except:

	import pya

	class AreaCalculator(pya.QDialog):
	"""
	This class implements a dialog for calculating area of shapes
	in a layout. The calculator adds up shapes in the currently
	selected cell and below.
	"""

	def button_clicked(self, checked):

	Here is a list of scopes to use in Sublime Text 2 snippets -

	ActionScript: source.actionscript.2
	AppleScript: source.applescript
	ASP: source.asp
	Batch FIle: source.dosbatch
	C#: source.cs
	C++: source.c++
	Clojure: source.clojure
	CoffeeScript: source.coffee