Andrew Schell datatalking

Python3 Virtualenv Setup

Requirements

Python 3
Pip 3

$ brew install python3

Tensorflow 1.8 with CUDA on macOS High Sierra 10.13.4

Largely based on the Tensorflow 1.6 gist, and Tensorflow 1.7 gist for xcode and Tensorflow 1.7 gist for eGPU, this should hopefully simplify things a bit.

Requirements

NVIDIA Web-Drivers 387.10.10.10.30.106 for 10.13.4 (17E199) (w/o Security Update)
CUDA-Drivers 387.128

Git

Global Settings

Related Setup: https://gist.github.com/hofmannsven/6814278
Related Pro Tips: https://ochronus.com/git-tips-from-the-trenches/
Interactive Beginners Tutorial: http://try.github.io/
Git Cheatsheet by GitHub: https://services.github.com/on-demand/downloads/github-git-cheat-sheet/

Reminder

Press minus + shift + s and return to chop/fold long lines!

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.

	from scipy.stats.distributions import binom

	data = [
	{'name': 'ISTJ', 'count': 12, 'expected': 0.1160},
	{'name': 'ISFJ', 'count': 4, 'expected': 0.1380},
	{'name': 'INFJ', 'count': 14, 'expected': 0.0150},
	{'name': 'INTJ', 'count': 206, 'expected': 0.0210},
	{'name': 'ISTP', 'count': 16, 'expected': 0.0540},
	{'name': 'ISFP', 'count': 1, 'expected': 0.0880},
	{'name': 'INFP', 'count': 56, 'expected': 0.0430},

	#!/usr/bin/python
	"""Convert CSV table to MindMap format

	Usage: python csv_to_mm.py sometable.csv > mymap.mm

	CSV format is rows representing tree leaves, e.g.:
	A1,
	A1,B1
	A1,B1,C1
	A1,B1,C2

	# checkDuplicates.py
	# Python 2.7.6

	"""
	Given a folder, walk through all files within the folder and subfolders
	and get list of all files that are duplicates
	The md5 checcksum for each file will determine the duplicates
	"""

	import os

	#!/usr/bin/env python
	# -- coding: utf-8 --

	"""
	Outputs some information on CUDA-enabled devices on your computer,
	including current memory usage.

	It's a port of https://gist.github.com/f0k/0d6431e3faa60bffc788f8b4daa029b1
	from C to Python with ctypes, so it can run without compiling anything. Note
	that this is a direct translation with no attempt to make the code Pythonic.