Christian Stade-Schuldt Tafkas

BENCHMARK PERFORMANCE (NEW!)

Prompts and metrics included in the abstract so you can benchmark yourself!

Beast Mode

Beast Mode is a custom chat mode for VS Code agent that adds an opinionated workflow to the agent, including use of a todo list, extensive internet research capabilities, planning, tool usage instructions and more. Designed to be used with 4.1, although it will work with any model.

Below you will find the Beast Mode prompt in various versions - starting with the most recent - 3.1

Installation Instructions

Go to the "agent" dropdown in VS Code chat sidebar and select "Configure Modes".
Select "Create new custom chat mode file"

TL;DR
Zsh initializations
- Zsh way of $PATH
- the special macOS
/usr/libexec/path_helper

The .*shrc file

pg_dump is a nifty utility designed to output a series of SQL statements that describes the schema and data of your database. You can control what goes into your backup by using additional flags.
Backup: pg_dump -h localhost -p 5432 -U postgres -d mydb > backup.sql

Restore: psql -h localhost -p 5432 -U postgres -d mydb < backup.sql

-h is for host.
-p is for port.
-U is for username.
-d is for database.

Comprehensions in Python the Jedi way

by Bjørn Friese

Beautiful is better than ugly. Explicit is better than implicit.

-- The Zen of Python

I frequently deal with collections of things in the programs I write. Collections of droids, jedis, planets, lightsabers, starfighters, etc. When programming in Python, these collections of things are usually represented as lists, sets and dictionaries. Oftentimes, what I want to do with collections is to transform them in various ways. Comprehensions is a powerful syntax for doing just that. I use them extensively, and it's one of the things that keep me coming back to Python. Let me show you a few examples of the incredible usefulness of comprehensions.

These links no longer work. Springer have pulled the free plug.

Graduate texts in mathematics

duplicates = multiple editions

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

	# Copyright 2019 Google LLC.
	# SPDX-License-Identifier: Apache-2.0

	# Author: Anton Mikhailov

	turbo_colormap_data = [[0.18995,0.07176,0.23217],[0.19483,0.08339,0.26149],[0.19956,0.09498,0.29024],[0.20415,0.10652,0.31844],[0.20860,0.11802,0.34607],[0.21291,0.12947,0.37314],[0.21708,0.14087,0.39964],[0.22111,0.15223,0.42558],[0.22500,0.16354,0.45096],[0.22875,0.17481,0.47578],[0.23236,0.18603,0.50004],[0.23582,0.19720,0.52373],[0.23915,0.20833,0.54686],[0.24234,0.21941,0.56942],[0.24539,0.23044,0.59142],[0.24830,0.24143,0.61286],[0.25107,0.25237,0.63374],[0.25369,0.26327,0.65406],[0.25618,0.27412,0.67381],[0.25853,0.28492,0.69300],[0.26074,0.29568,0.71162],[0.26280,0.30639,0.72968],[0.26473,0.31706,0.74718],[0.26652,0.32768,0.76412],[0.26816,0.33825,0.78050],[0.26967,0.34878,0.79631],[0.27103,0.35926,0.81156],[0.27226,0.36970,0.82624],[0.27334,0.38008,0.84037],[0.27429,0.39043,0.85393],[0.27509,0.40072,0.86692],[0.27576,0.41097,0.87936],[0.27628,0.42118,0.89123],[0.27667,0.43134,0.90254],[0.27691,0.44145,0.913

	{
	"emojis": [
	{"emoji": "👩‍👩‍👧‍👧", "name": "family: woman, woman, girl, girl", "shortname": ":woman_woman_girl_girl:", "unicode": "1F469 200D 1F469 200D 1F467 200D 1F467", "html": "👩&zwj;👩&zwj;👧&zwj;👧", "category": "People & Body (family)", "order": ""},
	{"emoji": "👩‍👩‍👧‍👦", "name": "family: woman, woman, girl, boy", "shortname": ":woman_woman_girl_boy:", "unicode": "1F469 200D 1F469 200D 1F467 200D 1F466", "html": "👩&zwj;👩&zwj;👧&zwj;👦", "category": "People & Body (family)", "order": ""},
	{"emoji": "👩‍👩‍👦‍👦", "name": "family: woman, woman, boy, boy", "shortname": ":woman_woman_boy_boy:", "unicode": "1F469 200D 1F469 200D 1F466 200D 1F466", "html": "👩&zwj;👩&zwj;👦&zwj;👦", "category": "People & Body (family)", "order": ""},
	{"emoji": "👨‍👩‍👧‍👧", "name": "family: man, woman, girl, girl", "shortname": ":man_woman_girl_girl:", "unicode": "1F468 200D 1F469 200D 1F467 200D 1F467", "html": "👨&zwj;👩&z

	#!/usr/bin/python3
	#
	# Simple Bloom filter implementation in Python 3
	# Copyright 2017 Hector Martin "marcan" <[email protected]>
	# Licensed under the terms of the MIT license
	#
	# Written to be used with the Have I been pwned? password list:
	# https://haveibeenpwned.com/passwords
	#
	# Download the pre-computed filter here (968MB, k=11, false positive p=0.0005):

	#!/usr/bin/env bash
	#Code adapted from https://gist.github.com/yangj1e/3641843c758201ebbc6c (Modified to Python3.5)

	cd ~
	#wget https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda2-2.4.0-Linux-x86_64.sh
	wget https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda3-2.4.1-Linux-x86_64.sh
	bash Anaconda3-2.4.1-Linux-x86_64.sh -b
	echo 'PATH="/home/ubuntu/anaconda3/bin:$PATH"' >> .bashrc
	. .bashrc