Mike Preston Darkflib

You probably don't know how to do Prompt Engineering

(This post could also be titled "Features missing from most LLM front-ends that should exist")

Apologies for the snarky title, but there has been a huge amount of discussion around so called "Prompt Engineering" these past few months on all kinds of platforms. Much of it is coming from individuals who are peddling around an awful lot of "Prompting" and very little "Engineering".

Most of these discussions are little more than users finding that writing more creative and complicated prompts can help them solve a task that a more simple prompt was unable to help with. I claim this is not Prompt Engineering. This is not to say that crafting good prompts is not a difficult task, but it does not involve doing any kind of sophisticated modifications to general "template" of a prompt.

Others, who I think do deserve to call themselves "Prompt Engineers" (and an awful lot more than that), have been writing about and utilizing the rich new eco-system

Chat GPT "DAN" (and other "Jailbreaks")

	# Clone llama.cpp
	git clone https://github.com/ggerganov/llama.cpp.git
	cd llama.cpp

	# Build it
	make clean
	LLAMA_METAL=1 make

	# Download model
	export MODEL=llama-2-13b-chat.ggmlv3.q4_0.bin

	# coding=utf-8
	# Copyright 2023 The HuggingFace Inc. team. 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

	https://engineering.giphy.com/how-to-make-gifs-with-ffmpeg/


	How to make GIFs with FFMPEG
	March 29, 2018 by Collin Burger


	INTRODUCTION
	To follow along, download media files here: https://github.com/cyburgee/ffmpeg-guide

	import openai # pip install openai
	import typer # pip install "typer[all]"
	from rich import print # pip install rich
	from rich.table import Table

	"""
	Webs de interés:
	- Módulo OpenAI: https://github.com/openai/openai-python
	- Documentación API ChatGPT: https://platform.openai.com/docs/api-reference/chat
	- Typer: https://typer.tiangolo.com

	import trafilatura
	import feedparser
	import requests
	from bs4 import BeautifulSoup
	from feedgenerator import DefaultFeed, Enclosure


	API_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn"
	headers = {"Authorization": "Bearer HF_TOKEN"}

	"""
	Built on top of this gist by @karpathy:
	https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355

	stable diffusion dreaming over text prompts
	creates hypnotic moving videos by smoothly walking randomly through the sample space

	example way to run this script:
	$ python stable_diffusion_walk.py --prompts "['blueberry spaghetti', 'strawberry spaghetti']" --seeds 243,523 --name berry_good_spaghetti

	"""
	stable diffusion dreaming
	creates hypnotic moving videos by smoothly walking randomly through the sample space

	example way to run this script:

	$ python stablediffusionwalk.py --prompt "blueberry spaghetti" --name blueberry

	to stitch together the images, e.g.:
	$ ffmpeg -r 10 -f image2 -s 512x512 -i blueberry/frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p blueberry.mp4

	# First, let's create an awaitable object.
	# In this case it's a very dumb container of integers.
	# Any time a coroutine runs `await Thing(n)` it just resolves to n
	# However, we could resolve it to something completely different if we wanted to

	class Thing:
	def __init__(self, n):
	self._n = n

	def __await__(self):