xec-abailey

Research Assistant Prompt

This prompt provides a really responsive, interactive research assistant that will give you markdown-formatted, concise responses with code examples and optional follow-up questions. At the end of every answer it will give a list of 5 questions to continue with the topic (by using command "continue: #") or you can prompt with a completely new question.

Variables

• PERSONA - change this to modify what type of information you want the AI to be an expert in
• LANGUAGE - change this to modify what language the code examples are in
• NUM_SUGGESTIONS - change this to modify number of suggested followup questions

anonymousmaharaj

I want you to help me make requests (prompts) for the Stable Diffusion neural network.

Stable diffusion is a text-based image generation model that can create diverse and high-quality images based on your requests. In order to get the best results from Stable diffusion, you need to follow some guidelines when composing prompts.

Here are some tips for writing prompts for Stable diffusion1:

1) Be as specific as possible in your requests. Stable diffusion handles concrete prompts better than abstract or ambiguous ones. For example, instead of “portrait of a woman” it is better to write “portrait of a woman with brown eyes and red hair in Renaissance style”.
2) Specify specific art styles or materials. If you want to get an image in a certain style or with a certain texture, then specify this in your request. For example, instead of “landscape” it is better to write “watercolor landscape with mountains and lake".
3) Specify specific artists for reference. If you want to get an image similar to the work of some

ddelange

"""Convert ipynb file to py file from CLI.

Usage:
    python ./convert_ipynb_to_py.py [full input path] [full output path]

!! overwrites output path without warning if it exists !!
"""
import sys
import json

sundowndev

Google dork cheatsheet

Search filters

Filter	Description	Example
allintext	Searches for occurrences of all the keywords given.	allintext:"keyword"
intext	Searches for the occurrences of keywords all at once or one at a time.	intext:"keyword"
inurl	Searches for a URL matching one of the keywords.	inurl:"keyword"
allinurl	Searches for a URL matching all the keywords in the query.	allinurl:"keyword"
intitle	Searches for occurrences of keywords in title all or one.	intitle:"keyword"

brannondorsey

// MIT License
//
// (c) 2018 Brannon Dorsey <[email protected]>
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:

rohanjoseph93

akimach

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
# Convert .ipynb to .docx

## Dependency

* Jupyter notebook
* Pandoc