Suppose we were to write a 1000 pages book called {name} what would the table of content be like? It should have 3 parts, each part will have 6 sections, each section will have 4 chapters, each chapter will have 3 headings. give me the full table of content with all 216 headings, 72 chapters, 18 sections and 3 parts.
your output should be in the following format
Table of Contents: {name}
PART 1: <part_title>
Section 1.1: <section_title>
Chapter 1.1.1:
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>PeerJS Messaging Demo</title> | |
| <script src="https://cdnjs.cloudflare.com/ajax/libs/peerjs/1.5.4/peerjs.min.js"></script> | |
| <style> | |
| body { |
"Just do it" - Prompt
Please implement <blank>
"Into natural language" Prompt
Please describe it in natural language in such a way to it will be able to be reconstruct exactly
| #!/bin/bash | |
| dirs=($(sudo du -h --max-depth=1 ~/ | sort -h | awk '{print $2}' | tail -n 10 | head -n 9)) | |
| for dir in "${dirs[@]}"; do | |
| if [ ! -d "$dir" ]; then | |
| echo "Warning: $dir is not a directory or doesnt exist" | |
| continue | |
| fi | |
| echo "Largest file in $dir:" |
| # pip install svgelements | |
| import io | |
| from svgelements import SVG, Path | |
| import matplotlib.pyplot as plt | |
| from matplotlib.animation import FuncAnimation | |
| import numpy as np | |
| def read_svg(svg_file_path): | |
| try: | |
| with io.open(svg_file_path, "r", encoding="utf-8") as f: |
| #!/bin/bash | |
| # create_enc_script.sh - Creates an encrypted script containing API keys and uploads to GitHub Gist | |
| # Usage: bash create_enc_script.sh --path_to_dot_env_file .my_tmp --password your_password --github_token $GITHUB_ACCESS_TOKEN | |
| # Function to show usage | |
| show_usage() { | |
| echo "Usage: $0 --path_to_dot_env_file <path> --password <password> --github_token <token>" | |
| exit 1 | |
| } |
Hmm... Well, let me see... I'm wondering if... This makes me think of... Building on that... Wait a minute... Going back to what I said about... Something's not quite right... Hold that thought... Here's where it gets interesting...
| Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output. | |
| - Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure. | |
| - Reasoning Before Conclusions: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS! | |
| - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed. | |
| - Conclusion, classifications, or results should ALWAYS appear last. | |
| - Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements. | |
| - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from p |
| def add_line_numbers(input_file, output_file): | |
| with open(input_file) as f: | |
| text = f.read() | |
| output_text = "\n".join([f"{i} {line}" for i, line in list(enumerate(text.split("\n")))]) | |
| with open(output_file, "w") as f: | |
| f.write(output_text) |
| class BTreeNode: | |
| def __init__(self, t, leaf=False): | |
| """ | |
| Initialize a B-tree node. | |
| Args: | |
| t (int): Minimum degree of B-tree node. | |
| leaf (bool, optional): True if node is a leaf. Defaults to False. | |
| """ | |
| self.t = t # Minimum degree (defines the range for number of keys) |