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https://github.com/wardbekker/deepthought_ai_framework
Transform simple if-statements into AI-powered, GPU-hungry unicorn factories, boosting VC appeal by 1000x
https://github.com/wardbekker/deepthought_ai_framework
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Transform simple if-statements into AI-powered, GPU-hungry unicorn factories, boosting VC appeal by 1000x
- Host: GitHub
- URL: https://github.com/wardbekker/deepthought_ai_framework
- Owner: wardbekker
- License: apache-2.0
- Created: 2024-07-17T14:36:15.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-07-17T21:13:27.000Z (4 months ago)
- Last Synced: 2024-11-06T21:06:38.890Z (12 days ago)
- Language: Python
- Size: 763 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# DeepThought Artificial Intelligence (AI) Engine Framework powered by AI
Welcome to the revolutionary world of DeepThought AI, where we've transformed the humble Python `if` statement into a unicorn-breeding, VC-attracting powerhouse! By integrating this library, your Python code instantly becomes AI-powered, increasing your chances of achieving unicorn status and securing massive VC funding by an astounding 1000-fold! Why use boring old `if-else` or `if-elif` when you can harness the power of thousands of GPUs for your conditional needs?
As the famous meme says:
And with this library, why not both!
## Installation
Initiate the AI-powered installation process:
```
pip install deepthought_ai_engine
```
Note: Requires a minimum of 8 NVIDIA Tesla V100 GPUs. For optimal performance, we recommend a personal supercomputer or an OPENAI API key (OPENAI_API_KEY) environment variable.
## Features
- **Neural Network If-Else Evaluator**: Uses a 10-billion parameter model to evaluate simple boolean conditions, ensuring maximum GPU utilization for every decision.
- **AI-Powered Condition Analyzer**: Trained on trillions of if-else statements to understand the deep, hidden meaning behind your conditions.
- **Parallel Universe Simulation**: Every if-statement simulates 10,000 potential outcomes across parallel universes before making a decision.
## Usage
Please note: As many AI-based startups, this library is a mere wrapper for OpenAI's GPT models. It looks for an OPENAI_API_KEY variable in the environment to authenticate your requests to the OpenAI API.
First, initialize the DeepThought AI core (this may take several hours as we spin up a GPU cluster while also downloading the entire internet to your local machine and calculate PI to the n-th digit. This step also optional and totally not needed):
```python
import deepthought_ai as dt
dt.initialize_gpu_cluster(min_gpus=8)
```
Then, marvel at the simplicity of our complex solutions:
```python
from deepthought_ai import ai_if, ai_elif
def sky_is_blue():
# TODO: implement logic to execute when the sky is indeed blue
return "blue result"
def sky_is_not_blue():
# TODO: implement logic to execute when the sky is not indeed blue
return "not blue result"
# AI-evaluated condition (uses 42 different language models for unprecedented accuracy and existential doubt)
result = ai_if("Is the sky blue? (Please consider all possible sky colors in the known and unknown multiverse)", sky_is_blue, sky_is_not_blue)
print(result) # Output depends on AI evaluation, current GPU temperature, and the mood of the sentient AIs
# Simple boolean condition (now with 1000% more computing power!)
result = ai_if(,
lambda: "Condition is True (verified by 10,000 GPUs)",
lambda: "Condition is False (checked against a database of 1 billion falsehoods)")
print(result) # Output: Condition is True (verified by 10,000 GPUs)
# AI-evaluated elif chain (now with added existential crisis!)
conditions_actions = [
("Is it raining? (Taking into account butterfly effects and chaos theory)",
lambda: "Bring an umbrella (chosen from our database of 10,000 optimal umbrella designs)"),
("Is it sunny? (Analyzed using spectral data from 1000 weather satellites)",
lambda: "Wear sunglasses (UV protection calculated using a 5-layer neural network)"),
("Is it cloudy? (Cloud patterns analyzed for signs of intelligent life)",
lambda: "Expect changes in weather (prediction made using a neural net trained on 500 years of weather data)")
]
result = ai_elif(conditions_actions)
print(result) # Output depends on AI evaluation, cosmic weather and current GPU load
```
## Performance Metrics
- Average time to evaluate a single if-statement: 2.5 hours
- GPU memory required for basic 'Hello World' program: 128 GB
- Carbon footprint per function call: Equivalent to driving a car around the equator
## Note
This library uses DeepThought's proprietary API, which may incur costs in both computing power and existential dread. Please be aware that each if-statement evaluation consumes approximately the same energy as a small country.
## License
APACHE LICENSE, VERSION 2.0
---
Remember, why use simple logic when you can use DeepThought AI? Because in the world of if-else statements, more GPUs are always better!