https://github.com/velocitatem/straightagpt

Bringing the Feynman Method to AI.
https://github.com/velocitatem/straightagpt

Last synced: 2 months ago
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Bringing the Feynman Method to AI.

• Host: GitHub
• URL: https://github.com/velocitatem/straightagpt
• Owner: velocitatem
• Created: 2023-04-22T18:23:42.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-05-10T11:09:17.000Z (almost 2 years ago)
• Last Synced: 2025-02-14T16:48:35.898Z (2 months ago)
• Language: Python
• Size: 38.1 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# StraightAGPT

![Beta badge](https://img.shields.io/badge/beta-0.0.1-blue) ![Unsatble badge](https://img.shields.io/badge/stability-unstable-red) ![License badge](https://img.shields.io/badge/license-MIT-green) ![Python badge](https://img.shields.io/badge/python-3.7-blue)

Bringing the Feynman Method to AI. You teach an AI how to solve problems for you.

## Usage

This is how the cli works:

1. Enter task (solve, exit, custom command)

  + Solve: Solve a problem (Provide the question context and goals)

  + Exit: Exit the program

  + Custom command: Enter a custom command for the AI to evaluate

## Installation

1. Clone the repository

2. Install the requirements (`pip install -r requirements.txt`)

3. Run the program (`python student.py`)

## Contributing

1. Fork the repository

2. Create a new branch

3. Make your changes

4. Create a pull request

# Operations for Data Analysis

Here is how the model~user interaction should go:

1. User provides a problem

2. Model identifies the hypotheses

3. Based on the hypotheses and problem, identifies the test statistic

4. Uses tools to compute test-statistic and p-value

5. Interprets the results in context of the problem and hypotheses

We want the model to: Solve complex problems, provide simple calculations