semvec/pypi-packages huggingface dataset:
zevihanthosa 1.2.0.0.1
Zevihanthosa AI
Requires Python: >=3.6

# Zevianthosa  

## What is Zevianthosa?  

Zevianthosa is an advanced artificial intelligence system developed by aertsimon90. It is designed to push the boundaries of machine learning by creating a flexible, adaptive, and highly efficient AI architecture. The core feature of Zevianthosa is its **dynamic, weight-based learning process**, enhanced by a **momentum-driven training mechanism**, which improves learning speed and accuracy. The model also integrates **customizable activation functions** and **multiple decision-making strategies**, providing flexibility for various tasks.  

The Zevianthosa framework supports both **single-layer and multi-layer architectures**, allowing it to be tailored to a wide range of applications. With a strong focus on **scalability**, this AI system is designed to **evolve and improve** continuously through updates, enhancing its performance for diverse use cases.  

## Current Models and Versions  

### **Latest Release: ZevihaNut/1.2**    

ZevihaNut/1.2 is the latest iteration of the Zevianthosa AI framework, introducing key enhancements:    

- **Neural Processing System:** A refined "Brain" module with improved weight distribution, activation functions, and dynamic memory.    
- **Hybrid Vectorization:** A combination of grammar-based learning, hash-based vectorization, and adaptive tokenization for efficient language understanding.    
- **Momentum-Based Training:** An optimized training process that balances learning speed and stability.    
- **Multi-Layer Support:** The `AdvancedChatbot` class enables deeper learning with automatic layer structuring.    
- **Improved Backpropagation:** Enhanced error correction and stability mechanisms for long-term adaptation.    
- **Flexible Decision-Making:** Multiple strategies for response generation, including probabilistic and similarity-based methods.    

### **Upcoming Versions**    

Future updates will focus on:    

- **Faster training algorithms** to improve learning efficiency.    
- **Advanced deep learning techniques** for complex decision-making.    
- **Improved adaptability** to various data structures and use cases.    

## Is Zevianthosa Open Source?    

Yes! Zevianthosa is completely **open-source**, allowing developers to contribute, modify, and integrate the AI system into their own projects. The goal is to foster collaboration and make cutting-edge AI accessible to all.    

## Installation & Usage    

To use Zevianthosa, clone the repository and follow the installation guide in the documentation.    

```bash  
git clone https://github.com/aertsimon90/Zevianthosa.git  
cd Zevianthosa  
pip install -r requirements.txt
```
Contact Information

For inquiries or contributions, feel free to contact:
Email: simon.scap090@gmail.com


---

Explore the repository, experiment with the code, and contribute to the future of Zevianthosa!
zevianthosa 1.2
Zevianthosa AI
Requires Python: >=3.6

# Zevianthosa  

## What is Zevianthosa?  

Zevianthosa is an advanced artificial intelligence system developed by aertsimon90. It is designed to push the boundaries of machine learning by creating a flexible, adaptive, and highly efficient AI architecture. The core feature of Zevianthosa is its **dynamic, weight-based learning process**, enhanced by a **momentum-driven training mechanism**, which improves learning speed and accuracy. The model also integrates **customizable activation functions** and **multiple decision-making strategies**, providing flexibility for various tasks.  

The Zevianthosa framework supports both **single-layer and multi-layer architectures**, allowing it to be tailored to a wide range of applications. With a strong focus on **scalability**, this AI system is designed to **evolve and improve** continuously through updates, enhancing its performance for diverse use cases.  

## Current Models and Versions  

### **Latest Release: ZevihaNut/1.2**    

ZevihaNut/1.2 is the latest iteration of the Zevianthosa AI framework, introducing key enhancements:    

- **Neural Processing System:** A refined "Brain" module with improved weight distribution, activation functions, and dynamic memory.    
- **Hybrid Vectorization:** A combination of grammar-based learning, hash-based vectorization, and adaptive tokenization for efficient language understanding.    
- **Momentum-Based Training:** An optimized training process that balances learning speed and stability.    
- **Multi-Layer Support:** The `AdvancedChatbot` class enables deeper learning with automatic layer structuring.    
- **Improved Backpropagation:** Enhanced error correction and stability mechanisms for long-term adaptation.    
- **Flexible Decision-Making:** Multiple strategies for response generation, including probabilistic and similarity-based methods.    

### **Upcoming Versions**    

Future updates will focus on:    

- **Faster training algorithms** to improve learning efficiency.    
- **Advanced deep learning techniques** for complex decision-making.    
- **Improved adaptability** to various data structures and use cases.    

## Is Zevianthosa Open Source?    

Yes! Zevianthosa is completely **open-source**, allowing developers to contribute, modify, and integrate the AI system into their own projects. The goal is to foster collaboration and make cutting-edge AI accessible to all.    

## Installation & Usage    

To use Zevianthosa, clone the repository and follow the installation guide in the documentation.    

```bash  
git clone https://github.com/aertsimon90/Zevianthosa.git  
cd Zevianthosa  
pip install -r requirements.txt
```
Contact Information

For inquiries or contributions, feel free to contact:
Email: simon.scap090@gmail.com


---

Explore the repository, experiment with the code, and contribute to the future of Zevianthosa!
xedix-versioning-system 0.1.2
A simple version control system
Requires Python: >=3.7

# XediX Versioning System

A lightweight version control system implemented in Python.

## Installation

```bash
pip install xedix-versioning-system
```

## Usage

After installation, you can use the `xvs` command:

```bash
xvs branch main                            # Switch to or create 'main' branch
xvs commit "file1.txt" "Initial commit"    # Commit changes
xvs stage "file1.txt" "Work in progress"   # Stage changes
xvs init
xvs status
```
gpt-repository-loader 0.10.0
A utility to convert a Git repository into a text representation.
Requires Python: >=3.6

# gpt-repository-loader

## Installation

`pip install gpt-repository-loader`

## Linux Requirements
On Linux, ensure that you have `xclip` installed for clipboard functionality. You can install it using:
```bash
sudo apt-get install xclip  # Debian/Ubuntu
sudo yum install xclip      # Fedora/CentOS
```

## How to use?
Go to the directory you are interested in, run
```
gpt-repository-loader . -c
```
This will copy ALL the git tracked content in the repository on clipboard and then you can use [Gemini](https://aistudio.google.com/app/prompts/new_chat)/[Claude](https://claude.ai)/[ChatGPT](https://chatgpt.com) to ask questions on it.

### Available Command Line Flags
* `repo_path`: (Required) Path to the Git repository.
* `-p`, `--preamble`: Path to a preamble file to include before the repository content.
* `-c`, `--copy`: Copies the repository contents to the clipboard. If not provided, the output will be written to a file named `output.txt` in the current directory.
* `-i`, `--ignore`: Additional file paths or patterns to ignore. You can specify multiple paths or patterns.
* `--include-js-ts-config`: Include JavaScript and TypeScript config files (which are ignored by default).
* `-l`, `--list`: List all files with their token counts.

### Examples
```bash
# Ignore specific files or directories
gpt-repository-loader . -c -i "*.log" "temp_files/" "config.ini"

# Include JS/TS config files and ignore a specific directory
gpt-repository-loader . --include-js-ts-config -i "node_modules/"

# List all files with their token counts
gpt-repository-loader . -l
```

## What to use it for?
- Build a README for codebases
- Work with Legacy code
- Debug issues

Gemini's 1M context window is REALLLY big, and it under utilized.
django-admin-bulk-io 1.3
This package allows you to import and export data in your Django admin.

# Django Admin Bulk IO

This package allows you to import and export data in your Django admin. It supports various formats like CSV, JSON, and XML.

## Features

* **Import data:** Easily import data into your Django models from CSV, JSON, and XML files.
* **Export data:** Export data from your Django models to CSV, JSON, and XML files.
* **Admin integration:** Seamlessly integrates with the Django admin interface.
* **Customizable serializers:**  Allows customization of how data is serialized and deserialized.
* **Support for related fields:** Handles import/export of related model data.
* **Error handling and reporting:** Provides detailed error messages during import/export operations.

## Installation

    ```bash
    pip install django-admin-bulk-io
    ```

## Configuration

1. Add django_admin_bulk_io to your INSTALLED_APPS in settings.py:

    ```python
    INSTALLED_APPS = [
        # ... other apps
        'django_admin_bulk_io',
    ]
    ```

2. In your admin.py, import and use the BulkImportMixin and BulkExportMixin:

    ```python
    from django.contrib import admin
    from django_admin_bulk_io.admin import BulkImportMixin, BulkExportMixin

    @admin.register(YourModel)
    class YourModelAdmin(admin.ModelAdmin):
        pass
    ```

## Usage

### Import

In the Django admin, navigate to the list view of the model you want to import data into.

1. Click on the "Import" button.
2. Choose the file you want to import and select the format (CSV, JSON, or XML).
3. Click "Import" to begin the import process.
Export

### Export

1. Select the objects you want to export (or select all).
2. From the "Action" dropdown, choose "Export selected objects".
Choose the desired export format (CSV, JSON, or XML).

## Contributing

Contributions are welcome! Please submit bug reports and pull requests.

## License

This project is licensed under the MIT License.
print-btree 0.0.14
A package that prints binary trees in terminal
Requires Python: >=3.8

# print-btree

This packages allows you to print and visualize your binary tree

```
from print_btree import print_btree

class Node:
    def __init__(self, val):
        self.val = val
        self.left = None
        self.right = None

root = Node(1)
root.left = Node(2)
root.right = Node(3)
root.left.left = Node(4)
root.left.right = Node(5)

print_btree(root)

'''

 __1__
 |   |
_2_  3 
| |    
4 5   

'''
```

Ideally, your Node definition should be something like:

```
class Node:
    def __init__(self, val):
        self.val = val
        self.left = None
        self.right = None
```

But it's ok if your Node has different attribute names:

```
from print_btree import print_btree

class BNode:
    def __init__(self, value):
        self.value = value
        self.left_node = None
        self.right_node = None

root = BNode(1)
root.left_node = BNode(2)
root.right_node = BNode(3)
root.left_node.left_node = BNode(4)
root.left_node.right_node = BNode(5)
root.right_node.right_node = BNode(100)

print_btree(root, 
            val='value',
            left='left_node',
            right='right_node')

'''

 __1__
 |   |
_2_  3__
| |    |
4 5   100

'''
```

It's ok if you values are longer

```
from print_btree import print_btree

class Node:
    def __init__(self, val):
        self.val = val
        self.left = None
        self.right = None

root = Node('apple')
root.left = Node('pie')
root.right = Node('juice')
root.left.left = Node('pear')
root.left.right = Node('pineapplejuice')
root.right.right = Node('durian')

print_btree(root)
'''
       ________apple________
       |                   |
 _____pie_____           juice_
 |           |                |
pear   pineapplejuice       durian

'''
```
openoperator 0.1.4
Operate any browser, desktop, device or robot.
Requires Python: <4.0,>=3.11

<picture>
  <source media="(prefers-color-scheme: dark)" srcset="./static/banner-dark.png">
  <source media="(prefers-color-scheme: light)" srcset="./static/banner.png">
  <img alt="Shows a black OpenOperator Logo in light color mode and a white one in dark color mode." src="./static/banner.png"  width="full">
</picture>

[![GitHub stars](https://img.shields.io/github/stars/j0yk1ll/openoperator?style=social)](https://github.com/j0yk1ll/openoperator/stargazers)
[![Discord](https://img.shields.io/discord/1332994187712397354?color=7289DA&label=Discord&logo=discord&logoColor=white)](https://discord.gg/Jdx62Bjj8v)
[![Documentation](https://img.shields.io/badge/Documentation-📕-blue)](https://openoperator.co)
[![Twitter Follow](https://img.shields.io/twitter/follow/Gregor?style=social)](https://x.com/gregpr07)
[![Twitter Follow](https://img.shields.io/twitter/follow/Magnus?style=social)](https://x.com/mamagnus00)
[![Twitter Follow](https://img.shields.io/twitter/follow/j0yk1ll?style=social)](https://x.com/j0yk1ll)

> [!IMPORTANT]
> openoperator is a fork of [browser-use](https://github.com/browser-use/browser-use)

Enable AI to control your browser 🤖.

To learn more, check out the [documentation 📕](https://openoperator.co).

# Quick start

Install the openoperator:

```bash
uv pip install openoperator
```

Install playwright:

```bash
playwright install
```

Add your API keys to your `.env` file.

```bash
OPENAI_API_KEY=
```

For other settings, models, and more, check out the [documentation 📕](https://openoperator.co).

# Examples

For examples see the [examples](examples) folder.

# Vision

A general purpose agent, that can operate any browser, desktop, device or robot in the world.

## Contributing

We love contributions! Feel free to open issues for bugs or feature requests. To contribute to the docs, check out the `/docs` folder.

## Local Setup

To learn more about the library, check out the [local setup 📕](https://openoperator.co/development/local-setup).

## Citation

If you use OpenOperator in your research or project, please cite:

```bibtex
@software{browser_use2024,
  author = {Müller, Magnus and Žunič, Gregor},
  title = {OpenOperator: Enable AI to control your browser},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/browser-use/browser-use}
}

@software{openoperator2025,
  author = {Raetz, Kevin},
  title = {OpenOperator: A general purpose computer use agent},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/j0yk1ll/openoperaotr}
}
```

---

<div align="center">
  Made with ❤️ in Germany
</div>