semvec/pypi-packages huggingface dataset:
hackbyte 2.0a1
HackByte is a powerful CLI memory scanner and modifier for rooted Linux and Android.
Requires Python: >=3.6
Keywords: memory scanner cli android root linux hacking freeze reverse-engineering

# 🧠 HackByte - Memory Hacker CLI

A powerful Command Line Interface (CLI) tool for memory scanning, editing, and freezing — built in Python.

🎯 Designed for Linux, Termux, or Android NetHunter environments with root access.
🧠 Perfect for users who need full control over memory processes directly from the terminal.

---

## ✨ Key Features

* 🔍 Search memory values (`int`, `float`, `string`, etc.)
* ✏️ Edit memory addresses
* 📌 Freeze memory values (prevent changes)
* 🧠 Attach to process by PID or process name
* 🛠️ Interactive shell interface powered by Python `cmdloop()`

---

## 🧪 Example Usage

```bash
hackbyte
```

Then inside the interactive shell:

```
> list
> attach 1234
> scan dword 100
> edit 0 999
> freeze 0
```

---

## 🚀 Installation

### From PyPI:

```bash
pip install hackbyte
```

### From Source:

```bash
git clone https://github.com/DX4GREY/hackbyte
cd hackbyte
pip install .
```

---

## 💻 Requirements

* Python 3.6+
* Root access (Linux/Android)
* Access to `/proc/[pid]/mem` and `/proc/[pid]/maps`
* `ptrace` or `process_vm_readv` support (depending on features)

---

## 🔒 Disclaimer

This tool is provided for educational and research purposes only.
**The author is not responsible for any misuse or illegal activities.**

---

## 🙌 Credits

Created by [Dx4Grey](https://github.com/DX4GREY)
Inspired by low-level memory manipulation and reverse engineering workflows.

---
pyhoneybee 0.1.0
HoneyBee: Misconfigured App Generator
Requires Python: >=3.9

# HoneyBee: Misconfigured App Generator


HoneyBee is a tool for creating misconfigured environments to test vulnerabilities in technologies like Jenkins, Jupyter Notebook, and more. 

With the help of LLMs, HoneyBee generates:
- **Dockerfiles** and **Docker-compose** files to replicate misconfigured applications.
- **Nuclei templates** to detect vulnerabilities (credit to a great template from the project [Fabric](https://github.com/danielmiessler/fabric)).
- **README files** with instructions on how to use the generated files.

## How It Works
- Choose a technology and a misconfiguration from a curated list of known issues, or write your own.
- HoneyBee uses LLMs to generate the required files and instructions to use them.

![HoneyBee](
images/img.png)

## Key Features

- **Misconfiguration Generator**:
  - Choose from a list of commonly misconfigured apps (e.g., Jenkins, Jupyter Lab).
  - Select a well-known misconfiguration (e.g., weak authentication, improper access control).
  - Automatically generate Dockerfiles and Docker-compose files tailored to your selections.

- **Detection Template Generator**:
  - Generate **Nuclei templates** to detect the created misconfiguration.

- **One-click test deployment**:
  - Deploy generated **docker-compose** files with one click and without leaving the browser.

## Getting Started

### Prerequisites

- Python 3.10 or above
- OpenAI API key

### Installation

1. Clone the repository:

   ```bash
   git clone https://github.com/yaaras/honeybee.git
   cd honeybee
    ```

2. Install using pip:

      ```bash
      pip install -e .
      ```

3. Set up your OpenAI API key:

    For OpenAI:

    ```bash
    export OPENAI_API_KEY=your_openai_api_key
    ```

    For Azure OpenAI:

    ```bash
    export AZURE_OPENAI_API_KEY=your_azure_api_key
    export AZURE_OPENAI_ENDPOINT=your_azure_endpoint
    ```

4. Setup docker compose (Optional):

    To enable the use of the local deployment feature simply [Install Docker Compose](https://docs.docker.com/compose/install/).


### Usage
Simply run the honeybee command
   ```bash
    honeybee
   ```
Open your browser and navigate to the URL provided by Streamlit (typically http://localhost:8501).
gunertensors 3.0.0
Advanced AI Optimization Toolkit
Requires Python: >=3.9
Keywords: ai nlp deep learning gpu optimization

 Gunertensors - Advanced AI Optimization Toolkit

- **Multi-GPU Training**: Automatic optimal device selection
- **Advanced Quantization**: QAT, Pruning, Dynamic Quantization
- **Multilingual NLP**: Language detection, spelling correction, sentiment analysis
- **Distributed Training**: Multi-GPU/TPU support
- **Autonomous Memory Management**: Real-time optimization
- **Comprehensive Monitoring**: Resource usage tracking


```bash
pip install gunertensors[cuda]