aidk-framework

Artificial Intelligence Development Kit (AIDK)

Version: 1.0
Author: Akshansh Nandan
License: GNU General Public License v3.0 (GPLv3)

Copyright (C) 2026 Akshansh Nandan

---

1. Overview

The Artificial Intelligence Development Kit (AIDK) is a comprehensive AI/ML development framework designed to provide a unified interface for a wide spectrum of AI models, including but not limited to:

• Handcrafted Neural Networks (FCNN, CNN, RNN, ResFCN, DeepRNN)
• Transformers with advanced meta-learning and pretraining capabilities
• Predictive Models (Linear Regression, Decision Trees, Logistic Regression, Classifiers)
• Neuromorphic Neural Networks for hardware-accurate inference
• Generative Adversarial Networks (GANs) and other generative architectures

AIDK allows rapid AI experimentation, training, fine-tuning, and deployment under a single, production-ready interface.

---

2. Key Features

• Unified Interface: Manage 23+ AI model types from one framework
• Minimal Code Required: Models can be created, trained, and deployed in under 10 lines of code
• Advanced Training Capabilities: Includes GPU/CPU acceleration, pretraining, fine-tuning, and custom loss functions
• Hardware-Accurate Neuromorphic Support: Run simulations on Loihi or equivalent neuromorphic hardware
• Custom Prompting & Reasoning: Built-in pipeline for stepwise reasoning, summarization, and multi-output workflows
• High Generalization Rates: Optimized for small and medium-sized datasets
• Production-Ready Output: Supports model saving, loading, and deployment seamlessly
• Secure & Reliable: Designed with stability and robustness for enterprise environments

---

3. Installation

AIDK requires Python 3.10+ and standard AI/ML dependencies. Example:

git clone --filter=blob:none --sparse https://github.com/Akshansh-Nandan/Tech.git
cd Tech
git checkout AI
git sparse-checkout set AI/AI_lib

cd AI/AI_lib/aidk
pip install -r requirements.txt

---

4. Usage & Examples

AIDK provides a unified API for creating, training, fine-tuning, and running inference across multiple AI model types.

All usage examples, workflows, and demonstrations are provided in the demo.py file, including:

• Model initialization and configuration
• Training and fine-tuning pipelines
• Saving and loading trained models
• Inference and prompting workflows
• Advanced and experimental use cases

To get started, run:

python demo.py

The demo.py file serves as the primary reference implementation for understanding AIDK’s design and usage patterns.

---

5. Documentation

For full usage, model types, APIs, and examples, see the demo.py file.

---

6. Licensing

AIDK is free and open-source software licensed under the GNU General Public License v3.0 (GPLv3). See LICENSE.md for the full license text.

Key points:

• You are free to use, study, modify, and redistribute AIDK.
• Source code must remain open for any redistributed or modified versions.
• Any derivative work must also be licensed under GPLv3.
• There is no warranty for the software, to the extent permitted by law.
• Commercial use is allowed, provided GPLv3 terms are fully respected.

AIDK is distributed to promote software freedom, transparency, and collaborative development.

---

7. Contact & Support

For licensing, corporate inquiries, or support:

• Author: Akshansh Nandan
• Email: [microcodehack@gmail.com]
• Website / Portfolio: [https://www.linkedin.com/in/akshansh-nandan-11403b289]

---

8. Contribution

AIDK is open source under the GPLv3 license. Contributions are welcome from the community.

• All contributions should follow the contribution guidelines.
• By contributing, you agree to license your changes under GPLv3, ensuring AIDK remains free and open-source.
• Community contributions may be merged, improved, or redistributed under the same license.

---

9. Disclaimer

AIDK is provided “AS IS”, without any express or implied warranties. Use at your own risk. The authors do not guarantee results or suitability for any specific purpose.

---

10. Credits and Acknowledgements

The development of AIDK (Artificial Intelligence Development Kit) acknowledges the use of concepts, APIs, tooling, and reference implementations from the following third-party technologies and ecosystems:

• PyTorch
• TensorFlow
• Scikit-learn
• NumPy
• Lava (Neuromorphic Computing Framework)
• SLAYER (Spiking Neural Network Framework)
• Larn2Learn

These tools and frameworks are independently developed and maintained by their respective authors and communities.

AIDK is not affiliated with, endorsed by, or sponsored by any of the above projects.
All trademarks, service marks, and names remain the property of their respective owners.

The inclusion of this acknowledgement does not imply redistribution, relicensing, or incorporation of third-party source code, except where explicitly required by their respective licenses.

---

NOTE

AIDK is not intended to replace core libraries like PyTorch or TensorFlow—it is built on top of them. For optimal workflow:

1. Use core libraries for experimentation, research, and testing new ideas.
2. Use AIDK for production-ready models, rapid development, and deployment.
3. If a new architecture proves robust and production-grade, AIDK may integrate it—only if permitted.

This ensures you get the best of both worlds: flexibility during experimentation and reliability in production.