Let's collaborate on a comprehensive, step-by-step prompt guide for developing a software using an AI coding tool.  We'll take our time to ensure clarity and completeness.

This guide will focus on using effective prompts to guide an AI in creating software with specific features we'll define together.  My development environment is Windows 11.  The software is for personal home service management and won't be used commercially.

Crucially, the software should leverage free and open-source tools, libraries, and APIs. We can achieve this by:

* Providing the AI with a list of relevant APIs and libraries, including their documentation.
* Enabling the AI to access and search online repositories like GitHub for suitable open-source code and libraries.                                                                                                                                                                                                                                                                                            The project: 
# A.I. Software Creator

## Key Components

This AI software comprises two primary components:

1. **AI Chat Interface:** This conversational interface allows users to interact naturally with the AI, submitting requests, instructions, or questions.  The AI processes these inputs to generate code snippets or provide explanations.

2. **Multi-Tab Interface:** This interface offers a visually organized project workspace divided into three tabs:

    - **Code Generation:**  Provides real-time visibility into the AI's code generation process.
    - **File Explorer:** A standard file system view of the project's files and folders created by the AI, facilitating easy navigation and access.
    - **Software Preview:** Displays a live preview of the software under development, enabling immediate feedback on UI elements and functionality.

## Core Functionality

The AI can create files and folders directly on the user's computer within a specified directory.  By leveraging its programming knowledge and best practices, it generates clean, efficient, and functional code.  Furthermore, the AI can access and manage projects within a GitHub repository.

## Underlying Technologies

This AI software combines natural language processing (NLP), machine learning (ML), and coding expertise to automate and accelerate software development. This allows users to concentrate on high-level design and logic while the AI handles the technical implementation details.

## Integration with Open-Source Services

To maximize the benefits of open-source resources, the AI assistant integrates with various free and open-source services, APIs, tools, and resources. This includes services from GitHub repositories, Hugging Face datasets and models, LMStudio, Ollama, and other future open-source additions.

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## Tool and Service Examples

**Main Resources:**

- GitHub: https://github.com
- Hugging Face: https://huggingface.co

**Useful GitHub Repositories:**

- duckduckgo-ai-chat: https://github.com/mumu-lhl/duckduckgo-ai-chat
- TensorFlow: https://github.com/tensorflow/tensorflow
- PyTorch: https://github.com/pytorch/pytorch
- Hugging Face Transformers: https://github.com/huggingface/transformers
- OpenCV: https://github.com/opencv/opencv
- Open Source AI Repository Collection: https://github.com/thebigbone/opensourceAI
- Awesome Machine Learning: https://github.com/josephmisiti/awesome-machine-learning
- LangChain: https://python.langchain.com/en/latest/index.html
- Electron: https://www.electronjs.org/
- Tauri: https://tauri.app/
- Node.js: https://nodejs.org/
- Node.js fs: https://nodejs.org/api/fs.html
- simple-git: https://github.com/steveukx/git-js
- Python: https://www.python.org/
- Python os: https://docs.python.org/3/library/os.html
- PyGithub: https://github.com/PyGithub/PyGithub
- Hugging Face Hub: https://huggingface.co/docs/hub/
- AI Assistant: https://github.com/Anuken/MegaLinter
- ONNX Runtime: https://github.com/microsoft/onnxruntime
- Ray: https://github.com/ray-project/ray
- Horovod: https://github.com/horovod/horovod
- MLflow: https://mlflow.org/
- Kubeflow: https://www.kubeflow.org/
- Scikit-learn: https://scikit-learn.org/stable/
- Pandas: https://pandas.pydata.org/
- NumPy: https://numpy.org/
- XGBoost: https://xgboost.ai/
- LightGBM: https://lightgbm.readthedocs.io/en/latest/
- DVC (Data Version Control): https://dvc.org/
- spaCy: https://spacy.io/
- NLTK: https://www.nltk.org/
- Gensim: https://radimrehurek.com/gensim/
- Flair: https://github.com/flairNLP/flair
- Detectron2: https://github.com/facebookresearch/detectron2
- MMDetection: https://github.com/open-mmlab/mmdetection
- Papers with Code: https://paperswithcode.com/
- Streamlit: https://streamlit.io/
- Gradio: https://gradio.app/


## AI Platform Accounts:

- Together.ai: https://together.ai
- Fireworks.ai: https://fireworks.ai
- Cohere.com: https://cohere.com
- GLHF.chat: https://glhf.chat/
- Deepinfra.com: https://deepinfra.com
- Openrouter.ai: https://openrouter.ai
- Groq.com: https://groq.com
- AI Studio (Google): https://aistudio.google.com
- Opper.ai: https://opper.ai


## More Useful Tools:

- Firecrawl: https://www.firecrawl.dev
- Readwise: https://readwise.io
- Serper: https://serper.dev
- ElevenLabs: https://elevenlabs.io
- Langfuse: https://langfuse.com
- Botpress: https://botpress.com/
- Tyk: https://tyk.io/
- Keras: https://keras.io/
- Scikit-learn: https://scikit-learn.org/
- MLflow: https://mlflow.org/
- Rasa: https://rasa.com/
- DeepPavlov: https://deeppavlov.ai/
- FairSeq: https://github.com/pytorch/fairseq
- Kubeflow: https://www.kubeflow.org/
- MLRun: https://github.com/mlrun/mlrun
- OpenVINO Toolkit: https://openvino.ai/
- NVIDIA CUDA and cuDNN: https://developer.nvidia.com/cuda-zone
- Electron: https://www.electronjs.org/
- Monaco Editor: https://microsoft.github.io/monaco-editor/
- React: https://reactjs.org/
- Vue.js: https://vuejs.org/