Key Features

• AI engineers can upload pre-trained static model files along with an index.html and JavaScript-based frontend UI.
• Example: Hosting a computer vision model that runs inference directly in the browser using WebAssembly or TensorFlow.js.

Key Features

• AI engineers training generative models (e.g., GPT-powered chatbots, text-to-image AI) can instantly deploy their applications by uploading static files.
• Removes the need for backend hosting or cloud GPU instances.

Key Features

• Researchers can upload interactive Jupyter Notebooks (converted to HTML) with live D3.js, Matplotlib, and Seaborn visualizations.
• Enables easy sharing of datasets, model results, and findings.

Key Features

• AI teams can host interactive dashboards built with Plotly Dash, Streamlit (converted to HTML), or D3.js to visualize model performance.
• Removes reliance on complex deployment pipelines.

Key Features

• Upload explainability visualizations such as SHAP, LIME, or Grad-CAM reports for stakeholders to analyze AI decision-making.
• Example: Hosting an interactive SHAP values UI to highlight model feature importance.

Key Features

• AI engineers deploying edge AI applications can upload WASM-optimized AI models (e.g., TensorFlow.js, ONNX.js) for in-browser inference.
• Example: Real-time face recognition or pose estimation without backend servers.

Key Features

• AI teams can host annotation tools like CVAT, LabelMe, and VIA in static mode for dataset labeling without requiring a server.
• Useful for small teams needing dataset labeling without relying on SaaS tools.