Using Scribbler for AI Workflows

Scribbler isn’t just a JavaScript notebook for small coding experiments — it can also be a powerful playground for AI development and exploration like Jupyter Notebook is used for AI workflows in Python. Because it runs directly in the browser with no backend dependencies, it gives developers and researchers a lightweight environment to test ideas, integrate AI APIs, or even run AI models in-browser.

1. Accessing AI APIs

Many AI services (like OpenAI, Hugging Face, Cohere, etc.) expose their functionality through REST APIs or WebSocket interfaces. With Scribbler, you can:

• Write JavaScript snippets that call these APIs directly via fetch()
• Prototype prompts, pipelines, or conversational interfaces without needing a Node.js or server-side setup.
• Embed results interactively alongside your notes and visualizations (e.g., generate text with GPT, then analyze or visualize it inline).

Example use case: Quickly testing Gemini API calls inside a Scribbler notebook to experiment with different prompts, and documenting results right next to the code.

2. Using Scribbler to Access WebLLMs and WebGPU based image generation

With the rise of WebLLM (large language models that run directly in the browser using WebGPU/WebAssembly), Scribbler becomes a natural testing ground:

• You can load pre-trained models (like WebLLM or transformers.js) directly in the browser.
• Run inference locally without requiring a backend or cloud service.
• Compare outputs from different models interactively, while logging results in the same notebook.
• Create small demos where users can interact with LLMs inside the Scribbler notebook environment.

Example use case:

1. Stable diffusion in the browser using Scribbler
2. LLMs using WebLLM on Scribbler

3. Data Analysis & Machine Learning in JavaScript

JavaScript has a growing ecosystem of ML and data science libraries, and Scribbler can act as the notebook interface for them.

• Data Analysis: Libraries like Danfo.js (pandas-like dataframes) and Arquero can be used to clean, manipulate, and visualize datasets inside Scribbler.
• Machine Learning Models: Use TensorFlow.js or ML5.js to build, train, and run machine learning models — from regression to neural networks — directly in the browser.
• Visualization: Combine ML experiments with libraries like D3.js, Plotly, or Chart.js for dynamic data visualization, all integrated into one notebook environment.

Example use cases:

• Training a small image classifier using TensorFlow.js inside Scribbler.
• Building interactive dashboards where ML predictions update live in response to user input.

Why Scribbler Fits AI Workflows

• Lightweight: No need for heavy installs or server setups.
• Interactive: Code + results + documentation in one place.
• Open-source & flexible: Can be extended with custom libraries or adapted for specific use cases.
• Shareable: Notebooks can be shared online, making it easier to collaborate on AI experiments.

Scribbler, by combining the notebook paradigm with JavaScript’s rapidly growing AI ecosystem, offers a frictionless entry point into prototyping, experimenting, and teaching AI concepts.