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This Blueprint demonstrates how to run AI agents in your browser using WebAssembly (WASM) through Pyodide and the OpenAI Agents Python SDK.

This Blueprint enables you to re-adjust Whisper models to user-predefined texts, improving transcription accuracy for uncommon terms or names.

This Blueprint provides a locally runnable pipeline for parsing structured data from scanned or digital documents using open-source OCR and LLMs.

This Blueprint demonstrates how to build a task-specific agent and compare different agent frameworks for a given use case, like finding a surfing spot.

This blueprint guides you through training and using a machine learning model that effectively detects synthetic and modified audio content.

This Blueprint shows you how to transcribe audio files using open-source Whisper models via Speaches, a self-hosted server that mimics the OpenAI Whisper API.

This blueprint guides you to convert unstructured documents to Markdown format using the Docling command-line interface.

This blueprint introduces an approach to personal, local timeline algorithms that people can either run out-of-the-box or customize.

This Blueprint demonstrates how to finetune LLMs using federated AI with Flower, enabling collaboration on private datasets without sharing raw data