Hardskills Integrations

Flower is an open source federated learning framework designed to simplify the development and deployment of machine learning models across decentralized data sources. It enables training on data located on devices or servers without transferring the data itself, thereby enhancing privacy and reducing bandwidth usage. Flower supports a wide range of machine learning frameworks, including PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, and XGBoost, and is compatible with various platforms and cloud services like AWS, GCP, and Azure. It offers flexibility through customizable strategies and supports both horizontal and vertical federated learning scenarios. Flower's architecture allows for scalable experiments, with the capability to handle workloads involving tens of millions of clients. It also provides built-in support for privacy-preserving techniques like differential privacy and secure aggregation.

Osaurus is a native AI harness for macOS that lets users run open models entirely on their Mac, connect cloud models when greater capability is needed, and carry shared memory across them. Built in Swift for Apple Silicon, it works fully offline with local models through Ollama, MLX, or LM Studio, keeping conversations, code, files, settings, agents, skills, and provider keys on the device unless the user explicitly chooses a cloud provider. A system-wide chat overlay provides quick access from any app, while separate agents can be created for coding, research, file organization, and other jobs, each with its own prompt, history, and memory. Osaurus distills past conversations into relevant facts, loads packaged skills when tasks require them, and gives agents scoped access to working folders, file search, Git, and other tools. Agents can run code in an isolated sandbox, delegate work to subagents, operate on schedules, respond to folder changes, use voice input, and generate images.