Skill UI

Outlines a comprehensive, fast local development loop for applications hosted on Fly.io. It guides users through testing Docker containers locally, proxying remote services like Postgres and Redis to localhost, configuring dev environments, and performing fast deployment cycles. Essential for modern edge computing development.

This skill provides advanced integration for deploying applications on Fly.io, focusing on edge computing architectures. It covers building production-ready Docker images, configuring `fly.toml` for services and health checks, and executing sophisticated deployment strategies like blue-green and canary releases. It ensures reliable, scalable deployment across multiple regions, including automated rollbacks and micro-VM optimization.

A comprehensive TypeScript SDK providing production-ready patterns for interacting with the Fly.io Machines REST API. It includes a typed client for managing the entire machine lifecycle—creation, state polling, and deletion—across multiple global regions. Features like robust error handling, rate-limit retries, and deployment builders ensure reliable multi-region orchestration for edge computing applications.

This skill provides real-time object detection capabilities by leveraging the Google Coral Edge TPU accelerator. It operates natively within Windows Subsystem for Linux (WSL), allowing high-speed, low-latency inference on live camera frames. By utilizing hardware acceleration, it achieves impressive performance (e.g., ~4ms inference at 320x320), making it ideal for edge computing projects requiring rapid visual analysis and resource efficiency.

This guide provides a comprehensive framework for positioning mobile and embedded systems manuscripts for ACM MobiSys. It details venue fit, contribution framing, necessary real-device evidence, and how to differentiate the work from related conferences (e.g., MobiCom, UbiComp). Use this when your research value depends on surviving real-world device constraints, sensing, or edge computing environments.

This skill guides users on deploying OpenMed models completely on-device for maximum privacy and edge computing. It details running workflows using three backends: MLX (Apple Silicon), CoreML (iOS/macOS), and ONNX/WebGPU (cross-platform/browser). The process involves converting, optionally quantizing, and running models locally, ensuring PHI never leaves the host device.

Expert hybrid cloud architect specializing in complex multi-cloud solutions across AWS/Azure/GCP, private clouds (OpenStack/VMware), and edge computing. Provides guidance on hybrid connectivity, workload placement, compliance, cost management, and Infrastructure as Code.