Add shimmy - AI microservice runtime framework

[Michael-A-Kuykendall]

Adds shimmy to the Frameworks/Runtimes section.

Project Details:

• Repository: https://github.com/Michael-A-Kuykendall/shimmy
• Stars: 2,918 ⭐
• Language: Rust
• Category: AI Microservice Framework

Why Shimmy enhances awesome-microservices:

AI-First Microservice Runtime:

• Specialized Runtime: Purpose-built for AI inference microservices
• OpenAI API Compatibility: Standard REST API for seamless service integration
• Hot Model Swapping: Dynamic service updates without downtime
• Auto-Discovery: Simplified service configuration and deployment
• Resource Efficient: Rust performance ideal for microservice architectures

Microservice Architecture Benefits:

• Single Binary Deployment: No complex dependencies or runtime requirements
• Container-Native: Perfect for Docker/Kubernetes microservice orchestration
• Stateless Design: Horizontally scalable across multiple instances
• Service Discovery Ready: Standard HTTP endpoints for load balancing
• Cross-Platform: Consistent deployment across cloud and edge environments

Production Microservice Features:

• Health Checks: Built-in endpoints for service monitoring
• Observability: Logging and metrics for microservice monitoring
• Configuration: Environment variable and CLI-based configuration
• Security: Memory-safe Rust implementation with minimal attack surface
• Performance: Low latency, high throughput for AI service demands

AI Microservice Use Cases:

• Conversational AI services - Chat and completion endpoints
• Text Processing pipelines - NLP microservices for content analysis
• Multi-tenant AI platforms - Isolated model serving per customer
• Edge AI deployment - Lightweight inference at the edge
• API Gateway integration - AI capabilities behind REST interfaces

Positioning in Microservice Ecosystem: Shimmy represents the next generation of domain-specific microservice runtimes. While general frameworks like Vert.X and Micronaut provide broad capabilities, Shimmy offers specialized runtime optimizations for AI workloads, making it easier to build and deploy AI-powered microservices.

This addition expands the microservice toolkit with AI-native capabilities, enabling developers to build sophisticated AI microservices with production-grade reliability and performance.