This is a monorepo containing over 40 independent MCP (Model Context Protocol) servers for AWS services, hosted under awslabs/mcp. Each server exposes tools, resources, and prompts that allow AI agents (coding assistants, chatbots) to interact with AWS APIs, databases, documentation, and best practices. The architecture is a collection of standalone Python packages, each with its own entry point (server.py/main.py), tests, Dockerfile, and PyPI distribution. Servers are built using either FastMCP or the lower-level MCP Python SDK, with consistent patterns for read-only enforcement, error handling, AWS client creation, and user-agent tracking. The codebase demonstrates solid engineering with extensive test coverage, but suffers from significant copy-paste boilerplate across servers (healthcheck scripts, Context singletons, test_init.py) and inconsistent use of MCP frameworks. Notable high-craftsmanship components include the AWS API MCP server's custom CLI-to-IR parser and execution engine, the OpenAPI MCP server's pluggable authentication provider framework, and the DynamoDB MCP server's schema-driven code generation tool. The repository is actively maintained with frequent commits and issue triage, though some servers are marked as deprecated or in initial setup, and the announcement of a successor ('Agent Toolkit for AWS') suggests potential future migration.

LLRT is a low-latency JavaScript runtime built in Rust around the QuickJS engine, designed primarily for AWS Lambda serverless workloads. The actual code confirms it is both a CLI runtime and a Lambda custom runtime: the `llrt` binary enters Lambda mode when `AWS_LAMBDA_RUNTIME_API` is present, and `runtime_client.rs` implements the full invoke/handler/response loop. It also provides `llrt compile` to emit QuickJS bytecode and self-contained executables, and `llrt test` to run a built-in Jest-compatible test runner. The runtime reimplements a broad surface of Node.js and Web APIs in Rust: `fs`, `net`, `http`, `crypto`/WebCrypto, `fetch`, `stream`/Web Streams, `events`, `timers`, `buffer`, `url`, `os`, `path`, `temporal`, `zlib`, `child_process`, `dgram`, and `intl`.

This repository is a collection of AWS EC2 Spot Instance best practices and workshop examples. It contains diverse implementations showing how to use Spot Instances across various AWS services including EC2 Auto Scaling, EC2 Fleet, ECS, EKS, Lambda, SageMaker, AWS Batch, and EMR. The examples demonstrate patterns for handling Spot Instance interruptions, cost optimization, fault tolerance, and workload-specific optimizations. While not a single cohesive application, it serves as a practical reference for deploying cost-effective, interruption-resilient workloads on AWS Spot Instances.

This repository has been archived and contains no source code in the provided file tree. The only non-Git file is a README.md indicating the archival status. Without any source code, it is impossible to determine the original purpose of the repository (whether it was an application or data research project). The repository is currently read-only and not maintained.

Renate is a Python library for continual learning that provides implementations of various algorithms (Experience Replay, DER, Super-ER, etc.) to mitigate catastrophic forgetting when updating neural networks on sequential data. It features a modular architecture separating models, data handling, update strategies, memory buffers, and drift detection, enabling users to customize continual learning pipelines. The framework supports local execution and cloud deployment via Amazon SageMaker, includes hyperparameter optimization through Syne Tune, and offers benchmarking tools for evaluating continual learning methods across diverse datasets and scenarios. Key reusable components include a memory buffer system with reservoir and class-balancing strategies, Maximum Mean Discrepancy (MMD) based drift detectors for monitoring data distribution shifts, and a composable loss component system for constructing complex continual learning objectives. The library integrates with PyTorch Lightning for training and provides abstract base classes for models and data modules to facilitate extension. Although the project is explicitly archived and no longer maintained, the codebase demonstrates solid engineering practices with well-defined interfaces and decoupled components. It has been tested on vision and NLP benchmarks (CIFAR, CLEAR, DomainNet, Hugging Face datasets) and includes synthetic scenario generators for controlled experimentation. The archival status indicates that users should expect no further updates or security patches, but the core functionality remains usable for continual learning research and applications.