Gin is a high-performance HTTP web framework for Go, designed for building REST APIs and microservices. It provides a zero-allocation radix tree router, a flexible middleware chain, comprehensive request binding (JSON, XML, YAML, TOML, Protobuf, BSON, form, query, header, URI), and multiple response renderers (JSON, XML, YAML, TOML, Protobuf, HTML, String, File, SSE, etc.). The framework is production-ready with features like panic recovery, logging, trusted proxy detection, content negotiation, and graceful shutdown. Its architecture is library-based: users embed the Engine in their Go applications, register routes and middleware, and run the server. The codebase is well-structured, extensively tested (99% coverage target), and actively maintained with frequent dependency updates and performance optimizations. Key external dependencies include go-playground/validator for validation, and various serialization libraries (goccy/go-json, goccy/go-yaml, pelletier/go-toml, ugorji/go/codec, etc.) that are pluggable via build tags.
Kuà is a lightweight Python web framework that supports both ASGI (async) and WSGI (sync) modes with a nearly identical API. It provides type-safe parameter binding via typing_extensions.Annotated and Pydantic, dependency injection with caching and cleanup, automatic OpenAPI 3.1.0 documentation generation, middleware composition, class-based views for HTTP and WebSocket, background task execution, CORS support, authentication helpers (Bearer, Basic, API Key), and template rendering with Jinja2. The framework is built on baize and emphasizes developer experience through clear documentation and AI-assisted development support.
This repository contains a JVM agent that samples thread stack traces and sends profiling data to a Riemann server. The agent is implemented in Java (Agent.java) which boots a Clojure runtime to perform the actual profiling logic. The core functionality involves periodically sampling stack traces of all runnable threads (excluding the sampler thread itself), aggregating them into a radix tree to estimate time spent per function, and sending the top functions as events to Riemann via HTTP. The design is lightweight and configurable, intended for production use in distributed systems like Hadoop clusters to identify CPU hotspots across thousands of nodes. The agent requires no application code changes and works with any JVM language.