Seldon Core v2 is a production-grade, Kubernetes-native MLOps platform for deploying, scaling, and managing machine learning inference at scale. The system is architected as a set of microservices: a Kubernetes operator manages CRDs (Model, Pipeline, Server, Experiment), a centralized scheduler coordinates model placement and scaling across server replicas, an agent sidecar runs on each inference server to handle model loading/unloading, caching, and reverse proxying, and a Kafka Streams-based dataflow engine executes multi-step inference pipelines with joins, batching, and triggers. Inference requests enter via an Envoy proxy that routes to model servers or pipeline gateways, with Kafka as the underlying data plane for pipeline flows. The platform supports the V2 inference protocol, A/B experiments, explainability, drift detection, and OpenAI API translation. It integrates with MLServer, Triton Inference Server, and custom runtimes, and provides a CLI for lifecycle management.
Parca is a continuous profiling platform that collects, stores, queries, and visualizes pprof profiles in real time. The Go backend ingests profiles via gRPC (both pprof and OpenTelemetry OTLP), normalizes them into Apache Arrow columnar format, and stores them in either FrostDB (default) or ClickHouse. It provides a rich gRPC+JSON API for querying profiles (flamegraph, table, source view, callgraph, pprof download), scraping Prometheus-style targets, managing debug information for symbolization, and sharing profiles via links. The TypeScript/React frontend delivers an interactive profiling UI with flamegraphs, tables, source views, metrics graphs, sandwich views, and comparison mode, all backed by efficient Arrow-based querying. The codebase demonstrates strong engineering with reusable patterns (generic caching, signed request abstraction, Arrow compaction), but carries some drift risk from dual storage backends, generated protobuf code, and a complex UI state layer built with multiple state management strategies.
This repository contains the public-facing source code for Draw Things, a production-grade AI image and video generation application for macOS/iOS/Linux. The codebase implements a comprehensive suite of diffusion models (Stable Diffusion, FLUX, HiDream, Qwen, MiniMax, etc.), samplers, LoRA training, model conversion, and a gRPC-based distributed inference server. It is written primarily in Swift, using Bazel for builds, and includes extensive use of FlatBuffers for data serialization, SQLite for persistence, and a custom neural network compiler (NNC) for on-device inference. The architecture is modular, with separate libraries for tokenization (CLIP, Tiktoken, SentencePiece), model zoos (downloadable model specifications), image generation pipelines, and cloud authentication. The repository also includes CLI tools (draw-things-cli, gRPCServerCLI, model converters) and server management scripts for deploying GPU-backed inference services with Tailscale, Envoy, and Docker. While the core application is a consumer-facing app, the codebase also contains research-grade components such as instruction count estimators for performance profiling, formal verification tools (Z3, Lean), and web search integration with browser fallback. The code is well-structured with many reusable patterns, though it carries some maintenance overhead from vendored dependencies and generated code.
Hiddify is a cross-platform proxy/VPN client built with Flutter and a Go core (Sing-box). It supports multiple proxy protocols (Vless, Vmess, Reality, TUIC, Hysteria, WireGuard, SSH, etc.), subscription management, per-app proxy, routing rules, chain configuration, and advanced features like Warp, Psiphon, and TLS tricks. The architecture is a Flutter frontend communicating with a Go backend via gRPC/protobuf over a local socket. The app uses Riverpod for state management, drift for local SQLite storage, freezed for immutable models, and go_router for navigation. It has platform-specific native code for Android (Kotlin), iOS/macOS (Swift), Windows (C++), and Linux (C). The codebase is well-organized into feature modules, core infrastructure, and generated protobuf bindings. It is actively maintained with CI/CD pipelines and multi-platform releases, though it contains some commented-out code and generated file churn.
Jupiter is a governance-oriented microservice framework for Go, developed and used at Douyu. It provides a comprehensive set of components for building production-grade microservices, including multiple HTTP server wrappers (Gin, Echo, fasthttp, GoFrame), gRPC server and client, service discovery via etcd, distributed task scheduling (XXL-JOB), caching (freecache, LRU), configuration management (file, etcd, Apollo, HTTP), structured logging with rotation, Prometheus metrics, OpenTelemetry tracing, and resilience patterns (Sentinel circuit breaker/flow control). The framework also includes a CLI tool for project scaffolding and protobuf code generation plugins for Gin, Echo, error codes, and field masks. The architecture follows a modular, pluggable design with consistent patterns for configuration (StdConfig/RawConfig/Build), lifecycle hooks, interceptor chains, and singleton management. It emphasizes observability and governance out of the box, with built-in health checks, pprof endpoints, and metric export. The codebase is well-structured with extensive unit and e2e tests, though it carries some technical debt from supporting multiple HTTP frameworks and legacy dependencies. While primarily an application framework, it includes reusable utility packages (retry, field mask, load balancing, goroutine management) that can be extracted independently. The framework is actively maintained with regular commits and CI/CD pipelines, but some dependencies are outdated and there is code duplication in log rotation and cache implementations.
Goravel is a full-featured web application framework for Go that follows Laravel-inspired patterns and conventions. It provides a comprehensive set of tools for building web applications including HTTP routing, controllers, middleware, ORM, authentication, authorization, caching, queues, events, scheduling, and more. The framework uses a service container with facade pattern for easy access to services while maintaining testability, and employs service providers for modular bootstrapping of application components. The architecture is modular monolith with clear separation of concerns: application logic in app/, configuration in config/, routes in routes/, database migrations in database/migrations/, and tests in tests/. Entry points include main.go for application startup and artisan for CLI commands. The framework integrates with popular Go libraries like Gin for HTTP routing, GORM for ORM, and OpenTelemetry for observability.
This repository implements the Agent-to-Agent (A2A) protocol in Python. It is a comprehensive SDK providing both server and client libraries for building interoperable agent systems. The architecture is modular with abstract interfaces (AgentExecutor, TaskStore, ClientTransport) and supports multiple transports: JSON‑RPC, REST/HTTP+JSON, and gRPC, with a full backward‑compatibility layer for v0.3. Key features include agent card signing/verification (JCS canonicalization), push notifications, event streaming, and database persistence via SQLAlchemy. The codebase is well-structured with extensive automated testing (unit, integration, cross‑version, and conformance), CI/CD pipelines, and release automation. While primarily an SDK for protocol compliance, it also includes a sample agent application and an interop test kit.
MixPHP is a high-performance PHP framework designed for CLI-mode development, supporting multiple server drivers including Swoole, Swow, WorkerMan, PHP-FPM, and the built-in CLI server. The framework is built as a modular monorepo with over 30 independent packages covering HTTP routing (Vega), WebSocket, gRPC, database access, Redis, CLI commands, validation, event dispatching, caching, dependency injection, and microservice infrastructure (service registry, circuit breaker, distributed tracing). The architecture emphasizes coroutine-based concurrency via Swoole, allowing seamless switching between server runtimes with minimal code changes. All components follow PSR standards (PSR-7, PSR-14, PSR-17, PSR-3) and are designed to be used independently, enabling developers to pick only what they need. The framework provides full-stack capabilities for building REST APIs, web applications, WebSocket services, gRPC servers, CLI tools, and microservices. It includes connection pooling for database and Redis, a PSR-14 event dispatcher, a standalone validation library, a generic object pool pattern, and a bean container for dependency injection. Examples and skeleton projects (api, web, cli, grpc, websocket) are provided to accelerate development. The codebase is well-tested with PHPUnit and includes continuous integration via GitHub Actions for splitting the monorepo into individual packages. While the framework is actively maintained and demonstrates strong craftsmanship, some modules show signs of technical drift such as deprecated classes (e.g., FileSystemApplicationContext, legacy cache handlers), reliance on specific Swoole versions, and copied Composer utilities that may diverge over time. The documentation is primarily in Chinese, and the Go counterpart (mix-go/mix) may split community attention.
This repository is a suite of Kafka cluster management tools developed by DataDog. It provides four primary tools: `topicmappr` for partition reassignment and rebalancing with storage-aware placement, `registry` for a gRPC/HTTP API that exposes Kafka metadata and custom tagging, `autothrottle` for dynamically adjusting replication throttles based on Datadog network metrics, and `metricsfetcher` for pulling partition size and broker storage metrics from Datadog into ZooKeeper. The architecture is a collection of independent CLI binaries and a gRPC server, all sharing well-factored Go libraries (`kafkaadmin`, `kafkazk`, `mapper`) that abstract Kafka admin operations, ZooKeeper interactions, and partition mapping logic. The codebase is production-grade, with extensive unit and integration tests, Docker-based development environments, and CI pipelines. It is tightly coupled to Datadog for metrics and ZooKeeper for metadata, which introduces some drift risk as Kafka evolves away from ZooKeeper. The last commit date (2026-09-16) appears anomalous and may indicate a timestamp issue, but the repository shows active maintenance with recent CI configurations and issue triage.
This is a multi-platform Solana trading bot designed for sniping newly created tokens on pump.fun and letsbonk.fun (Raydium LaunchLab). It listens for token creation events in real-time via multiple mechanisms (gRPC Geyser, WebSocket logs/block subscription, third-party PumpPortal feed), then automatically executes buy and sell trades using program instructions (v2 with full account lists). The codebase emphasizes a platform-agnostic architecture with abstract interfaces for address derivation, instruction building, event parsing, and curve management, allowing new platforms to be added without altering core trading logic. It includes optional 'extreme fast mode' for zero-RPC buys, configurable exit strategies (take-profit, stop-loss, time-based), priority fee management (fixed or dynamic), compute unit optimization, and an interactive cleanup manager. The repository is explicitly educational ('NOT FOR PRODUCTION') and ships with an extensive suite of standalone learning examples and verification scripts that offline-test edge cases such as v1/v2 transaction cutover, optional trailing arguments, price feed failures, and retry bounding. Despite its purpose as a demonstration, the code demonstrates solid engineering practices: abstraction layers, configuration-driven bots, a token bucket rate limiter, plugin-based fee engines, and generic Anchor IDL decoders.
Protoconf is a configuration-as-code framework that uses protobuf schemas and Starlark (a Python dialect) to define, compile, and deliver configuration to services. In production, it operates as a multi-component system: a compiler that transforms Starlark config files into protobuf messages, an agent that serves compiled configs to applications via gRPC streaming, a server that handles config mutations (with git-based workflows and staged rollouts), and an inserter that pushes configs to key-value stores (Consul, etcd, Zookeeper, Kubernetes ConfigMaps). The architecture is modular with clear separation between compilation, storage, and delivery layers, all communicating via gRPC and protobuf. External dependencies include protobuf tooling, Starlark runtime, gRPC, and multiple KV store backends. The project also includes a web IDE (compiled to WASM) for browser-based config authoring and a Python client library for config consumption.
Berty is a full-stack, privacy-focused, peer-to-peer messaging application. The system consists of a Go backend implementing the Wesh protocol (based on IPFS, libp2p, and OrbitDB) that provides core messaging, push notifications, authentication, replication, and directory services. The backend is exposed via gRPC and consumed by multiple clients: a React Native mobile app (with both a legacy JS variant and a newer Expo-based variant), a Go CLI with a terminal UI (mini) and a desktop GUI (Fyne), and native bridges for iOS and Android. The architecture is modular, with separate packages for error handling, authentication, push relay, and bot framework. Key entry points include the `berty daemon` CLI command, the mobile app entry points (`App.tsx` in both `js/packages` and `berty-bridge-expo/mobile`), and the bridge framework (`go/framework/bertybridge`). Data flows involve gRPC streams from the Go backend to the mobile clients, with event-driven state management and local encrypted storage via SQLCipher. External dependencies include libp2p, OrbitDB, gRPC, protobuf, and various platform-specific native modules for BLE, mDNS, and push notifications. The repository also contains auxiliary tools for infrastructure testing, rendezvous servers, and debugging (Tyber).
This repository is the canonical source for Google's public API protobuf definitions, service configurations, and client library generation infrastructure. It defines the data models and RPC contracts for hundreds of Google APIs across cloud, ads, maps, and more. The codebase is almost entirely composed of .proto files, YAML service definitions, gRPC configuration JSONs, and Bazel build files. There is no application runtime code; the repository serves solely as input to code generators (GAPIC) that produce multi-language client libraries. It exhibits high consistency in API design patterns (e.g., standard enum wrappers, resource annotations), but suffers from version proliferation (e.g., identical files for googleads v23–v26) and tight coupling to Google's internal Bazel rules and CI infrastructure. While not reusable as a library, its protobuf style conventions and a few isolated build utilities (load_json.bzl) can inform other API-first projects.
This repository is a comprehensive multi-module Maven project that serves as a learning and demonstration resource for the Java ecosystem, specifically focusing on Spring Boot, microservices, middleware, and AI integration. It contains over 20 independent modules, each showcasing a specific technology or pattern: from core Spring features (AOP, events, lifecycle) to advanced distributed systems (Dubbo, gRPC, Netty, RocketMQ, Kafka, Redis, Elasticsearch, ShardingSphere, Sentinel, Nacos, Curator) and modern AI capabilities (LangChain4j-based RAG, agentic workflows, guardrails, MCP client, image generation, TTS). The architecture is a modular monolith where each module is independently runnable via its own Spring Boot application class, sharing a common utility library (whatsmars-common) for serialization, caching, consistent hashing, encryption, and logging. The AI module is particularly notable for implementing advanced patterns such as query compression, smart routing, re-ranking, structured output, and configurable agentic workflows (sequential, parallel, conditional, loop, supervisor). While the repository is not a single deployable product, it provides high-quality, well-documented examples that demonstrate non-obvious craftsmanship in areas like custom gRPC load balancing, consistent hashing with virtual nodes, and programmatic logger creation. The codebase is professionally written with good separation of concerns, though testing coverage is limited to utility classes. Overall, it is a valuable reference for Java developers exploring modern frameworks and AI integration patterns.
EGO is a Go microservices framework designed around component-based lifecycle management. It provides servers (HTTP via Gin, gRPC), clients (HTTP, gRPC), scheduled tasks (cron, one-off jobs), and built-in observability (structured logging, Prometheus metrics, OpenTelemetry tracing). The framework uses a configuration-driven pattern (Load → Build) and includes protoc plugins for generating error types, HTTP handlers, and test scaffolding. Production usage typically involves defining components in TOML/YAML config files and wiring them via the ego.New().Serve(...) entry point. The architecture is modular with clear abstractions for configuration, logging, metrics, registry, and resilient interceptors.
ASRT (Automatic Speech Recognition Toolkit) is a Chinese speech recognition system that provides both training and inference capabilities. It uses a deep CNN acoustic model with Connectionist Temporal Classification (CTC) and an N-gram language model based on a maximum entropy hidden Markov model. The system supports multiple backends (TensorFlow/Keras and experimental PyTorch) and can be trained on several Chinese speech corpora (THCHS30, ST-CMDS, etc.). It includes HTTP and gRPC servers for real-time inference, a speech recorder utility, and client libraries. The architecture is monolithic with separate scripts for training, evaluation, and serving. The codebase is functional but shows signs of technical drift, such as using deprecated Keras APIs (fit_generator) and hardcoded paths. The language model implementation is a notable highlight, offering a non-trivial statistical decoding approach. The repository is popular but has a single maintainer and a high number of open issues.
This is a Go-based blockchain node for the Mezon MainNet (MMN), implementing Proof-of-History (PoH) for transaction ordering, libp2p for peer-to-peer networking, gRPC and JSON-RPC for external APIs, and a leader-based consensus protocol with vote collection. The node supports three transaction types: Ed25519 key-based transfers, Groth16 ZK-proof transfers, and user content (social interactions). It uses either LevelDB or RocksDB as the persistent storage backend, with the latter bundled as vendored C headers. The codebase includes a CLI (init, bootnode, node, transfer), a client library, a stress-test tool with HTML reports, and a migration tool for importing user balances from CSV. The architecture is modular with clear interfaces for storage, mempool, events, and networking, making it a well-structured example of a modern blockchain validator node.
This repository implements a lightweight gRPC connection pool for Go applications. The library provides a thread-safe pool of gRPC client connections that can be reused across multiple goroutines, with configurable pool size, idle timeouts, and maximum connection lifetimes. It manages the full lifecycle of connections including creation, reuse, expiration, and cleanup. The implementation handles edge cases such as connection health checking, automatic replacement of stale connections, and proper error handling for timeouts and pool exhaustion. The core abstraction consists of a Pool struct that manages a channel of ClientConn wrappers, which track connection metadata and pool membership.
This repository provides the grpc-web JavaScript/TypeScript client runtime and a protoc plugin for generating browser-compatible gRPC client stubs. It enables browser clients to communicate with gRPC services through an HTTP/1.1 proxy (Envoy or grpcwebproxy) by implementing the gRPC-Web protocol. The codebase includes a runtime library (npm package `grpc-web`) with support for unary and server-streaming calls, interceptors, metadata, deadlines, and two wire formats (grpcwebtext, grpcweb). The protoc plugin (`protoc-gen-grpc-web`) is a C++ binary that outputs client code in Closure, CommonJS, or TypeScript styles. Examples and interop tests demonstrate end-to-end usage with Node.js backends, Envoy proxy, and browser clients. The project is now in maintenance mode (no new features) with several drift risks: dual implementation (Closure JS and TypeScript), archived dependencies (Closure library, Protobuf JS), and incomplete TypeScript migration. Despite this, the core components are well-architected and the gRPC-Web stream parser and interceptor pattern are reusable.
This is a large, sprawling collection of tutorial and demo projects authored by a single developer (blogger '程序员欣宸'). It covers dozens of technologies including Spring Boot internals, Flink, Kafka, gRPC, JavaCV/OpenCV, Kubernetes controllers, LangChain4j, JUnit 5, and more. Each subdirectory is an independent, self-contained demo typically with a single main class or test. The code is educational: full of Chinese comments, simplified logic, and hardcoded paths. While not a cohesive application, it serves as a rich reference for learning how to use specific frameworks and patterns. The quality is average overall—functional but boilerplate-heavy, with many outdated dependencies and duplicated code across projects.