Yellowstone gRPC Geyser is a Solana blockchain data streaming service implementing the Geyser plugin interface. It provides a high-performance gRPC server for subscribing to blockchain events (accounts, transactions, slots, blocks) with advanced filtering, commitment levels, and deshred (pre-execution) streams. The architecture features a modular Rust workspace with separate crates for the core plugin, client libraries, protobuf definitions, and tooling. Key capabilities include TLS hot-reloading, token-bucket rate limiting, Prometheus metrics, authentication layers, and multi-language client support (Rust, Go, Node.js, TypeScript). The system handles complex data routing through broadcast channels, load-aware streams, and block reconstruction logic to ensure ordered delivery of blockchain events.
Eclipse Open VSX is a full-stack, vendor-neutral open-source registry for VS Code extensions, serving as an alternative to the Visual Studio Marketplace. The system comprises three main components: a Java/Spring Boot server that manages extension publishing, security scanning, rate limiting, download analytics, and trusted publishing via OIDC; a React/TypeScript web UI (MUI, TanStack Query) for browsing, searching, and administering extensions; and a Node.js CLI for publishing, verifying, and managing extensions. The server uses PostgreSQL with Flyway migrations, optional Elasticsearch for search, TimescaleDB for time-series download analytics, and Valkey/Redis for caching and rate limiting. Storage is abstracted over local filesystem, AWS S3, Azure Blob, and Google Cloud Storage. The architecture is a monolith with background job processing via JobRunr, and it exposes both a custom REST API and a VS Code Gallery-compatible API for seamless integration with VS Code and other editors.
This is a Avalanche blockchain faucet service that allows users to request test tokens for various EVM-compatible networks and subnets. The application consists of a Node.js/Express.js backend with a React.js frontend. It implements sophisticated rate limiting (global, IP-based, wallet-based, and common token disbursal), captcha verification (reCAPTCHA v2/v3), and a pipeline check system requiring either mainnet balance validation or coupon redemption for token requests. The service supports multiple EVM chains and ERC20 tokens, with automatic nonce and balance management, transaction queuing, and mempool limit handling. Key architectural features include modular middleware, decoupled services for coupon and mainnet checking, and clean separation between blockchain interaction logic and API handling.
Keydd is an HTTP(s) proxy tool that performs sensitive information detection and AI-driven API business logic analysis. It acts as a MITM proxy to intercept traffic, matches content against 30+ regex patterns for credentials/API keys, and optionally uses LLMs to analyze API endpoints for business purpose, sensitivity, and penetration priority. The architecture features separate layers for proxy handling, rule-based detection, AI analysis (with rate limiting, token tracking, and metrics), database persistence (SQLite/MySQL/PostgreSQL), and notifications. Key entry points include main.go for startup, proxy/handler.go for traffic processing, and cmd/match_rules.go for sensitive pattern matching.
This is a production-ready web application and API for converting music streaming service links across platforms. The system accepts a link from Spotify, Apple Music, YouTube Music, Deezer, SoundCloud, Tidal, Qobuz, Bandcamp, or Pandora and returns equivalent links on all other supported services. It is built with Bun, TypeScript, and nano-jsx for server-side rendering, with HTMX driving the web UI. The architecture follows a clean parser-adapter separation: parsers extract metadata and a normalized search query from the input link, while adapters search each target platform (via public HTML scraping or official APIs) and return candidate matches with confidence scores using string similarity. A notable implementation detail is the Spotify adapter, which circumvents API restrictions by extracting a TOTP-based anonymous access token from the Spotify web player JavaScript bundle—a clever workaround that avoids requiring a premium developer account. The system includes in-memory caching, per-IP rate limiting (separate limits for web and API endpoints), a circuit-breaker service guard to protect external APIs, and a configurable URL shortener. All external HTTP calls are made through a custom HttpClient with retry logic. Tests cover integration scenarios with mocked snapshots.
This repository is a production-grade FastAPI boilerplate (named 'Fastro') designed to accelerate building SaaS backends. It provides a complete, modular architecture with three layers: infrastructure (config, caching, rate limiting, auth, logging, database), interfaces (REST API, admin panel), and vertical-slice modules (user, tier, API keys, rate limits). The codebase is fully async, uses SQLAlchemy 2.0 with `MappedAsDataclass`, Pydantic v2, and FastCRUD for generic CRUD operations. Authentication is session-based via the external `crudauth` library, with server-side sessions, CSRF, OAuth (Google wired), and API key support. Caching and rate limiting follow a provider pattern with swappable Redis/Memcached backends. The repo also includes a CLI tool (`bp`) with plugin discovery for commands and features, a multi-stage Dockerfile, Alembic migrations, and extensive documentation (though some docs describe aspirational features not yet implemented). A paid fork (FastroAI) adds payments, email, and frontend, but the free boilerplate is self-contained and functional.
express-slow-down is an Express.js middleware library that rate-limits clients by slowing down responses instead of blocking them outright. It wraps `express-rate-limit` internally, using a custom handler that computes a delay based on request count and configurable options like `delayAfter`, `delayMs`, and `maxDelayMs`. The middleware increments a per-client counter (via a store, defaulting to an in-memory store), and when the count exceeds a threshold, it delays subsequent responses. It supports custom stores, async functions for dynamic delays, and standard/legacy rate-limit headers. The codebase is well-structured TypeScript with comprehensive unit and integration tests, and follows modern ES module and TypeScript practices.
NyaProxy is a lightweight API gateway/proxy service built with FastAPI and Uvicorn that manages authentication, credential rotation, load balancing, rate limiting, and observability for HTTP APIs. It acts as an intermediary between clients and upstream services, injecting credentials, enforcing quotas, and providing features like retry logic, key quarantine, and request/response transformation. The system includes a web dashboard for monitoring traffic, metrics, and queue status, and exposes Prometheus metrics for external monitoring. The architecture follows a modular design with separate concerns: core proxy logic (queue-first request handling), traffic management (load balancing and rate limiting), configuration management (using Nacho for layered config and schema validation), services (metrics, state persistence), and a dashboard interface. Requests are processed via a priority queue with worker pools, enabling prioritization of retries and high-priority requests. State persistence saves rate limit windows and key cooldowns to disk using SHA256-hashed keys to avoid storing secrets, allowing survival across restarts. Key entry points include the CLI (`python -m nya` or `nyaproxy` script) and the main application factory in `nya/server/app.py`. The system relies on dependencies like FastAPI, Uvicorn, Pydantic, Nacho, and the Prometheus client, with explicit version pins in `pyproject.toml` and regular updates shown in the changelog. The codebase demonstrates strong engineering practices including comprehensive test coverage (unit and end-to-end), secure secret handling, and clean separation of concerns.
RiotSharp is a C# wrapper for the Riot Games API, providing access to League of Legends data including summoner information, match history, champion stats, leagues, and static game data. The library implements automatic rate limiting to comply with Riot's API constraints and includes caching for static data to minimize API calls. It features a layered architecture with separate projects for the core library, ASP.NET Core integration, and tests. The main entry point is the `RiotApi` class, which aggregates all endpoint interfaces via dependency injection. Key reusable components include a sophisticated rate limiter, a pluggable caching abstraction, a base class for static data endpoints, and custom JSON converters for enum serialization.
Steam Economy Enhancer is a single-file userscript (~4,500 lines) that runs via Violentmonkey on Steam Community pages. It detects whether the current URL is an inventory, market, market-listing, or trade-offer page and initializes a matching enhancement module. On the inventory it injects bulk-sell/card-sell/crate-sell buttons, duplicate detection, gem conversion, booster pack unpacking, per-item price labels, and quick-sell panels populated from Steam's order book. On the market it identifies overpriced/underpriced listings against a price heuristic, supports mass removal and auto-relisting, adds client-side search/sort/select-all controls, and aggregates total listing values. On trade offers it renders per-party item counts and price summaries. All server actions are executed through Steam's internal AJAX endpoints using the user's own session cookie. Under the hood, the script is organized as an IIFE with region annotations. Every external call flows through a hand-built serialized request queue with adaptive rate limiting (300ms baseline, 1s for market endpoints, 5s after errors) and a circuit breaker that halts after repeated HTTP 400/401/403/404/405/429 responses. Long-running operations (selling, relisting, grinding, pricing) are run through async.queue pipelines with concurrency 1, jittered delays, and 30-45s backoff retries. A rolling session cache built on localforage keeps price histories and order books across tab restarts while rotating through six bounded IndexedDB buckets to prevent unbounded growth. The fee engine precisely mirrors Steam's market fee math, including the December 2025 rule changes for 11 currencies that switched from floor to round and introduced a $0.01 minimum per fee. The code is heavily coupled to Steam's page globals (g_rgAssets, g_ActiveInventory, g_rgWalletInfo) and DOM structure, so the script cannot run outside steamcommunity.com. However, several embedded patterns are self-contained and instructive: the request queue, the rolling session cache, the fee calculation engine, and the pure-function price heuristic. Maintenance is healthy: the code incorporates December 2025 Steam rule changes, uses modern library versions, and CI runs ESLint on every push. There are no unit tests—the only automated check is linting.
stravalib is a well-crafted Python client library for the Strava V3 REST API. It provides a high-level, Pythonic interface for authenticating (via OAuth2), retrieving, and modifying Strava data such as activities, athletes, clubs, segments, routes, and streams. The architecture is layered: a Client class orchestrates higher-level operations, delegating HTTP communication to a lower-level protocol module (ApiV3) that handles authentication, rate limiting, automatic token refresh, and error mapping. Data is modeled using Pydantic v2, with both auto-generated models from the official Strava Swagger spec and handcrafted domain models that add lazy-loading, unit conversion (via Pint), and custom types like Distance and Duration. The library includes a configurable rate limiter with priority levels, a generic paginated iterator (BatchedResultsIterator), and extensive unit and integration tests including a mock API stub that leverages the Swagger spec for realistic testing. It is not a standalone application but a library intended for integration into scripts, web apps, or data analysis pipelines. While primarily an API client, its unit conversion and stream retrieval features make it useful for fitness data research. The codebase is actively maintained with frequent releases and CI, though it shows signs of technical drift due to deprecation of certain Strava endpoints and reliance on an auto-generated model that may lag behind API changes.
This repository provides reference implementations of common system design patterns in Python and Java. It is primarily an educational resource containing standalone algorithm implementations for consistent hashing, rate limiting (token bucket, leaky bucket, sliding window variants), and load balancing algorithms (round robin, weighted round robin, least connections, least response time, IP hash). The code serves as a learning tool for system design interviews and concepts rather than a production-ready application. Each implementation is self-contained with usage examples embedded in the source files. The repository has no external dependencies beyond standard libraries, making it easy to extract and study individual patterns.
ThinkWatch is a Rust workspace implementing an AI gateway platform that fronts multiple model providers and MCP (Model Context Protocol) servers. It exposes OpenAI-compatible chat/completion endpoints, streaming, MCP tool-call routing, content filtering, and provider failover. The backend is split into crates: `common` (shared lifecycle/limits/access), `auth` (PoW login, API keys), `gateway` (model routing), `mcp-gateway` (MCP server proxying), `server` (REST admin/audit/trace endpoints), and `test-support` (integration test client). A React/TypeScript frontend in `web/` provides an admin console, setup wizard, limits panels, and MCP server management. Data flows: requests enter the gateway, pass through `check_limits`, `check_budget`, `check_access`, then `run_post_invoke`; `trace_id` is propagated across gateway, MCP, audit, and app logs stored in ClickHouse; Postgres holds users, API keys, budgets, and MCP server configs; Redis/Lua scripts enforce rate limits.
AREPL is a VS Code extension that evaluates Python code in real-time as you type, displaying variables, errors, and print output in a side panel. It provides a scratchpad-like experience for Python development, with features like real-time evaluation, variable inspection, error display, and support for dumping variables from functions. The extension uses a Python backend (arepl-backend npm package) to execute code and communicate results via JSON. The TypeScript architecture includes a PreviewManager that orchestrates execution and preview updates, a PreviewContainer that manages the webview, and separate modules for inline errors (PythonInlinePreview) and panel display (PythonPanelPreview). The codebase is well-structured with clear separation of concerns, includes unit tests (mocha), and uses telemetry for diagnostics. The extension is maintained (last release November 2024) and uses modern dependencies.
This is a demo application showcasing ActiveAdmin functionality for Ruby on Rails. It provides an admin interface for managing AdminUser records with Devise authentication, featuring CRUD operations, a customizable dashboard, and proper error handling. The application follows standard Rails MVC architecture with ActiveAdmin as an engine/plugin, uses Importmap for JavaScript bundling, and CSS Bundling with TailwindCSS for styling. It includes comprehensive test coverage and is actively maintained with CI/CD pipelines.
Llama Tutor is an open-source AI personal tutor built as a Next.js application. It allows users to enter a topic and select an education level (elementary to graduate), then uses Exa search to fetch relevant web content and Together AI (Qwen2.5-7B-Instruct-Turbo) to generate a personalized lesson in streaming markdown. The application features conversational follow-ups, source display, and rate limiting via Upstash Redis. It integrates Helicone for observability and Plausible for analytics. The codebase is a monolith with App Router, client-side state, and edge-deployed API routes. The architecture is straightforward: the main page orchestrates chat and source retrieval, relying on a custom streaming utility for Together AI responses. While the components are functional and well-structured for a demo, only the streaming utility stands out as a potentially reusable pattern for other AI-powered applications.
This repository is a production-grade RESTful API service designed to serve Holy Bible text across multiple translations (NVI, RA, KJV, etc.) and languages (Portuguese, English, French, Spanish). Built on a Node.js/Express stack with MongoDB for persistence and Redis for caching and rate limiting, it follows a standard MVC architecture. The system includes JWT-based authentication for user management and request tracking, alongside a static frontend served via Pug templates. While the API contract is well-documented via OpenAPI and Insomnia, the codebase exhibits significant technical debt, specifically regarding cryptographic security (MD5 password hashing) and outdated core dependencies (Jest v24, Mongoose v5). The architecture is functional and modular but relies on legacy frontend technologies (jQuery/Pug) rather than modern SPA frameworks.
BlossomGateway is a custom API gateway built on Netty, supporting HTTP and Dubbo protocols. It uses Nacos for service registration and configuration, Redis for distributed rate limiting, and provides a pluggable filter chain for routing, load balancing (random, round-robin, weighted, consistent hash), authentication (JWT), gray release, flow control (Guava/Redis), circuit breaking (Hystrix), monitoring (Prometheus), and mocking. The gateway core is a Netty server that processes incoming HTTP requests, creates a GatewayContext, and runs through a chain of filters before routing to backend services. It also includes a client SDK (BlossomGateway-Client) for downstream services to register themselves. Additionally, the repository contains a separate custom RPC framework (BlossomRPC) with its own protocol, serialization, and dynamic proxy, which appears to be experimental/educational. The project is structured as a multi-module Maven project with clear separation of concerns. However, the codebase shows signs of being a learning project with many TODOs, hardcoded values, and personal promotional content, indicating it may not be production-ready.
APIMyLlama is a Node.js application that serves as a proxy and management layer for the Ollama API. It provides an endpoint (/generate) to interact with LLMs using API keys, which are managed via a command-line interface. The application includes features such as API key generation, validation, rate limiting, webhook notifications, and dynamic configuration of server port and Ollama URL. The architecture is monolithic, leveraging Express for HTTP handling, SQLite for persistence, and a custom CLI for administrative tasks. Key components include rate limiting middleware and a webhook notification system, which demonstrate common patterns in API management. However, the project suffers from dependency issues where the version constraints for axios and express are set to versions (>=1.13.5 and >=5.2.1) that are not currently available, preventing installation with the latest stable packages. This indicates a lack of maintenance in terms of dependency updates, despite recent commits.
This repository implements an API Gateway based on Openresty and NGINX with LuaJIT for high-performance request processing. The system provides service discovery via Marathon integration, API key validation, HMAC/OAuth authentication, rate limiting, request/response transformation, and metrics collection. Architecture-wise, it uses a modular design with NGINX configuration files defining request processing phases, Lua modules for extensible functionality (validation, tracking, metrics), and external dependencies for service discovery (Marathon), caching (Redis), and metrics reporting (StatsD). Key entry points include the health-check endpoint (/health-check), metrics endpoint (/api-gateway-stats), and admin API (/tracking/* on port 5000). The gateway processes client requests through validation phases, proxies them to backend services, collects metrics throughout, and returns responses while supporting dynamic configuration reloading and remote config sync from S3.