Storybook is a frontend workshop for building UI components and pages in isolation. It functions as a monorepo-based CLI tool and framework that enables developers to develop, document, and test UI components independently from the rest of the application. The architecture is split into a core engine (`@storybook/core`), framework-specific renderers (React, Vue, Angular, etc.), build tools (Vite, Webpack), and a modular addon system. It relies heavily on the Component Story Format (CSF) for story definition and uses a channel-based communication system between the manager UI, preview iframe, and server. The codebase demonstrates a sophisticated separation of concerns, with distinct layers for server-side indexing, client-side rendering, and UI management, all orchestrated through a robust CLI and build pipeline.
This repository contains two distinct Unity projects: the Unity Playground, an educational framework for teaching 2D physics game development through modular scripts and example scenes (Adventure, Defender, Maze, Lander, etc.), and the com.unity.asset-store-tools, an Editor extension for publishing packages to the Unity Asset Store. The Playground provides atomic movement, condition, action, and attribute components that can be combined visually in the Editor, with custom inspectors to guide beginners. The Asset Store Tools implement a full publishing workflow including package validation (30+ automated tests), preview generation (native and custom), package exporting, and upload with authentication and analytics. Both projects are well-structured with clear separation of concerns, but the Asset Store Tools show signs of version-specific conditional compilation and reliance on internal Unity APIs, indicating medium technical drift. The Playground examples are simple but functional, while the tools demonstrate more advanced patterns like service providers and interface-based validation frameworks.
This repository is a comprehensive React workshop containing three main applications: a vacation search SPA, an e-commerce store using React Router's data APIs (loaders/actions), and a Next.js app demonstrating React Server Components. It also includes extensive course materials covering React fundamentals, advanced hooks, form abstractions, and performance optimization. The applications showcase modern React patterns including context API, custom hooks, component composition, and data fetching evolution from useEffect to React Query to loader functions.
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 contains the lab materials for the AWS Data Engineering Immersion Day, a guided workshop demonstrating how to build a data lake and analytics pipeline on AWS using services like AWS Database Migration Service (DMS), AWS Glue, Amazon Athena, Amazon QuickSight, Amazon Redshift Spectrum, and AWS Lake Formation. The repo includes step-by-step PDF lab guides, CloudFormation templates for infrastructure provisioning, sample data loading scripts, and a self-contained anomaly detection example using Apache Flink with Random Cut Forest. While the Flink streaming job is a real application, the primary purpose of the repo is instructional, not a production deployment or a self-contained research project. The data lineage involves ingesting ticketing data from a PostgreSQL database via DMS into S3, transforming with Glue into Parquet, and analyzing with Athena/Redshift. Clickstream data is generated into Kinesis streams and processed in real-time with Kinesis Analytics (Flink) to detect anomalies. The repo is hybrid: it has both application code (Flink Java) and data pipeline orchestration, but the dominant character is a workshop with infrastructure-as-code and documentation.
This repository is a structured workshop for learning to write Rust procedural macros. It contains five project skeletons covering derive macros (Builder, CustomDebug), function-like macros (seq!), attribute macros (#[sorted], #[bitfield]). Each project includes incremental test cases using the trybuild test harness, guiding learners from basic parsing to advanced code generation and error reporting. The actual macro implementations are stubs (unimplemented!()), so the repository serves as a starting template for hands-on learning rather than a functional codebase. The architecture demonstrates best practices for organizing proc-macro projects, including separation of proc-macro and library crates in the bitfield project. The codebase is intentionally incomplete, intended for educational use.