EP226: API Concepts Every Software Engineer Should Know

Designing dependable APIs requires careful attention to fundamental HTTP concepts, structural design styles, and early architectural choices. Engineers must evaluate paradigms such as REST, GraphQL, gRPC, webhooks, and WebSockets to match specific system requirements. In addition to core protocols, long-term usability depends on security implementations like OAuth and JWTs, operational reliability practices, and thorough documentation. Neglecting aspects like versioning, idempotency, and error handling can make APIs fragile and costly to maintain.

Prompt injection tops the OWASP LLM Top 10 and cannot be solved with a single defense mechanism. Mitigating it requires stacking multiple complementary defenses across model-level and system-level categories. Model-level approaches include spotlighting and instruction hierarchy, which instruct or fine-tune models to treat untrusted inputs as data. System-level architectures such as least-privilege tools, human-in-the-loop checks, and planner/executor splits restrict agent capabilities and isolate sensitive tasks. Production applications like Gmail combine these layered defenses to manage the threat of indirect prompt injections.

An upcoming educational course titled 'Rebuild YouTube with AI' is scheduled to launch in approximately one week. The course begins on September 26, 2026. It is scheduled to conclude on October 24, 2026. The curriculum centers on rebuilding the YouTube platform leveraging artificial intelligence techniques.

The five-week course titled Rebuild YouTube with AI teaches participants to build and ship a production-ready YouTube clone. The curriculum emphasizes AI-assisted software development using Cursor agents for task planning, implementation, and code reviews. Students build a React frontend alongside a Postgres backend, incorporating multimodal embeddings for semantic search and recommendations. The deployment pipeline leverages Vercel for hosting, an admin dashboard for analytics, and Playwright for AI-driven testing.

This section outlines twelve seminal research papers that significantly shaped the modern artificial intelligence landscape. Covering foundational milestones from 2012 to 2022, the list spans core developments in computer vision, generative modeling, natural language architectures, and scaling behaviors. Key innovations highlighted include early deep learning breakthroughs like AlexNet and GANs, the Transformer architecture, diffusion models, and retrieval-augmented generation. Together, these papers established the architectural and methodological foundations underlying contemporary AI systems.

A monolithic architecture combines a single codebase, database, and deployment, providing simplicity for small teams but risking system-wide failures and cumbersome deployments as the application grows. Microservices decompose systems into independently deployable and scalable components, though they introduce operational overhead such as service discovery, request routing, and distributed tracing. Serverless architectures execute functions on demand with automated cloud scaling and pay-per-use pricing, but present drawbacks like cold-start latency, debugging complexity, and vendor lock-in. In practice, most production environments use a hybrid model, centering on a monolith while adopting microservices and serverless for targeted scaling, background jobs, or notifications.

Load balancers serve critical infrastructural roles across modern distributed systems by managing how client traffic interacts with backend servers. Key capabilities include evenly distributing incoming traffic, offloading SSL termination, and preserving session state across user interactions. Furthermore, they improve system resiliency, security, and scalability through health checks, failover routing, horizontal scaling support, and DDoS mitigation.