Back to Engineering
4 min read
Updated recently

Fundamental Concepts of Software Systems Engineering

Step-by-step guide from full-stack web development to building a forward-deployed AI product.

Fundamental Concepts of Software Systems Engineering (Step-by-Step)

From full-stack → Next.js → system design → microservices → ML → agents → production AI product.

STEP 1 — Build the Base Web Product (React + Flask + DB)

(Weeks 1–2) Your very first foundation: a real app that everything else builds on.

  • Frontend (React fundamentals): JSX, Hooks, Controlled forms, Client-side routing, Context API, Data fetching.
  • Backend (Flask): REST endpoints, Request validation, SQLAlchemy ORM, Auth, Error handling, DB design.
  • Testing: React Testing Library, Pytest.
  • Deployment: Vercel (Frontend), Render/Heroku (Backend).
  • Deliverable: User + Posts + Comments product built on React + Flask + Postgres.

STEP 1.5 — Upgrade to Next.js (Full-Stack React + SSR/ISR/Edge)

(Week 2.5) Transform your React SPA into a scalable, high-performance Next.js app.

  • Concepts: App Router, Server vs Client Components, SSR/SSG/ISR, API routes, Edge middleware, Image optimization.
  • Integration: Auth pages (SSR), User profiles (ISR), Feed (Client + Streaming), API proxy.
  • Deliverable: Next.js frontend + Flask backend.

STEP 2 — Add Architecture Fundamentals

(Week 3) Architect your existing app like a senior engineer.

  • Analysis: Latency heatmap, Scalability constraints, Caching opportunities, Database bottlenecks.
  • Deliverable: Architecture document (UML, Request flows, Caching plan, Scaling plan).

STEP 3 — Add Classic Distributed System Building Blocks

(Week 4) Extend your platform from "web app" to "distributed system".

  • Components: Redis caching, Rate limiting, Messaging queue (RabbitMQ/Kafka-lite), CDN, Search service.
  • Skills: Pub-sub, Async processing, Indexing, Fault tolerance.
  • Deliverable: Your single app becomes a distributed system.

STEP 4 — API & Product Architecture Deepening

(Week 5) Upgrade your API to a true product-grade interface.

  • Enhancements: API versioning, Pagination/Sorting/Filtering, GraphQL/gRPC, OpenAPI docs, OAuth2/JWT rotation, Rate limiting, Circuit breakers.
  • Deliverable: A clean, well-designed, client-centric API.

STEP 5 — Frontend System Design (REDCAAP + Next.js Optimization)

(Week 6) Design your frontend like FAANG-level engineers.

  • Performance: Code splitting, Reduce hydration, Skeleton loading, Virtualized lists.
  • Architecture: Micro-frontends, i18n, Error boundaries, Accessibility.
  • Next.js Specifics: Server Components, Route caching, Edge middleware, Streaming.
  • Deliverable: High-performance Next.js frontend.

STEP 6 — Mobile System Design (React Native)

(Week 7) Create a mobile app that interacts with your microservices and agents.

  • Why React Native: Shares mental model with React, Single codebase.
  • Features: Offline-first, Local caching, Background sync, Push notifications, Conflict resolution.
  • Deliverable: Fully functional RN mobile client.

STEP 7 — Low-Level Design (OOD + Design Patterns)

(Week 8) Apply LLD to your own system.

  • Backend: Refactor into SOLID services. Patterns: Strategy, Factory, Observer, Adapter, Command.
  • Frontend: Custom hooks, Container-presentation, Compound components.
  • Deliverable: Production-quality architecture + maintainable codebase.

STEP 8 — Convert to Microservices + Event-Driven Architecture

(Weeks 9–10) Split into microservices.

  • Services: User, Post, Feed Ranking, Notification, Search.
  • Infrastructure: Kafka, Docker, Kubernetes, API Gateway, Service Registry.
  • Patterns: Event sourcing, Sagas, Outbox, Async consumers, Retries & DLQ.
  • Deliverable: Fully containerized, event-driven microservice architecture.

STEP 9 — Machine Learning System Design Integration

(Week 11) Build ML services into your architecture.

  • Features: Feed ranking model, Recommendation engine, Content embeddings.
  • Monitoring: Drift detection, Latency monitoring, Shadow inference.
  • Deliverable: Live ML powering your feed + notification ranking.

STEP 10 — Agentic System Design (LLM Agents + Tools + Memory)

(Week 12) Integrate LLM agents into your system.

  • Agents: Recommendation agent, Moderation agent, Support chatbot.
  • Capabilities: Web actions, Vector DB (RAG), Long-term memory, Human-in-the-loop.
  • Deliverable: Distributed, ML-powered product with autonomous agents.

FINAL PROJECT — Forward-Deployed AI Product

All pieces combine into an AI-Powered Social/Content Platform:

  • Next.js frontend
  • React Native mobile app
  • Microservices on Kubernetes
  • Kafka event streams
  • Redis caching
  • ML recommendations
  • Embeddings + vector search
  • Multi-agent LLM layer
  • Identity, logging, and monitoring
Back to Overview