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AI-Powered Microservices with Vibe Coding & Software 3.0

Name: AI-Powered Microservices with Vibe Coding & Software 3.0
Rating: 3.9 (175 reviews)

Master API Integration, GraphQL, Observability & AI-Driven Architecture

Created byVivian Aranha, School of AI

Last updated 2/2026

English

English [Auto],Spanish [Auto],

What you'll learn

Learn how to write natural language specifications and prompt AI to generate clean, modular, and testable code across services and components.
Design, containerize, and deploy microservices with secure APIs using OpenAPI, GraphQL, Docker, and Kubernetes, enhanced by AI-assisted code generation.
Set up distributed tracing, logging, performance monitoring, and root cause analysis using tools like OpenTelemetry, Prometheus, and Grafana.
Use AI to auto-generate OpenAPI docs, maintain prompt libraries, build knowledge graphs, and even deploy chatbots to support dev teams in real time.
Analyze and build systems for domains like e-commerce, IoT, healthcare, and gaming—featuring Redis sharding, HIPAA compliance, gRPC, and anti-corruption layers.
Communicate your technical decisions with clarity, using visual architecture diagrams, AI-generated docs, and structured walkthroughs.

Course content

9 sections • 44 lectures • 2h 17m total length

Certificate of Completion0:29
Intro to Building AI-Powered Microservices with Vibe Coding & Software 3.03:14

What is Vibe Coding?3:35
Introduction to Software 3.0 Paradigm4:00
Role of AI in Modern Development3:55
Specification-Driven Development Principles3:49
Discover specification driven development (SDD) that turns natural language into reliable, testable, and auditable software for software 3.0. Use specs with YAML, JSON, and OpenAPI to guide AI-generated code.
Why Microservices + APIs Matter in the AI Era3:47
Clawdbot and the Shift to Agent-First Computing8:15
For years, artificial intelligence has been positioned as a conversational tool—answering questions, generating content, and offering recommendations. While impressive, most AI systems today remain fundamentally passive. They talk, but they don’t act.
This presentation explores a critical shift now underway: the transition from chat-based AI to agentic AI systems—AI that can reason, make decisions, and execute real-world actions. Using Clawdbot (also known as Moltbot) as a concrete example, this session introduces a new operating model for AI: one where agents serve as persistent, proactive operators rather than temporary conversational assistants.
Clawdbot represents a new class of AI systems. It is an open-source, self-hosted AI agent designed to live alongside users and teams, integrate with existing tools, maintain long-term context, and perform actions autonomously when instructed. Instead of navigating dozens of applications, dashboards, and workflows, users interact with a single intelligent agent through familiar messaging interfaces such as WhatsApp, Slack, or Telegram. The agent interprets intent, reasons using large language models, executes tasks across systems, and reports results back in natural language.
This presentation breaks down how this agentic model works in practice. Attendees will see how human intent flows into AI reasoning and ultimately results in real execution—such as managing emails, scheduling tasks, running scripts, coordinating workflows, or integrating with enterprise systems. The architecture behind these systems will be explained at a high level, highlighting how reasoning, memory, and execution are intentionally separated to enable flexibility, control, and governance.
Beyond the technology, the session focuses on why this shift matters. Agentic AI challenges the app-centric model of computing that has dominated for decades. Instead of humans adapting to software interfaces, software adapts to human goals through intelligent agents. This has profound implications for productivity, privacy, system design, and organizational workflows. AI moves from being a feature embedded in products to becoming a foundational layer of infrastructure.
The presentation also addresses the risks and responsibilities that come with powerful AI agents. Topics such as security, permissioning, governance, and human-in-the-loop control are discussed to ensure that autonomy is introduced safely and intentionally—especially in enterprise environments.
By the end of this session, attendees will leave with a clear understanding of what AI agents are, how systems like Clawdbot work, and why this paradigm represents one of the most important evolutions in AI adoption. More importantly, they will gain a new mental model for the future of computing—one where AI doesn’t just assist, but actively operates.

Core RESTful Design + AI Auto-Generation3:38
Core RESTful Design + AI Auto-Generation3:25
Authentication (API Keys, JWT, OAuth 2.0)3:20
Explore API authentication methods—API keys, JWT, JSON Web Tokens, and OAuth 2.0—and learn to implement secure access layers for AI-generated APIs, with role-based access, auditing, and token validation.
Versioning and Auto-Documentation with Swagger3:16
Version your rest APIs with Swagger and OpenAPI to keep docs automatically up to date, enabling v1 and v2 routes, version aware routing, and AI-assisted live documentation, supporting semantic versioning.
GraphQL: Schema, Resolvers, Subscriptions with Vibe Coding3:22
Discover how GraphQL schemas, resolvers, and subscriptions enable precise data fetching and real-time updates in AI-driven vibe coding, auto-generated by AI.

Microservice Principles (SRP, Loose Coupling)3:07
Apply microservice principles by enforcing single responsibility and loose coupling to build AI-driven, autonomous services (products, inventory, carts) that scale, stay resilient, and interface cleanly.
Tech Stack Heterogeneity and AI Code Choice3:26
Polyglot Persistence & AI-Guided DB Selection3:10
Service Discovery, Load Balancing (e.g., K8s)3:10
Explore how service discovery and load balancing orchestrate microservices with Kubernetes, DNS-based routing, and scalable traffic distribution across replicas to ensure fast, reliable AI-powered systems.
AI-Assisted Saga Pattern & Distributed Transactions3:19

What is Monitoring vs Observability2:42
Clarify the difference between monitoring and observability in ai-infused microservices, and design introspective architectures using logs, metrics, and traces to diagnose latency and model failures, including prompt drift.
Error Tracking, Logging & Performance Metrics2:35
Develop structured logs with timestamps, request IDs, and user IDs to enable observable monitoring of distributed AI pipelines, and automate error tracking and alerting to forecast latency.
Distributed Tracing with OpenTelemetry3:01
Instrument your system with OpenTelemetry to generate spans and build end-to-end traces that reveal latency, failures, and root causes across microservices, with visualizations like flame graphs.
Profiling AI-Generated Code & Prompt Monitoring2:47
Profile AI generated code and prompts for efficiency, reliability, and cost awareness by tracking prompt health, latency, token usage, failures, retries, drift, blue scores, and semantic similarity.
AI-Assisted Root Cause Analysis & Explainable Dashboards3:19

From Code to Docs with AI3:01
Turn code into living documentation with AI, generating input/output types, usage examples, and API specs in real time. Bridge the code-to-doc gap for onboarding and debugging.
Context-Aware and Multimodal Documentation2:42
Develop context-aware, multimodal documentation that adapts to developers, designers, and decision makers. Leverage AI to generate architecture diagrams, UI flows, and live API sandboxes.
Creating and Managing Prompt Libraries2:38
Build a structured, versioned prompt library as the source of truth for scalable ai behavior. Organize prompts by function with input-output examples and testing status to enable reuse and quality.
Knowledge Graphs and Tacit Knowledge2:52
Developer Chatbots and Prompt QA Systems2:47
Build developer chatbots and prompt QA systems as AI co-pilots delivering real-time, code-aware answers from your docs and workflows. Use Rag pipelines, vector databases, and session memory for production-grade prompts.

SDD Workflow and AI Integration3:06
Explore specification driven development with AI integration, turning user intents into formal specs, auto-generated code, simulations, and test harnesses for edge, embedded, and distributed systems.
SDD in Embedded & Edge Systems3:05
Master specification driven development for embedded and edge systems by modeling with state machines or block diagrams, validating constraints with AI, and auto generating memory-efficient, portable code.
Model-Based Design with Stateflow, TVM, and TinyML3:20
Explore model-based design that models behavior before coding, then generates implementation from diagrams and blocks. See how Stateflow, TVM, and TinyML enable simulation, optimization, and edge deployment for embedded ai.
Low-Code and AI IDEs for Embedded Programming3:00
Explore how low-code platforms and AI-powered IDEs reshape embedded programming by visualizing logic, simulating in the cloud, and generating optimized firmware from natural language prompts for specification-driven workflows.
Testing and Safety in Spec-Driven Development3:06

E-Commerce: From Monolith to Microservices2:50
Migrate from monoliths to microservices using AI-powered, specification-driven design to isolate catalog, cart, orders, and payments, enabling scalable, independent deployments and event-driven communication with Kafka or RabbitMQ.
Real-Time IoT Monitoring with GraphQL2:55
Legacy + AI Integration via Anti-Corruption Layers2:47
Learn how anti-corruption layers isolate AI from legacy systems, enabling safe modernization without rewriting. ACLs translate between modern APIs and legacy data, supporting AI-powered modules and gradual refactors.
High-Frequency Trading with gRPC2:36
MMO Gaming Backends and Redis Sharding2:43
HIPAA-Compliant Healthcare APIs2:46

Choose a Domain (IoT, E-Commerce, Healthcare, etc.)2:21
Define Spec + Prompt AI to Generate Code2:41
Define a natural language system spec and prompt AI to generate modular, testable code from microservices to REST and GraphQL endpoints, with contracts, tests, and maintainable docs.
Build Microservices and Secure APIs2:34
Build clean, resilient microservices with secure AI-generated APIs and contracts, enforcing data ownership, authentication, and modular, observable communication via gRPC, Kafka/Nats, and gateways.
Deploy, Observe, and Monitor2:25
Deliver Presentation with Documentation + Architecture Map2:21
Learn to present an ai-powered microservices system with a clear architecture map and ai-assisted documentation, delivering a compelling technical walkthrough that shows impact, stability, and scalability.

Requirements

A basic understanding of programming concepts (any language—Python, JavaScript, etc.)
Familiarity with REST APIs or modern web development practices
Curiosity about AI-assisted development, automation, and prompt engineering
A modern computer with internet access, Docker installed, and optionally a GitHub account

Description

Welcome to Vibe Coding and Software 3.0, a cutting-edge course that will redefine how you build, scale, and maintain intelligent systems in the age of AI. This isn’t just another programming tutorial—it’s a deep dive into the future of software engineering, where AI-assisted development, specification-driven architecture, and microservices come together to form the foundation of Software 3.0.

In a world where AI tools like ChatGPT, Copilot, and Claude are reshaping how developers write code, manage APIs, and design infrastructure, it’s no longer enough to just “know how to code.” Today’s top engineers must understand how to collaborate with AI, generate code from prompts, build autonomous systems, and orchestrate microservice architectures that are scalable, secure, and observable.

This course introduces you to the Vibe Coding paradigm—a developer workflow that begins with human intent, uses natural language specifications, and leverages AI to scaffold everything from API contracts to documentation. Whether you’re designing a RESTful service, building a GraphQL backend, or deploying to Kubernetes, Vibe Coding enables you to move faster while maintaining clarity, control, and code quality.

You’ll learn how to:

Use AI prompts to generate and test APIs using OpenAPI and GraphQL schemas
Build secure, scalable microservices with AI-guided code generation
Implement best practices in authentication, including API Keys, OAuth2, and JWTs
Leverage AI to auto-document your services with Swagger and context-aware chatbots
Apply Specification-Driven Development (SDD) in embedded, edge, and cloud systems
Monitor performance, detect anomalies, and visualize observability data with OpenTelemetry
Use distributed tracing, root cause analysis, and explainable dashboards powered by AI
Build your own prompt libraries, knowledge graphs, and developer chatbots for ongoing productivity
Deploy production-grade systems across diverse domains like e-commerce, IoT, healthcare, and gaming

By the end of the course, you’ll complete a Capstone Project where you select a real-world use case, define its spec, prompt AI to generate code, build a full-stack system, and present it with documentation and an architecture map. You’ll not only gain technical skills, but also the ability to communicate your systems like a modern software architect.

This course is perfect for software engineers, DevOps professionals, tech leads, and product-minded developers who want to stay ahead of the curve. Whether you're transitioning into AI-powered development or leveling up your microservice architecture game, this course will equip you with the mindset, tools, and hands-on skills to thrive in the AI-first software era.

Are you ready to code with intent, not syntax? To lead in the age of intelligent systems and developer-AI collaboration?

Then welcome to Vibe Coding and Software 3.0—where your specs become software, and your ideas scale with AI.

Who this course is for:

Product-minded engineers who want to combine speed with scalability
Developers exploring tools like OpenAPI, GraphQL, Kubernetes, and OpenTelemetry
Professionals aiming to build secure, scalable systems powered by AI-generated code
Early-career developers eager to learn the mindset and tooling behind Software 3.0

AI-Powered Microservices with Vibe Coding & Software 3.0

What you'll learn

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Course content

Intro to Building AI-Powered Microservices with Vibe Coding & Software 3.02 lectures • 4min

Module 1: Introduction to Vibe Coding and Software 3.06 lectures • 27min

Module 2: REST APIs and GraphQL in the Age of AI5 lectures • 17min

Module 3: Building Microservice Architectures with AI5 lectures • 16min

Module 4: Observability and Monitoring in AI Systems5 lectures • 14min

Module 5: Automating Documentation & Prompt Libraries5 lectures • 14min

Module 6: Specification-Driven Development (SDD)5 lectures • 16min

Module 7: Case Studies and Industry Applications6 lectures • 17min

Module 8: Capstone Project – Build Your Vibe-Coded System5 lectures • 12min

Requirements

Description

Who this course is for: