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Be the Engineer the World Needs.

AI That Elevates Learning.
Results That Launch Careers.

At Developers Stack, AI isn’t just a subject. it’s your personal co-pilot, tutor, architect, and career coach.

+70%

Code Productivity with AI Assistance

+85%

Understanding of Complex Concepts via AI Explanations

+60%

Improvement in Interview Performance & Confidence

Become a Future-Proof Developer with AI-Powered Software Engineering
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1
Module (M1501)
  • ✅ Setup & Use
    • GitHub Copilot / Tabnine: Real-time code suggestions for Java and C#; auto-completes loops, classes, and JavaFX handlers.
    • ChatGPT / Gemini / Claude: Explain Java/C# concepts, debug errors, translate Java ↔ C#, and generate documentation for classes/functions.
    • Scene Builder + GPT: Describe UI layout in text → receive auto-generated FXML or JavaFX code; get design/layout improvement tips.
  • ✅ Testing & Collaboration
    • AI-generated test templates (e.g., JUnit/NUnit) from methods or pseudocode.
    • Generate `.gitignore`, commit messages, and README files using prompt-based tools.
    • Use AI to explain Git workflows (e.g., pull, merge, rebase) and simulate collaboration scenarios.
  • ✅ Personalized Learning
    • AI tutors explain code errors and suggest fixes, with support in Sinhala, Tamil, or English.
    • GPT-based assistant reviews common mistakes in student submissions and recommends practice topics.
    • Adaptive hints for Java/C# exercises based on coding pattern recognition.
In this foundational module, students explore core programming skills using Java and C#, along with tools like IntelliJ IDEA, Scene Builder, and Git. Generative AI enhances the learning process by acting as a real-time coding assistant—suggesting syntax, fixing logic errors, and generating reusable code blocks for loops, classes, and UI elements. Students can describe JavaFX layouts in plain English and receive instant FXML or Java code, making UI design more intuitive and accessible. AI also supports collaboration by generating .gitignore files, writing meaningful commit messages, and explaining Git workflows like branching and merging. As a personal tutor, AI provides explanations in Sinhala, Tamil, or English, reviews student submissions, and adapts feedback based on coding patterns. This dramatically boosts productivity, clarity, and confidence for beginners taking their first steps into software development.
Module (M1502)
2
  • ✅ Setup & Use
    • ChatGPT / Claude / Gemini: Convert natural language queries into optimized SQL (e.g., “Show all customers from Colombo in last 30 days” → SQL).
    • AI Schema Designer: Describe app requirements → auto-generate normalized SQL schema or NoSQL document models.
    • Jasper Reports + GPT: Explain report structure, auto-generate queries and layout XML; review report design for improvements.
  • ✅ Testing & Collaboration
    • Generate test data (dummy users, orders, etc.) using AI prompts for realistic table population.
    • Use AI to write and explain complex joins, indexes, and constraints with visual ERD suggestions.
    • Generate Git commits, README files for DB projects (e.g., “Hospital DB Design”) with prompt-based input.
  • ✅ Personalized Learning
    • AI tutor explains DB normalization (1NF-3NF), indexing, and transactions using analogies and local examples.
    • GPT-based assistant reviews student SQL queries, finds logic errors, and suggests improvements.
    • Adaptive hints when students get SQL syntax or logic wrong (e.g., “You might be missing a GROUP BY clause”).
In this module, students gain hands-on experience with relational and non-relational databases like MySQL, PostgreSQL, SQL Server, and MongoDB. Generative AI acts as a powerful co-pilot throughout the learning journey—converting plain-language questions into optimized SQL queries, auto-generating schemas based on app requirements, and simplifying complex joins, constraints, and indexing through intelligent prompts. AI tools also accelerate report generation with Jasper by suggesting queries, layouts, and structure improvements. Students receive real-time guidance from AI tutors that explain errors, recommend best practices, and adapt feedback based on query patterns. Whether designing a normalized schema or analyzing a NoSQL document model, learners can leverage AI to work faster, collaborate better, and develop a deep, practical understanding of modern data systems.
3
Module (M1503)
  • ✅ Setup & Use
    • Prompt-to-HTML/CSS: Describe a landing page → receive responsive HTML/CSS with Tailwind/Bootstrap.
    • Three.js + GPT: Generate 3D objects or animations from natural language descriptions.
    • JavaScript + AI: Explain DOM events, async functions, and browser behavior using interactive prompts.
  • ✅ Testing & Collaboration
    • AI tools auto-generate testable JavaScript functions from user stories or pseudocode.
    • SEO advisor bot analyzes your code and suggests optimizations for Google ranking.
    • AI helps build GitHub READMEs, commit logs, and deployment scripts (Netlify/Vercel).
  • ✅ Personalized Learning
    • GPT agent provides accessibility feedback and explains layout/debugging errors.
    • Offers hints for improving responsiveness and cross-browser support.
    • Live coach bot explains key frontend concepts in multiple languages.
This module focuses on frontend web development using HTML, CSS, JavaScript, SASS, Bootstrap, Tailwind, and JavaScript libraries like jQuery and Three.js. With Generative AI, students can describe UI requirements in plain text and instantly generate semantic, responsive HTML/CSS code. AI-powered tools explain layout bugs, recommend performance improvements, and assist in designing accessible web interfaces. Students use prompts to generate animations with Three.js, simulate SEO strategies, and get feedback on responsive design. AI helps demystify complex JavaScript interactions and generates deploy-ready builds—greatly accelerating the creation of professional-grade web apps.
Module (M1504)
4
  • ✅ Setup & Use
    • AI Backend Generator: Generate CRUD APIs (Spring/Node/.NET) from model descriptions.
    • Prompt-to-Firebase Rules: Auto-generate security rules and Firestore queries from requirements.
    • Component Builders: GPT helps create Angular/React components and services using prompts.
  • ✅ Testing & Collaboration
    • AI writes unit and integration tests (JUnit, Mocha, Jasmine) from controller/service code.
    • Generates API documentation (Swagger/OpenAPI) and explains error-handling logic.
    • GPT compares and suggests improvements to Redux or NGRX logic flows.
  • ✅ Personalized Learning
    • AI explains request/response cycles, token-based auth, and lifecycle hooks in simple terms.
    • Smart assistant recommends refactoring or modularizing code.
    • Helps students translate backend logic across stacks (e.g., Node → Spring).
Students dive into backend and enterprise web frameworks like Spring Boot, Node.js (Express, NestJS), .NET (Razor), TypeScript, Angular, React, and Firebase. AI dramatically streamlines this complexity: it scaffolds projects, builds secure APIs, generates boilerplate code, and explains concepts like JWT, authentication flows, and state management (Redux, NGRX). Students can request unit tests, simulate test data, and review backend logic using AI code reviewers. Real-world full-stack app development becomes faster, smarter, and more scalable with GPT-like tools supporting each layer of the enterprise stack.
5
Module (M1505)
  • ✅ Setup & Use
    • Prompt-to-App Generator: "Build a todo app with local storage" → Flutter/React Native scaffold.
    • AI UI Designer: Generate mobile layouts and themes via prompt (Material, Cupertino, NativeBase).
    • Flutter Gems + GPT: Recommend and integrate 3rd-party packages based on use case.
  • ✅ Testing & Collaboration
    • Generate test cases and mocks for Dart/JS services using AI tools.
    • Auto-create changelogs, app store descriptions, and onboarding docs.
    • Simulate API behavior for mobile testing using AI-generated stubs.
  • ✅ Personalized Learning
    • GPT clarifies widget trees, hooks, state, and lifecycles.
    • Code coach explains Flutter build errors or React Native styling issues.
    • Language support for Sinhala/Tamil for mobile-specific guidance.
This module covers React Native and Flutter (Dart), empowering students to build cross-platform mobile apps. With AI, they can describe app features in natural language and generate working mobile UIs, navigation flows, and API integrations. GPT helps troubleshoot build errors, generate FCM logic, and explain state management. Students also receive real-time guidance on layout responsiveness, native feel, and platform differences. AI shortens the gap between idea and prototype—helping students move faster and build smarter mobile experiences.
Module (M1506)
6
  • ✅ Setup & Use
    • Prompt-to-Architecture: “Design an Uber-like system” → GPT generates HLD, flow diagrams, DB schema.
    • Design Pattern Assistant: Use AI to explain and generate code for patterns (Factory, Observer, etc.).
    • System Comparison Tool: Compare monolithic vs. microservice impacts on performance, scalability, etc.
  • ✅ Testing & Collaboration
    • Generate sequence diagrams, deployment diagrams from markdown or prompt.
    • Get peer-reviewed feedback on trade-offs via AI simulation.
    • Collaborate on GitHub with GPT-generated architecture readmes and comments.
  • ✅ Personalized Learning
    • AI agent explains each design decision using real-world analogies.
    • Adaptive practice: describe any system → GPT gives a rough HLD you refine.
    • Pattern explanation and usage review based on student errors.
This module introduces architectural thinking—monolithic vs. microservices, DDD, design patterns, message queues, and more. Generative AI acts as a powerful thought partner: it visualizes architectures, generates design pattern examples, simulates real-time architectures (like Uber/WhatsApp), and validates trade-offs. Students can describe business domains, and AI will propose scalable architectures, HLD/LLD diagrams, and messaging flows. This unlocks system-level thinking early and prepares students for technical leadership roles.
7
Module (M1501)
  • ✅ Setup & Use
    • OWASP Scanner + GPT: Describe a web app → get vulnerability report and patch advice.
    • RBAC Generator: Describe access control rules → receive enforcement code or policy file.
    • Performance Advisor: GPT suggests caching, database tuning, load balancing strategies.
  • ✅ Testing & Collaboration
    • Generate security unit tests and performance benchmarking scripts.
    • Use AI to simulate DoS attack impact or replay common exploits.
    • AI suggests CI/CD security best practices for pipeline hardening.
  • ✅ Personalized Learning
    • AI explains security models (Zero Trust, Defense-in-Depth) with analogies.
    • Students can ask: “Why is this code vulnerable?” → GPT explains & rewrites securely.
    • Personalized feedback on missing headers, validation, or auth checks.
Students explore security principles (CIA, OWASP Top 10, RBAC) and performance topics (load balancing, caching, optimization). AI plays a vital role by scanning code for vulnerabilities, generating mitigation strategies, and explaining exploits in layman's terms. It also benchmarks application performance, suggests caching strategies, and simulates DoS/DDOS scenarios. Students can describe access rules or threat models, and AI builds enforcement logic, RBAC flows, and alerts. GPT transforms complex security and performance engineering into digestible, actionable guidance.
Module (M1508)
8
  • ✅ Setup & Use
    • Full Project Generator: GPT scaffolds entire microservice-based project (API, DB schema, frontend).
    • Infra Builder Bot: Input architecture prompt → receive CloudFormation or Terraform templates.
    • Queue Integrator: AI explains Kafka/RabbitMQ and writes integration code with event flows.
  • ✅ Testing & Collaboration
    • Generate test suites for APIs and services using AI prompts.
    • GPT auto-generates API Gateway configs, rate limit policies, and backend error responses.
    • Produce README, ERD, Swagger, and deployment docs using prompt-based AI assistance.
  • ✅ Personalized Learning
    • AI explains scaling strategies and reviews database sharding/indexing decisions.
    • GPT gives suggestions for improving latency, availability, and fault tolerance.
    • Students ask “Why is this service failing?” → GPT diagnoses and suggests fixes.
This module challenges students to build and deploy a full-stack, microservices-based e-commerce system using modern technologies like Angular/React, Spring Boot/NestJS, PostgreSQL/MongoDB, and AWS/GCP. Generative AI acts as a powerful project co-pilot: helping with architecture, database design, CI/CD, deployment, and debugging. Students use AI to generate documentation, scaffold microservices, and optimize queries. AI agents can validate OAuth2/JWT/MFA security setups, simulate message queues (Kafka/RabbitMQ), and analyze Cloud deployment strategies. GPT transforms this into a production-grade development experience with intelligent automation and design feedback.
9
Module (M1509)
  • ✅ Setup & Use
    • Prompt-to-Pipeline: “Deploy Node + React on AWS with GitHub Actions” → get full CI/CD YAML config.
    • Docker & K8s Wizard: GPT generates Dockerfiles, Helm charts, and explains pod/container lifecycle.
    • AWS Assistant: Ask GPT to create EC2, RDS, VPC setups; get SDK code + CLI commands.
  • ✅ Testing & Collaboration
    • Create unit tests for Terraform and Jenkins pipelines with prompt-based AI.
    • GPT explains IAM, WAF, Security Hub rules — and generates compliant policies.
    • Use AI to compare monitoring tools (Datadog, Prometheus, ELK) and generate setup guides.
  • ✅ Personalized Learning
    • Students can ask: “Why is ECS failing?” → GPT analyzes logs and gives resolution steps.
    • Real-time feedback on pipeline errors, misconfigurations, and cost-saving opportunities.
    • Multi-cloud deployment advisor simulates AWS, GCP, Azure differences.
In this module, students gain experience with CI/CD tools (Jenkins, GitHub Actions), containerization (Docker, Kubernetes), IaaC (Terraform, Ansible), and cloud services across AWS, GCP, and Azure. Generative AI revolutionizes DevOps by generating pipelines, Dockerfiles, Helm charts, IAM policies, and deployment scripts from natural language. Students simulate DevOps workflows, automate error resolution, and create secure, production-ready infrastructures using AI. GPT serves as a DevOps assistant for monitoring, cost optimization, secrets management, and security analysis, turning students into efficient cloud-native engineers.
Module (M1510)
10
  • ✅ Setup & Use
    • Resume Optimizer: Upload or paste resume → GPT reviews for ATS keywords, formatting, and impact.
    • LinkedIn Coach: AI suggests headline, summary, experience rewrite based on job target.
    • GitHub Auditor: GPT reviews public repos and gives feedback on README, project quality.
  • ✅ Testing & Collaboration
    • Generate and solve coding questions (LeetCode-style) with hints and time tracking.
    • Simulate whiteboard system design: GPT acts as the interviewer and evaluator.
    • Practice DevOps/AWS/cloud scenario questions with live explanations.
  • ✅ Personalized Learning
    • AI identifies common patterns in student mistakes and tailors next practice topics.
    • Get voice-based interview coaching with GPT (behavioral + technical).
    • AI creates custom career roadmap based on skills, goals, and market trends.
This module prepares students for real-world job interviews with resume building, GitHub and LinkedIn optimization, DSA prep, mock interviews, and system design challenges. AI becomes the ultimate interview coach—analyzing resumes, optimizing LinkedIn profiles, generating DSA challenges, and simulating interviews with live feedback. GPT-based tools help students prepare for cloud, DevOps, and system design rounds using realistic case studies. Students can role-play with an AI HR manager or tech lead, receiving personalized tips and growth plans. This transforms interview prep into an interactive, performance-driven experience.