AI CODING Agents with Google Cloud AWS Heroku Vercel Netlify Xano Railway Cloudflare Render _Host code on GitHub Gitlab
No Code and Low Code Vibe Coding for non coder’s and professional and advanced : Plan, Create, Develop Build and Launch Any App, SaaS, Automations, Tools, Game, Website, Programs with AI
What you’ll learn
- Reliable applications must be tested to ensure that features behave exactly as intended. In this module you will learn how to generate and run automated tests.
- You will use Git together with GitHub to track changes, manage revisions, and safely experiment with new features. The course demonstrates how to instruct AI.
- The process includes identifying incorrect dependencies installed through npm, resolving runtime errors in Node.js applications, and improving inefficiency.
- After defining your idea—such as a task manager, note-taking tool, or simple API—you will provide structured prompts that instruct the AI to generate softwares.
- The course demonstrates how AI tools can accelerate prototyping, debugging, and project setup while maintaining professional development practices..
- Founders, creators, or product managers who want to quickly transform ideas into working prototypes without hiring a full development team.
- You will learn how Git tracks code changes, stores commit history, and synchronizes your project with repositories hosted on GitHub.
Course Content
- With Xano AI Build a ChatGPT App for App SDK –> 1 lecture • 9min.
- Getting Started with Supabase Database –> 1 lecture • 11min.
- How to Build_ AI Apps (For Beginners) –> 1 lecture • 7min.
- Visual Studio 2026 – The Intelligent Development Environment –> 1 lecture • 23min.
- 10 open source tools –> 1 lecture • 10min.
Requirements
No Code and Low Code Vibe Coding for non coder’s and professional and advanced : Plan, Create, Develop Build and Launch Any App, SaaS, Automations, Tools, Game, Website, Programs with AI
Building and Launching Applications with AI Coding Assistants
Course Overview
This course teaches a practical workflow for building software with the help of AI-powered development environments. Instead of manually writing every function, you will instruct AI tools using clear prompts to generate code, configure environments, install dependencies, and execute development tasks.
The focus is not just on generating code. You will learn how to guide the AI step-by-step through the full software lifecycle—from creating a project repository to deploying a live application that users can access.
During the course, you will instruct AI assistants to:
• create project folders and files
• generate application logic
• install required packages
• run development commands
• debug errors
• prepare builds for production
• deploy and launch applications online
The goal is to treat the AI as a development partner that carries out instructions while you supervise the architecture and direction of the project.
What You Will Do in This Course
1. Starting a Project with AI
You will begin by giving AI tools structured instructions such as:
- initialize a new project directory
- generate configuration files
- create the main application entry point
- prepare a dependency list
Example workflow:
Create a new project called ai-task-manager
Initialize a Node.js environment
Generate package.json
Create src/app.js
Add Express as a dependency
The AI assistant will generate the files and install the required packages.
2. Generating Application Code
Instead of writing each function manually, you will instruct the AI to build specific components.
Example prompt:
Create an API endpoint that allows users to add tasks.
Store tasks in memory.
Return all tasks in JSON format.
The AI produces the code while you review, modify, and test it.
You will learn how to:
• refine prompts when code is incomplete
• request improvements or optimizations
• isolate bugs in generated code
3. Running and Testing the Application
Once the application is generated, you will instruct the AI to run development commands.
Typical commands include:
npm install
npm run dev
npm test
You will also learn how to ask the AI to:
• generate basic test scripts
• simulate user requests
• verify API responses
This step ensures the application behaves correctly before deployment.
4. Preparing the Project for Deployment
Before launching an application, production configuration is required.
In this stage you will instruct the AI to:
• create environment variable files
• configure production builds
• optimize dependencies
• remove development-only packages
Example instruction:
Prepare the project for production deployment.
Create a .env example file.
Configure a production start command.
5. Deploying the Application
The course demonstrates how to instruct AI to configure deployment environments.
Typical deployment instructions include:
Create a deployment configuration for a cloud hosting platform
Generate a Dockerfile
Add start and build commands
You will also learn how to ask AI to:
• configure continuous deployment pipelines
• connect your repository to a hosting service
• automatically deploy updates after each commit
6. Launching the Application
Once deployment is configured, you will launch the application and verify that it runs online.
You will instruct the AI to:
• verify server logs
• check endpoint responses
• confirm the application is publicly accessible
Example verification step:
Send a request to /tasks endpoint
Confirm the API returns the expected JSON response
This step ensures the application is fully operational for real users.
Skills You Will Develop
By completing the course you will learn how to:
• translate software ideas into precise AI prompts
• supervise AI-generated code instead of writing everything manually
• debug issues in AI-generated projects
• structure projects so they remain maintainable
• deploy applications to live environments
• launch functioning software products
Rather than treating AI as a shortcut, the course focuses on supervising and structuring AI-generated development so the resulting applications remain reliable, maintainable, and production-ready.
Tools and Software Used
During the course you will work directly with professional development tools including:
- Cursor for AI-assisted coding and file generation
- Windsurf for prompt-driven development workflows
- Lovable for rapid app prototyping
- GitHub Copilot for inline code generation
- Git and GitHub for version control and repositories
Core Technologies and Libraries
Applications created in the course will use widely adopted technologies such as:
- Node.js for backend execution
- Express.js for building APIs
- React for frontend interfaces
- npm for dependency management
- Docker for packaging applications
Development Workflow You Will Practice
Project Initialization
- Create a project repository
- Generate configuration files such as package.json
- Set up folders like src, routes, and components
AI-Generated Coding
- Generate backend endpoints using Express
- Build UI components with React
- Request improvements and bug fixes from AI assistants
Running and Testing
- Execute commands such as
npm install
npm run dev
npm test
Version Control
- Track changes using Git
- Push code to GitHub repositories
- Manage updates safely with commits and branches
Deployment Preparation
- Configure environment variables
- Create production start scripts
- Generate Docker configuration when required
Deployment and Launch Applications will be deployed to modern hosting platforms such as:
- Vercel
- Railway
- Render
You will verify the deployment by testing API endpoints and confirming the application runs publicly online.
Who This Course Is For
- Beginners who want to build real applications using AI coding tools
- Developers interested in integrating AI into professional development workflows
- Entrepreneurs who want to prototype and launch digital products quickly