How to Get Started Programming with AI: Complete Beginner's Guide

Want to build your own website, app, or SaaS product but don’t know where to start? In 2025, AI has made programming more accessible than ever. You don’t need years of coding experience anymore—with the right AI tools and approach, you can start building real projects from day one.

This comprehensive guide will take you from complete beginner to deploying your first project, step by step. Whether you want to build a blog, a web app, or something more complex, AI can be your coding partner throughout the journey.

Why Programming with AI is Perfect for Beginners

Traditional programming has a steep learning curve. You’d spend months learning syntax, debugging cryptic errors, and understanding complex concepts before building anything useful. AI changes this completely.

With AI assistance, you can:

Think of AI as your senior developer mentor who’s available 24/7, infinitely patient, and eager to help you learn.

Step 1: Choosing the Right Technology Stack

The first decision you’ll make is what technology stack to use. Your choice depends on what you’re building.

For Websites and Blogs: Astro

If you’re building a content-focused website or blog, Astro is your best bet. It’s fast, beginner-friendly, and perfect for sites that don’t need complex interactivity.

Why Astro?

Perfect for:

• Personal blogs
• Portfolio sites
• Documentation sites
• Marketing websites
• Affiliate sites

I’ve created a comprehensive guide on building an Astro blog completely free: How to Build a Free Blog with Astro & Cloudflare in 30 Minutes

For Web Applications: TanStack Start

Building something more interactive? TanStack Start is a modern full-stack React framework that gives you both frontend and backend capabilities.

Why TanStack Start?

Perfect for:

• SaaS applications
• Dashboards and admin panels
• Interactive web apps
• Projects with user authentication
• Apps that need a database

Check out my guides:

• Deploy TanStack Start on Your VPS with Dokploy
• TanStack Start Getting Started Guide

For Svelte Fans: SvelteKit

If you prefer Svelte’s approach to reactivity and want a simpler mental model than React, SvelteKit is excellent.

Why SvelteKit?

Perfect for:

• Developers who prefer simpler syntax
• Projects where bundle size matters
• Apps that need excellent performance
• Teams familiar with Svelte

Quick Decision Tree

Not sure which to pick? Use this simple guide:

Step 2: Set Up Your Code Repository

Before writing any code, create a GitHub repository. This gives you version control, backup, and enables automatic deployments.

Create a GitHub Account

1. Visit github.com
2. Sign up for a free account
3. Verify your email address

Set Up SSH Keys (Recommended)

SSH keys let you push code securely without entering passwords every time:

# Generate a new SSH key
ssh-keygen -t ed25519 -C "[email protected]"

# Start the ssh-agent
eval "$(ssh-agent -s)"

# Add your SSH key
ssh-add ~/.ssh/id_ed25519

# Copy your public key
cat ~/.ssh/id_ed25519.pub

Add the public key to GitHub:

1. Go to GitHub Settings → SSH and GPG keys
2. Click “New SSH key”
3. Paste your public key
4. Save

For more details: Link GitHub with SSH on Mac/Linux

Create Your Repository

1. Click the ”+” icon in GitHub
2. Select “New repository”
3. Name it (e.g., my-first-ai-project)
4. Keep it public or private (your choice)
5. Don’t initialize with README (you’ll create it locally)
6. Click “Create repository”

Step 3: Choose Your AI Coding Assistant

This is crucial. The right AI assistant makes all the difference in your learning and building experience.

Recommended: GitHub Copilot Pro ($10/month)

GitHub Copilot Pro is the best value for money, and it’s what I personally use daily.

What you get for $10/month:

Recommended IDE: Zed - It’s blazing fast, modern, and has native Copilot integration.

Read my complete guide: GitHub Copilot Pro: Best $10 AI Coding Plan with Zed IDE & CLI

Free Alternatives

If you want to start completely free, you have excellent options:

1. Amp Code Free (by Sourcegraph)

What you get:

Trade-off: Your code is used to train models. Don’t use for proprietary work.

Learn more: Amp Code Free: The AI Coding Agent That Works in Your Editor

2. Free Access to Claude Sonnet 4.5 and GPT-5

There are legitimate ways to use premium models for free:

• Droid CLI - 20 million free tokens first month
• Windsurf IDE - 25 free prompts monthly
• AgentRouter - $200 in free API credits

Full details: How to Use Claude Sonnet 4.5 and GPT-5 for FREE

My Recommendation

For beginners: Start with Droid CLI (20M free tokens) or Amp Code Free to learn without spending money.

When you’re building seriously: Upgrade to GitHub Copilot Pro ($10/month). The investment pays for itself in time saved within the first week.

Step 4: Create Your Documentation Folder

This is a game-changer that most beginners skip. A well-maintained documentation folder helps AI understand your project and make better decisions.

Why Documentation Matters

AI is powerful, but it doesn’t automatically know:

• How your specific project is structured
• What commands to run for testing
• What conventions you’re following
• What libraries and versions you’re using

By creating clear documentation, you transform your AI assistant from “generally helpful” to “expertly tailored to your project.”

Create a docs/ Folder

In your project root, create a docs folder with these files:

mkdir docs

Essential Documentation Files

1. Framework Documentation

If you’re using Astro, create docs/astro.md:

# Astro Framework Guide

## Installation & Setup
npm create astro@latest

## Development Commands
- `npm run dev` - Start dev server (http://localhost:4321)
- `npm run build` - Build for production
- `npm run preview` - Preview production build

## Project Structure
- `src/pages/` - File-based routing
- `src/layouts/` - Reusable page layouts
- `src/components/` - React/Vue/Svelte components
- `src/content/` - Markdown content collections
- `public/` - Static assets

## Key Concepts
- Islands Architecture - Only hydrate interactive components
- Content Collections - Type-safe markdown content
- Zero JS by default - Ship less JavaScript

## Common Patterns
- Use `---` frontmatter for component scripts
- Import components: `import Header from '../components/Header.astro'`
- Access props: `const { title } = Astro.props`

2. Stack-Specific Guides

For TanStack Start, create docs/tanstack-start.md:

# TanStack Start Guide

## Project Structure
- `src/routes/` - File-based routing
- `src/components/` - React components
- `src/server/` - Server-side functions
- `src/lib/` - Utilities and helpers

## Key Features
- Server functions with `createServerFn()`
- Type-safe routing
- Built-in data loading
- SSR and SSG support

## Backend Options
- Convex - Serverless backend
- Drizzle + PostgreSQL - Traditional database
- Supabase - Backend as a service

3. Styling Documentation

Create docs/tailwind.md if using Tailwind CSS:

# Tailwind CSS v4 Guide

## Configuration
- CSS-first config using `@theme` directive
- No more `tailwind.config.js` needed

## Common Patterns
- Use utility classes: `bg-blue-500 text-white p-4`
- Responsive: `sm:text-lg md:text-xl lg:text-2xl`
- Dark mode: `dark:bg-gray-800`
- Hover states: `hover:bg-blue-600`

## Custom Theme
```css
@theme {
  --color-primary-500: #3b82f6;
  --font-sans: 'Inter', sans-serif;
}

Example: Complete Project Documentation

Here’s what good documentation looks like. This is from my TanStack project bitbuddies.me:

# Project Overview

## Tech Stack
- Framework: TanStack Start (React 19)
- Backend: Convex
- Auth: Clerk
- Styling: Tailwind CSS v4 + shadcn/ui
- Type Safety: TypeScript strict mode

## Commands
- `bun run dev` - Development server (port 3000)
- `bun run build` - Production build
- `bun run test` - Run tests
- `bun convex dev` - Start Convex backend

## Code Conventions
- Use React Server Components by default
- Client components must have 'use client' directive
- Keep server functions in `src/server/`
- Use Zod for validation
- Follow existing file naming patterns

## Important Notes
- Always run tests before committing
- Use TypeScript strict mode
- Database schema changes require migration
- Environment variables go in `.env.local`

Step 5: Use the AGENTS.md File

The AGENTS.md file is your AI assistant’s instruction manual for your project. It tells the AI exactly how to work with your codebase.

Create AGENTS.md in Your Root

touch AGENTS.md

What to Include

Here’s a template based on my production projects:

# Agent Instructions

You don't need to create any documentation unless I specifically ask. Just provide short summaries of what you did.

## Commands

- **dev**: `npm run dev` (runs on http://localhost:4321)
- **build**: `npm run build`
- **test**: `npm test`

## Architecture

- **Framework**: Astro v5 static site generator
- **Content**: Markdown files in `src/content/posts/`
- **Styling**: Tailwind CSS v4
- **Components**: Reusable components in `src/components/`

## Code Style

- Use TypeScript for type safety
- Follow existing file naming conventions
- Use Tailwind utilities for styling
- Keep components simple and focused

## Important Notes

- Always test changes locally before committing
- Follow existing code patterns in similar files
- Check that all images exist before referencing them
- Run build to verify no errors

## Testing Workflow

1. Make changes
2. Run `npm run dev` to test locally
3. Run `npm run build` to check for errors
4. Review changes carefully
5. Commit with descriptive message

## Common Pitfalls

- Don't use libraries that aren't already installed
- Check TypeScript errors before committing
- Verify all imports are correct
- Test responsive design at different screen sizes

Why This Matters

Without AGENTS.md, AI makes assumptions. With it, AI follows your project’s specific patterns and conventions. This dramatically improves code quality and reduces errors.

Step 6: Add MCP Servers to Your AI

Model Context Protocol (MCP) servers give your AI superpowers by connecting it to external services and tools.

What Are MCP Servers?

MCP servers extend what your AI can do. Instead of just writing code, your AI can:

• Search the web for current information
• Access up-to-date framework documentation
• Test your application automatically
• Interact with databases and APIs

Essential MCP Servers for Beginners

1. Context7 - Up-to-Date Documentation

Context7 provides fresh documentation for frameworks and libraries. Instead of AI using outdated training data, it fetches current docs.

What it helps with:

• Latest API references
• Current best practices
• Recent framework updates
• New features and deprecations

2. BrightData MCP - Web Search & Data

BrightData MCP lets your AI search the web and extract data from websites. Perfect for research and gathering information.

What it helps with:

• Searching for solutions to problems
• Finding examples and tutorials
• Researching competitors
• Gathering data for your project

Learn more: BrightData MCP Guide

3. Playwright MCP - Application Testing

Playwright MCP enables your AI to test your application automatically by controlling a browser.

What it helps with:

• Testing user workflows
• Verifying forms work correctly
• Checking responsive design
• Catching bugs before deployment

GitHub: playwright-mcp

How to Add MCP Servers

The setup process depends on your AI tool. Here’s how to do it for popular options:

{
  "mcpServers": {
    "brightdata-mcp": {
      "command": "npx",
      "args": ["-y", "@brightdata/mcp"],
      "env": {
        "API_TOKEN": "your_brightdata_token"
      }
    },
    "playwright": {
      "command": "npx",
      "args": ["-y", "@playwright/mcp@latest"]
    }
  }
}

{
  "mcpServers": {
    "brightdata-mcp": {
      "command": "npx",
      "args": ["-y", "@brightdata/mcp"],
      "env": {
        "API_TOKEN": "your_brightdata_token"
      }
    }
  }
}

{
  "mcpServers": {
    "context7": {
      "command": "npx",
      "args": ["-y", "context7-mcp"]
    }
  }
}

Don’t Overdo It

For a complete list of available MCP servers, visit: mcpservers.org

Step 7: Start Building Features Incrementally

This is where most beginners make mistakes. They try to build everything at once, overwhelming both themselves and the AI.

Think Small and Focused

Break your project into tiny, manageable features. Instead of “build a blog,” think:

1. Create the homepage layout
2. Add a navigation menu
3. Create a single blog post page
4. Add a blog post listing page
5. Implement search functionality
6. Add categories and tags

Create a Project Architecture First

Before writing code, ask your AI to help plan the structure:

Good prompt:

I want to build a personal blog with Astro. It should have:
- Homepage with recent posts
- Individual post pages
- About page
- Contact form
- Categories and tags

Please create a project architecture document that outlines:
- Folder structure
- Key components needed
- Content organization
- Routing approach

Don't write code yet, just plan the structure.

This gives you a roadmap and helps you understand what you’re building.

Work on One Feature at a Time

When building, focus on a single feature completely before moving on:

Bad approach:

Build the homepage, blog pages, contact form, and about page

Good approach:

Create the homepage layout with:
- Header with site title and navigation
- Hero section with welcome message
- Grid of latest 3 blog posts
- Footer with copyright

Use Tailwind for styling and make it responsive.

Example: Building a Blog Post Page

Here’s how to approach a single feature step by step:

Step 1: Create the basic page structure

Create a blog post page template at src/pages/blog/[slug].astro
- It should accept a slug parameter
- Display post title, date, and author
- Render markdown content
- Use the main layout

Step 2: Add styling

Style the blog post page:
- Use Tailwind typography plugin for content
- Add a max-width container
- Style the header with gradient
- Make code blocks look good

Step 3: Add metadata

Add SEO metadata to the blog post template:
- Title tag with post title
- Meta description from frontmatter
- Open Graph tags for social sharing
- Canonical URL

Step 4: Test and refine

Test the blog post page with different content:
- Long posts with many headings
- Posts with images
- Posts with code blocks
- Short posts

Each step is focused and manageable. AI can handle these easily without getting confused.

Keep the AI Focused

If you notice the AI starting to add features you didn’t ask for, rein it back:

That's good, but I only asked for the header right now.
Let's finish the header completely before moving to other sections.
Can you remove the footer and sidebar code and focus only on the header?

Step 8: Review and Understand Every Line of Code

This is crucial for learning. Don’t just accept code from AI without understanding it.

Ask for Explanations

After AI generates code, ask it to explain:

Good follow-up prompts:

Explain what this component does line by line

Why did you use this approach instead of [alternative]?

What are the potential issues with this code?

How would I modify this to add [feature]?

Request Comments

For complex code, ask AI to add explanations:

Add comments to this code explaining what each section does.
Focus on the "why" not just the "what."

Identify Learning Opportunities

When you see something you don’t understand:

I don't understand this TypeScript syntax: `const { data }: { data: Post[] }`
Can you explain what this means and why we use it?

Keep a Learning Log

Create a LEARNING.md file in your project:

# Things I've Learned

## TypeScript Destructuring
```typescript
const { title, date } = post;

This extracts specific properties from an object. More concise than:

const title = post.title;
const date = post.date;

Component Props in Astro

Props are passed to components and accessed via Astro.props:

---
const { title } = Astro.props;
---
<h1>{title}</h1>

## Tailwind Responsive Classes
`sm:`, `md:`, `lg:` prefixes apply styles at breakpoints:
- `sm:` = 640px and up
- `md:` = 768px and up
- `lg:` = 1024px and up

This log becomes your personal reference as you learn.

Step 9: Get a Second Opinion

AI can make mistakes or use suboptimal approaches. Getting a second AI’s perspective catches issues early.

Use a Different AI for Code Review

If you built features with Claude Sonnet 4.5, ask GPT-5 to review. If you used GPT-5, ask Claude to review.

Code review prompt:

Please review this [component/feature/file] for:

1. Security vulnerabilities
2. Performance issues
3. Code quality and maintainability
4. Potential bugs
5. Best practice violations

Be thorough and critical. I want to improve this code.

Create a Review Document

Ask the second AI to create REVIEW.md:

After reviewing the entire codebase, create a REVIEW.md file with:

## Security Issues
List any security concerns found

## Performance Optimizations
Suggest improvements for speed and efficiency

## Code Quality
Note areas that could be cleaner or more maintainable

## Best Practices
Identify where we deviate from best practices

## Recommended Changes
Prioritized list of improvements

Be specific with file names and line numbers where relevant.

Iterate Based on Feedback

Don’t just collect feedback—act on it:

Based on the review, let's fix the top 3 security issues first.
Start with [specific issue from review].

Example Review Process

Here’s what a typical review session looks like:

1. Build feature with AI #1 (e.g., Claude Sonnet 4.5)
2. Switch to AI #2 (e.g., GPT-5) and share the code
3. Request detailed review focusing on security and performance
4. Review the feedback and ask questions about anything unclear
5. Prioritize fixes starting with security issues
6. Implement improvements with AI #1
7. Verify fixes with AI #2

This back-and-forth catches issues that a single AI might miss.

Step 10: Deploy Your Project

Time to make your project live! Deployment gets your work on the internet for others to use.

Push to GitHub

First, make sure all your changes are committed:

# Stage all changes
git add -A

# Commit with descriptive message
git commit -m "Initial project setup with homepage and blog"

# Push to GitHub
git push origin main

Get a Domain Name (Optional)

While you can use free subdomains, a custom domain looks more professional:

Where to buy domains:

• Namecheap - $1-15/year
• Cloudflare - At-cost pricing
• Porkbun - Low prices, good service

Choose a .com if available, or .dev for tech projects.

Deployment Options

Configure Your Domain

After deploying, point your custom domain to your hosting:

For Vercel:

1. Go to Project Settings → Domains
2. Add your domain
3. Follow DNS configuration instructions
4. Wait for DNS propagation (5-30 minutes)

For Cloudflare Pages:

1. Go to Custom Domains
2. Add your domain
3. Cloudflare handles DNS automatically if you’re using Cloudflare nameservers

For Self-Hosted:

1. Add A record pointing to your VPS IP
2. Configure SSL with Let’s Encrypt (Dokploy does this automatically)
3. Set up your domain in Dokploy

Verify Deployment

After deployment, check that everything works:

Step 11: Monitor Your Application

Once deployed, monitoring helps you catch issues before users complain.

Application Monitoring Options

npm install @sentry/browser

# Add to your main.js/index.js
import * as Sentry from "@sentry/browser";

Sentry.init({
  dsn: "your-sentry-dsn",
  environment: "production",
});

Set Up Alerts

Configure notifications for critical issues:

Essential alerts:

Where to send alerts:

• Email (always)
• Slack/Discord (for teams)
• SMS (critical issues only)
• PagerDuty (on-call scenarios)

Regular Health Checks

Weekly or monthly, manually check:

Step 12: Continue Adding Features Carefully

Your project is live! Now you’ll iterate and improve. But do it carefully to avoid breaking what works.

Always Work on a Branch

Never commit directly to main when your site is live:

# Create a new feature branch
git checkout -b feature/add-dark-mode

# Make your changes and test locally

# Commit to the branch
git add -A
git commit -m "Add dark mode toggle"

# Push the branch
git push origin feature/add-dark-mode

Then create a Pull Request on GitHub and deploy from there.

Test Locally First

Before deploying new features:

Only after all these checks pass should you deploy.

Use Staging Environments

For serious projects, have a staging site:

Setup:

1. Create a separate branch (e.g., staging)
2. Deploy it to a different URL (e.g., staging.yourdomain.com)
3. Test new features on staging first
4. Merge to main only after verification

Most hosting platforms make this easy:

• Vercel: Automatic preview deployments for branches
• Cloudflare Pages: Can deploy multiple branches
• Self-hosted: Use Dokploy to deploy multiple branches

Keep Features Small

Remember Step 7? This applies forever:

Bad:

Add user authentication, profile pages, and social sharing all at once

Good:

Step 1: Add user registration form with email/password
Step 2: Add login functionality
Step 3: Add password reset flow
Step 4: Add email verification

Small changes are easier to test, debug, and roll back if needed.

Document New Features

Update your AGENTS.md when adding new patterns:

## Recent Additions

### Dark Mode (Added 2025-11-10)
- Theme toggle in header
- Saved to localStorage
- CSS variables in `theme.css`
- Usage: Add `dark:` prefix to Tailwind classes

### User Authentication (Added 2025-11-08)
- Using Clerk for auth
- Protected routes in `src/middleware/auth.ts`
- User object available in `Astro.locals.user`

This helps AI (and future you) understand the full project context.

Common Pitfalls and How to Avoid Them

Pitfall 1: Overwhelming the AI

Symptom: AI generates buggy code or misses requirements

Solution: Break tasks into smaller pieces. If AI is struggling, your request is too big.

Pitfall 2: Not Testing Locally

Symptom: Deployments fail or bugs appear in production

Solution: Always run npm run dev and npm run build locally first.

Pitfall 3: Trusting AI Blindly

Symptom: Accumulating technical debt or security issues

Solution: Review all code, ask questions, get second opinions.

Pitfall 4: Too Many Dependencies

Symptom: Project becomes slow, bloated, or breaks easily

Solution: Only add libraries when necessary. Prefer solutions with fewer dependencies.

Pitfall 5: No Version Control Discipline

Symptom: Lost code, unclear what changed, hard to roll back

Solution: Commit often with descriptive messages. Use branches for features.

Pitfall 6: Ignoring Performance

Symptom: Slow load times, poor user experience

Solution: Test with Lighthouse, optimize images, minimize JavaScript.

Pitfall 7: Skipping Documentation

Symptom: AI makes wrong assumptions, you forget how things work

Solution: Keep AGENTS.md and docs/ updated. Document as you build.

Real-World Success Example

Let me share my experience to show what’s possible.

The Project: SmoothieBlenderGuide.com

I recently rebuilt my affiliate site SmoothieBlenderGuide.com from scratch using AI.

Starting Point:

• 40 articles on WordPress
• Slow performance (3-4 second load times)
• $15/month hosting
• Outdated product information

Goal:

• Migrate to Astro for speed
• Update all product data from Amazon
• Improve article quality
• Reduce hosting costs to $0

Tools Used:

• Factory.ai Droid CLI (40M free tokens)
• BrightData MCP (for Amazon data)
• Claude Sonnet 4.5 (for article writing)

Process:

1. Asked AI to create project architecture
2. Set up Astro with Tailwind CSS
3. Used BrightData MCP to fetch current Amazon data
4. Rewrote articles with updated information
5. Created comparison tables from review data
6. Generated optimized images
7. Deployed to Cloudflare Pages

Results:

• 40 articles completed in ~3 hours of actual work
• Total cost: Under $2 (mostly AI tokens)
• Hosting: $0 (free on Cloudflare Pages)
• Load times: Under 1 second (from 3-4 seconds)
• Better content: More comprehensive with current data

Key Takeaway: With AI and the right approach, you can build professional-quality projects as a beginner.

Read the full case study: How to Build AI-Powered Affiliate Websites with Amazon Products

Your Action Plan: Get Started Today

Ready to begin? Here’s your immediate next steps:

Today (30 minutes)

• Create a GitHub account
• Sign up for an AI assistant (Droid CLI or Amp Code Free to start)
• Decide what you want to build (blog, portfolio, app?)
• Install Node.js if you haven’t yet

This Week

• Choose your stack (Astro for sites, TanStack Start for apps)
• Create your first repository
• Initialize your project with AI help
• Create AGENTS.md file
• Build your first page

This Month

• Complete your first feature end-to-end
• Set up documentation folder
• Add 1-2 MCP servers
• Deploy to production
• Set up basic monitoring

Conclusion

Programming with AI in 2025 is fundamentally different from traditional learning. You can start building real projects immediately, learn by doing, and iterate quickly without getting stuck on syntax or obscure errors.

The key principles:

1. Choose the right stack for what you’re building
2. Use version control from day one
3. Pick a good AI assistant (start free, upgrade when serious)
4. Document your project with AGENTS.md and docs/
5. Add MCP servers strategically (don’t overdo it)
6. Build incrementally - small focused features
7. Review and understand every line of code
8. Get second opinions from different AI models
9. Deploy early and often to production
10. Monitor your application to catch issues
11. Iterate carefully without breaking what works

The barrier to entry has never been lower. You don’t need a computer science degree or months of studying. With AI as your coding partner and this guide as your roadmap, you can start building today.

What will you build first?

Related Resources

Continue your learning journey with these guides:

The future of programming is collaborative. You + AI = unstoppable. Start building today! 🚀