Part 0: Intro

Never coded before? Perfect! That's exactly who this is for.

What this tutorial will show you is:

✅ Understand what AI coding assistants are and how they work
✅ Use AI (Claude or ChatGPT) to build a real web application
✅ Deploy Your Own App to a public URL you can share
✅ Know how to repeat this process for your own business ideas

The Big Reveal: We're going to build and deploy a complete app from scratch. No coding experience required - AI does the heavy lifting, you make the decisions.

What to expect: This tutorial will guide you step by step. If something breaks, ask your Ai to troubleshooting.

Tutorial Path

Follow the flow below in full if it is your first time programming. The Requirements Installation Checklist is required before you start. If you know what a IDE is, Programming Languages & Frameworks are, Data bases, feel free to skip to Part 2 after you completed the Requirements Installation Checklist. It's the fastest path. All action prompts are labeled Run this prompt in your AI.

Part 1: Understanding the Landscape (20 min)
Part 2: The App We're Building (10 min)
Part 3: The Art of Prompting AI (25 min)
Part 4: Getting Started with VS Code and Ready to Prompting
Part 5: Planning Phase (Align Before You Build) (10 min)
Part 6: Build Phase (Implement the App) (45 min)
Part 7: Deployment to Production (30 min)
Part 8: What Just Happened? (15 min)
Part 9: Make It Your Own (20 min)
Part 10: Wrap-Up and Next Steps (15 min)
Part 11: Advanced: Level Up

Requirements Installation Checklist

All items will be explained in the section below.

1. VS Code (Code Editor)

Download: https://code.visualstudio.com
Run installer, accept defaults, open it once

2. Python 3.11+ (Programming Language)

Download: https://python.org
⚠️ IMPORTANT: Check "Add Python to PATH" during install
Test: Open Terminal/Command Prompt, type python3 --versionClick to copy (Mac) or python --versionClick to copy (Windows)

3. Git (Version Control)

Download: https://git-scm.com
Run installer, accept defaults
Test: Type git --versionClick to copy in Terminal/Command Prompt

4. Create Free Accounts

GitHub (for storing your code):

Sign up: https://github.com/signup
Pick a username you're comfortable sharing

PythonAnywhere (for hosting your app):

Sign up: https://www.pythonanywhere.com
Click "Create a Beginner account" (free forever)
Your username becomes your URL: yourname.pythonanywhere.comClick to copy

5. Setup the AI Assistant

You'll want a subscription to Claude, ChatGPT, or Gemini. If you are already paying for one of these, use that one.

Install the VS Code Extension

If this is your first time in VS Code, here's how to install an extension:

Open VS Code
Look at the left sidebar and click the Extensions icon (it looks like four squares)
A search box will appear at the top of the Extensions panel
Type the extension name as displayed in the check boxes below
Locate the one with the green check box (this means its the office extension) and click Install

Install ONE of these:

Claude: Search for Claude CodeClick to copy
- Direct link: https://marketplace.visualstudio.com/items?itemName=anthropic.claude-code
ChatGPT: Search for CodexClick to copy
- Direct link: https://marketplace.visualstudio.com/items?itemName=openai.chatgpt
Gemini: Search for Gemini Code AssistClick to copy
- Direct link: https://marketplace.visualstudio.com/items?itemName=Google.geminicodeassist

Note: All three AI assistants have free tiers available, but a paid subscription gives you higher usage limits and better models.

Claude Code - Great for longer context and coding
ChatGPT Codex - Well-known, reliable and context writing
Gemini Code Assist - Google's AI with 180K/month free code completions

Optional Context: If you already know what an IDE is, how AI assistants work, what a database is, how Git/GitHub works, and what a hosting platform does, skip to Part 3.

Part 1: Understanding the Landscape (20 min)

Now that you have your tools installed, let's talk about what each piece actually does and how they work together.

What is an IDE (Integrated Development Environment)?

Simple Definition: An IDE is a fancy text editor specifically designed for writing code.

VS Code (What We're Using):

Free, made by Microsoft
Most popular editor in the world
Works with every programming language
Has built-in terminal
Supports extensions (like Claude, GitHub Copilot)

Alternatives You Might Hear About:

Cursor - VS Code fork with AI built-in
Windsurf - New AI-first IDE
PyCharm - Popular for Python (heavy but powerful)
Sublime Text - Fast and lightweight

What Makes an IDE Better Than Notepad?

Syntax highlighting (colors make code readable)
Auto-completion (suggests as you type)
Debugging tools (find and fix errors)
Terminal access (run commands without leaving editor)
Git integration (version control in the UI)

What is AI and Why Does It Matter for Building Apps?

The AI Companies You Should Know:

OpenAI (ChatGPT)
- Created GPT-4, GPT-4o, and o1
- Most well-known consumer AI
- Great for general questions and brainstorming
- ChatGPT available for coding help
- https://chatgpt.com/
Anthropic (Claude)
- Created Claude (Opus, Sonnet, Haiku models)
- Known for longer context and coding ability
- Claude for coding help
- https://claude.ai/
Google (Gemini)
- Formerly Bard
- Strong multimodal capabilities
- Deep Google integration
- https://gemini.google.com/
X (Grok)
- Created by xAI (Elon Musk's AI company)
- Real-time access to X (Twitter) data
- Known for more conversational, less filtered responses
- https://x.ai
Meta (Llama)
- Open source models
- Free to use and customize
- Popular with developers
- https://www.llama.com/

Why This Matters: These AIs have gone from "chatting" to "doing" – they can now write production code, debug issues, and help deploy applications.

How We'll Use AI to Code Today

We'll use a VS Code extension. It's the simplest and most reliable option for this tutorial.

What it is: A visual panel inside VS Code where you chat with AI and see changes happen in real-time.

How it works:

Open VS Code, start a new project.
Open the Claude, Codex, or Gemini panel in VS Code (click the icon in the sidebar, on the left)
Type what you want in plain English
AI shows you what it's going to do
You click "Accept" or "Reject"
Changes appear in your files automatically

Programming Languages & Frameworks

The Landscape:

Python + Django ⭐ (What We're Using)
- Language: Python (easy to read, popular for data/AI)
- Framework: Django (full-featured web framework)
- Philosophy: "Batteries included" – everything you need built-in
- Best for: MVPs, internal tools, apps that will need user auth
- Used by: Instagram, Spotify, Dropbox
Ruby + Rails
- Language: Ruby (elegant and readable)
- Framework: Ruby on Rails
- Philosophy: "Convention over configuration" – smart defaults
- Best for: Rapid prototyping, startups
- Used by: GitHub, Shopify, Basecamp
JavaScript + Node.js Stack
- Language: JavaScript (same language as web browsers)
- Frameworks: Express, Nest, Next.js
- Philosophy: Same language front and back-end
- Best for: Real-time apps, single-page applications
- Used by: Netflix, PayPal, LinkedIn

The Real Reason: If your Investment Board works and you want to add user authentication, team permissions, deal room access – Django makes that pivot easy.

What is a Database?

Simple Definition: A database is like a smart filing cabinet where your app stores and organizes information.

Think of it like this:

Your code is the storefront of a shop
The database is the warehouse in the back
When a customer (user) asks for something, your code goes to the warehouse (database) to get it

Common Databases:

SQLite ⭐ (for simple apps)
- Type: File-based database
- Think of it as: A single Excel file that your app uses
- Why it's great for learning:
  - No setup required - it just works
  - Stored as a simple file (db.sqlite3)
  - Perfect for small to medium apps
  - Easy to understand and debug
- Used by: Firefox bookmarks, iPhone apps, small websites
PostgreSQL
- Type: Server-based database
- Think of it as: A dedicated warehouse with its own address
- Why professionals use it:
  - Handles millions of records efficiently
  - Multiple apps can share the same database
  - More features for complex queries
  - Better for teams and large projects
- Used by: Instagram, Spotify, Reddit
MySQL
- Type: Server-based database
- Think of it as: Similar to PostgreSQL, slightly different features
- Why it's popular:
  - Been around forever (reliable)
  - Easy to find hosting
  - Good for content-heavy sites
- Used by: WordPress, Facebook (historically)

When you'd upgrade to PostgreSQL/MySQL:

More than 100,000 records
Multiple people writing to database simultaneously
Need advanced features (full-text search, JSON columns)
Running on platforms that don't support SQLite

SQLite is perfect. One less thing to worry about.

What is Version Control (Git/GitHub)?

Simple Definition: Version control is like "Track Changes" in Google Docs, but for your entire project.

Think of it like this:

Git = Time machine for your code (save points you can go back to)
GitHub = Google Drive for code (cloud storage + sharing)

What Git Does:

Imagine you're writing a novel:

Git keeps a copy of every draft you've ever saved
If Chapter 5 breaks, you can roll back to yesterday's version
You can try experimental ideas on a "branch" without ruining the main story
You can see exactly what changed between drafts

What GitHub Does:

GitHub takes your local Git history and:

Stores it in the cloud - your code is backed up
Lets you share - teammates can see your work
Enables deployment - platforms like PythonAnywhere can pull your code automatically
Tracks collaboration - who changed what and when

The Basic Git Workflow :

1. git init           → "Start tracking this project"
2. git add .          → "Mark all files to be saved"
3. git commit         → "Save a snapshot with a message"
4. git push           → "Upload to GitHub (cloud backup)"
Click to copy

Why This Matters:

Without Git/GitHub:

❌ Code only on your laptop (no backup)
❌ Can't deploy to the internet easily
❌ Hard to share or collaborate
❌ Can't undo mistakes easily

With Git/GitHub:

✅ Code backed up in the cloud
✅ Deployment platforms (like PythonAnywhere) can grab your code
✅ Share a link to your code
✅ Roll back if something breaks

What is a Hosting Platform?

Simple Definition: A hosting platform is where your app lives on the internet so other people can use it.

Think of it like this:

Your laptop = Your home office
Hosting platform = Renting office space downtown where customers can visit

When you run your app locally:

Only works on your computer
URL is localhost:8000Click to copy (only you can see this)
Stops working when you close your laptop
Can't share with teammates or clients

When you deploy to a hosting platform:

Works from anywhere in the world
URL is yourname.pythonanywhere.comClick to copy (public link)
Runs 24/7 even when your laptop is off
Anyone can visit and use it

Popular Hosting Platforms:

PythonAnywhere ⭐ (What We're Using Tonight)
- Best for: Django apps, beginners, learning
- Free tier: Forever free with one app
- Why we chose it:
  - Built specifically for Python/Django
  - SQLite works perfectly (no database setup)
  - Free forever (no credit card required)
  - Easy web-based interface
- Perfect for: Workshops, demos, small projects

Why PythonAnywhere?

Factor	PythonAnywhere	Others
Setup time	5 minutes	15-30 minutes
Works with SQLite	✅ Yes	❌ Most don't
Free forever	✅ Yes	⚠️ Limited or paid
Beginner-friendly	✅ Very	⚠️ Medium
Your app stays online	✅ 24/7	⚠️ Some sleep

What Happens When You Deploy:

You push your code to GitHub (cloud storage for code)
We then pull from GitHub to PythonAnywhere
PythonAnywhere runs your Django app on their servers
You get a public URL: yourname.pythonanywhere.comClick to copy
Anyone can visit that URL and use your app

The Tools We Use

Tool	Purpose	What It Does
AI Assistant (Claude, ChatGPT, or Gemini)	AI coding assistant	Writes code, creates files, runs commands
VS Code	Code editor	Where you view and edit files
Python + Django	Programming language + framework	What the app is built with
SQLite	Database	Where data is stored (simple, no setup)
Git + GitHub	Version control	Saves history, enables cloud storage
PythonAnywhere	Hosting platform	Makes your app publicly accessible

The Workflow:

You (business idea)
    ↓
AI assistant (generates code)
    ↓
VS Code (you review it)
    ↓
Git/GitHub (save and push to cloud)
    ↓
PythonAnywhere (pull from GitHub and deploy)
    ↓
Live URL (share with team)
Click to copy

Part 2: The App We're Building (10 min)

A Simple Introducing Investment Opportunities Board

The Business Need: You're evaluating multiple investment opportunities – real estate deals, stock picks, business acquisitions, private equity. Right now:

Opportunities tracked in spreadsheets or email
No clear workflow from sourcing to decision
Hard to see pipeline value or compare opportunities
No record of why deals passed or moved forward
Can't easily see what's stuck in due diligence

The Solution: A Kanban-style board that tracks investment opportunities through evaluation stages with key metrics and decision tracking.

Core Features (In Scope):

✅ Add investment opportunity (name, type, amount, ROI estimate)
✅ Assign stage: Sourced → Screening → Due Diligence → Committee Review → Decision
✅ Move opportunities between stages
✅ Mark final decision: Invest / Pass / Hold
✅ View by stage or filter by type
✅ See total capital in pipeline
✅ Add notes/thesis for each opportunity

Explicitly Out of Scope (For Now):

❌ No user accounts or login (single-user MVP)
❌ No file uploads or document storage
❌ No notifications or alerts
❌ No external data integrations (stock APIs, etc.)
❌ No complex financial modeling

Why These Constraints?

Gets us from idea → deployed in 2 hours
Forces clarity on core workflow
Can add complexity later once we validate the concept
AI works better with clear boundaries

What Success Looks Like: By end of today, you'll have a URL like yourname.pythonanywhere.comClick to copy where you can:

Add "Industrial Warehouse - Denver" → Type: Real Estate → Amount: $2.5M → ROI: 12%
Move it from "Sourced" to "Due Diligence"
See total pipeline value across all stages
Mark it as "Invest" with a note on why
Filter to see only Real Estate opportunities

Part 3: The Art of Prompting AI (25 min)

Before we write any code, we need to learn how to communicate with the AI. A clear, detailed prompt gives it the context and constraints it needs to build the right thing the first time.

Why AI Needs Good Prompts: The New Hire Analogy

Here's the truth that most people miss: AI is incredibly powerful, but it's not a mind reader.

Imagine this scenario:

You hire a smart developer. On their first day, you walk up and say:

"Build me an investment tracking app."

Then you walk away.

What happens?

They have NO idea what features you want
They don't know your industry (real estate? stocks? both?)
They don't know how YOU think about investments
They'll make assumptions (probably wrong ones)
They'll build SOMETHING, but it won't be what you wanted

This is exactly what happens when you give AI a vague prompt.

The mindset shift:

❌ Wrong: "AI should just figure it out" ✅ Right: "I need to onboard AI like a new team member"

❌ Wrong: "This AI is dumb, it didn't understand me" ✅ Right: "I didn't give it enough context to succeed"

❌ Wrong: "Just build me a Django app" ✅ Right: "Let me explain the problem, constraints, and what success looks like"

The Right Way: Structured Prompting

Think of prompting as a conversation, not a command.

Use this order:

What you want (the simple goal in one sentence)
What it must do (the must-have features)
What it should NOT do (the off-limits list)
How it should feel (simple, clean, fast, etc.)
Ask AI to repeat back (so you can confirm)

Why this works: clear goals and limits stop the AI from guessing.

Simple prompt example:

Do not code anything, we are in the planing phase. I want a simple investment board to track deals using Django programming language. It should let me add an opportunity, move it through stages, and see totals. I do not want login, file uploads, or notifications. Keep it simple and clean. Do not try to please me. If something I said is unclear, incomplete, or you need more detial,tell me and ask questions. Challenge my assumptions before you write any code. Provide me a break down of the app with workflows and features. We will continue to talk back and forth until we both agree the app is ready for developement.
Click to copy

Prompting Tips

These are the high-leverage prompts that prevent confusion and bad assumptions. They should be incorporated into your prompt during the planning process before you start coding.

Ask the AI to challenge you:

Do not try to please me. If something I said is unclear, incomplete, or risky,tell me and ask questions. Challenge my assumptions before you write any code.
Click to copy

Ask the AI to interview you:

Before you start coding, ask me clarifying questions about features, users, data fields, edge cases, and constraints. Do not guess.
Click to copy

Ask for beginner-friendly language (if needed):

Explain things to me like I have never coded before. Avoid jargon.
Click to copy

Ask for a read-back summary:

Summarize what you think I want to build in 5-7 bullets, then ask me to confirm or correct anything before you implement.
Click to copy

Ask for a short plan:

Give me a simple, step-by-step plan before you start coding.
Click to copy

Ask for tradeoffs when choices exist:

If there are multiple ways to build something, show me 2-3 options with pros/cons and your recommendation.
Click to copy

Part 4: Getting Started with VS Code and Ready to Prompt

Get the prompt window open

On your desktop create a folder and name it test-financial-appClick to copy
Now open VS Code
In the top menu, click File → Open Folder. Navigate to the Desktop folder named test-financial-appClick to copy, select it, and click Open.
- For every app you are creating, you must create a new folder. All code for that app will live in that folder. Mixing 2 apps in 1 folder will brake the app.
If Copilot chat automatically opened, close the window as we will not be using Copilot.
On the left-hand menu bar, you should see the logo for the AI extension you installed. Click it.
If this is your first time opening the extension, the AI agent will ask you to log in.
If you have already logged in, type testClick to copy in the prompt box to confirm the AI responds.
Congratulations, you are ready to start prompting!

Get the terminal window open

The terminal is a simple text window where you run commands to set up and test your app. It's not hacking. It's just how you talk to your computer. The AI may ask you to run commands from time to time. This is where you run them if the AI can't.

In VS Code, click Terminal in the top menu bar
Click New Terminal
A panel opens at the bottom of VS Code – that's your terminal
You'll see a blinking cursor and a prompt (often a $Click to copy or >Click to copy). That's normal.
Leave it open. You'll use it to run commands like starting the app

Part 5: Planning Phase (Align Before You Build) (10 min)

What this is: A short back-and-forth with AI to confirm you both agree on the app before any code is written.

Copy and paste the prompt below into the AI panel in VS Code.

Do not write any code yet. I'm in the planning phase. I want a simple investment board to track deals using Django programming language. It should let me add an opportunity, move it through stages, and see totals. I do not want login, file uploads, or notifications. Keep it simple and clean. Do not try to please me. If anything is unclear or missing, ask me questions and challenge my assumptions. First, summarize what you think I want to build, then give me a simple breakdown of features and user workflow. We'll keep refining until we agree it's ready to build.
Click to copy

Continue the conversation with the AI agent. Ask for ideas and clarifications until the plan feels solid.

If you like where your planning conversation ended, skip the optional prompt below and go to Part 6, Step 1. If you did NOT do your own planning, use the optional prompt below, then go to Part 6, Step 1. Do NOT run both prompts.

Optional Course Planning Prompt (Only use this if you didn't already do your own planning) (5 min)

Run this prompt in your AI:

Do not write any code yet. I'm in the planning phase. I want to build an investment tracking app called Investment Opportunities Board
to manage deal flow from sourcing through decision.

## In-Scope Features
- Add opportunity (name, type, amount, ROI%, risk level, thesis/notes)
- Types: Real Estate, Stocks, Business Acquisition, Private Equity
- Stages: Sourced → Screening → Due Diligence → Committee Review → Decision
- Move opportunities between stages
- Mark final decision: Invest / Pass / Hold
- View by stage (Kanban-style) or filter by type
- See total capital in pipeline per stage
- Add/edit notes on each opportunity

## Out of Scope
- No user accounts or authentication
- No file uploads or document storage
- No notifications
- No external APIs (stock data, etc.)
- No complex financial calculations beyond simple totals

## Technical Stack
- Framework: Django
- Database: SQLite
- Templates: Django templates + HTMX for smooth stage transitions
- Styling: Tailwind CSS via CDN

Please help me:
1. Define the Django data models needed (Opportunity model)
2. Describe the page structure (Kanban board view, detail view)
3. Outline the user workflow
4. Create an implementation plan

Keep it simple and readable. Optimize for shipping quickly.
Click to copy

If the AI asks questions, answer them. Then continue to Part 6, Step 1.

Part 6: Build Phase (Implement the App) (45 min)

Use AI for command blocks only. Any clicks in the browser or VS Code are done by you.

Step 1: Project Initialization (8 min)

What we're doing: Now we actually create the project files.

Run this prompt in your AI:

Create a new Django project called investment_board with:
- A Django app called 'opportunities'
- SQLite database (default)
- requirements.txt with Django and other dependencies
- Basic settings configured
- Ready for server-rendered templates
- Include Tailwind CSS via CDN in base template
Click to copy

Press Enter and wait for AI to create the files. If your AI asks for confirmation, approve the changes.

Watch AI work:

Create project structure
Generate manage.pyClick to copy, settings.pyClick to copy, etc.
Set up the opportunitiesClick to copy app
Create initial templates folder
Write requirements.txtClick to copy

What you'll see:

investment_board/
├── investment_board/    # Project settings
│   ├── settings.py
│   ├── urls.py
│   ├── wsgi.py
├── opportunities/       # Our app
│   ├── models.py
│   ├── views.py
│   ├── urls.py
│   ├── templates/
├── manage.py            # Django management tool
├── requirements.txt     # Dependencies
Click to copy

Install dependencies:

Ask your AI to run the commands below. If it can't, run them manually in your terminal.

On Mac:

Press Command + SpaceClick to copy
Type TerminalClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

cd Desktop/test-financial-app
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
Click to copy

On Windows:

Press the WindowsClick to copy key
Type cmdClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

cd Desktop\test-financial-app
python -m venv venv
venv\Scripts\activate
pip install -r requirements.txt
Click to copy

What this does: Creates a safe "sandbox" for your project and installs Django. You'll see (venv)Click to copy appear in your terminal - that's good!

Checkpoint: ✅ Project structure created

Step 2: Define Data Models (10 min)

What we're doing: Creating the database structure for investment opportunities.

Run this prompt in your AI:

Create a Django model for Investment Opportunities with:
- name (CharField, max 200 characters)
- opportunity_type (CharField with choices: Real Estate, Stocks, Business Acquisition, Private Equity)
- amount (DecimalField for investment amount)
- roi_estimate (DecimalField for ROI percentage)
- risk_level (CharField with choices: Low, Medium, High)
- stage (CharField with choices: Sourced, Screening, Due Diligence, Committee Review, Decision)
- decision (CharField with choices: Pending, Invest, Pass, Hold)
- thesis_notes (TextField for investment thesis and notes)
- created_at (DateTimeField, auto-populated)
- updated_at (DateTimeField, auto-updated)

Register it in admin.py so I can manage opportunities.
Include __str__ method that shows name and type.
Click to copy

What AI creates in opportunities/models.pyClick to copy:

from django.db import models

class Opportunity(models.Model):
    TYPE_CHOICES = [
        ('real_estate', 'Real Estate'),
        ('stocks', 'Stocks'),
        ('business', 'Business Acquisition'),
        ('private_equity', 'Private Equity'),
    ]

    STAGE_CHOICES = [
        ('sourced', 'Sourced'),
        ('screening', 'Screening'),
        ('due_diligence', 'Due Diligence'),
        ('committee', 'Committee Review'),
        ('decision', 'Decision'),
    ]

    RISK_CHOICES = [
        ('low', 'Low'),
        ('medium', 'Medium'),
        ('high', 'High'),
    ]

    DECISION_CHOICES = [
        ('pending', 'Pending'),
        ('invest', 'Invest'),
        ('pass', 'Pass'),
        ('hold', 'Hold'),
    ]

    name = models.CharField(max_length=200)
    opportunity_type = models.CharField(max_length=20, choices=TYPE_CHOICES)
    amount = models.DecimalField(max_digits=12, decimal_places=2)
    roi_estimate = models.DecimalField(max_digits=5, decimal_places=2)
    risk_level = models.CharField(max_length=10, choices=RISK_CHOICES)
    stage = models.CharField(max_length=20, choices=STAGE_CHOICES, default='sourced')
    decision = models.CharField(max_length=10, choices=DECISION_CHOICES, default='pending')
    thesis_notes = models.TextField(blank=True)
    created_at = models.DateTimeField(auto_now_add=True)
    updated_at = models.DateTimeField(auto_now=True)

    class Meta:
        ordering = ['-created_at']
        verbose_name_plural = 'Opportunities'

    def __str__(self):
        return f"{self.name} ({self.get_opportunity_type_display()})"
Click to copy

Why Django is great here:

Multiple CHOICESClick to copy create dropdowns automatically
DecimalFieldClick to copy handles money and percentages precisely
auto_nowClick to copy tracks when opportunities are updated
get_opportunity_type_display()Click to copy shows human-readable labels
Admin interface is automatically generated

Run migrations:

Ask your AI to run the commands below. If it can't, run them manually in your terminal.

On Mac:

Press Command + SpaceClick to copy
Type TerminalClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

python3 manage.py makemigrations
python3 manage.py migrate
Click to copy

On Windows:

Press the WindowsClick to copy key
Type cmdClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

python manage.py makemigrations
python manage.py migrate
Click to copy

What this does: Tells Django to create the database tables. You'll see some green text - that's good!

Create admin user:

Use the same terminal window you already opened above. Make sure you are in the test-financial-appClick to copy folder.

Ask your AI to run the commands below. If it can't, run them manually in your terminal.

On Mac:

Press Command + SpaceClick to copy
Type TerminalClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

On Windows:

Press the WindowsClick to copy key
Type cmdClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

# Mac users: use python3
# Windows users: use python
python manage.py createsuperuser
Click to copy

Then answer the prompts:

Username: Just type adminClick to copy and press Enter
Email: Type your email (or make one up) and press Enter
Password: Type a password (it won't show on screen - that's normal!)
Password again: Type the same password

Don't forget your password! Write it down if needed.

Test the admin panel:

Use the same terminal window you already opened above. Make sure you are in the test-financial-appClick to copy folder.

Ask your AI to run the commands below. If it can't, run them manually in your terminal.

On Mac:

Press Command + SpaceClick to copy
Type TerminalClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

On Windows:

Press the WindowsClick to copy key
Type cmdClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

# Mac: python3 manage.py runserver
# Windows: python manage.py runserver
python manage.py runserver
Click to copy

You'll see: Some text ending with "Starting development server at http://127.0.0.1:8000/"

Now: Open your web browser and go to http://localhost:8000/adminClick to copy

Try this now: Add a sample opportunity through the admin panel (3 min)

Example to add:

Name: "Industrial Warehouse - Denver"
Type: Real Estate
Amount: 2500000
ROI: 12
Risk: Medium

Checkpoint: ✅ Data model created and working

Step 3: Build the Board View (12 min)

What we're doing: Creating the main Kanban-style board page that shows opportunities by stage.

Run this prompt in your AI:

Create a Django view and template for the Investment Board homepage that:
- Shows opportunities grouped by stage (Sourced, Screening, Due Diligence, Committee Review, Decision)
- Display as Kanban board with columns for each stage
- Show opportunity name, type, amount, ROI%, risk level in each card
- Filter buttons by type (All, Real Estate, Stocks, Business, Private Equity)
- Form to add new opportunity
- Show total capital in pipeline at the top
- Uses Tailwind CSS for professional styling with cards and columns
- Make it look like an investment dashboard
Click to copy

Step 4: Add HTMX for Smooth Stage Transitions (10 min)

What is HTMX?

Don't let the name scare you! HTMX is just a tool that makes your app feel modern without you needing to know JavaScript.

In plain English:

Without HTMX: Click button → whole page refreshes (like old websites)
With HTMX: Click button → just that part updates smoothly (like modern apps)
You don't need to understand how it works - AI will add it for us

What we're doing: Making stage transitions smooth - move opportunities between stages without the page refreshing.

Run this prompt in your AI:

Add HTMX to enable smooth stage transitions:
- Include HTMX via CDN in base template
- Create a move_stage view that updates opportunity stage
- Add next/previous stage buttons on each opportunity card
- Update the board without full page reload
- Show visual feedback during transition
- Update total pipeline value if needed
Click to copy

Step 5: Polish and Test (5 min)

Final touches:

Run this prompt in your AI:

Review the app and:
- Make sure all pages look consistent
- Add empty states (what shows when there are no opportunities?)
- Add basic validation (don't allow empty submissions)
- Make sure type filter shows active state
- Test that everything works together
Click to copy

Testing checklist:

Try creating opportunities
Try filtering by type
Try moving stages
Try breaking it (empty fields, huge numbers, etc.)

Fix any issues with AI:

Just paste the error message to your AI:

I'm getting this error: [paste error]. Fix it.
Click to copy

Checkpoint: ✅ App is fully functional locally

Part 7: Deployment to Production (30 min)

Step 1: Prepare for Deployment (5 min)

What we're doing: Making sure our code is ready to push to GitHub.

Run this prompt in your AI:

Prepare this Django app for deployment:
- Make sure requirements.txt is complete
- Create .gitignore for Python/Django projects
- Update settings.py to allow PythonAnywhere domain
Click to copy

Step 2: Push to GitHub (8 min)

What we're doing: Uploading your code to GitHub (think of it as Google Drive for code).

Why? PythonAnywhere needs to grab your code from somewhere to deploy it. GitHub is that somewhere!

Step 1: Tell Git about your project

Ask your AI to run the commands below. If it can't, run them manually in your terminal.

On Mac:

Press Command + SpaceClick to copy
Type TerminalClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

On Windows:

Press the WindowsClick to copy key
Type cmdClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

git init
git add .
git commit -m "Initial commit: Investment Opportunities Board MVP"
Click to copy

What this does:

git initClick to copy = "Hey Git, track this project"
git add .Click to copy = "Include all my files"
git commitClick to copy = "Save a snapshot with this message"

You might see some text scroll by - that's normal!

Step 2: Create a GitHub Repository

Easiest Way (GitHub Website):

Go to: https://github.com/new
Log in if prompted
Repository name: Type investment-boardClick to copy
Keep it Public (already selected)
DO NOT check "Initialize with README"
Click "Create repository"

You'll see a page with commands. IGNORE THEM! Do this instead:

Step 3: Connect your code to GitHub

In your terminal, type these commands one at a time:

Ask your AI to run the commands below. If it can't, run them manually in your terminal.

On Mac:

Press Command + SpaceClick to copy
Type TerminalClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

On Windows:

Press the WindowsClick to copy key
Type cmdClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

# Replace YOUR_USERNAME with your actual GitHub username
git remote add origin https://github.com/YOUR_USERNAME/investment-board.git
git branch -M main
git push -u origin main
Click to copy

Example: If your GitHub username is jsmithClick to copy, the first command would be: Ask your AI to run the commands below. If it can't, run them manually in your terminal.

On Mac:

Press Command + SpaceClick to copy
Type TerminalClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

On Windows:

Press the WindowsClick to copy key
Type cmdClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

git remote add origin https://github.com/jsmith/investment-board.git
Click to copy

What to expect:

You might be asked to log in to GitHub - do it!
You'll see text scrolling - that's your code uploading
When it's done, you'll see "Branch 'main' set up to track..."

Step 4: Verify it worked

Go to https://github.com/YOUR_USERNAME/investment-boardClick to copy (use YOUR username)

You should see: All your project files listed!

Checkpoint: ✅ Code is on GitHub

Step 3: Deploy on PythonAnywhere (15 min)

What we're doing: Taking our app and putting it on the internet so anyone can access it!

Step-by-Step Deployment:

All commands in this section run inside PythonAnywhere, not on your computer.

1. Create PythonAnywhere Account

Open https://www.pythonanywhere.com in your browser
Click "Start running Python online" or "Pricing & signup"
Click "Create a Beginner account" (the free one)
Fill in username, email, password
Choose a good username - this will be in your URL: username.pythonanywhere.comClick to copy

2. Open a Bash Console

After logging in, click "Consoles" tab at the top
Click "Bash" to open a new console
You'll see a terminal (looks like Mac/Windows terminal)

This is where we'll set up your app! This is a separate terminal inside PythonAnywhere (not your computer).

3. Clone Your Code from GitHub

You are now in PythonAnywhere, not on your computer.

In the PythonAnywhere Bash console, run:

git clone https://github.com/YOUR_USERNAME/investment-board.git
cd investment-board
Click to copy

Replace YOUR_USERNAME with your actual GitHub username!

What this does: Downloads your code from GitHub onto PythonAnywhere's servers.

4. Create a Virtual Environment (PythonAnywhere)

Ask your AI to run the commands below in your PythonAnywhere Bash console. If it can't, run them manually in the PythonAnywhere Bash console.

python3.10 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
Click to copy

If python3.10Click to copy fails, try python3.11Click to copy.

5. Run Migrations (PythonAnywhere)

Ask your AI to run the commands below in your PythonAnywhere Bash console. If it can't, run them manually in the PythonAnywhere Bash console.

python manage.py migrate
python manage.py collectstatic --noinput
Click to copy

What this does: Sets up your SQLite database on PythonAnywhere.

6. Create a Superuser

Ask your AI to run the commands below in your PythonAnywhere Bash console. If it can't, run them manually in the PythonAnywhere Bash console.

On Mac:

Press Command + SpaceClick to copy
Type TerminalClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

On Windows:

Press the WindowsClick to copy key
Type cmdClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

python manage.py createsuperuser
Click to copy

Fill in:

Username: adminClick to copy
Email: (your email)
Password: (same as you used locally)

7. Set Up the Web App

Now go back to the PythonAnywhere dashboard:

Click "Web" tab at the top
Click "Add a new web app"
Click "Next" (accept the domain yourusername.pythonanywhere.comClick to copy)
Select "Manual configuration"
Select "Python 3.10"
Click "Next"

8. Configure the Web App

You'll see a configuration page. Scroll down and fill in:

Code section:

Source code: /home/yourusername/investment-boardClick to copy
Working directory: /home/yourusername/investment-boardClick to copy

Virtualenv section:

Virtualenv path: /home/yourusername/investment-board/venvClick to copy

WSGI configuration file:

Click the link that says /var/www/yourusername_pythonanywhere_com_wsgi.pyClick to copy
Delete everything in the file
Paste this code:

import os
import sys

# Add your project directory to the sys.path
project_home = '/home/yourusername/investment-board'
if project_home not in sys.path:
    sys.path.insert(0, project_home)

# Set environment variable to tell Django where settings are
os.environ['DJANGO_SETTINGS_MODULE'] = 'investment_board.settings'

# Import Django's WSGI handler
from django.core.wsgi import get_wsgi_application
application = get_wsgi_application()
Click to copy

Replace yourusernameClick to copy with your actual PythonAnywhere username! (appears in 2 places)

Click "Save" (top right)

9. Reload Your Web App

Go back to the "Web" tab
Scroll to the top
Click the big green "Reload yourusername.pythonanywhere.com" button

THE BIG MOMENT

YOUR APP IS LIVE ON THE INTERNET!

Your app is now at:

https://yourusername.pythonanywhere.com
Click to copy

Click the link at the top of the Web tab! Or type it into your browser.

🥳 CELEBRATE! Click your URL and see it live.

Now let's test it together:

Share your URL in the group chat - We all want to see what you built!
Add an investment opportunity - Try it from your phone
Move it between stages - Click the Next/Previous buttons
Filter by type - See only Real Estate or only Stocks
Check the pipeline value - See the total at the top

Here's the crazy part: Anyone with this URL can now use your app. Your friend across the country could add an opportunity right now.

What you just accomplished:

✅ Built a database-backed web application ✅ Made it interactive with HTMX ✅ Deployed it to production ✅ Got a public URL anyone can visit

And you did it in under 2 hours.

This is REAL software. Not a tutorial. Not a toy. A real, working application on the internet.

Checkpoint: ✅ Investment Opportunities Board is live on the internet

Screenshot time! 📸 Take a screenshot of your app and post it!

Step 4: Updating Your App Later (5 min)

What we're covering: How to update your live app when you make changes.

The scenario: You're home after the tutorial. You add a new feature locally and want to deploy it.

The 3-Command Update Process:

Whenever you make changes and want to update your live site:

1. Push your changes to GitHub (on your laptop): Ask your AI to run the commands below. If it can't, run them manually in your terminal.

On Mac:

Press Command + SpaceClick to copy
Type TerminalClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

On Windows:

Press the WindowsClick to copy key
Type cmdClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

git add .
git commit -m "Added new feature"
git push
Click to copy

2. Log into PythonAnywhere (web browser):

Go to https://www.pythonanywhere.com
Click "Consoles" → "Bash"

3. Run these 3 commands (in PythonAnywhere console): Ask your AI to run the commands below. If it can't, run them manually in your terminal.

On Mac:

Press Command + SpaceClick to copy
Type TerminalClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

On Windows:

Press the WindowsClick to copy key
Type cmdClick to copy
Press EnterClick to copy
Type the commands and press EnterClick to copy after each line

cd ~/investment-board
git pull
touch /var/www/yourusername_pythonanywhere_com_wsgi.py
Click to copy

Replace yourusernameClick to copy with your actual username!

That's it! Your site is updated.

What each command does:

cd ~/investment-boardClick to copy → Go to your project folder
git pullClick to copy → Download latest code from GitHub
touch /var/www/yourusername_pythonanywhere_com_wsgi.pyClick to copy → Tell PythonAnywhere to reload

Pro tip: Create an update script

To make it even easier, create a file called update.shClick to copy:

In your PythonAnywhere Bash console:

cd ~/investment-board
nano update.sh
Click to copy

#!/bin/bash
cd ~/investment-board
git pull
touch /var/www/yourusername_pythonanywhere_com_wsgi.py
echo "✅ Site updated!"
Click to copy

Press Ctrl+OClick to copy to save, EnterClick to copy, then Ctrl+XClick to copy to exit.

Make it executable: Ask your AI to run the commands below. If it can't, run them manually in your terminal.

chmod +x update.sh
Click to copy

Now updating is ONE command: Ask your AI to run the commands below. If it can't, run them manually in your terminal.

bash ~/investment-board/update.sh
Click to copy

Checkpoint: ✅ You know how to update your app forever

Part 8: What Just Happened? (15 min)

The Honest Reality Check

What AI is Great At:

✅ Boilerplate and setup (90% of project initialization)
✅ Following established patterns (Django conventions)
✅ Explaining code (better than documentation sometimes)
✅ Debugging common errors
✅ Generating test data
✅ Writing repetitive code

Where You Still Need Skills:

⚠️ Business logic (what makes your app unique)
⚠️ Edge cases and validation
⚠️ Performance optimization
⚠️ Security considerations
⚠️ Knowing when something is wrong
⚠️ Asking the right questions

The New Skill Set:

Clear communication (describing intent precisely)
Critical thinking (does this make sense?)
Product sense (what should we build?)
Testing mindset (how do I break this?)
Pattern recognition (spotting AI mistakes)

You're not replacing developers. You're becoming a different kind of builder.

Part 9: Make It Your Own (20 min)

Optional Challenges

Option 1: Add a Feature to Investment Board

Pick one:

Add ability to mark decision (Invest/Pass/Hold) and reason
Show days in current stage for each opportunity
Create a dashboard with total invested vs. passed
Add expected close date and highlight overdue opportunities
Add ability to archive completed opportunities
Show ROI comparison across all opportunities

How to do it:

Just tell your AI:

Add [feature description] to Investment Opportunities Board.
Keep it simple and consistent with existing code.
Click to copy

Option 2: Start Your Own App

Brainstorm ideas:

Expense tracker for team
Simple inventory system
Meeting notes repository
Client status board
Project milestone tracker

Use the same process:

Describe it to your AI
Define core features (max 5)
Explicit non-goals
Let AI structure it
Build it step by step

Part 10: Wrap-Up and Next Steps (15 min)

What You Accomplished Tonight

You:

✅ Learned about AI coding tools and how they work
✅ Understood programming languages and frameworks
✅ Built a full Django web application
✅ Deployed it to a production URL
✅ Have a working example to reference
✅ Know the process to repeat this

That's legitimately impressive.

Most people talk about "learning to code someday." You shipped a product today.

Optional Reflection

Quick recap of what you did:

Described a business need in plain English
- Not technical specs
- Just "what" and "why"
AI translated that into structure
- Data models
- Page layouts
- User workflows
AI wrote the implementation
- Django models, views, templates
- HTMX interactions
- Deployment configs
You reviewed and tested
- Made sure it matched your intent
- Fixed issues as they came up
- Understood what was happening
Deployed to production
- Not localhost anymore
- A real URL anyone can visit
- Ready to share

Time breakdown:

Traditional way: 40-80 hours for a developer
This tutorial: a few hours with AI assistance
Speed-up: 20-40x faster

Part 11: Advanced: Level Up

If you want to go deeper after the tutorial, try one of these advanced paths.

Try: AI Writes a Build Instruction Summary

Have AI turn your planning conversation into a single build instruction you can reuse.

Difficulty: ⭐⭐⭐⭐⭐ Power Level: ⭐⭐⭐⭐⭐

Prompt to use:

Summarize our plan into a single build instruction I can reuse.
Include the tech stack, data model, key pages, and workflow.
Do not write code yet.
Click to copy

Try: CLI Workflow (Terminal) CLI stands for command-line interface. It means running prompts and commands by typing in a terminal.

Difficulty: ⭐⭐⭐⭐⭐ Power Level: ⭐⭐⭐⭐⭐

Try: MD File Planning (4-File System)

Ask AI to create structured MD files during planning, then build from those specs.

Difficulty: ⭐⭐⭐⭐⭐ Power Level: ⭐⭐⭐⭐⭐

4-File Documentation System (Optional)

Take it to the next level after the tutorial. This is the spec-first workflow we skipped today.

The Essential 4-File Documentation Stack

These aren't toy examples - these are production-grade specifications that seasoned architects actually use.

1. product-spec.mdClick to copy – The "What" & "Why"

Purpose: Business requirements that a non-technical person can read and understand.

What's inside:

Problem statement (current state + impact on business)
Target users (who will use this and how)
Core features (must-have vs. nice-to-have)
Explicitly out of scope (prevents feature creep)
Success criteria (measurable outcomes)
User stories (5 key scenarios)
Constraints (timeline, budget, technical skill level)

2. technical-spec.mdClick to copy – The "How"

Purpose: Documents architecture decisions, tech stack choices, and technical constraints.

What's inside:

Complete tech stack with "why we chose this" explanations
System design diagrams (request flow, file structure)
Key technical decisions (Django vs Flask, SQLite vs Postgres, HTMX vs React)
Non-functional requirements (scalability, security, performance)
Deployment strategy (local development + production)
Testing approach

3. data-schema.mdClick to copy – The "What We Store"

Purpose: Complete database blueprint that AI can implement directly.

What's inside:

Field-by-field specification table (types, constraints, descriptions, examples)
Choice field mappings (internal values → display labels)
Explicit validation rules ("amount must be positive", "ROI between 0-999.99%")
Database indexes with reasoning
Business logic (stage transitions, auto-calculations)
Sample data (3 realistic examples for testing)
Complete Django model code (ready to implement)

4. user-flows.mdClick to copy – The "How Users Interact"

Purpose: Complete UX specification documenting every user interaction.

What's inside:

7 complete user flows (view, filter, add, move forward, move backward, mark decision, details)
Step-by-step interactions for each flow
Visual specifications (card layouts in ASCII art, color codes, badge styles)
Edge cases (no data, network errors, rapid clicks)
Error states (validation errors, network failures, empty results)
Loading states (skeleton screens, fade transitions)
Form validation table (field → validation → error message)
Modal UI mockups (in ASCII art)
Responsive behavior (desktop/tablet/mobile breakpoints)

How to use these 4 files with AI:

Step 1: Create the documentation first (before any code)

I want to build an investment tracking app. Before we write any code,
let's create documentation files:

1. Create product-spec.md that explains:
   - The business problem
   - Who the users are
   - What features we need (in-scope and out-of-scope)
   - Success criteria

2. Create technical-spec.md that documents:
   - Tech stack decisions and why
   - Architecture overview
   - Deployment strategy
   - Security and performance considerations

3. Create data-schema.md with:
   - Complete database models
   - Field types, constraints, and validation rules
   - Relationships and indexes
   - Sample data

4. Create user-flows.md detailing:
   - Every user interaction
   - Page layouts and UI components
   - Edge cases and error states
   - Visual design specifications
Click to copy

Step 2: Review and refine the docs together

"Let's review the product-spec.md together. Read it back to me and
ask clarifying questions about anything that's unclear or missing."
Click to copy

Step 3: Reference docs when building

"Now let's start building. Please:
1. Read all 4 documentation files
2. Confirm you understand the requirements
3. Ask any questions before writing code
4. Generate the Django models based on data-schema.md"
Click to copy

Step 4: Keep AI aligned throughout

"Before you create the board view, review user-flows.md Flow #1
and confirm your implementation matches the spec."
Click to copy

Why this 4-file stack is superior:

✅ Clear separation - Business (product-spec) vs. Technical (technical-spec) vs. Data (data-schema) vs. UX (user-flows)

✅ No overlap - Each file has a distinct purpose, no confusion about where to document something

✅ Complete specifications - AI has everything needed to generate production-ready code

✅ Easy maintenance - Change UI? Update user-flows.md. Change database? Update data-schema.md.

✅ Works for teams - Non-technical stakeholders can edit product-spec.md, developers edit technical-spec.md

✅ Scalable - These 4 files work for small MVPs and large applications

✅ Version controlled - Track how requirements evolve over time

✅ AI-friendly - Structured format with tables, code blocks, and clear hierarchies

What makes these docs "architect-grade":

Explicit validation rules - Not just "string", but "max 200 chars, trim whitespace, required"
Edge cases documented - What happens when user clicks outside modal? What if network fails?
Visual specifications - Card layouts, color codes, responsive breakpoints
Business logic - Stage transition rules, calculation formulas, decision flows
Non-functional requirements - Performance targets, security posture, browser support
Sample data - Concrete examples AI can use for testing
Error states - Validation messages, network errors, empty states
Future considerations - What's out of scope now but might be needed later

Resources to Take Home

The Prompt Library:

Starting a new Django project:

Create a new Django project for [description].
Use SQLite, server-rendered templates, and Tailwind CSS.
Include HTMX for smooth interactions.
Set it up for deployment on PythonAnywhere.
Click to copy

Adding features:

Add [feature] to my Django app.
It should [describe behavior].
Keep code simple, readable, and consistent with existing patterns.
Click to copy

Debugging:

I'm getting this error: [paste error]
Here's the relevant code: [paste code]
Explain what's wrong and how to fix it.
Click to copy

Deployment:

Prepare my Django app for deployment on [platform].
Generate all necessary configuration files.
Update settings for production.
Click to copy

Understanding code:

Explain this code like I'm a business professional, not a developer:
[paste code]
Click to copy

Community and Support

Join our community on Discord: https://discord.gg/CHzuu9G9 Post questions, share what you build, and help each other get unstuck. There's a lot of helpful context and examples inside the Discord.

AI Noob Programming

Part 0: Intro

Tutorial Path

Requirements Installation Checklist

1. VS Code (Code Editor)

2. Python 3.11+ (Programming Language)

3. Git (Version Control)

4. Create Free Accounts

5. Setup the AI Assistant

Part 1: Understanding the Landscape (20 min)

What is an IDE (Integrated Development Environment)?

What is AI and Why Does It Matter for Building Apps?

How We'll Use AI to Code Today

Programming Languages & Frameworks

What is a Database?

What is Version Control (Git/GitHub)?

What is a Hosting Platform?

The Tools We Use

Part 2: The App We're Building (10 min)

A Simple Introducing Investment Opportunities Board

Part 3: The Art of Prompting AI (25 min)

Why AI Needs Good Prompts: The New Hire Analogy

The Right Way: Structured Prompting

Prompting Tips

Part 4: Getting Started with VS Code and Ready to Prompt

Get the prompt window open

Get the terminal window open

Part 5: Planning Phase (Align Before You Build) (10 min)

Optional Course Planning Prompt (Only use this if you didn't already do your own planning) (5 min)

Part 6: Build Phase (Implement the App) (45 min)

Step 1: Project Initialization (8 min)

Step 2: Define Data Models (10 min)

Step 3: Build the Board View (12 min)

Step 4: Add HTMX for Smooth Stage Transitions (10 min)

Step 5: Polish and Test (5 min)

Part 7: Deployment to Production (30 min)

Step 1: Prepare for Deployment (5 min)

Step 2: Push to GitHub (8 min)

Step 3: Deploy on PythonAnywhere (15 min)

THE BIG MOMENT

Step 4: Updating Your App Later (5 min)

Part 8: What Just Happened? (15 min)

The Honest Reality Check

Part 9: Make It Your Own (20 min)

Optional Challenges

Part 10: Wrap-Up and Next Steps (15 min)

What You Accomplished Tonight

Optional Reflection

Part 11: Advanced: Level Up

Resources to Take Home

Community and Support