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Frequently Asked Questions (FAQ)

Common questions about DevBoot LLM - a coding education platform with 2,107 verified lessons.

Table of Contents

Getting Started

What is DevBoot LLM?

DevBoot LLM is an open-source coding education platform with 2,107 verified lessons covering Python and Java. Every lesson includes:

  • Interactive code editor
  • Detailed tutorials
  • Working code examples
  • Real-time code execution
  • Optional AI assistance

Who is this for?

  • Self-learners: Learn programming fundamentals at your own pace
  • Bootcamp students: Supplement your curriculum with hands-on practice
  • Teachers: Deploy for your students in a controlled environment
  • Companies: Use for employee training and onboarding

How much does it cost?

It's completely free! This is an open-source project. You can:

  • Use it for free online (when available)
  • Self-host it for free
  • Modify it for your needs
  • Deploy it in your organization

No subscriptions, no hidden costs, no account required.

What do I need to get started?

To use the platform:

  • Web browser (Chrome, Firefox, Safari, Edge)
  • Internet connection (or local deployment)

To self-host:

  • Docker (recommended) OR
  • Node.js 18+ and Python 3.12+ and Java 17+

Deployment

How do I deploy this?

Option 1: Docker (Recommended)

git clone https://github.com/undergroundrap/devbootLLM-app.git
cd devbootLLM-app
docker build -t devboot-llm .
docker run -p 3000:3000 devboot-llm

Visit http://localhost:3000

Option 2: Manual Setup

git clone https://github.com/undergroundrap/devbootLLM-app.git
cd devbootLLM-app
npm install
npm start

Requirements:

  • Node.js 18+
  • Python 3.12+
  • Java 17+

Can I deploy this on my company's servers?

Yes! The platform is designed to be self-hosted. You can:

  • Deploy on internal servers
  • Use behind a firewall
  • Run completely offline
  • Customize for your needs

No external services required (except optional AI features).

Can I use this offline?

Yes! Once deployed, the platform works completely offline:

  • All lessons are local
  • Code execution is local
  • No external API calls (except optional AI)

Perfect for:

  • Corporate environments with restricted internet
  • Classroom settings
  • Areas with limited connectivity
  • Privacy-conscious deployments

How do I update to the latest version?

git pull origin main
npm install
docker build -t devboot-llm .  # If using Docker
npm start  # Or restart Docker container

Using the Platform

How do I start learning?

  1. Visit the platform (local or online)
  2. Select Python or Java
  3. Choose a lesson category
  4. Pick a lesson based on difficulty
  5. Read the tutorial
  6. Write code in the editor
  7. Click "Run Code" to test
  8. Submit when your output matches expected output

How do I track my progress?

Currently: Progress tracking is manual - you remember which lessons you've completed.

Coming soon:

  • Mark lessons as complete
  • Track completion percentage
  • "Continue where you left off"
  • Learning streaks
  • Category completion stats

See CHANGELOG.md for planned features.

Can I save my code?

Currently: Code is saved in your browser's local storage temporarily. Refreshing the page clears it.

Workaround: Copy your code to a text editor or IDE to save it permanently.

Coming soon: User accounts with code persistence (optional feature).

How do I search for lessons?

Currently: Browse by category and difficulty.

Coming soon:

  • Full-text search
  • Filter by keywords, tags, difficulty
  • Search suggestions
  • "Most popular" and "Recommended for you"

Can I use this on mobile?

Partially. The platform works on mobile browsers but the experience is optimized for desktop.

Limitations:

  • Code editor is small on phones
  • Keyboard typing can be awkward
  • Layout not fully responsive

Coming soon: Mobile-optimized experience with touch-friendly controls.

Lessons and Content

How many lessons are there?

2,107 verified lessons:

  • 1,030 Python lessons
  • 1,077 Java lessons

100% working - Every lesson compiles and runs correctly.

What topics are covered?

Python (1,030 lessons):

  • Core fundamentals
  • Object-oriented programming
  • Web development (Flask, Django, FastAPI)
  • Asynchronous programming
  • Data science (pandas, NumPy, scikit-learn)
  • Database (SQLAlchemy)
  • Testing (pytest)
  • DevOps and deployment

Java (1,077 lessons):

  • Core fundamentals
  • Object-oriented programming
  • Spring Boot (REST APIs, MVC)
  • Spring Security (authentication, JWT)
  • Spring Data (JPA, Hibernate)
  • Spring Cloud (microservices)
  • Reactive programming (WebFlux)
  • Testing (JUnit, Mockito)
  • Advanced features (reflection, streams, generics)

What difficulty levels are available?

  • Beginner: First steps, basic syntax
  • Intermediate: Combining concepts, frameworks
  • Advanced: Complex patterns, architecture
  • Expert: Optimization, advanced topics

Are the lessons up-to-date?

Yes! Lessons use:

  • Python 3.12+
  • Java 17+
  • Current framework versions (Spring Boot 3.x, etc.)

Can I suggest new lessons?

Yes! See CONTRIBUTING.md for how to:

  • Propose new lesson topics
  • Submit lesson contributions
  • Improve existing lessons

Code Execution

How does code execution work?

Python:

  • Runs in a containerized Python 3.12 environment
  • Uses exec() to execute your code
  • Captures stdout and stderr
  • 30-second timeout per execution

Java:

  • Compiles with javac (Java 17)
  • Runs with java command
  • Captures output
  • 30-second timeout per execution

Why isn't my code executing?

Common reasons:

  1. Syntax errors: Check for typos, missing colons, unmatched brackets
  2. Runtime errors: Division by zero, null pointers, etc.
  3. Infinite loops: Code times out after 30 seconds
  4. Wrong output: Output must match expected output EXACTLY
  5. Docker not running: If self-hosting, ensure Docker is running

Debugging tips:

  • Read error messages carefully
  • Use print statements to debug
  • Compare with tutorial examples
  • Check expected output format (including newlines)

Why does my output not match?

Output must match EXACTLY, including:

  • Case sensitivity ("hello" ≠ "Hello")
  • Spacing
  • Newlines (\n)
  • Punctuation

Example:

Expected: "Hello, World!\n"
Your output: "Hello World"  ❌ Missing comma and newline

Your output: "Hello, World!\n"  ✅ Exact match

Can I use external libraries?

Standard libraries: Yes, all standard library modules/packages work.

External packages: Limited to pre-installed packages:

Python:

  • Flask, Django, FastAPI
  • pandas, NumPy, scikit-learn
  • SQLAlchemy, psycopg2
  • pytest, requests
  • boto3 (AWS SDK)

Java:

  • Spring Boot ecosystem
  • JUnit, Mockito
  • Jackson, Lombok
  • Common Apache libraries

Want to add a package? See CONTRIBUTING.md or open an issue.

Is my code executed securely?

Yes. Code runs in:

  • Docker containers (isolated from host)
  • Limited resource allocation
  • 30-second timeout
  • No file system access outside container
  • No network access (except for web framework lessons)

However: This is a learning platform, not a production environment. Don't use it to run untrusted code from others.

AI Features

How do I enable AI features?

AI features are optional and require either:

  1. Ollama (local AI)

    # Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Pull a model
ollama pull codellama

# Configure in platform
Set OLLAMA_URL environment variable

  2. LM Studio (local AI)

    • Install LM Studio
    • Load a coding model
    • Start API server
    • Configure platform endpoint

Do I need AI to use the platform?

No! AI is completely optional. The platform works perfectly without it:

  • All lessons are complete
  • Tutorials explain everything
  • Code examples are provided
  • Solutions are available

AI just provides additional hints if you get stuck.

What can AI help with?

  • Explain error messages
  • Suggest debugging steps
  • Provide alternative approaches
  • Answer questions about concepts

AI does NOT:

  • Give you the complete solution
  • Do the lesson for you
  • Track your progress

Is my code sent to external servers?

No! If you use local AI (Ollama/LM Studio), everything stays on your machine.

No external API calls means:

  • Your code stays private
  • Works offline
  • No API costs
  • Complete privacy

Contributing

How can I contribute?

Many ways to help:

  1. Add lessons: Create new Python or Java lessons
  2. Improve content: Fix typos, clarify tutorials
  3. Fix bugs: Report or fix issues
  4. Add features: Progress tracking, search, etc.
  5. Improve documentation: Help others understand the project

See CONTRIBUTING.md for detailed guidelines.

Do I need permission to contribute?

No! Just:

  1. Fork the repository
  2. Make your changes
  3. Test thoroughly
  4. Submit a pull request

All contributions are reviewed and welcomed.

How do I add a new lesson?

See the Adding New Lessons section in CONTRIBUTING.md.

Quick summary:

  1. Add lesson JSON to public/lessons-python.json or public/lessons-java.json
  2. Ensure code compiles and runs
  3. Verify expected output
  4. Write clear tutorial
  5. Test in the app
  6. Submit PR

What if I find a bug in a lesson?

Please report it!

  1. Open a GitHub Issue
  2. Include:
    • Lesson ID and title
    • What's wrong
    • Expected vs actual behavior
    • Screenshots if helpful

Or fix it yourself and submit a PR!

Troubleshooting

Platform won't start

Docker:

# Check if Docker is running
docker ps

# Check logs
docker logs <container_id>

# Rebuild
docker build -t devboot-llm .

Manual setup:

# Check Node.js version
node --version  # Should be 18+

# Check Python version
python --version  # Should be 3.12+

# Check Java version
java -version  # Should be 17+

# Reinstall dependencies
rm -rf node_modules
npm install

Code editor not loading

Check:

  • Browser console for errors (F12 → Console)
  • Network tab for failed requests
  • Ad blocker isn't blocking resources
  • Try different browser

Code execution fails

Check:

  1. Code compiles locally first

    # Python
python -c "exec('''your code here''')"

# Java
javac Main.java && java Main

  2. Expected output is correct

  3. No infinite loops

  4. Syntax is valid

Docker build fails

Common issues:

  1. Insufficient disk space: Free up space
  2. Docker daemon not running: Start Docker
  3. Port 3000 in use: Change port or stop other service
  4. Network issues: Check internet connection
# Use different port
docker run -p 8080:3000 devboot-llm

Lesson not displaying correctly

Check:

  1. Browser cache - try hard refresh (Ctrl+F5)
  2. JSON syntax - ensure lessons file is valid JSON
  3. Browser console for errors
  4. Try different lesson to isolate issue

Technical Questions

What's the tech stack?

Backend:

  • Node.js + Express.js
  • SQLite database (JSON fallback)
  • Child process for code execution

Frontend:

  • Vanilla JavaScript
  • Tailwind CSS
  • No framework (lightweight, fast)

Code Execution:

  • Python 3.12
  • Java 17
  • Docker containerization

Can I integrate this with my LMS?

Possible, but requires development. You could:

  • Embed in iframe (limited)
  • Use as content source
  • Fork and customize
  • Build API integration

No built-in LTI or LMS support currently. See CHANGELOG.md for planned API features.

How does the validation work?

// Simplified validation flow
1. User submits code
2. Server executes code in container
3. Captures output
4. Compares to expectedOutput
5. Returns success/failure + actual output

Can I customize the UI?

Yes! Fork the project and modify:

  • public/index.html - Main UI
  • public/styles.css - Custom styles (in addition to Tailwind)
  • Frontend JavaScript files

Where is data stored?

Lessons: public/lessons-python.json, public/lessons-java.json

User progress: Currently browser localStorage (temporary)

Database: SQLite at coding-platform.db (created on first run)

How do I backup my data?

Lessons:

cp public/lessons-python.json backup/
cp public/lessons-java.json backup/

Database:

cp coding-platform.db backup/

Docker volumes:

docker cp <container_id>:/app/coding-platform.db backup/

Can I add support for other languages?

Yes! You'll need to:

  1. Create lessons-<language>.json
  2. Add execution logic in server.js
  3. Update frontend language selector
  4. Test thoroughly

Pull requests welcome! See CONTRIBUTING.md.

Still Have Questions?

Happy learning! 🚀