Testing Modern Development Tools

No-Code Revolution: Testing Modern Development Tools in a Weekend Hackathon

As the software development landscape evolves, no-code and AI-assisted development tools are gaining significant attention.

Table of contents

    At Curiosum, we're always eager to explore new technologies that could enhance our development capabilities. This led us to organize a weekend hackathon where we tested different approaches to building applications: AI-powered development with Bolt and Lovable, traditional no-code with Bubble.io and Flutterflow.

    Table of contents

    • Introduction
    • The Project: Wrocław Bike Routes Platform
    • Tools Overview and Experiences
    • Key Findings
    • Future Perspectives
    • FAQ

    Introduction

    The rise of no-code and AI-assisted development tools presents both opportunities and challenges for traditional software development companies. These platforms promise faster development cycles, lower entry barriers, and reduced costs. But can they deliver on these promises?

    To answer these questions hands-on, we organized a hackathon with a diverse team including our CTO, CEO, designer and developer. Each participant brought a different perspective and level of technical expertise, allowing us to evaluate these tools from multiple angles.

    The Project: Wrocław Bike Routes Platform

    Instead of creating a generic test project, we chose to address a real problem: the lack of a centralized platform for bike routes around Wrocław. The project requirements came from actual user needs:

    Current Problems

    • Route information scattered across multiple platforms (Strava, Komoot, Facebook groups)
    • Incomplete or outdated route descriptions
    • Missing GPX files
    • Difficulty in filtering routes based on specific criteria

    Planned Features

    MVP Requirements:

    • User authentication (Google + standard)
    • GPX file upload functionality
    • Route listing with map integration
    • Basic filtering (length, difficulty, surface type)
    • Rating and commenting system
    • Responsive design for mobile users

    This project provided an excellent test case because it required both frontend and backend functionality, needed map integration, included file handling, required user authentication, and needed a database with filtering capabilities.

    Tools Overview and Experiences

    AI-Powered Development with Bolt and Lovable

    These platforms represent a new generation of development tools that use conversational AI to generate and modify code. The development process is similar to having a conversation with an AI assistant - you describe what you want to build, and the AI understands and implements it.

    AI-Powered Development

    AI-Powered Development with Bolt and Lovable

    Development Process

    "The development process was surprisingly intuitive. Starting with a basic Product Requirements Document, I could generate features through natural language prompts. What would typically take a week of design work and another week of development was mostly done in 8 hours." - Dawid, Head of Design

    The process typically involved:

    1. Providing initial project requirements
    2. Breaking down features into smaller prompts
    3. Iterative development through conversation
    4. Real-time adjustments and refinements

    Key Achievements

    In approximately 8 hours of work, we managed to build:

    • Complete authentication system with email verification
    • GPX file upload and processing functionality
    • Route listing with filtering capabilities
    • Basic map integration
    • Responsive user interface

    Challenges and Observations

    "Working with AI tools requires a different mindset. The key lies in writing effective prompts and maintaining context throughout the development process." - Jakub, Elixir Developer

    Main challenges included:

    • Learning effective prompt engineering
    • Managing context in longer development sessions
    • Handling complex feature interactions
    • The need to retry prompts for better results

    AI-Powered development process

    Bubble.io: Traditional No-Code Approach

    "Bubble proves capable of handling approximately 70-80% of typical web application requirements. The platform particularly shines in its AI-powered page generation capabilities, though it can feel cumbersome compared to newer, more streamlined tools." - Szymon, CEO

    Key observations:

    • Comprehensive platform for web application development
    • Strong database capabilities
    • AI-assisted page generation features
    • Extensive plugin ecosystem
    • Programming knowledge helps in platform utilization
    • Platform lock-in considerations due to no code export option

    AI-Powered Development app

    Flutterflow: Mobile-First Development

    "Specialized tools significantly outperform general-purpose platforms in their niche. FlutterFlow excels in mobile development, offering impressive integration capabilities with services like Firebase and Stripe." - Michał, CTO

    Notable features:

    • Native mobile app development capabilities
    • Code export functionality
    • Extensive widget library
    • Strong integration options
    • Mobile-first design approach

    Key Findings

    Technical Insights

    1. AI-Powered Development Impact
    • Significantly reduces development time
    • Lowers technical barriers to entry
    • Requires skill in prompt engineering
    • Most effective when combining technical knowledge with good prompting
    1. Platform Specialization
    • AI-powered tools show impressive versatility
    • Traditional no-code platforms offer robust feature sets
    • Mobile-first tools excel in their specific domain
    1. Development Efficiency
    • AI tools excel at rapid prototyping
    • Traditional no-code platforms provide stability
    • Mobile-first tools optimize for specific use cases

    Business Considerations

    1. Risk Assessment
    • Vendor lock-in concerns
    • Platform longevity
    • Scalability limitations
    1. Team Impact
    • Reduced technical requirements
    • Changed skill set needs
    • New collaboration patterns

    Future Perspectives

    The hackathon demonstrated that modern development tools, especially AI-powered ones, have evolved significantly. While they may not replace traditional development for complex applications, they show promise for:

    1. Rapid Prototyping
    • MVP development
    • Proof of concept creation
    • Design validation
    1. Internal Tools
    • Administrative dashboards
    • Workflow automation
    • Data management systems
    1. Simple to Moderate Applications
    • Content management systems
    • Basic e-commerce
    • Community platforms

    Conclusion

    Our hackathon experience showed that modern development tools, particularly AI-powered ones, have matured significantly. While they may not completely replace traditional development, they offer compelling advantages for certain use cases. The key is understanding each tool's strengths and limitations to make informed decisions about when and how to use them effectively.

    FAQ

    How do AI-powered development tools differ from traditional no-code platforms? AI-powered tools use conversational interfaces where developers describe what they want to build in natural language. This differs from traditional no-code platforms that use visual programming interfaces. The AI-powered approach can be faster and more flexible but requires skill in writing effective prompts.

    Which type of tool would you recommend for different projects? It depends on the project needs:

    • AI-powered tools (Bolt/Lovable): Best for rapid prototyping and MVPs
    • Bubble.io: Suitable for comprehensive web applications
    • Flutterflow: Ideal for mobile-first applications

    What are the main challenges when using these tools? Key challenges include:

    • AI tools: Prompting and context management
    • Traditional no-code: Platform limitations and learning curve
    • Mobile-first: Specialized knowledge requirements

    How do these tools impact development team structure? These tools can enable:

    • Faster prototyping and validation
    • More involvement from non-technical team members
    • Changed focus from implementation to product design
    • New emphasis on prompt engineering skills

    What are the main considerations for adoption? Consider:

    • Project complexity and requirements
    • Team technical expertise
    • Long-term maintenance needs
    • Integration requirements
    • Scalability needs

    How do deployment and maintenance work with these tools? Each platform has different approaches:

    • AI-powered tools generate standard code
    • Traditional no-code platforms often provide hosting
    • Mobile-first tools may allow code export Consider long-term maintenance requirements when choosing a platform
    Curiosum Head of Design
    Dawid Noculak Head of Design

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