Building a Professional RWA Investment Platform in Days, Not Months: My Journey with Kiro AI

yu gumin — Sun, 10 Aug 2025 15:00:32 +0000

Tags: #kiro #ai #development #fintech #python #streamlit #defi

The Challenge: Building Complex Financial Software Fast

As a developer, I've always dreamed of building sophisticated financial applications, but the complexity and time investment seemed overwhelming. Traditional development meant months of architecture planning, countless hours debugging, and the constant struggle between speed and code quality.

That changed when I discovered Kiro AI.

What I Built: RWA Yield Optimizer

Using Kiro, I built a complete Real-World Assets (RWA) investment analysis platform featuring:

Real-time Dashboard: Live protocol monitoring with interactive charts
Multi-Model AI Predictions: Integration of GPT-4, Claude-3.5, and Gemini for yield forecasting
Portfolio Optimizer: Modern Portfolio Theory implementation with 3D visualizations
Protocol Comparison: Multi-dimensional analysis with heatmaps and radar charts
Professional UI: Dark theme interface with responsive design

The entire platform - over 1,700 lines of production-ready code - was built in days, not months.

The Kiro Difference: Spec-Driven Development

Before Kiro: The Traditional Struggle

Idea → Code → Debug → Refactor → More Bugs → Frustration

With Kiro: Structured Collaboration

Idea → Spec → Implementation → Production-Ready Code

Kiro's spec-driven approach transformed my workflow:

1. Requirements Phase

# requirements.md
## User Story
As an investor, I want AI-powered yield predictions, 
so that I can make informed investment decisions.

## Acceptance Criteria
1. WHEN I select a protocol THEN the system SHALL display predictions from multiple AI models
2. WHEN predictions are generated THEN confidence scores SHALL be provided

2. Design Phase

# design.md
## Architecture
- Multi-model AI integration with ensemble logic
- Confidence scoring system
- Error handling and fallback mechanisms

3. Implementation Phase

# tasks.md
- [ ] Implement multi-model prediction engine
- [ ] Create confidence scoring algorithm
- [ ] Add error handling for API failures

The Most Impressive Code Generation

The standout moment was when Kiro generated the complete multi-model AI prediction engine:

async def get_ai_prediction(self, protocol: str, timeframe: str) -> Dict[str, Any]:
    """Multi-model AI prediction with ensemble logic"""
    try:
        models = ['gpt-4', 'claude-3.5', 'gemini-pro']
        predictions = []

        for model in models:
            prediction = await self._get_single_model_prediction(
                protocol, timeframe, model
            )
            predictions.append(prediction)

        # Weighted ensemble with confidence scoring
        final_prediction = self._ensemble_predictions(predictions)

        return {
            "success": True,
            "predicted_apy": final_prediction['apy'],
            "confidence": final_prediction['confidence'],
            "reasoning": final_prediction['reasoning'],
            "risk_factors": final_prediction['risks']
        }
    except Exception as e:
        return {"success": False, "error": str(e)}

This wasn't just code generation - it was architectural thinking. Kiro understood:

The need for ensemble methods in AI predictions
Proper error handling for production systems
Clean, maintainable code structure
Integration with existing codebase patterns

Agent Hooks: Automation That Actually Works

Kiro's agent hooks eliminated the tedious parts of development:

Code Quality Hook: Automatically triggered on file save

Linting and formatting
Documentation updates
Consistency checks

Test Generation Hook: Created comprehensive tests

Unit tests for new functions
Mock data generation
Test coverage reports

Documentation Hook: Kept docs current

API documentation from code comments
Feature descriptions
User guides

These hooks created a "development safety net" - I could focus on creative problem-solving while Kiro handled the maintenance tasks.

Real Impact: The Numbers Don't Lie

Development Speed: 10x faster than traditional methods
Code Quality: Production-ready from day one
Architecture: Consistent patterns throughout
Testing: Automated test generation
Documentation: Always up-to-date

The Professional GUI: Beyond Expectations

The generated Streamlit application wasn't just functional - it was beautiful:

# Professional dark theme with custom CSS
DARK_THEME_CSS = """
<style>
    .stApp {
        background: linear-gradient(135deg, #1a1d29 0%, #252a3a 50%, #2d3748 100%);
        color: #ffffff;
        font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif;
    }

    .metric-card {
        background: rgba(37, 42, 58, 0.8);
        border: 1px solid rgba(255, 255, 255, 0.1);
        border-radius: 12px;
        padding: 1.5rem;
        box-shadow: 0 4px 20px rgba(0, 0, 0, 0.15);
        transition: all 0.3s ease;
    }
</style>
"""

Kiro generated not just the CSS, but the entire component architecture with:

Responsive layouts
Interactive charts using Plotly
State management
Error boundaries
Loading states

Lessons Learned: Best Practices for AI-Assisted Development

1. Start with Clear Specs

Don't jump straight into coding. Invest time in writing clear requirements and design documents. Kiro works best with structured input.

2. Embrace Iterative Development

Use Kiro's context awareness. Each conversation builds on previous work, creating increasingly sophisticated solutions.

3. Trust the Architecture

Kiro often suggests better architectural patterns than I would have chosen. Don't micromanage - let the AI architect.

4. Leverage Domain Knowledge

Provide context about your domain (finance, in my case). Kiro adapts its suggestions to industry best practices.

5. Use Hooks Liberally

Set up automation early. The compound benefits of automated testing, documentation, and code quality checks are enormous.

The Future of Development

Working with Kiro changed my fundamental approach to software development. Instead of:

Writing code line by line
Debugging for hours
Struggling with architecture decisions
Maintaining documentation manually

I now:

Collaborate with an AI architect
Focus on creative problem-solving
Build production-ready systems rapidly
Maintain high quality automatically

Code Examples: See It in Action

Here's the complete dashboard implementation Kiro generated:

def show_realtime_dashboard():
    """Real-time data dashboard with professional styling"""
    st.markdown('<h1 class="main-title">🏠 Real-Time Dashboard</h1>', 
                unsafe_allow_html=True)

    # Key metrics with professional cards
    col1, col2, col3, col4 = st.columns(4)

    with col1:
        st.markdown(f"""
        <div class="metric-card success-card">
            <div class="metric-label">Total Protocols</div>
            <div class="big-metric">{len(protocols)}</div>
        </div>
        """, unsafe_allow_html=True)

    # Interactive charts with real-time updates
    historical_data = get_protocol_trends()
    line_fig = create_dynamic_line_chart(historical_data)
    st.plotly_chart(line_fig, use_container_width=True)

The attention to detail is remarkable - from the CSS classes to the data flow architecture.

Conclusion: A New Era of Development

Kiro AI represents a fundamental shift in how we build software. It's not just about generating code faster - it's about elevating the entire development process.

The RWA Yield Optimizer project proved that with the right AI collaboration, individual developers can build enterprise-grade applications that would traditionally require entire teams.

If you're still writing software the old way, you're missing out on a revolution. The future of development is here, and it's collaborative, intelligent, and incredibly powerful.

Ready to transform your development workflow? Try Kiro AI and experience the future of software development.

Built with Kiro AI - where human creativity meets artificial intelligence.

Technical Stack Used

Frontend: Streamlit with custom CSS
Visualization: Plotly for interactive charts
AI Integration: Multi-model approach (GPT-4, Claude-3.5, Gemini)
Data: SQLAlchemy with real-time updates
Architecture: Modular, spec-driven design

Project Repository

[Link to GitHub repository with full source code]

Tags: #kiro #ai #development #fintech #python #streamlit #defi #rwa #portfolio #investment

built TailorMyResume.online Magic with Bolt.new

yu gumin — Mon, 21 Jul 2025 03:48:50 +0000

🧠 Project Overview

Our team built TailorMyResume.online, a web tool that helps job seekers improve their chances by tailoring their existing resume to specific job descriptions. Users simply paste their current resume and the job requirements, and the tool automatically reformats and highlights the most relevant experiences and skills, making their application more compelling.

🧩 Technical Challenges & Breakthroughs
• Text Parsing & Structure Recognition: Resumes come in countless formats: bullet points, paragraphs, inconsistent punctuation. We tackled this by combining regular expressions with AI-enhanced semantic parsing to extract section headers and key content reliably.
• Contextual Alignment with OpenAI: We fine-tuned prompts to ask the Bolt/OpenAI model, “Given this resume and this JD, suggest a restructured version prioritizing role-specific keywords and accomplishments.” Through prompt engineering, we achieved clear, ATS-friendly output.
• Formatting Output: We use HTML + CSS Grid for layout, and when users want a PDF or Word download, we convert the formatted output via a headless browser (Puppeteer) to preserve styling and printability.

🔧 Sponsor Tech Integration – Building with Bolt
• Bolt Functions: We leveraged Bolt’s serverless infrastructure to deploy both our resume-formatter and PDF-generator as separate functions. This gave us scalable performance during peak usage.
• Bolt KV Store: We used Bolt KV for temporary storage of user inputs and outputs, minimizing latency and avoiding full database deployment.
• Bolt Logging & Analytics: Instant tracking of API performance and user flows—vital during demo at the hackathon! We could see error rates decline as we tweaked prompts and regex patterns.

❤️ Favorite Bolt Features
1. Fast cold-start performance—critical when users edit resumes and expect instant feedback.
2. Granular monitoring—dashboards helped us quickly diagnose slowdown in PDF conversion.
3. Easy secrets management—OpenAI API key and Puppeteer settings were securely handled, simplifying deployment.

💻 Sample Prompt Snippet

const prompt = `
You are an expert resume coach.
User Resume:

${userResume}

Job Description:

${jobDesc}

Output: A revised resume emphasizing relevant skills, responsibilities, and achievements. Use bullet points and include at most one achievement per bullet. Make it ATS compliant.
`;
const response = await bolt.openai.createChatCompletion({ prompt, model: "gpt‑4‑turbo" });

🎓 Teammates
• @guminyu88

DEV Community: yu gumin