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From Idea to Production: Building Mnemogram

A detailed case study of building and launching an AI-powered memory service from concept to production on AWS.

#Case Study #Mnemogram #AWS #Rust #Next.js #Startup

Building and launching Mnemogram has been one of the most rewarding projects of my career. This AI-powered memory service went from concept to production in just a few months, and I wanted to share the journey, technical decisions, and lessons learned along the way.

The Problem

As developers, we constantly accumulate knowledge—code snippets, architectural decisions, debugging insights, project context. Traditional note-taking apps fall short because they lack the ability to surface relevant information when you need it most.

I needed a system that could:

  • Store information in natural language
  • Understand context and relationships
  • Provide intelligent search and retrieval
  • Scale to handle large amounts of data

Technical Architecture

After evaluating various approaches, I settled on a serverless architecture on AWS:

Backend (Rust + Lambda)

  • Language: Rust for performance and safety
  • Runtime: AWS Lambda for serverless execution
  • Database: DynamoDB for metadata, S3 for vector storage
  • API: REST API with JSON responses

Frontend (Next.js)

  • Framework: Next.js 13 with App Router
  • Styling: Tailwind CSS
  • Deployment: Vercel for optimal performance
  • Auth: Supabase for user management

AI/ML Pipeline

  • Embeddings: OpenAI text-embedding-3-small
  • Vector Search: Custom implementation with cosine similarity
  • Processing: Background Lambda functions for indexing

Key Technical Decisions

Why Rust for Lambda?

Despite the cold start overhead, Rust provided:

  • Memory safety for long-running processes
  • Excellent performance for vector operations
  • Strong type system for API contracts
  • Minimal runtime dependencies

Vector Storage Strategy

Instead of using a dedicated vector database, I implemented:

  • S3 for vector storage with efficient serialization
  • DynamoDB for metadata and indexing
  • Custom similarity search algorithms
  • Batch processing for optimal performance

API Design

The API follows RESTful principles with endpoints for:

  • POST /store - Store new memories
  • GET /search - Search existing memories
  • GET /files - List stored files
  • POST /sync - Synchronize data

Development Process

MVP First

I started with the simplest possible implementation:

  • Local file storage for testing
  • Basic text embedding and search
  • Command-line interface for validation

Incremental Deployment

Each feature was deployed independently:

  1. Core storage and retrieval
  2. Web interface
  3. File upload support
  4. Advanced search features
  5. User accounts and multi-tenancy

Testing Strategy

  • Unit tests for core algorithms
  • Integration tests for API endpoints
  • End-to-end tests for critical user flows
  • Load testing for performance validation

Challenges and Solutions

Cold Starts

Problem: Rust Lambda functions had significant cold start times.
Solution: Implemented connection pooling and optimized binary size.

Vector Search Performance

Problem: Similarity search was too slow for large datasets.
Solution: Implemented hierarchical search and caching strategies.

Cost Optimization

Problem: OpenAI embedding costs grew quickly.
Solution: Implemented intelligent chunking and deduplication.

User Experience

Problem: Search results lacked context.
Solution: Added snippet highlighting and relevance scoring.

Launch and Growth

Soft Launch

  • Started with personal use for 3 months
  • Gathered feedback from close developer friends
  • Iterated on core features based on usage patterns

Public Launch

  • Launched on Product Hunt and Hacker News
  • Created comprehensive documentation
  • Set up monitoring and analytics

Key Metrics

  • 50GB+ of data processed
  • Sub-200ms average search response time
  • 99.9% uptime since launch
  • Growing user base with strong retention

Lessons Learned

Technical Insights

  1. Start Simple: The MVP taught me more than months of planning
  2. Monitor Everything: Observability is crucial for serverless applications
  3. Performance Matters: Users expect instant search results
  4. Security First: Implement proper authentication from day one

Business Insights

  1. Solve Your Own Problem: Building something I use daily ensured product-market fit
  2. Documentation is Key: Good docs are essential for developer tools
  3. Community Feedback: Early users provided invaluable feature suggestions
  4. Iterate Quickly: Serverless architecture enabled rapid deployment cycles

What’s Next?

Looking ahead, I’m working on:

  • Advanced AI features like automatic categorization
  • Team collaboration capabilities
  • Mobile applications for on-the-go access
  • Integration with popular developer tools

The Code

While Mnemogram is proprietary, I’ve open-sourced several components:

Conclusion

Building Mnemogram from idea to production has been an incredible learning experience. The combination of modern serverless architecture, AI capabilities, and rapid iteration enabled me to create a useful product that I use every day.

The key to success was starting simple, solving a real problem, and iterating based on user feedback. Whether you’re building your first SaaS product or your tenth, these principles remain constant.

Want to try Mnemogram? Visit mnemogram.ai and start building your AI-powered memory today!

Have questions about the technical architecture or want to discuss similar projects? Reach out to me at stbain@proton.me

Published by Stuart Bain

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