Case Studies

Challenge: AI assistants lacked direct integration with Gamma, a popular presentation platform. Users had to manually copy content between tools, breaking their workflow and reducing productivity.

Solution: Developed a production-ready MCP server that connects AI assistants (Claude Desktop, Cursor, etc.) directly to the Gamma API. The server enables AI to create, update, and manage presentations programmatically.

Key Features:

• Create new presentations with AI-generated content
• Update existing slides programmatically
• Search and retrieve presentation data
• Full TypeScript implementation with comprehensive error handling
• Complete documentation and usage examples

Results:

• 500+ npm downloads in first month after release
• Open sourced on GitHub, benefiting the entire MCP community
• Zero production issues reported by users
• Active community contributions and feature requests

Technologies: TypeScript, Model Context Protocol, Gamma API, npm packaging

View on GitHub →

Challenge: Developers struggling to choose between rapidly evolving AI tools, frameworks, and services. Information scattered across blog posts, documentation, and marketing materials made objective comparison difficult.

Solution: Created a curated comparison framework documenting AI tools, coding assistants, and development frameworks. Structured tables enable side-by-side feature comparison with objective criteria.

Key Features:

• Side-by-side comparisons of major AI coding assistants
• Framework evaluations (CrewAI, LangChain, AutoGen, etc.)
• Updated regularly as tools evolve
• Community-driven with contribution guidelines
• Markdown format for easy updates and version control

Results:

• Referenced by developer communities for tool selection
• Active community engagement with suggestions and updates
• Helps developers make informed decisions based on objective criteria
• Growing resource as AI tooling ecosystem expands

Technologies: Markdown, Documentation, GitHub

View on GitHub →

These case studies represent open source contributions. For client projects, I provide detailed case studies covering:

Problem & Context — What challenge was the client facing? What were the constraints, requirements, and success criteria?

Solution Design — How did we approach the problem? What architecture, frameworks, and technologies did we choose, and why?

Implementation — What did we build? Key features, technical decisions, and how we addressed challenges during development.

Measurable Results — What was the impact? Quantified outcomes like time saved, costs reduced, processes improved, or revenue generated.

Client Testimonial — Direct feedback from stakeholders about the project outcome and collaboration experience.

Lessons Learned — What insights emerged? What would we do differently? What best practices emerged?