Engineering Solutions for AI Bottlenecks

Identifying critical friction points in AI development and architecting robust solutions

3
Core Challenges
3
Proven Solutions
100%
Open Source

The Technical Landscape

The shift to AI-native development has introduced new classes of engineering problems. Context windows are finite, token costs are real, and the complexity of managing Model Context Protocol (MCP) servers is growing. My work focuses on solving these specific infrastructure challenges.

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Challenge 1: The Context Bottleneck

The Problem: Traditional MCP setups load all tool definitions into the context window for every request. This wastes massive amounts of tokens (often 10k+ per session), hits rate limits quickly, and degrades model performance due to context clutter.

Solution: NCP (Natural Context Protocol)

An intelligent router that dynamically selects only the relevant tools based on the user's prompt.

  • ✅ 94.8% Token Reduction (measured in production)
  • ✅ O(1) Scaling regardless of tool count
  • ✅ 2x-50x Longer Coding Sessions
View Solution
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Challenge 2: MCP Development Friction

The Problem: Building MCP servers currently involves significant boilerplate, complex configuration, and multi-file setups. This friction slows down the creation of custom tools and agents, hindering the adoption of the MCP standard.

Solution: Photon

A single-file TypeScript runtime that treats business logic as the API.

  • ✅ Zero Boilerplate: Just write a class
  • ✅ Auto-Schema Generation: No manual JSON
  • ✅ Instant Deployment: npx run photon
View GitHub
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Challenge 3: API Documentation Drift

The Problem: (Foundational Work) Keeping API documentation in sync with implementation is a perennial struggle. Outdated docs lead to integration errors and developer frustration.

Solution: Luracast Restler

The pioneer PHP framework that generates API routes and documentation directly from code structure.

  • ✅ Code-First Design: Public methods become API endpoints
  • ✅ Auto-Documentation: Specs generated from PHPDoc
  • ✅ Standardized: Used by thousands of developers
View Legacy

Have a Challenge?

I'm constantly looking for the next bottleneck to solve in the AI developer ecosystem.

If you're facing infrastructure friction, let's discuss it.

Discuss a Challenge View Code