Tech Lead · Staff Software Engineer · AI Engineer · Leon, Mexico 🇲🇽
20 years building software — all remote. Grown alongside Ruby on Rails through every major evolution of the framework. Focused on architecting high-performance systems in Rust, scaling enterprise Ruby on Rails monoliths, and building developer-centric Agentic AI integrations & Model Context Protocol (MCP) infrastructure.
High-fidelity evaluation engine for benchmarking AI agent skills across any stack (Rails-first, but extensible). Compare baseline vs. skill-enhanced agent runs with 100% reproducibility via isolated Git sandboxes.
Turn any Rails app into an AI-ready codebase — one command, zero config. AI assistants waste tokens guessing your app structure and still give generic responses. rails-ai-bridge fixes the root cause.
Safe Rust CLI for Model Context Protocol (MCP) runtime management. Orchestrates communication between LLMs and external tools via standard I/O streams using the ReAct reasoning pattern.
Curated AI agent skill library for Ruby on Rails development. Structured SKILL.md files that give coding agents the context and conventions to generate production-quality Rails code.
npx tessl i igmarin/rails-agent-skills
Curated library of atomic development skills and orchestrated agents for AI coding assistants, enforcing TDD and DDD discipline for Hanami 2.x Ruby applications.
npx tessl i igmarin/hanakai-yaku
Core Ruby skills for AI agent evaluation. A structured library of language fundamentals, standard library mastery, and idiomatic patterns for benchmarking.
npx tessl i igmarin/ruby-core-skills
Framework-agnostic AI planning capabilities. Enforces PM and software engineering planning discipline with structured templates for PRDs and task estimation.
npx tessl i igmarin/agnostic-planning-skills
A syndicated bi-weekly segment about Artificial Intelligence and technology broadcasted on national FM radio, syndicated online as a podcast. We break down the implications of agentic workflows, machine learning models, and complex system architectures to bridge technical depth with public understanding.
In this segment, we analyze and demystify the common belief that our phones actively wiretap our daily audio. I explain how data brokers, web tracking networks, cookie syncing, and predictive machine learning models analyze non-audio signals to predict user desires with hyper-accuracy, mimicking active listening.
Listen to the Episode →Currently accepting contract work and full-time remote positions for Senior / Staff Engineer, Tech Lead, and AI Engineering roles.