Alan Szmyt is a founder and systems architect with a decade of experience building mission-oriented, reproducible systems that blend AI, automation, and thoughtful architecture. He has delivered full-stack geospatial and decision-support platforms at MIT Lincoln Laboratory—shipping field-tested, low-connectivity mobile tracking and investigative tools—and now runs an independent studio designing deterministic publishing pipelines and programmable AI workflows. Alan treats LLMs and generative models as composable engineering components with clear I/O contracts, and he emphasizes reproducible, local-first development environments using Docker and GitHub Actions. Comfortable at the intersection of platform engineering, developer experience, and creative systems, he pairs government-grade rigor with experimental multimodal media work such as beat-synchronized generative video pipelines.
10 years of coding experience
12 years of employment as a software developer
UMass Lowell
Master of Science (MS), Software Development, Master of Science (MS), Software Development at Boston University
This project contains my implementation of the NEAT-Python algorithm to use a recurrent neural network that enables an AI-controlled Mario to train itself to complete levels in "Super Mario World" on SNES. I used the gym-retro-integration program to create my own variables from the game's RAM values and then used those variables to reward/penalize the AI. If the AI complete's a level, the neural network is saved as the winner.
Contributions:16 commits, 1 PR, 13 pushes in 1 year 3 months
Contributions:171 pushes, 1 branch in 1 year 3 months
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Alan Szmyt - Founder & Systems Architect at Incompris LLC