Index/Writing·05 Notes
Notes on the gap between research and production.
Occasional writing about building ML systems that operate in the real world. On reinforcement learning in production, the realities of deploying AI in logistics, and lessons from bridging academic research with industrial operations.
MAY 2026
Why We Designed Our Systems to Be Human in the Loop
We built our RL systems to work with terminal operators, not around them, because the hardest problems in logistics need both computational power and human judgment.
10 min read
MAY 2026
The Data Problem No One Talks About
Getting clean, reliable data out of terminal operating systems, or any industrial environment, is harder than any model you will build on top of it. The gap between what is logged and what actually happened is where most projects quietly fail.
8 min read
APR 2026
The Trust Gap: Earning the Right to Optimise a Container Terminal
Deploying optimisation in container terminals means working within constraints that have nothing to do with mathematics. It is about earning the right to change how people work.
10 min read
MAR 2026
Reinforcement Learning Learns to Decide. Optimisation Is Just How It Gets There
We talk about RL as though it optimises things. But the real insight is subtler. RL is a framework for making decisions, and optimisation is just a by-product of making good ones consistently.
8 min read
MAR 2026
Reinforcement Learning in Container Terminals: When It Works, When It Doesn't, and Why That Matters
A practitioner's honest take on deploying RL in one of the most operationally complex environments in global logistics, and knowing when to reach for a different tool.
9 min read