04 · REA lands

Day 10 — Oct 15

REA dropped this week — the new reverse-engineering agent. Everyone in my corner of the field is talking about it, and after two evenings I get the hype: point it at a binary or a pcap and it reasons about structure the way a good junior reverser does, except it doesn’t get tired at hour three. Exceptional performance on exactly the class of problem this project is made of.

Day 11 — Oct 16

Used REA to sketch the client’s architecture end-to-end: connection lifecycle, heartbeat cadence, event push patterns, how the world map streams in blocks, how chat and rally events fan out. And somewhere in there the hobby flipped upside down on me: this game is a genuinely well-built high-concurrency system. Massive concurrent users, aggressive latency budgets, event-driven fan-out — I’ve paid for infrastructure training that taught me less. The the cross-venue arbitrage desk — feed-handler cluster could straight-up copy their event fan-out pattern.

Embarrassing-but-true: some weeks the game’s infrastructure teaches me more about high-availability, high-demand, low-latency design than my day job does. I keep telling myself it’s a hobby. It’s a lab that plays back.

Day 12 — Oct 17

REA-assisted pass over the tool’s public feature surface. Mapping features → wire behavior → what an officer could ever see. The feature-to-observable matrix is the real deliverable here; rules and detection both fall out of it. Draft matrix is 70% filled.

Day 13 — Oct 18

Off-topic evening that wasn’t: went down the damage-formula rabbit hole instead (hero skill vs. troop multiplier interactions, the diminishing-return knees). Zero direct progress on auto, but this is why the game holds me — the systems are deep and they’re honest about the math. A game this well-engineered deserves better neighbors than a bot farm.