If you work in IoT, environmental sensing, or data systems, forest soil monitoring is one of the most technically interesting problems you'll encounter. The system you're trying to measure is extraordinarily complex, the variables are deeply interdependent, and the consequences of getting it wrong — or not monitoring at all — are significant. The Problem Space: What You're Actually Measuring Soil
More rules should mean better output. That's the intuition. I spent weeks building a comprehensive CLAUDE.md — 200 lines covering naming conventions, security rules, error handling, architectural patterns, import ordering, type safety requirements, and more. I was proud of it. I'd thought through every scenario. Then I scored the output. 79.0 / 100. My carefully crafted documentation was actively
There's about $400 of meat, milk, and miscellaneous condiments in my kitchen fridge at any given time. It runs 24/7, makes a quiet humming noise, and gives no indication when something's wrong until you open the door three days later and recoil. The freezer compartment is worse: a slow failure can defrost everything before you notice the puddle. I already had a TP-Link P110 smart plug on the fridg
One of the recurring challenges while building IoT systems is testing device communication, telemetry handling, MQTT flows, and event-driven architectures without constantly relying on physical hardware. To solve this problem, I recently started building a lightweight IoT Simulator CLI focused on helping developers simulate virtual devices directly from the terminal. The project is designed for de
Every device you own has a speaker and a microphone. I decided to use them for something useful. Natural disasters knock out cell towers. WiFi dies at conferences. Underground sensors need to offload data where nothing reaches. Bluetooth pairing is painful and range-limited. LoRa is great but requires hardware you don't have. Sound doesn't care about any of that. Every phone, every laptop, every e
Have you ever looked at code you wrote six months ago and thought: "Who wrote this monster?"? Relax, it happens to all of us. In software engineering, writing code that a machine understands is the easy part. The real challenge is writing code that other humans (including your future self) can understand, maintain, and scale. This is exactly where Software Design Principles come into play. In this
Part 1 of 5 in The New Engineering Contract — what it means to lead engineers when AI is doing more of the coding. SWE-CI tested 18 AI models across 71 consecutive commits. Most broke something on commit 47 they'd already broken on commit 1. That's not an intelligence problem. That's a learning system that isn't learning. A paper made me uncomfortable this month. Not because of what it found about