# Blog Posts Canonical blog index: https://deaneeth.vercel.app/blogs ## Building a Full-Stack ESP32 IoT Project for Smart Beehive Monitoring URL: https://deaneeth.vercel.app/blog/full-stack-esp32-iot-smart-beehive-monitor Canonical URL: https://deaneeth.com/blog/full-stack-esp32-iot-smart-beehive-monitor Published: 2026-07-05T00:00:00.000Z Category: ai-ml-engineering Tags: ESP32, IoT, Embedded Systems, Firebase, React Reading time: 14 minutes Source: medium; external URL: https://medium.com/@deaneeth/building-a-full-stack-esp32-iot-project-for-smart-beehive-monitoring-5a6f421b7be7?sharedUserId=deaneeth Summary: How I built a complete smart beehive monitoring system end-to-end: ESP32 firmware with environmental and activity sensors, solar-powered edge deployment, Firebase Realtime Database sync, and a React TypeScript dashboard for live hive metrics, bee traffic, weight tracking, GPS, and alerts. Excerpt: My dad’s apiary is not exactly a quiet place to test electronics. There is heat, dust, insects, sunlight, humidity, and the constant low noise of a living hive doing what a hive does. It is also the kind of place where a “ simple IoT project ” stops being simple very quickly. A sensor reading that looks perfect on a desk can become useless outside. A wire that behaves nicely during testing can loosen. A dashboard that looks clean with mock data suddenly feels fragile when the data is coming from real hardware. That was the reason I wanted to build this project properly. The goal was simple in one sentence: build a complete live telemetry system from PCB to browser, where an ESP32 collects beehive data, sends it to Firebase Realtime Database , and a real time monitoring dashboard turns that sensor data into something a beekeeper can actually understand. TL;DR: this is a production style E... ## I Built a TPU You Can Watch Run: Every Signal in Real Time URL: https://deaneeth.vercel.app/blog/tiny-tpu-visual-simulator-real-time-signals Canonical URL: https://deaneeth.com/blog/tiny-tpu-visual-simulator-real-time-signals Published: 2026-06-08T00:00:00.000Z Category: ai-ml-engineering Tags: TPU Simulation, AI Accelerators, Computer Architecture, Systolic Arrays, Hardware Visualization Reading time: 10 minutes Source: medium; external URL: https://deaneeth.medium.com/i-built-a-tpu-you-can-watch-run-every-signal-is-real-3bf2e83b2805?sharedUserId=deaneeth Summary: A hands-on engineering breakdown of TinyTPU, a visual TPU simulator that shows matrix multiplication, systolic arrays, chip signals, and AI accelerator behavior in real time. Excerpt: I had been using TPUs for almost a year before I realized I had absolutely no idea what was happening inside one. I knew the math. I could write the \ nn.Linear\ call. I understood that matrix multiplication maps cleanly to hardware. But why it maps cleanly, what’s actually happening in the silicon when those multiplications fire was a complete mystery to me. The standard explanation is a diagram with arrows. Weights go in here, activation's go in there, results come out the bottom. Clean. Tidy. Completely useless for actually understanding anything. So I decided to build one. A real one. And make it run in the browser so I could watch it cycle by cycle. That became TinyTPU . What I Actually Wanted to Understand TPUs use something called a systolic array to execute matrix multiply. It’s the architecture behind why TPUs are efficient at exactly the workloads that matter for deep learning.... ## I Built a Tiny GPU Simulator to See What Happens When Threads Execute in Parallel URL: https://deaneeth.vercel.app/blog/tiny-gpu-simulator-parallel-threads Canonical URL: https://deaneeth.com/blog/tiny-gpu-simulator-parallel-threads Published: 2026-01-24T00:00:00.000Z Category: ai-ml-engineering Tags: GPU Simulation, Parallel Computing, Computer Architecture, Systems Engineering, GPU Reading time: 10 minutes Source: medium; external URL: https://deaneeth.medium.com/i-built-a-tiny-gpu-simulator-to-see-what-happens-when-threads-execute-in-parallel-c2f204ab3bc3?sharedUserId=deaneeth Summary: A hands-on engineering breakdown of TinyGPU, a small GPU simulator that visualizes how threads, warps, memory, and parallel execution work inside GPU-style systems. Excerpt: I was three hours into debugging a CUDA kernel when I realized I had no idea what was actually happening. The code compiled. It ran. It even produced output just not the right output. Somewhere inside that GPU, thousands of threads were executing my instructions in parallel, but I was blind to it all. No print statements. No step through debugger. Just a black box that either worked or didn’t. That’s when I started sketching what eventually became TinyGPU . The Problem with Learning GPU Programming GPUs are incredible machines. They can execute thousands of operations simultaneously, making them essential for everything from graphics rendering to machine learning. But there’s a catch: they’re nearly impossible to understand by just reading documentation. The mental model is weird. Unlike CPUs where you think about one instruction executing at a time, GPUs run the same instruction across... ## From Bees to Bytes: My Journey Building a Simple AI to Protect a Beehive URL: https://deaneeth.vercel.app/blog/from-bees-to-bytes-building-ai-beehive Canonical URL: https://deaneeth.com/blog/from-bees-to-bytes-building-ai-beehive Published: 2025-06-01T00:00:00.000Z Category: ai-ml-engineering Tags: AI, Computer Vision, MLOps, IoT, Python Reading time: 5 minutes Source: medium; external URL: https://deaneeth.medium.com/from-bees-to-bytes-my-journey-building-a-simple-ai-to-protect-a-beehive-cf8f072ab0ca?sharedUserId=deaneeth Summary: How I built a simple AI beehive monitor using Azure Custom Vision to detect Varroa mites from hive images - an IoT + ML side project focused on shipping fast instead of chasing perfection. Excerpt: I was sitting there, feeling the warm sun on my skin and listening to the buzzing bees in my dad’s apiary. But my mind was a million miles away, stuck on a huge, overwhelming problem: building my first real world AI project. I had this big idea for a smart beehive monitor, but going from what I’d learned in tutorials to actually building something that worked felt impossible. How do you even get started? What tools do you pick? How do you turn a vague idea into something that helps someone? This isn’t a story about a perfect project. It’s about taking one giant problem, chopping it down to a tiny, manageable piece, and learning what real AI work is all about. Related build: Smart Beehive Monitor project. The Problem: When Big Ideas Hit Reality My first plan was super ambitious. I wanted to build a system that did everything: track hive temperature, check humidity, count bee traffic, and...