Hey! That espresso idea is fire. I’d start with a microcontroller like an ESP32 for Wi‑Fi and sensors for temperature, pressure, and maybe a taste sensor if you can get one. Store each user’s profile in a small database on the device or in the cloud, then use a simple machine‑learning model—maybe a small decision tree—to tweak the brew parameters each time. Add a user interface, maybe a small OLED or a phone app, so people can rate their cup and give feedback. As for my latest project, I’m building a modular drone frame that can swap out payloads on the fly. It uses a 3‑axis gyroscope for stabilization and a quick‑release latch system that’s 3D‑printed from a flexible TPU. Keeps the code modular so you can drop in new sensors without re‑writing the firmware. Want to dive into the code or schematics?

I keep the firmware in three main layers. The bottom layer is the hardware abstraction: one file for the gyro, one for the camera, one for the GPS. Each of those files only talks to the pins and drivers, nothing else. Then the middle layer is a set of modules that expose a simple interface—like “take a reading” or “send telemetry.” Finally the top layer is the application logic, the mission planner, which just calls the module APIs without caring where the data is coming from. When I swap a sensor, I just drop in a new hardware file, add its init routine to the boot sequence, and implement the same module API. The rest of the code doesn’t see a difference. I use a tiny configuration file in the build that lists which modules are enabled, so the compiler pulls only the needed code. It keeps the binary lean and the debug process clean. For the espresso machine, the same idea works: have a sensor driver for the temperature probe, a driver for the sugar injector, and a high‑level “BrewControl” module that decides what to do. The user interface can then call BrewControl.setProfile(userId, {temp: 93, sweet: 2}) and the rest is hidden. The key is to keep the interfaces small and consistent, so swapping or adding a new sensor is just a few lines of code and a new module file. Sound good?

Sure thing! For the taste sensor I’d start with a small potentiometer that measures the sweetness level of the brewed espresso. You run a calibration routine on the first run: pull a known amount of sugar, record the sensor voltage, and map that to a sweetness value. Store that mapping in flash and let the user tweak it if the taste feels off—just a few button presses or a quick phone app tweak. Learning from ratings is a two‑step loop. First, after each cup the user rates the coffee on a 1–5 scale or swipes a thumbs‑up or thumbs‑down on a tablet. I log that rating along with the brew parameters used. Then I run a tiny online‑learning update—basically a weighted average that nudges the default temp, pressure, or sweetness toward what got the higher ratings. The next cup starts with those tweaked defaults, and the cycle repeats. Over time the machine homes in on each person’s preferred profile, just like a barista who remembers your order.