Millions of people all over the world don’t have access to clean drinking water, and it’s largely because of pollution by corporations and individuals. Solving this problem requires an affordable, scalable way to quickly judge water quality, package the data, and present it to an authority that can crack down on the polluters before the evidence dissipates. Ideally, the solution would be open source and easy to replicate. The more citizen scientists, the better.

[Andrei Florian]’s WaterAid flows directly from this line of thinking. Dip this small handheld device below the surface, and it quickly takes a bunch of water quality and atmospheric readings, averages them, and sends the data to a web dashboard using an Arduino MKR GSM.

WaterAid judges quality by testing the pH and the turbidity of the water, which gauges the amount of impurities. Commercial turbidity sensors work by measuring the amount of light scattered by the solids present in a liquid, so [Andrei] made a DIY version with an LED pointed at a photocell. WaterAid also reads the air temperature and humidity, and reports its location along with a timestamp.

This device can run in one of two modes, depending on the application. The enterprise mode is designed for a fleet of devices placed strategically about a body of water. In this mode, the devices sample continuously, taking readings every 15 minutes, and can send notifications that trigger on predefined thresholds. There’s also a one-and-done individual mode for hikers and campers who need to find potable water. Once WaterAid takes the readings, the NeoPixel ring provides instant color-coded judgment. Check out the demo after the break.

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Soldering irons are a personal tool. Some folks need them on the cool side, and some like it hot. Getting it right takes some practice and experience, but when you find a tip and temp that works, you stick with it. [Riccardo Pittini] landed somewhere in the middle with his open-source soldering station, Soldering RT1. When you start it up, it asks what temperature you want, and it heats up. Easy-peasy. When you are ready to get fancy, you can plug in a second iron, run off a car battery, record preset temperatures, limit your duty-cycle, and open a serial connection.

The controller has an Arduino bootloader on a 32u4 processor, so it looks like a ProMicro to your computer. The system works with the RT series of Weller tips, which have a comprehensive lineup. [Riccardo] also recreated SMD tweezers, and you can find everything at his Tindie store.

Soldering has a way of bringing out opinions from novices to masters. If we could interview our younger selves, we’d have a few nuggets of wisdom for those know-it-alls. If ergonomics are your priority, check out TS100 3D-printed cases, which is an excellent iron, in our opinion.

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Gesture recognition and machine learning are getting a lot of air time these days, as people understand them more and begin to develop methods to implement them on many different platforms. Of course this allows easier access to people who can make use of the new tools beyond strictly academic or business environments. For example, rollerblading down the streets of Atlanta with a gesture-recognizing, streaming TV that [nate.damen] wears over his head.

He’s known as [atltvhead] and the TV he wears has a functional LED screen on the front. The whole setup reminds us a little of Deep Thought. The screen can display various animations which are controlled through Twitch chat as he streams his journeys around town. He wanted to add a little more interaction to the animations though and simplify his user interface, so he set up a gesture-sensing sleeve which can augment the animations based on how he’s moving his arm. He uses an Arduino in the arm sensor as well as a Raspberry Pi in the backpack to tie it all together, and he goes deep in the weeds explaining how to use Tensorflow to recognize the gestures. The video linked below shows a lot of his training runs for the machine learning system he used as well.

[nate.damen] didn’t stop at the cheerful TV head either. He also wears a backpack that displays uplifting messages to people as he passes them by on his rollerblades, not wanting to leave out those who don’t get to see him coming. We think this is a great uplifting project, and the amount of work that went into getting the gesture recognition machine learning algorithm right is impressive on its own. If you’re new to Tensorflow, though, we have featured some projects that can do reliable object recognition using little more than a Raspberry Pi and a camera.

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If you’ve ever had surgery, and you’re over a certain age, chances are good you’re familiar with the dreaded incentive spirometer. It’s a little plastic device with one or more columns, each of which has a plastic ball in it. The idea is to blow into the thing to float the balls, to endure that your lungs stay in good shape and reduce the chance of pneumonia. This unique air-powered clock reminds us a little of that device, without all the pain.

Like a spirometer, [Nir Tasher]’s clock has three calibrated tubes, each big enough to hold a foam ball loosely. At the bottom of each tube is a blower whose motor is under PWM control. A laser rangefinder sits below each ball and measures its height; the measurement is used by a PID loop to control the speed of each fan and thus the height of each ball. The video below shows that the balls are actually pretty steady, making the clock easy to read. It doesn’t, however, reveal what the clock sounds like; we’re going to go out on a limb here and guess that it’s pretty noisy. Still, we think it’s a fantastic way to keep time, and unique in the extreme.

[Nir]’s Air Flow clock is an early entry in the 2020 Hackaday Prize, the greatest hardware design contest on Earth. Everyone should enter something, or at least check out the cool things people are coming up with. It’s still early in the process, but there are so many neat projects already. What are you waiting for?

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