What’s Your Favorite Kind Of Hack?

April 11, 2026 by Elliot Williams 31 Comments

Talking with [Tom Nardi] on the podcast this week, he mentioned his favorite kind of hack: the community-developed open-source firmware that can be flashed into a commercial product that has crappy firmware, thus saving it. The example, just for the record, is the CrossPoint open e-book reader firmware that turns a mediocre cheap e-book into something that you can do anything you want with. Very nice!

And that got me thinking about “kinds of hacks” in general. Do we have a classification scheme for the hacks that we see here on Hackaday? For instance, the obvious precursor to many of Tom’s favorite hacks is the breaking-into-the-locked-firmware hack, where a device that didn’t want you loading your own firmware on it is convinced to let you do so. Junk-hacking is probably also a category of its own, where instead of finding your prey on AliExpress, you find it on eBay, or in the alleyway. And the save-it-from-the-landfill repair and renovation hacks are close relatives.

The doing-too-much-with-too-little hacks are maybe my personal favorite. I just love to see when someone manages to get DOOM running in Linux on a computer made with only 8-pin microcontrollers. Because of the nature of the game, these often also include a handful of abusing-a-component-to-do-something-it’s-not-meant-to-do hacks. Heck, we even had a challenge for just exactly those kind of hacks.

Then there are fine-art-hacks, where the aesthetic outcome is as important as the technical, or games-hacks where fun is the end result.

What other broad categories of hacks are we missing? And which are your favorite?

AI System Drops A Dime On Noisy Neighbors

April 25, 2024 by Dan Maloney 4 Comments

“There goes the neighborhood” isn’t a phrase to be thrown about lightly, but when they build a police station next door to your house, you know things are about to get noisy. Just how bad it’ll be is perhaps a bit subjective, with pleas for relief likely to fall on deaf ears unless you’ve got firm documentation like that provided by this automated noise detection system.

OK, let’s face it — even with objective proof there’s likely nothing that [Christopher Cooper] is going to do about the new crop of sirens going off in his neighborhood. Emergencies require a speedy response, after all, and sirens are perhaps just the price that we pay to live close to each other. That doesn’t mean there’s no reason to monitor the neighborhood noise, though, so [Christopher] got to work. The system uses an Arduino BLE Sense module to detect neighborhood noises and Edge Impulse to classify the sounds. An ESP32 does most of the heavy lifting, including running the UI on a nice little TFT touchscreen.

When a siren-like sound is detected, the sensor records the event and tries to classify the type of siren — fire, police, or ambulance. You can also manually classify sounds the system fails to understand, and export a summary of events to an SD card. If your neighborhood noise problems tend more to barking dogs or early-morning leaf blowers, no problem — you can easily train different models.

While we can’t say that this will help keep the peace in his neighborhood, we really like the way this one came out. We’ve seen the BLE Sense and Edge Impulse team up before, too, for everything from tuning a bike suspension to calming a nervous dog. Continue reading “AI System Drops A Dime On Noisy Neighbors” →

Machine Learning Helps You Track Your Internet Misery Index

February 14, 2021 by Dan Maloney 8 Comments

We all seem to intuitively know that a lot of what we do online is not great for our mental health. Hang out on enough social media platforms and you can practically feel the changes your mind inflicts on your body as a result of what you see — the racing heart, the tight facial expression, the clenched fists raised in seething rage. Not on Hackaday, of course — nothing but sweetness and light here.

That’s all highly subjective, of course. If you’d like to quantify your online misery more objectively, take a look at the aptly named BrowZen, a machine learning application by [Nick Bild]. Built around an NVIDIA Jetson Xavier NX and a web camera, BrowZen captures images of the user’s face periodically. The expression on the user’s face is classified using a facial recognition model that has been trained to recognize facial postures related to emotions like anger, surprise, fear, and happiness. The app captures your mood and which website you’re currently looking at and stores the results in a database. Handy charts let you know which sites are best for your state of mind; it’s not much of a surprise that Twitter induces rage while Hackaday pushes [Nick]’s happiness button. See? Sweetness and light.

Seriously, we could see something like this being very useful for psychological testing, marketing research, or even medical assessments. This adds to [Nick]’s array of AI apps, which range from tracking which surfaces you touch in a room to preventing you from committing a fireable offense on a video conference.

Continue reading “Machine Learning Helps You Track Your Internet Misery Index” →

Training Bats In The Random Forest With The Confusion Matrix

November 21, 2019 by Pat Whetman 4 Comments

When exploring the realm of Machine Learning, it’s always nice to have some real and interesting data to work with. That’s where the bats come in – they’re fascinating animals that emit very particular ultrasonic calls that can be recorded and analysed with computer software to get a fairly good idea of what species they are. When analysed with an FFT spectogram, we can see the individual call shapes very clearly.

Nyctalus noctula (noctule bat)

Myotis nattereri (natterera’s bat)

Plecotus auritus (brown long eared)

Pipistrellus pipistrellus (common pipistrelle)

Creating an open source classifier for bats is also potentially useful for the world outside of Machine Learning as it could not only enable us to more easily monitor bats themselves, but also the knock on effects of modern farming methods on the natural environment. Bats feed on moths and other night flying insects which themselves have been decimated in numbers. Even in the depths of the countryside here in the UK these insects are a fraction of the population that they used to be 30 years ago, but nobody seems to have monitored this decline.

So getting back to our spectograms, it would be perfectly reasonable to throw these images at a convolutional neural network (CNN) and use an image feature-recognition strategy. But I wanted to explore the depths of the mysterious Random Forest. Continue reading “Training Bats In The Random Forest With The Confusion Matrix” →