If anything ends up on the beds of hobbyist-grade laser cutters more often than birch plywood, it’s probably sheets of acrylic. There’s something strangely satisfying about watching a laser beam trace over a sheet of the crystal-clear stuff, vaporizing a hairs-breadth line while it goes, and (hopefully) leaving a flame-polished cut in its wake.
Acrylic, more properly known as poly(methyl methacrylate) or PMMA, is a wonder material that helped win a war before being developed for peacetime use. It has some interesting chemistry and properties that position it well for use in the home shop as everything from simple enclosures to laser-cut parts like gears and sprockets.
Most people are familiar with the idea that machine learning can be used to detect things like objects or people, but for anyone who’s not clear on how that process actually works should check out [Kurokesu]’s example project for detecting pedestrians. It goes into detail on exactly what software is used, how it is configured, and how to train with a dataset.
The application uses a USB camera and the back end work is done with Darknet, which is an open source framework for neural networks. Running on that framework is the YOLO (You Only Look Once) real-time object detection system. To get useful results, the system must be trained on large amounts of sample data. [Kurokesu] explains that while pre-trained networks can be used, it is still necessary to fine-tune the system by adding a dataset which more closely models the intended application. Training is itself a bit of a balancing act. A system that has been overly trained on a model dataset (or trained on too small of a dataset) will suffer from overfitting, a condition in which the system ends up being too picky and unable to usefully generalize. In terms of pedestrian detection, this results in false negatives — pedestrians that don’t get flagged because the system has too strict of an idea about what a pedestrian should look like.
[Kurokesu]’s walkthrough on pedestrian detection is great, but for those interested in taking a step further back and rolling their own projects, this fork of Darknet contains YOLO for Linux and Windows and includes practical notes and guides on installing, using, and training from a more general perspective. Interested in learning more about machine learning basics? Don’t forget Google has a free online crash course to get you up to speed.
Many a budding electronics maker got their start not with a soldering iron, but with the humble breadboard. With its push connections, the breadboard enables electronics experimentation without requiring the specialised skill of soldering or any dangerous hot tools. What it lacks is a certain robustness that can make all but the simplest projects rather difficult to execute. [Runtime Micro] have shared a few tips on making things just a little more robust, however.
The fundamental principle behind this process is replacing point-to-point jumper wires with custom cables, made using 0.1″ pitch headers and wire-wrapping techniques. Other techniques include pinning down components with Blu-tack, and selecting components with the appropriate wire diameter to avoid them falling out of the breadboard’s spring clip contacts. There are also useful tips on using foam tape for appropriate strain relief.
While breadboards aren’t really suitable for projects dealing with high frequencies and can rapidly become unmanageable, these basic techniques should improve a project’s chance of success. These simple ways of improving connection quality and reducing the likelihood of things falling apart are likely to reduce frustration immensely.
When I began programming microcontrollers in 2003, I had picked up the Atmel STK-500 and learned assembler for their ATtiny and ATmega lines. At the time I thought it was great – the emulator and development boards were good, and I could add a microcontroller permanently to a project for a dollar. Then the ESP8266 came out.
I was pretty blown away by its features, switched platforms, except for timing-sensitive applications, and it’s been my chip of choice for a few years. A short while ago, a friend gave me an ESP32, the much faster, dual core version of the ESP8266. As I rarely used much of the computing power on the ESP8266, none of the features looked like game changers, and it remained a ‘desk ornament’ for a while.
About seven weeks ago, support for the libSodium Elliptic Curve Cryptography library was added. Cryptography is not the strongest feature of IoT devices, and some of the methods I’ve used on the ESP8266 were less than ideal. Being able to more easily perform public-private key encryption would be enough for me to consider switching hardware for some projects.
Here at Hackaday we are willing to bet that in a universe free of all monetary constraints, many of our readers would leave their day jobs in order to pursue their hardware hobbies full time. Obviously this is only practical for a lucky minority of people (for a wide variety of reasons) but we’re willing to bet that a significant stumbling block is figuring how to do it in the first place. You quit your job, but then what? If more information about starting and sustaining small hardware business’ was available more people would take the plunge to start one. There are software companies with salary transparency but this is only part of the picture and we can’t think of many hardware companies that offer the same. What we really want is to get an image of the entire business end to end; from suppliers to COGS to salary. And we want to see it for hardware.
Years ago the first and second Hackaday Prizes captured an entrant named FarmBot whose goal was to build open source robotic farming equipment to make it easier for anyone to grow their own food. A few successful Kickstarters and years later they’ve been shipped multiple versions of the Genesis and Genesis XL robotic farming system and have a sustainable business! And now they’ve decided to open source their business operations too. Suffice to say, this provides quite an uncommon view into the guts of what makes a small open source hardware business tick. Let’s take a closer look!
There is a wealth of information exposed in the company documentation; it’s as though they took their internal wiki and made it public, which we suppose is exactly what happened. The most interesting part for our readers might be the statistics page that tracks costs and quantities for their products. This is where the magic lives. You can use to it see that so far they’ve sold 124 Genesis XL machines at an average selling price of $3,834.34 for $475,458.30 of revenue (it cost $187,200 to build their run of 200 machines). You can also see that each machine has 1,415 parts and takes about 25 hours to assemble. This page is where the true guts of the business live.
Everything else is here too. Here’s where you can learn about what vendors FarmBot uses use logistics, or power, or web infrastructure monitoring. And this is the page with the infamous salary calculation formulas if you want to guess what you’d make as an employee. Then there’s a bunch of boring but important stuff. Fulfillment processes live here, and the consumables they use to support that fulfillment are listed here (with costs!).
One reason we enjoy open source so much is that it affords a wonderful opportunity for people to learn instead of keeping the important parts of a product or process perpetually under wraps. We’re hoping that documentation like this becomes more prevalent and foster an explosion of small hardware companies to follow it.
When Python was created, [Guido van Rossum] knew that one day it would be fully realized and take its final form. Clearly, that day has arrived since there now exists a way to send a word query and receive a lengthy list of potential portmanteaus. Some may regard this as merely quaint, but it will be the most important thing to happen in binary until the singularity.
Perhaps we are overpromising a smidge, but it may be fun to spend an afternoon getting your own whimsicalibrated pun resource churning out some eye-roll-worthy word combos. The steps are broken up neatly and explained at a high level with links for more in-depth explanations so a novice can slog through it, but a whiz can wrap it up while the boss is looking the other way.
Just to be clear, the primary goal of the Papas Inventeurs (Inventor Dads) was to have the kids make something, have fun, and learn. In that light, they enjoyed a huge success. Four children designed, made, and sold laser-cut napkin rings from a booth at the Ottawa Maker Faire as a fun learning process (English translation, original link in French.) [pepelepoisson] documented the entire thing from beginning to end with plenty of photos. Things started at proof of concept, then design brainstorming, prototyping, manufacture, booth design, and finally sales. While adults were involved, every step was done by the kids themselves.
It all began when the kids were taken to a local fab lab at the École Polytechnique and made some laser-cut napkin holders from plywood for personal use. Later, they decided to design, manufacture, and sell them at the Ottawa Maker Faire. Money for the plywood came from piggy banks, 23 different designs made the cut, and a total of 103 rings were made. A display board and signs made from reclaimed materials rounded out the whole set.
In the end, about 20% of people who visited and showed interest made a purchase, and 60 of the 103 pieces were sold for a profit of $126. Of course, the whole process also involved about 100 hours of combined work between the kids and parents and use of a laser cutter, so it’s not exactly a recipe for easy wealth. But it was an incredibly enriching experience, at least figuratively, for everyone involved.