Microsoft’s late-90s consumer desktop operating system wouldn’t have been the first to come to mind as appropriate for a spacecraft, but ESA were quick to remind us that it was the development toolchain, not the craft itself, that depended upon it. It’s still quite a surprise to find Windows 98 being dusted off for such an unexpected purpose, and it’s led us to consider those now-almost-forgotten operating systems once more, and to question where else it might still be found. Continue reading “Windows 98 For Spaceships? Not Quite!”→
Needless to say, the views we get through modern lenses are a lot more realistic. So how did we get from simple magnifying systems to the complex lens systems we see today? We start with a quick journey through the history of the camera and the lens, and we’ll end up with the cutting edge in lens design for smartphone cameras and VR headsets.
At the risk of dating myself, I will tell you that grew up in the 80s — that decade of excess that was half drab and half brightly colored, depending on where you looked, and how much money you had for stuff like Memphis design. Technology seemed to move quickly in almost every aspect of life as the people of the Me decade demanded convenience, variety, and style in everything from their toilet paper (remember the colors?) to their telephones. Even though long distance cost a fortune back then, we were encouraged to ‘reach out and touch someone’.
A Healthy Fear of Bears
Looking back, it’s easy to see how all that advanced technology and excess filtered down to children. I may be biased, but the 80s were a pretty awesome time for toys, and for children’s entertainment in general. Not only were the toys mostly still well-made, even those that came in quarter machines — many of them were technologically amazing.
Take Teddy Ruxpin, which debuted in 1985. Teddy was the world’s first animatronic children’s toy, a bear that would read stories aloud from special cassette tapes, which moved his eyes and mouth along with the words. One track contained the audio, and the other controlled three servos in his face.
I remember watching the commercials and imagining Teddy suddenly switching from some boring bedtime story over to a rockin’ musical number a là the animatronic Rock-afire Explosion band at ShowBiz Pizza (a Chuck E. Cheese competitor). That’s the kind of night I wanted to be having.
The current lineup of the Rock-afire Explosion. Image via Servo Magazine
Which brings us to KC Bearifone, an animatronic teddy bear telephone. Honestly, part of the reason I bought the Bearifone was some sort of false nostalgia for Teddy. The main reason is that I wanted to own a Teleconcepts unit of some kind, and this one seemed like the most fun to mess around with. A robot teddy bear that only does speakerphone? Yes, please.
Four years ago when the idea of a pandemic was something which only worried a few epidemiologists, a group of British hardware hackers and robotic combat enthusiasts came up with an idea. They would take inspiration from the American Power Racing Series to create their own small electric racing formula. Hacky Racers became a rougher version of its transatlantic cousin racing on mixed surfaces rather than tarmac, and as an inaugural meeting that first group of racers convened on a cider farm in Somerset to give it a try. Last weekend they were back at the same farm after four years of Hacky Racer development with racing having been interrupted by the pandemic, and Hackaday came along once more to see how the cars had evolved. Continue reading “How Far Can You Push A £500 Small Electric Car; Four Years Of The Hacky Racer”→
“With the power of edge AI in the palm of your hand, your business will be unstoppable.”
That’s what the marketing seems to read like for artificial intelligence companies. Everyone seems to have cloud-scale AI-powered business intelligence analytics at the edge. While sounding impressive, we’re not convinced that marketing mumbo jumbo means anything. But what does AI on edge devices look like these days?
Being on the edge just means that the actual AI evaluation and maybe even fine-tuning runs locally on a user’s device rather than in some cloud environment. This is a double win, both for the business and for the user. Privacy can more easily be preserved as less information is transmitted back to a central location. Additionally, the AI can work in scenarios where a server somewhere might not be accessible or provide a response quickly enough.
Google and Apple have their own AI libraries, ML Kit and Core ML, respectively. There are tools to convert Tensorflow, PyTorch, XGBoost, and LibSVM models into formats that CoreML and ML Kit understand. But other solutions try to provide a platform-agnostic layer for training and evaluation. We’ve also previously covered Tensorflow Lite (TFL), a trimmed-down version of Tensorflow, which has matured considerably since 2017.
For this article, we’ll be looking at PyTorch Live (PTL), a slimmed-down framework for adding PyTorch models to smartphones. Unlike TFL (which can run on RPi and in a browser), PTL is focused entirely on Android and iOS and offers tight integration. It uses a react-native backed environment which means that it is heavily geared towards the node.js world.
Stand by the shore and watch the waves roll in, and you’ll notice that most come in at roughly the same size. There’s a little variation, but the overwhelming majority don’t stand out from the crowd. On all but the stormiest of days, they have an almost soothing regularity about them.
Every so often though, out on the high seas, a rogue wave comes along. These abnormally large waves can strike with surprise, and are dangerous to even the largest of ships. Research is ongoing as to what creates these waves, and how they might be identified and tracked ahead of time.
Perhaps rather unexpectedly, on the 14th of March this year the GCC mailing list received an announcement regarding the release of the first ever COBOL front-end for the GCC compiler. For the uninitiated, COBOL saw its first release in 1959, making it with 63 years one of the oldest programming language that is still in regular use. The reason for its persistence is mostly due to its focus from the beginning as a transaction-oriented, domain specific language (DSL).
Its acronym stands for Common Business-Oriented Language, which clearly references the domain it targets. Even with the current COBOL 2014 standard, it is still essentially the same primarily transaction-oriented language, while adding support for structured, procedural and object-oriented programming styles. Deriving most of its core from Admiral Grace Hopper‘s FLOW-MATIC language, it allows for efficiently describing business logic as one would encounter at financial institutions or businesses, in clear English.
Unlike the older GnuCOBOL project – which translates COBOL to C – the new GCC-COBOL front-end project does away with that intermediate step, and directly compiles COBOL source code into binary code. All of which may raise the question of why an entire man-year was invested in this effort for a language which has been declared ‘dead’ for probably at least half its 63-year existence.
Does it make sense to learn or even use COBOL today? Do we need a new COBOL compiler?
