GPS is an incredible piece of modern technology. Not only does it allow for locating objects precisely anywhere on the planet, but it also enables the turn-by-turn directions we take for granted these days — all without needing anything more than a radio receiver and some software to decode the signals constantly being sent down from space. [Chris] took that last bit bit as somewhat of a challenge and set off to write a software-defined GPS receiver from the ground up.

As GPS started as a military technology, the level of precision needed for things like turn-by-turn navigation wasn’t always available to civilians. The “coarse” positioning is only capable of accuracy within a few hundred meters so this legacy capability is the first thing that [Chris] tackles here. It is pretty fast, though, with the system able to resolve a location in 24 seconds from cold start and then displaying its information in a browser window. Everything in this build is done in Python as well, meaning that it’s a great starting point for investigating how GPS works and for building other projects from there.

The other thing that makes this project accessible is that the only other hardware needed besides a computer that runs Python is an RTL-SDR dongle. These inexpensive TV dongles ushered in a software-defined radio revolution about a decade ago when it was found that they could receive a wide array of radio signals beyond just TV.

Got a Flipper Zero? Ever wanted to use a high-level but powerful scripting language on it? Thanks to [Oliver] we now have a MicroPython application for the Flipper, complete with a library for hardware and software feature support. Load it up, start it up, connect over USB, and you’ve got the ever-so-convenient REPL at your disposal. Or, upload a Python script to your Flipper and run them directly from Flipper’s UI at your convenience!

In the API docs, we’re seeing support for every single primitive you could want – GPIO (including the headers at the top, of course), a healthy library for LCD and LCD backlight control, button handling, SD card support, speaker library for producing tones, ADC and PWM, vibromotor, logging, and even infrared transmit/receive support. Hopefully, we get support for Flipper’s wireless capabilities at some point, too!

Check out the code examples, get the latest release from the Flipper app portal or GitHub, load it up, and play! Mp-flipper has existed for the better half of a year now, so it’s a pretty mature application, and it adds quite a bit to Flipper’s use cases in our world of hardware hacking. Want to develop an app for the Flipper in Python or otherwise? Check out this small-screen UI design toolkit or this editor we’ve featured recently!

Every few years or so, a development in computing results in a sea change and a need for specialized workers to take advantage of the new technology. Whether that’s COBOL in the 60s and 70s, HTML in the 90s, or SQL in the past decade or so, there’s always something new to learn in the computing world. The introduction of graphics processing units (GPUs) for general-purpose computing is perhaps the most important recent development for computing, and if you want to develop some new Python skills to take advantage of the modern technology take a look at this introduction to CUDA which allows developers to use Nvidia GPUs for general-purpose computing.

Of course CUDA is a proprietary platform and requires one of Nvidia’s supported graphics cards to run, but assuming that barrier to entry is met it’s not too much more effort to use it for non-graphics tasks. The guide takes a closer look at the open-source library PyTorch which allows a Python developer to quickly get up-to-speed with the features of CUDA that make it so appealing to researchers and developers in artificial intelligence, machine learning, big data, and other frontiers in computer science. The guide describes how threads are created, how they travel along within the GPU and work together with other threads, how memory can be managed both on the CPU and GPU, creating CUDA kernels, and managing everything else involved largely through the lens of Python.

Getting started with something like this is almost a requirement to stay relevant in the fast-paced realm of computer science, as machine learning has taken center stage with almost everything related to computers these days. It’s worth noting that strictly speaking, an Nvidia GPU is not required for GPU programming like this; AMD has a GPU computing platform called ROCm but despite it being open-source is still behind Nvidia in adoption rates and arguably in performance as well. Some other learning tools for GPU programming we’ve seen in the past include this puzzle-based tool which illustrates some of the specific problems GPUs excel at.

Not that it’s the kind of thing that pops into your head often, but if you ever do think of a cyanotype print, it probably doesn’t conjure up thoughts of modern technology. For good reason — the monochromatic technique was introduced in the 1840s, and was always something of a niche technology compared to more traditional photographic methods.

The original method is simple enough: put an object or negative between the sun and a UV-sensitive medium, and the exposed areas will turn blue and produce a print. This modernized concept created by [Gabe] works the same way, except both the sun and the negative have been replaced by a lightly modified resin 3D printer.

A good chunk of the effort here is in the software, as [Gabe] had to write some code that would take an image and turn it into something the printer would understand. His proof of concept was a clever bit of Python code that produced an OpenSCAD script, which ultimately converted each grayscale picture to a rectangular “pixel” of variable height. The resulting STL files could be run through the slicer to produce the necessary files to load into the printer. This was eventually replaced with a new Python script capable of converting images to native printer files through UVtools.

On the hardware side, all [Gabe] had to do was remove the vat that would usually hold the resin, and replace that with a wooden lid to both hold the UV-sensitized paper in place and protect the user’s eyes. [Gabe] says there’s still some room for improvement, but you wouldn’t know it by looking at some of the gorgeous prints he’s produced already.

No word yet on whether or not future versions of the project will support direct-to-potato imaging.

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