Look out of a window, ask yourself the question, “Has a nuke gone off?”. Maybe, maybe not, and all of us here at Hackaday need to know the answer to these important questions! Introducing the hasanukegoneoff.com Indicator from [bigcrimping] to answer our cries.

An ESP32 running a MicroPython script handles the critical checks from hasanukegoneoff.com for any notification of nuclear mayhem. This will either power the INS-1 neon bulb, indicating “no” or “yes” in the unfortunate case of a blast. Of course, there is also the button required for testing the notification lights; no chance of failure can be left. All of this is fitted onto a custom dual-sided PCB and placed inside a custom 3D-printed enclosure.

Hasanukegoneoff.com’s detection system, covered before here, relies on an HSN-1000L Nuclear Event Detector to check for neutrons coming from the blast zone. [bigcrimping] also provides the project plans for your own blast detector to answer the critical question of “has a nuke gone off” from anywhere other than the website’s Chippenham, England location.

This entire project is open sourced, so keep sure to check out [bigcrimping]’s GitHub for both portions of this project on the detector and receiver. While this project provides some needed dark humor, nukes are still scary and especially so when disarming them with nothing but a hacksaw and testing equipment.

Thanks to [Daniel Gooch] for the tip.

The intersection between “woodworkers” and “programmers” is not a densely populated part of the Venn diagram, but [Michael Schiebler] is there with his Kerf Bend Wizard to help us make wood twist and bend like magic.

Kerf bending is a fine technique we have covered before: by cutting away material on the inside face of a piece of wood, you create an area weak enough to allow for bending. The question becomes: how much wood do I remove? And where? That’s where Kerf Bend Wizard comes to the rescue.

More after the break…

You feed it a spline– either manually or via DXF–and it feeds you a cut pattern that will satisfy that spline: just enough wood removed in just the right places that the edges of the cut should touch when the bend is achieved. This means less cut time and a stronger piece than eyeballing the kerfs. It works with both a table saw blade or a tapered end mill on a CNC or manual router. You can specify the kerf width of your table saw, or angle of your end mill, along with your desired cut depth.

The output is DXF, convenient for use with a CNC, and a simple table giving distances from the edge of the piece and which side to cut, which is probably easier for use on the table saw. (Kerf Bend Wizard is happy to handle complex bends that require kerfing both sides of the material, as you can see.)

This was [Michael]’s thesis project, for which he hopefully got a good grade. The code is “semi-open” according to [Michael]; there’s a GitHub where you can grab an offline version for your own use, but no open-source license is on offer. Being a broke student and an artist to boot, [Michael] also can’t promise he will be able to keep the web version available without ads or some kind of monetization, so enjoy it while you can!

If CNCs or table saws aren’t your thing, kerf bending has long been used with laser cutters, too.

Our thanks (which, as always, is worth its weight in gold) to [Michael] for the tip. If you’re in the intersection of the Venn diagram with [Michael], we’d love to hear what you’re up to.

When [Terence Grover] set out to build a Tamagotchi-inspired simulator, he didn’t just add a few modern tweaks. He ditched the entire concept and rebuilt it from the ground up. Forget cute wide-eyed blobby animals and pixel-poop. This Raspberry Pi-powered project ditches nostalgia in favour of brutal realism: inflation, burnout, capitalism, and the occasional existential crisis. Think Sims meets cyberpunk, rendered charmingly in Python on a low-res RGB LED matrix.

Instead of hunger and poop meters, this dystopian pet juggles Maslow’s hierarchy: hunger, rest, safety, social life, esteem, and money. Players make real-life-inspired decisions like working, socialising, and going into education – each affecting the stats in logical (and often unfair) ways. No free lunch here: food requires money, money requires mind-numbing labour, and labour tanks your rest. You can even die of overwork à la Amazon warehouse. The UI and animation logic are all hand-coded, and there’s a working buzzer, pixel-perfect sprite movement, and even mini-games to simulate job repetition.

It’s equal parts social commentary and pixel art fever dream. While we have covered Tamagotchi recreations some time ago, this one makes you the needy survivor. Want your own dystopia in 64×32? Head over to [Terence Grover]’s Github and fork the full open source code. We’ll be watching. The Tamagotchi certainly is.

Lots of microcontrollers will accept Python these days, with CircuitPython and MicroPython becoming ever more popular in recent years. However, there’s now a new player in town. Enter PyXL, a project to run Python directly in hardware for maximum speed.

What’s the deal with PyXL? “It’s actual Python executed in silicon,” notes the project site. “A custom toolchain compiles a .py file into CPython ByteCode, translates it to a custom assembly, and produces a binary that runs on a pipelined processor built from scratch.” Currently, there isn’t a hard silicon version of PyXL — no surprise given what it costs to make a chip from scratch. For now, it exists as logic running on a Zynq-7000 FPGA on a Arty-Z7-20 devboard. There’s an ARM CPU helping out with setup and memory tasks for now, but the Python code is executed entirely in dedicated hardware.

The headline feature of PyXL is speed. A comparison video demonstrates this with a measurement of GPIO latency. In this test, the PyXL runs at 100 MHz, achieving a round-trip latency of 480 nanoseconds. This is compared to MicroPython running on a PyBoard at 168 MHz, which achieves a much slower 15,000 nanoseconds by comparison. The project site claims PyXL can be 30x faster than MicroPython based on this result, or 50x faster when normalized for the clock speed differences.

Python has never been the most real-time of languages, but efforts like this attempt to push it this way. The aim is that it may finally be possible to write performance-critical code in Python from the outset. We’ve taken a look at Python in the embedded world before, too, albeit in very different contexts.

Strenghtening FDM prints has been discussed in detail over the last years. Solutions and results vary as each one’s desires differ. Now [TenTech] shares his latest improvements on his post-processing script that he first created around January. This script literally bends your G-code to its will – using non-planar, interlocking sine wave deformations in both infill and walls. It’s now open-source, and plugs right into your slicer of choice: PrusaSlicer, OrcaSlicer, or Bambu Studio. If you’re into pushing your print strength past the limits of layer adhesion, but his former solution wasn’t quite the fit for your printer, try this improvement.

Traditional Fused Deposition Modeling (FDM) prints break along layer lines. What makes this script exciting is that it lets you introduce alternating sine wave paths between wall loops, removing clean break points and encouraging interlayer grip. Think of it as organic layer interlocking – without switching to resin or fiber reinforcement. You can tweak amplitude, frequency, and direction per feature. In fact, the deformation even fades between solid layers, allowing smoother transitions. Structural tinkering at its finest, not just a cosmetic gimmick.

This thing comes without needing a custom slicer. No firmware mods. Just Python, a little G-code, and a lot of curious minds. [TenTech] is still looking for real-world strength tests, so if you’ve got a test rig and some engineering curiosity, this is your call to arms.

The script can be found in his Github. View his full video here , get the script and let us know your mileage!

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