Like to see dominoes fall? [JK Brickworks] has got what you need, in the form of a never-ending ring of falling and resetting tiles. LEGO pieces are the star in this assembly, which uses a circular track and moving ramp to reset tiles after they have fallen. Timed just right, it’s like watching a kinetic sculpture harmoniously generating a soliton wave as tiles fall only to be endlessly reset in time to fall again.
It’s true that these chunky tiles aren’t actually dominoes — not only are they made from LEGO pieces and hinged to their bases, they have a small peg to assist with the reset mechanism. [JK Brickworks] acknowledges that this does stretch the definition of “dominos”, but if you’re willing to look past that, it’s sure fun to see the whole assembly in action.
The central hub in particular is a thing of beauty. For speed control, an IR sensor monitors a single domino’s up/down state and a LEGO Mindstorms EV3 with two large motors takes care of automation.
The video does a great job of showing the whole design process, especially the refinements and tweaks, that demonstrate the truly fun part of prototyping. [JK Brickworks] suggests turning on subtitles for some added details and technical commentary, but if you’re in a hurry skip directly to 4:55 to see it in action.
Many AI systems require huge training datasets in order to achieve their impressive feats. This applies whether or not you’re talking about an AI that works with images, natural language, or just about anything else. AI developers are starting to come under scrutiny for where they’re sourcing their datasets. Unsurprisingly, stock photo site Getty Images is at the forefront of this, and is now suing the creators of Stable Diffusion over the matter, as reported by The Verge.
Stability AI, the company behind Stable Diffusion, is the target of the lawsuit for one good reason: there’s compelling evidence the company used Getty Images content without permission. The Stable Diffusion AI has been seen to generate output images that actually include blurry approximations of the Getty Images watermark. This is somewhat of a smoking gun to suggest that Stability AI may have scraped Getty Images content for use as training material.
The copyright implications are unclear, but using any imagery from a stock photo database without permission is always asking for trouble. Various arguments will likely play out in court. Stability AI may make claims that their activity falls under fair use guidelines, while Getty Images may claim that the appearance of perverted versions of their watermark may break trademark rules. The lawsuit could have serious implications for AI image generators worldwide, and is sure to be watched closely by the nascent AI industry. As with any legal matter, just don’t expect a quick answer from the courts.
Sometimes you absolutely, positively need to know the angle of the cutting edge on a knife. When you do, the best tool for the job is a laser goniometer, and [Felix Immler] shows us three different ways to build one. (YouTube)
The underlying principle of all three of these builds is to project reflected laser light off a knife blade onto a scale going from 0-45˚. [Immler] shows a basic demonstration of this concept with a hinge toward the beginning of the video (after the break). Blades with multiple bevels will reflect light to each of the appropriate points on the scale.
The simplest version of the tool is a printed PDF scale attached to a wooden box with a hole for the blade to pass through. The next uses a large pipe end cap and a drilled-out piece of wood to create a more manageable measuring tool. Finally, [Immler] worked with a friend to design a 3D printed goniometer with differently-sized adapters to fit a variety of laser pointers.
The core of the build is an ESP8266, which queries an NTP time server to keep itself synced up with the current time as accurately as possible. It then controls a WS2812B LED strip to display the time. The strip itself is hidden in a 3D-printed housing behind an opaque wooden ring, with the light from the LEDs diffusing out nicely on to the wall upon which the clock is mounted.
The display shows three “hands” in the colors it projects on the wall. The red second hand is projected inside and outside the ring. The minute hand is green, and projects outside the ring. Meanwhile, the hour hand is blue, and projects inside the ring. Without any numerical markings, you won’t get an exact reading of the time, but you can figure it out closely enough. As a bonus, the clock looks like a stylish light-based wall sculpture and your guests may not even realizes it tells the time.
There was a time when to take a British rail journey was to receive a ticket barely changed since Victorian times — a small cardboard rectangle printed with the destination through which the inspector on the train would punch a hole. In recent decades these were replaced by credit-card-sized thin card, and now increasingly with scanable 2D codes from an app. These caught the attention of [eta], and she set about reverse engineering their operation.
The codes themselves are Aztec barcodes, similar to a QR code but with a single central fiducial mark. At first glance they resemble the codes used by non-UK ticketing systems, but she soon found out that they don’t follow the same standard. There followed a lengthy but fascinating trail of investigation, involving app decompilation of a dodgy copy of the ticket inspector app to find public keys, and then some work with a more reputably sourced app from another ticketing company.
Along the way it revealed a surprising amount of traveler data that maybe shouldn’t be in the public domain, and raises the question as to why the ticketing standard remains proprietary. It’s well worth a read.
Smartphone features used to come thick and fast. Cameras proliferated, navigation got added, and then Apple changed the game by finally making touch computing just work. Since then, truly new features have slowed to a trickle, but Apple’s innovative crash detection system has been a big deal where safety is concerned.
The problem? It’s got a penchant for throwing false positives when iPhone and Apple Watch users are in no real danger at all. We first covered this problem last year, but since then, the wintery season has brought yet more issues for already-strained emergency responders.
ChatGPT has been put to all manner of silly uses since it first became available online. [Engineering After Hours] decided to see if its coding skills were any chop, and put it to work programming a circular saw. Pun intended.
The aim was to build a line following robot armed with a circular saw to handle lawn edging tasks. The circular saw itself consists of a motor with a blade on it, and precisely no safety features. It’s mounted on the front of a small RC car with a rack and pinion to control its position. [Engineering After Hours] has some sage advice in this area: don’t try this at home.
ChatGPT was not only able to give advice on what parts to use, it was able to tell [Engineering After Hours] on how to hook everything up to an Arduino and even write the code. The AI language model even recommended a PID loop to control the position of the circular saw. Initial tests were messy, but some refinement got things impressively functional.
As a line following robot, the performance is pretty crummy. However, as a robot programmed by an AI, it does pretty okay. Obviously, it’s hard to say how much help the AI had, and how many corrections [Engineering After Hours] had to make to the code to get everything working. But the fact that this kind of project is even possible shows us just how far AI has really come.