Machine Learning Makes Sure Your LOLs Are Genuine

There was a time not too long ago when “LOL” actually meant something online. If someone went through the trouble of putting LOL into an email or text, you could be sure they were actually LOL-ing while they were typing — it was part of the social compact that made the Internet such a wholesome and inviting place. But no more — LOL has been reduced to a mere punctuation mark, with no guarantee that the sender was actually laughing, chuckling, chortling, or even snickering. What have we become?

To put an end to this madness, [Brian Moore] has come up with the LOL verifier. Like darn near every project we see these days, it uses a machine learning algorithm — EdgeImpulse in this case. It detects a laugh by comparing audio input against an exhaustive model of [Brian]’s jocular outbursts — he says it took nearly three full minutes to collect the training set. A Teensy 4.1 takes care of HID duties; if a typed “LOL” correlates to some variety of laugh, the initialism is verified with a time and date stamp. If your LOL was judged insincere – well, that’s on you. See what you think of the short video below — we genuinely LOL’d. And while we’re looking forward to a ROTFL verifier, we’re not sure we want to see his take on LMAO.

Hats off to [Brian] for his attempt to enforce some kind of standards online. You may recall his earlier attempt to make leaving Zoom calls a little less awkward, which we also appreciate.

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Blender Builds LEGO Models

Blender is a free and open source computer graphics package that’s used in the production of everything from video games to feature films. Now, as demonstrated by [Joey Carlino], the popular program can even be used to convert models into LEGO.

This new feature available in Blender 3.4 allows for the use of instance attributes in a way that a large number of points on a model can be created without causing undue strain on (and possible crashing of) the software. Essentially, an existing model is split into discrete points at specific intervals. The spacing of the intervals is set to be exactly that of LEGO bricks, which gives the model the low-resolution look of a real LEGO set. From there, a model brick is created and placed at each of these points, and then colors can be transferred to the bricks individually.

The demonstration that [Joey] uses is converting a beach ball model to LEGO, but using these tools on other models delivers some striking results. He goes over a lot of the details on how to create these, and it would only be a short step from there to ordering the bricks themselves. Or, using these models and sending them over to a 3D printer straight from Blender itself. Not bad for free software!

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Scratch-Built RC Excavator Is A Model Making Tour De Force

Some projects just take your breath away with their level of attention to detail. This scratch-built RC-controlled model excavator is not only breathtaking in its detail, but also amazing for the materials and tools used to create it.

We’ve got to be honest, we’ve been keeping an eye on the progress [Vang Hà] has been making on this build for a few weeks now. The first video below is a full tour of the finished project, which is painstakingly faithful to the original, a Caterpiller 390F tracked excavator. As impressive as that is, though, you’ve got to check out the build process that starts with fabricating the tracks in the second video below. The raw material for most of the model is plain gray PVC pipe, which is sliced and diced into flat sheets, cut into tiny pieces using a jury-rigged table saw, and heat formed to create curved pieces. Check out the full playlist for a bounty of fabrication delights, like tiny hinges and working latches.

We can’t possibly heap enough praise onto [Vang Hà] for his craftsmanship, but that’s not all we love about this one. There are tons of helpful tips here, and plenty of food for thought for more practical builds. We’re thinking about that full set of working hydraulic cylinders that operates the boom, the dipper, and the bucket, as well as the servo-operated hydraulic control valves. All of it is made from scratch, of course, and mostly from PVC. Keep that in mind for a project where electric motors or linear actuators just won’t fill the bill.

If this construction technique seems familiar to you, it could because we featured a toolbox made out of similarly processed PVC pipes back in June.

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Mini Falcon 9 Uses NASA Software

[T-Zero Systems] has been working on his model Falcon 9 rocket for a while now. It’s an impressive model, complete with thrust vectoring, a microcontroller which follows a predetermined flight plan, a working launch pad, and even legs to attempt vertical landings. During his first tests of his model, though, there were some issues with the control system software that he wrote so he’s back with a new system that borrows software from the Space Shuttle.

The first problem to solve is gimbal lock, a problem that arises when two axes of rotation line up during flight, causing erratic motion. This is especially difficult because this model has no ability to control roll. Solving this using quaternion instead of Euler angles involves a lot of math, provided by libraries developed for use on the Space Shuttle, but with the extra efficiency improvements the new software runs at a much faster rate than it did previously. Unfortunately, the new software had a bug which prevented the parachute from opening, which wasn’t discovered until after launch.

There’s a lot going on in this build behind-the-scenes, too, like the test rocket motor used for testing the control system, which is actually two counter-rotating propellers that can be used to model the thrust of a motor without actually lighting anything on fire. There’s also a separate video describing a test method which validates new hardware with data from prior launches. And, if you want to take your model rocketry further in a different direction, it’s always possible to make your own fuel as well.

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Patents And The Missing Museum

A beautiful chapter of the history of invention in the United States ended with a fire in 1880. Well, the fire took place in 1877, but the wheels of government turn slowly. For the first 90 years that patents were granted in the USA, applications were required to be accompanied by a working model – to prove that the idea works and rule out “the perpetual motion cranks”.

During this time, the US Patent Office put all of these models on display, or at least as many of them as they could. The idea was that, alongside the printed documents, people would learn from seeing the inventions in the flesh. This tremendous resource got the Patent Office nicknamed the “Temple of Invention”, and rightly so. Many of the crucial innovations of the industrial revolution were there, in miniature. From Samuel Morse’s model telegraph, through Eli Whitney’s cotton gin, to more than a thousand inventions of Thomas Edison’s, working models were to be seen in the flesh, if in the small. We can only imagine how awe-inspiring it would have been to walk through those halls.

Two fires put significant dents in this tremendous collection. First in 1836, in a fire that consumed most of the approximately 10,000 patents that had been issued to that date, models and paper copies alike. Ironically, these included the patent for the first cast-iron fire hydrant. This fire was so devastating that it led to a dramatic patent reform in that same year, and to the building of a new fireproof Patent Office.

And the “new” Patent Office building still stands today, and proudly displayed patent models until the fire that broke out inside the building in 1877. (The contents of the building weren’t fireproof.) In this second fire, brave employees saved many of the works by staying and battling the fire from inside, but the second demoralizing beatdown, and the accelerating number of patent applications, it became obvious that there just wasn’t enough space to store a model of each patentable invention, and the requirement was dropped in 1880.

A small portion of the remaining patent models were put on display in one wing of the National Portrait Gallery, housed in the Patent Office building, and I had the wonderful opportunity to see it live in the early 2000s. I have no idea if the exhibit is still there – I’m guessing it’s not. The Smithsonian owns the lion’s share of the existing models, and we imagine they are in a warehouse somewhere, like at the end of Raiders of the Lost Ark.

A shame, because seeing a real 3D model of a thing is different from seeing line drawings. Maybe in the future, 3D CAD drawings will take their place? They’d be a lot easier to save in event of a fire.

Building A Better 3D Scanner With An IPhone, And Making Art

Apple’s FaceID system uses infrared depth-sensing technology to authenticate people via their faces. It can also be used for simple 3D scanning, and [Scott Yu-Jan] found a better way to do that.

The main problem with using an iPhone as a 3D scanner in this manner is that the sensor is built into the front side of the device. It’s great for scanning your own face, but if you’re trying to scan an object, you can no longer see the iPhone’s screen. [Scott] solved this problem by slapping together a handheld 3D printed device to hold the iPhone along with an external monitor. This allowed Scott to scan while still seeing what was going on.

Having noticed that some of the 3D scanning apps produced strange, glitchy results when scanning faces, [Scott] decided to innovate artistically. He employed [Andrea] to model, took some scans, and Photoshopped the results into some impressive posters.

Overall, [Scott] demonstrates that it’s relatively easy to repurposed the iPhone for improved 3D scanning. With a simple design, he has a handheld scanner that works way better than just the phone on its own. Alternatively, consider getting into photogrammetry instead.

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Using Statistics Instead Of Sensors

Statistics often gets a bad rap in mathematics circles for being less than concrete at best, and being downright misleading at worst. While these sentiments might ring true for things like political polling, it hides the fact that statistical methods can be put to good use in engineering systems with fantastic results. [Mark Smith], for example, has been working on an espresso machine which can make the perfect shot of coffee, and turned to one of the tools in the statistics toolbox in order to solve a problem rather than adding another sensor to his complex coffee-brewing machine.

To make espresso, steam is generated which is then forced through finely ground coffee. [Mark] found that his espresso machine was often pouring too much or too little coffee, and in order to improve his machine’s accuracy in this area he turned to the linear regression parameter R2, also known as the coefficient of determination. By using a machine learning algorithm tuned to this value, which assesses predictable variation in a data set, a computer can more easily tell when the coffee begins pouring out of the portafilter and into the espresso cup based on the pressure and water flow in the machine itself rather than using some other input such as the weight of the cup.

We have seen in the past how seriously [Mark] takes his coffee-making, and this is another step in a series of improvements he has made to his equipment. In this iteration, he has additionally produced a simulation in JupyterLab to better assist him in modeling the system and making even more accurate predictions. It’s quite a bit more effort than adding sensors, but since his espresso machine already included quite a bit of computing power it’s not too big a leap for him to make.