Solid Tips For Casting Colored Silicone Tires

For people who work with wheeled robots or RC vehicles, sooner or later one gets interested in making custom tires instead of dealing with whatever is available off the shelf.¬†[concreted0g]’s preferred method is to design and 3D print wheel hubs, then cast some custom silicone tires to fit over them. Of course, the devil is in the details and this process can be a bit messy, so he’s shared useful tips on how to get reliable results with simple materials.

The casting material is cheap silicone caulking from a hardware store, and color can be added with a small amount of cheap acrylic paint. A few drops of glycerin added to the silicone thins it out slightly and helps it flow into a mold better. Mix well (the paint will also serve as a visual indicator of how well it is mixed), then scoop the mixture into the mold while trying to avoid creating air pockets. If your mold is in two pieces, assemble the mold and remove any overflow, then let it sit undisturbed for at least several hours while it cures.

Mounting the resulting tire to a wheel hub can be done with a thin film of super glue, which seems to work perfectly well for small tires and is easy to apply.

The rules are going to be a bit different for big objects. We know that silicone caulking can have difficulty fully curing when it’s applied thickly, especially when sealed into a mold with little to no airflow. In such cases, adding cornstarch (in about a 5:1 ratio of silicone to cornstarch by volume) is all that it takes to cure even thick wads of goop in less than an hour. Stirring cornstarch in tends to introduce more air bubbles into the mixture, but for larger pieces that can be an acceptable tradeoff. Cheap silicone caulking is versatile stuff, one just needs to know what to expect, and take a few steps to deal with the messiness.

Need something tougher? Maybe check out using slices of automotive silicone hose for robot wheels to get something that works just as well, but is a lot more durable.

AI Upscaling And The Future Of Content Delivery

The rumor mill has recently been buzzing about Nintendo’s plans to introduce a new version of their extremely popular Switch console in time for the holidays. A faster CPU, more RAM, and an improved OLED display are all pretty much a given, as you’d expect for a mid-generation refresh. Those upgraded specifications will almost certainly come with an inflated price tag as well, but given the incredible demand for the current Switch, a $50 or even $100 bump is unlikely to¬†dissuade many prospective buyers.

But according to a report from Bloomberg, the new Switch might have a bit more going on under the hood than you’d expect from the technologically conservative Nintendo. Their sources claim the new system will utilize an NVIDIA chipset capable of Deep Learning Super Sampling (DLSS), a feature which is currently only available on high-end GeForce RTX 20 and GeForce RTX 30 series GPUs. The technology, which has already been employed by several notable PC games over the last few years, uses machine learning to upscale rendered images in real-time. So rather than tasking the GPU with producing a native 4K image, the engine can render the game at a lower resolution and have DLSS make up the difference.

The current model Nintendo Switch

The implications of this technology, especially on computationally limited devices, is immense. For the Switch, which doubles as a battery powered handheld when removed from its dock, the use of DLSS could allow it to produce visuals similar to the far larger and more expensive Xbox and PlayStation systems it’s in competition with. If Nintendo and NVIDIA can prove DLSS to be viable on something as small as the Switch, we’ll likely see the technology come to future smartphones and tablets to make up for their relatively limited GPUs.

But why stop there? If artificial intelligence systems like DLSS can scale up a video game, it stands to reason the same techniques could be applied to other forms of content. Rather than saturating your Internet connection with a 16K video stream, will TVs of the future simply make the best of what they have using a machine learning algorithm trained on popular shows and movies?

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Giant Nintendo Switch Is Actually Playable

The Nintendo Switch has been a hugely successful console for the century-old former playing card manufacturer. At least part of that success has come from its portability, of which [Michael Pick] has probably lost a bit with his 65-pound giant Nintendo Switch built for St. Jude’s Children’s Hospital. (Video, embedded below.) What he’s lost in portability has been more than made up in coolness-factor, though, and we’re sure the kids will appreciate that they can still play the monster gaming machine.

From its plywood body to the 3D-printed buttons, the supersized build looks solid. Docked inside the left Joy-Con is the actual console powering its big brother. Perhaps the biggest surprise, however, is that tiny (well, normal-sized) Joy-Cons are also hidden inside. These are manipulated via servos for the buttons and a direct pass-through setup for the joysticks to control games on the Switch.

While the Joy-Cons are unmodified and completely removable, [Michael] does recognize this isn’t necessarily the ideal solution. But he was certain it was a hack he could make work in the time he had, so he went for it. He’s looked into the controller emulation possible with Teensys and would probably use that solution for any giant Switch projects in the future. Of course, with this build, players can still pair regular Joy-Cons and pro controllers for more practical gaming.

Most Nintendo mods we see attempt to make the console smaller, not larger, so this is an eye-catching change of pace. Unfortunately, we don’t get to see the colossal console in action after it was installed, only some stills of hospital staff wheeling it in the front doors. But we can imagine that the children’s smiles are at least as big as ours were when we saw it.

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Honey, I Shrunk The Arduino Core

High-level programming languages do a great job of making a programmer’s job easier, but these languages often leave a lot of efficiency on the table as a compromise. While a common thought is to move into a lower-level language like assembly to improve on a program’s speed or memory use, there’s often a lot that can be done at the high level before resorting to such extremes. This, of course, is true of the Arduino platform as well, as [NerdRalph] demonstrates by shrinking the size of the Arduino core itself.

[NerdRalph] had noticed that the “blink” example program actually includes over 1 kB of extraneous code, and that more complicated programs include even more cruft. To combat this issue, he created ArduinoShrink, which seeks to make included libraries more modular and self-contained. It modifies some of the default registers and counters to use less memory and improve speed, and is also designed to improve interrupt latency as well by changing when the Arduino would otherwise disable interrupts.

While there are some limits to ArduinoShrink, such as needing to know specifics about the pins at compile time, for anyone writing programs for Arduinos that are memory-intensive or need improvements in timing, this could be a powerful new tool. If you’d prefer to go in the opposite direction to avoid ever having to learn C or assembly, though, you can always stick with running Python on your embedded devices.