How do you celebrate your YouTube channel passing the 7400 subscriber mark? If you are [Low Level JavaScript], the answer is obvious: You create a 7400 TTL logic simulator in JavaScript. The gate simulations progress from simple gates up to flipflops and registers. You could probably build a 7400-based computer virtually with this code.
In addition to just being fun and interesting, there were a lot of links of interest in the video (see below) and its comments. For one, someone watching the channel took the code and made a Verilog-like IDE that is impressive.
Students of ARM history will know that the origins of the wildly popular processor architecture lie in the British computer manufacturer Acorn (the original “A” in “ARM”). The first mass-market ARM-based products were their Archimedes line of desktop computers. A RISC-based computer in a school or home was significantly ahead of the curve in the mid 1980s and there was no off-the-shelf software, so alongside the new chips came a new operating system that would eventually bear the name Risc OS.
It’s since become one of those unexpected pieces of retrocomputing history that refuses to die, and remains in active development with a new version 5.28 of its open-source variant just released. Best of all, after supporting the Raspberry Pi since the earliest boards, it now runs on a Raspberry Pi 4. The original ARM operating system has very much kept up with the times, and can now benefit from the extra power of the latest hardware from Cambridge. The new release deals with a host of bugs, as well as bringing speed increases, security fixes, and other improvements. For those whose first experience of a GUI came via the Archimedes in British schools, the news that the built-in Paint package has received a thorough update will bring a smile.
The attraction of Risc OS aside from its history and speed lies in its being understandable in operation for those wishing to learn about how an OS works under the hood. It’s likely that for most of us it won’t replace our desktops any time soon, but it remains an interesting diversion to download and explore. If you’d like to read more about early ARM history then we’d like to point you at our piece on Sophie Wilson, the originator of the ARM architecture.
Recent price drops put entry level masked stereolithography (MSLA) resin 3D printers at around $200 USD, making them a very compelling tool for makers and hackers. But as you might expect, getting the price this low often involves cutting several corners. One of the ways manufacturers have made their machines so cheap is by simplifying the electronics and paring down the feature set to the absolute minimum.
So it was hardly a surprise for [Luiz Ribeiro] to find that his new Elegoo Mars Pro didn’t offer WiFi connectivity or a remote control interface. You’re supposed to just stick a USB flash drive into the printer and select the object you want to print from its menu system. But that doesn’t mean he couldn’t hack the capability in himself.
Monitoring a print with Mariner.
If this were a traditional 3D printer, he might have installed OctoPrint and been done with it. But resin printers are a very different beast. In the end, [Luiz] had to develop his own remote control software that worked around the unique limitations of the printer’s electronics. His software runs on a Raspberry Pi Zero and uses Linux’s “USB Gadget” system to make it appear as a flash drive when plugged into the USB port on the Elegoo Mars Pro.
This allows sending object files to the printer over the network, but there was a missing piece to the puzzle. [Luiz] still needed to manually go over to the printer and select which file he wanted to load from the menu. Until he realized there was an exposed serial port on control board that allowed him to pass commands to the printer. Between the serial connection and faux USB Mass Storage device, his mariner software has full control over the Mars Pro and is able to trigger and monitor print jobs remotely.
In the Star Wars universe, not everyone uses a lightsaber, and those who do wield them had to build them themselves. There’s something to be said about that strategy. Building a car or a radio is a great way to learn how those things work. That’s what [Low Level JavaScript] points out about regular expressions. Sure, a lot of people think they are scary. So why not write your own regular expression parser and engine? Get that under your belt and you’ll probably never fear another regular expression.
Of course, most of us probably won’t do it ourselves, but you can still watch the process in the video below. The code is surprisingly short, but don’t expect all the bells and whistles you might find in Python or even Perl.
At this point, you’ve likely heard that the GitHub repository for youtube-dl was recently removed in response to a DMCA takedown notice filed by the Recording Industry Association of America (RIAA). As the name implies, this popular Python program allowed users to produce local copies of audio and video that had been uploaded to YouTube and other content hosting sites. It’s a critical tool for digital archivists, people with slow or unreliable Internet connections, and more than a few Hackaday writers.
It will probably come as no surprise to hear that the DMCA takedown and subsequent removal of the youtube-dl repository has utterly failed to contain the spread of the program. In fact, you could easily argue that it’s done the opposite. The developers could never have afforded the amount of publicity the project is currently enjoying, and as the code is licensed as public domain, users are free to share it however they see fit. This is one genie that absolutely won’t be going back into its bottle.
In true hacker spirit, we’ve started to see some rather inventive ways of spreading the outlawed tool. A Twitter user by the name of [GalacticFurball] came up with a way to convert the program into a pair of densely packed rainbow images that can be shared online. After downloading the PNG files, a command-line ImageMagick incantation turns the images into a compressed tarball of the source code. A similar trick was one of the ways used to distribute the DeCSS DVD decryption code back in 2000; though unfortunately, we doubt anyone is going to get the ~14,000 lines of Python code that makes up youtube-dl printed up on any t-shirts.
It’s worth noting that GitHub has officially distanced themselves from the RIAA’s position. The company was forced to remove the repo when they received the DMCA takedown notice, but CEO Nat Friedman dropped into the project’s IRC channel with a promise that efforts were being made to rectify the situation as quickly as possible. In a recent interview with TorrentFreak, Friedman said the removal of youtube-dl from GitHub was at odds with the company’s own internal archival efforts and financial support for the Internet Archive.
But as it turns out, some changes will be necessary before the repository can be brought back online. While there’s certainly some debate to be had about the overall validity of the RIAA’s claim, it isn’t completely without merit. As pointed out in the DMCA notice, the project made use of several automated tests that ran the code against copyrighted works from artists such as Taylor Swift and Justin Timberlake. While these were admittedly very poor choices to use as official test cases, the RIAA’s assertion that the entire project exists solely to download copyrighted music has no basis in reality.
With the Jetson Nano, NVIDIA has done a fantastic job of bringing GPU-accelerated machine learning to the masses. For less than the cost of a used graphics card, you get a turn-key Linux computer that’s ready and able to handle whatever AI code you throw at it. But if you’re trying to set up a lab for 30 students, the cost of even relatively affordable development boards can really add up.
Spoiler: These things don’t exist.
Which is why [Tea Vui Huang] has developed jetson-emulator. This Python library provides a work-alike environment to NVIDIA’s own “Hello AI World” tutorials designed for the Jetson family of devices, with one big difference: you don’t need the actual hardware. In fact, it doesn’t matter what kind of computer you’ve got; with this library, anything that can run Python 3.7.9 or better can take you through NVIDIA’s getting started tutorial.
So what’s the trick? Well, if you haven’t guessed already, it’s all fake. Obviously it can’t actually run GPU-accelerated code without a GPU, so the library [Tea] has developed simply pretends. It provides virtual images and even “live” camera feeds to which randomly generated objects have been assigned.
The original NVIDIA functions have been rewritten to work with these feeds, so when you call something like net.Classify(img) against one of them you’ll get a report of what faux objects were detected. The output will look just like it would if you were running on a real Jetson, down to providing fictitious dimensions and positions for the bounding boxes.
If you’re a hacker looking to dive into machine learning and computer vision, you’d be better off getting a $59 Jetson Nano and a webcam. But if you’re putting together a workshop that shows a dozen people the basics of NVIDIA’s AI workflow, jetson-emulator will allow everyone in attendance to run code and get results back regardless of what they’ve got under the hood.
Here’s a neat little trick: take the jaggies out of scaled fonts on the fly! This technique is for use on graphic displays where you might want to scale your fonts up. Normally you’d just write a 2×2 block of pixels for every area where there would have been one pixel and boom, larger font. Problem is, that also multiplies each empty area and you end up with jagged edges in the transitions that really catch your eye.
[David Johnson-Davies] entered big-brain mode and did something much cleverer than the obvious solution of using multiple font files. Turns out if you analyze the smoothing problem you’ll realize that it’s only the angled areas that are to blame, horizontal and vertical scaling are nice and smooth. [David’s] fix looks for checker patterns in what’s being drawn, adding a single pixel in the blank spots to smooth out the edge incredibly well!
The technique has been packaged up in a simple function that [David] wrote to play nicely in the Arduino ecosystem. However, the routine is straightforward and would be quick to implement no matter the language or controller. Keep this one in your back pocket!
Now if all you have on hand is an HD44780 character LCD, that one’s arguably even more fun to hack around on just because you’re so limited on going beyond the hard-coded font set. We’ve seen amazing things like using the custom character slots to play Tetris.
[David Johnson-Davies] entered big-brain mode and did something much cleverer than the obvious solution of using multiple font files. Turns out if you analyze the smoothing problem you’ll realize that it’s only the angled areas that are to blame, horizontal and vertical scaling are nice and smooth. [David’s] fix looks for checker patterns in what’s being drawn, adding a single pixel in the blank spots to smooth out the edge incredibly well!
