The Need For Speed?

Por: Elliot Williams

24 Mayo 2025 at 14:00

We wrote up a video about speeding up Arduino code, specifically by avoiding DigitalWrite. Now, the fact that DigitalWrite is slow as dirt is long known. Indeed, a quick search pulls up a Hackaday article from 2010 demonstrating that it’s fifty times slower than toggling the pin directly using the native pin registers, but this is still one of those facts that gets periodically rediscovered from generation to generation. How can this be new again?

First off, sometimes you just don’t need the speed. When you’re just blinking LEDs on a human timescale, the general-purpose Arduino functions are good enough. I’ve written loads of useful firmware that fits this description. When the timing requirements aren’t tight, slow as dirt can be fast enough.

But eventually you’ll want to build a project where the old slow-speed pin toggling just won’t cut it. Maybe it’s a large LED matrix, or maybe it’s a motor-control application where the loop time really matters. Or maybe it’s driving something like audio or video that just needs more bits per second. One way out is clever coding, maybe falling back to assembly language primitives, but I would claim that the right way is almost always to use the hardware peripherals that the chipmakers gave you.

For instance, in the end of the video linked above, the hacker wants to drive a large shift register string that’s lighting up an LED matrix. That’s exactly what SPI is for, and coming to this realization makes the project work with timing to spare, and in just a few lines of code. That is the way.

Which brings me to the double-edged sword that the Arduino’s abstraction creates. By abstracting away the chips’ hardware peripherals, it makes code more portable and certainly more accessible to beginners, who don’t want to learn about SPI and I2C and I2S and DMA just yet. But by hiding the inner workings of the chips in “user friendly” libraries, it blinds new users to the useful applications of these same hardware peripherals that clever chip-design engineers have poured their sweat and brains into making do just exactly what we need.

This isn’t really meant to be a rant against Arduino, though. Everyone has to start somewhere, and the abstractions are great for getting your feet wet. And because everything’s open source anyway, nothing stops you from digging deeper into the datasheet. You just have to know that you need to. And that’s why we write up videos like this every five years or so, to show the next crop of new hackers that there’s a lot to gain underneath the abstractions.

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Understanding Linear Regression

Hackaday

Por: Al Williams

9 Mayo 2025 at 02:00

Although [Vitor Fróis] is explaining linear regression because it relates to machine learning, the post and, indeed, the topic have wide applications in many things that we do with electronics and computers. It is one way to use independent variables to predict dependent variables, and, in its simplest form, it is based on nothing more than a straight line.

You might remember from school that a straight line can be described by: y=mx+b. Here, m is the slope of the line and b is the y-intercept. Another way to think about it is that m is how fast the line goes up (or down, if m is negative), and b is where the line “starts” at x=0.

[Vitor] starts out with a great example: home prices (the dependent variable) and area (the independent variable). As you would guess, bigger houses tend to sell for more than smaller houses. But it isn’t an exact formula, because there are a lot of reasons a house might sell for more or less. If you plot it, you don’t get a nice line; you get a cloud of points that sort of group around some imaginary line.

There are mathematical ways to figure out what line you should imagine, but you can often eyeball it, too. The real trick is evaluating the quality of that imaginary line.

To do that, you need an error measure. If you didn’t know better, you’d probably think expressing the error in terms of absolute value would be best. You know, “this is 10 off” or whatever. But, as [Vitor] explains, the standard way to do this is with a squared error term R². Why? Read the post and find out.

For electronics, linear regression has many applications, including interpreting sensor data. You might also use it to generalize a batch of unknown components, for example. Think of a batch of transistors with different Beta values at different frequencies. A linear regression will help you predict the Beta and the error term will tell you if it is worth using the prediction or not. Or, maybe you just want to make the perfect cup of coffee.

Hackaday
Digital Squid’s Behavior Shaped by Neural Network
26 Abril 2025 at 11:00

Digital Squid’s Behavior Shaped by Neural Network

Hackaday

Por: Bryan Cockfield

26 Abril 2025 at 11:00

In the 90s, a video game craze took over the youth of the world — but unlike today’s games that rely on powerful PCs or consoles, these were simple, standalone devices with monochrome screens, each home to a digital pet. Often clipped to a keychain, they could travel everywhere with their owner, which was ideal from the pet’s perspective since, like real animals, they needed attention around the clock. [ViciousSquid] is updating this 90s idea for the 20s with a digital pet squid that uses a neural network to shape its behavior.

The neural network that controls the squid’s behavior takes a large number of variables into account, including whether or not it’s hungry or sleepy, or if it sees food. The neural network adapts as different conditions are encountered, allowing the squid to make decisions and strengthen its algorithms. [ViciousSquid] is using a Hebbian learning algorithm which strengthens connections between neurons which activate often together. Additionally, the squid’s can form both short- and long-term memories, and the neural network can even form new neurons on its own as needed.

[ViciousSquid] is still working on this project, and hopes to eventually implement a management system in the future, allowing the various behavior variables to be tracked over time and overall allow it to act in a way more familiar to the 90s digital pets it’s modeled after. It’s an interesting and fun take on those games, though, and much of the code is available on GitHub for others to experiment with as well. For those looking for the original 90s games, head over to this project where an emulator for Tamagotchis was created using modern microcontroller platforms.

LLMs Coming for a DNA Sequence Near You

Hackaday

Por: Navarre Bartz

25 Abril 2025 at 02:00

An illustration of two translucent blue hands knitting a DNA double helix of yellow, green, and red base pairs from three colors of yarn. Text in white to the left of the hands reads: "Evo 2 doesn't just copy existing DNA -- it creates truly new sequences not found in nature that scientists can test for useful properties."

While tools like CRISPR have blown the field of genome hacking wide open, being able to predict what will happen when you tinker with the code underlying the living things on our planet is still tricky. Researchers at Stanford hope their new Evo 2 DNA generative AI tool can help.

Trained on a dataset of over 100,000 organisms from bacteria to humans, the system can quickly determine what mutations contribute to certain diseases and what mutations are mostly harmless. An “area we are hopeful about is using Evo 2 for designing new genetic sequences with specific functions of interest.”

To that end, the system can also generate gene sequences from a starting prompt like any other LLM as well as cross-reference the results to see if the sequence already occurs in nature to aid in predicting what the sequence might do in real life. These synthetic sequences can then be made using CRISPR or similar techniques in the lab for testing. While the prospect of building our own Moya is exciting, we do wonder what possible negative consequences could come from this technology, despite the hand-wavy mention of not training the model on viruses to “to prevent Evo 2 from being used to create new or more dangerous diseases.”

We’ve got you covered if you need to get your own biohacking space setup for DNA gels or if you want to find out more about powering living computers using electricity. If you’re more curious about other interesting uses for machine learning, how about a dolphin translator or discovering better battery materials?

Easy With AI
Reminisce.ai
25 Agosto 2023 at 12:17

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