Get Over Your Fears

October 24, 2020 by Elliot Williams 23 Comments

Some projects are just too complex, that’s for sure. But I’d be willing to bet that some things you think are too difficult actually aren’t, and it may be that all you need to get over your personal hurdle is a good demonstration. Here come three cases in point.

I was looking at the new Raspberry Pi Compute Module last weekend. They have a whole bunch of high-speed traces: things like Gigabit Ethernet, HDMI, and those crazy-fast SDI serial camera interfaces. I have no experience in high-speed design and layout at all, and frankly it gives me the willies. But the Raspberries also shipped me an IO demo board, and concomitant KiCAD design files, with the review board. Looking at it, they were just wires — maybe pairwise length-matched and impedance controlled — but also just wires. Opening up the KiCAD board file and clicking on the traces just like I do with my own designs, I’m a lot less scared. That was a revelation for me.

In a great writeup of his experience building ten different Linux single-board-computers from scratch, Jay Carlson had a similar effect on me. I would never have considered breaking out the hotplate for some CPU-and-DRAM action, and I’ve never had to lay out a PCB with a high density BGA chip before either. I’m not quite into Dunning-Kruger territory yet; I still have a healthy respect for the layout intricacies in fanning out a tight BGA CPU into a DRAM. But Jay’s frank assessments of what is easy and what is hard make it all seem within the realm of the doable.

As Mike and I were talking on the podcast about Jay’s work, Mike came clean about his fear of BGAs. I’ve done enough reflow-plate soldering, with parts that have a lead pitch that’s a factor of two finer than the 0.8 mm pitch BGAs in question, so it doesn’t seem implausible to me. And I’m 100% sure Mike could pull it off too, but he is in need of a BGA guru. Any good hobbyist videos out there?

Being a nerdy type, I’m much more focused on the knowledge and the inspiration, but maybe the courage is equally important — at least I think I undervalue it. I don’t need to lay out HDMI lines, or build a from-scratch Linux box, but I am no longer afraid that I couldn’t, and that’s because I’ve seen detailed examples of fellow hackers who’ve done the same. I might not get it right on the first shot, but I’m not afraid to try, and I wouldn’t have said the same before looking over other folks’ shoulders. Forza e corragio!

Spare Parts Express

October 17, 2020 by Elliot Williams 33 Comments

I’ve got spare parts, and I cannot lie.

This week I’m sending out two care packages to friends and coworkers because I’ve got too many hackables on hand, and not enough time to hack them all. One is a funky keyboard, and the other is an FPGA dev board, but that’s not the point. The point is that the world is too interesting, and many of us have more projects piled up in the to-do box, with associated gear, than we’ll ever have time to complete.

Back in the before-times, we would meet up, talk about our ongoing hacks, and invariably someone would say “oh you need an X, I’ve got half a box of them” and send you one. Or maybe you’d be the one with the extra widgets on hand. I know I’ve happily been in both positions.

Either way, it’s a win for the giver, who gets to take a widget off the widget pile, for the receiver, who doesn’t have to go to the widget store, and for the environment, which has to produce fewer widgets. (My apologies to the widget manufacturers and middlemen.)

This reminded me of Lenore Edman and Windell Oskay’s Great Internet Migratory Box Of Electronics Junk back in the late aughts. Trolling through the wiki was like a trip down memory lane. This box visited my old hackerspace, and then ended up with Bunnie Huang. Good times, good people, good hacker junk! And then there’s our own Brian Benchoff’s Travelling Hacker Box and spinoffs.

These are great and fun projects, but they all end up foundering in one respect: to make sense, the value of goods taken and received has to exceed the cost of the postage, and if you’re only interested in a few things in any given box, that’s a lot of dead weight adding to the shipping cost.

So I was trying to brainstorm a better solution. Some kind of centralized pinboard, where the “have too many h-bridge drivers” folks can hook up with the “need an h-bridge” people? Or is this ad-hoc social network that we already have working out well enough?

What do you think? How can we get the goods to those who want to work on them?

Hardware Vs Software: Fight!

October 10, 2020 by Elliot Williams 41 Comments

It’s one of the great cliches in the hacker world: the hardware type and the software type. You can tell which of these two you are quite easily. When a project is actually 20% done, but you think it’s 90% done, and you say to yourself “And the rest is a simple matter of software”, you’re a hardware type. Ask anyone who has read my code, and they’ll tell you, I’m a hardware type.

Along with my blindness to the difficulties of getting the code right, I’ve also admittedly got an underappreciation of what powers lie in the dark typing arts. But I am not too proud to tip my hat when I see an awesome application of the soft stuff. Case in point: this Go board sequencer that we ran last week. An overhead webcam parses players’ moves as they put black and white stones down while playing the game of Go, and turns this into music.

The pure software type will be saying “but there’s a webcam and a Go board”. And indeed, that’s true. There are physical elements to this project that anchor it in the shared reality of the two people playing. But a hardware project this isn’t; it’s OpenCV and Max/MSP that make it work.

For comparison, look at the complexity of this similar physical sequencer. It’s got a 16 x 16 array of LEDs and switches and a CNC milled, primed, and painted surface that’s the size of a twin bed. Sawdust and hand-soldering: that’s a hardware project.

What I love about the Go sequencer is that it uses software just right. The piece is still physical. It could have just as easily been a VR world, where the two people would interact with each other only inside their goggles. But somehow that’s not quite as human as putting stones on a wooden board, sitting across from, and maybe even looking at, your opponent. The players aren’t forced to think about the software. They don’t feel like they’re playing a video game.

But at the same time, the software side of things makes all of the horrible hardware problems go away. Nobody is soldering a rat’s nest of 169 switches. There’s a webcam plugged into the USB port of a laptop. There’s a deep simplicity there.

Should you always trade out arcade buttons for OpenCV? Absolutely not! But is it worth considering the soft side when doing it in hardware is just too, well, hard? I’m open.

Paying It Forward

October 3, 2020 by Elliot Williams 21 Comments

It’s all those little things. A month ago, I was working on the axes for a foam-cutting machine. (Project stalled, will pick back up soon!) A week ago, somewhere else on the Internet, people were working on sliders that would ride directly on aluminum rails, a problem I was personally experiencing, and recommended using drawer-glide tape — a strip of PTFE or UHMW PE with adhesive backing on one side. Slippery plastic tape solves the metal-on-metal problem. It’s brilliant, it’s cheap, and it’s just a quick trip to the hardware store.

Just a few days ago, we covered another awesome linear-motion mechanical build in the form of a DIY camera rig that uses a very similar linear motion system to the one I had built as well: a printed trolley that slides on skate bearings over two rails of square-profile extruded aluminum. He had a very nice system of anchoring the spacers that hold the two rails apart, one of the sticking points in my build. I thought I’d glue things together, but his internal triangle nut holders are a much better solution because epoxy doesn’t like to stick to anodized aluminum. (And Alexandre, if you’re reading, that UHMW PE tape is just what you need to prevent bearing wear on your aluminum axes.)

Between these events, I got a message thanking me for an article that I wrote four years ago on debugging SPI busses. Apparently, it helped a small company to debug a problem and get their product out the door. Hooray!

So in one week, I got help from two different random strangers on a project that neither of them knew I was working on, and I somehow saved a startup. What kind of crazy marvelous world is this? It’s become so normal to share our ideas and experience, at least in our little corner of the Internet, that I sometimes fail to be amazed. But it’s entirely amazing. I know we’ve said it before, but we are living in the golden era of sharing ideas.

Thanks to all of you out there, and Read More Hackaday!

Twitter: It’s Not The Algorithm’s Fault. It’s Much Worse.

September 26, 2020 by Elliot Williams 160 Comments

Maybe you heard about the anger surrounding Twitter’s automatic cropping of images. When users submit pictures that are too tall or too wide for the layout, Twitter automatically crops them to roughly a square. Instead of just picking, say, the largest square that’s closest to the center of the image, they use some “algorithm”, likely a neural network, trained to find people’s faces and make sure they’re cropped in.

The problem is that when a too-tall or too-wide image includes two or more people, and they’ve got different colored skin, the crop picks the lighter face. That’s really offensive, and something’s clearly wrong, but what?

A neural network is really just a mathematical equation, with the input variables being in these cases convolutions over the pixels in the image, and training them essentially consists in picking the values for all the coefficients. You do this by applying inputs, seeing how wrong the outputs are, and updating the coefficients to make the answer a little more right. Do this a bazillion times, with a big enough model and dataset, and you can make a machine recognize different breeds of cat.

What went wrong at Twitter? Right now it’s speculation, but my money says it lies with either the training dataset or the coefficient-update step. The problem of including people of all races in the training dataset is so blatantly obvious that we hope that’s not the problem; although getting a representative dataset is hard, it’s known to be hard, and they should be on top of that.

Which means that the issue might be coefficient fitting, and this is where math and culture collide. Imagine that your algorithm just misclassified a cat as an “airplane” or as a “lion”. You need to modify the coefficients so that they move the answer away from this result a bit, and more toward “cat”. Do you move them equally from “airplane” and “lion” or is “airplane” somehow more wrong? To capture this notion of different wrongnesses, you use a loss function that can numerically encapsulate just exactly what it is you want the network to learn, and then you take bigger or smaller steps in the right direction depending on how bad the result was.

Let that sink in for a second. You need a mathematical equation that summarizes what you want the network to learn. (But not how you want it to learn it. That’s the revolutionary quality of applied neural networks.)

Now imagine, as happened to Google, your algorithm fits “gorilla” to the image of a black person. That’s wrong, but it’s categorically differently wrong from simply fitting “airplane” to the same person. How do you write the loss function that incorporates some penalty for racially offensive results? Ideally, you would want them to never happen, so you could imagine trying to identify all possible insults and assigning those outcomes an infinitely large loss. Which is essentially what Google did — their “workaround” was to stop classifying “gorilla” entirely because the loss incurred by misclassifying a person as a gorilla was so large.

This is a fundamental problem with neural networks — they’re only as good as the data and the loss function. These days, the data has become less of a problem, but getting the loss right is a multi-level game, as these neural network trainwrecks demonstrate. And it’s not as easy as writing an equation that isn’t “racist”, whatever that would mean. The loss function is being asked to encapsulate human sensitivities, navigate around them and quantify them, and eventually weigh the slight risk of making a particularly offensive misclassification against not recognizing certain animals at all.

I’m not sure this problem is solvable, even with tremendously large datasets. (There are mathematical proofs that with infinitely large datasets the model will classify everything correctly, so you needn’t worry. But how close are we to infinity? Are asymptotic proofs relevant?)

Anyway, this problem is bigger than algorithms, or even their writers, being “racist”. It may be a fundamental problem of machine learning, and we’re definitely going to see further permutations of the Twitter fiasco in the future as machine classification is being increasingly asked to respect human dignity.

Code For Hackers

September 19, 2020 by Elliot Williams 57 Comments

Mike and I were talking about two very similar clock projects we’d both built recently: they both use ESP8266 modules to get the time over WiFi and NTP, and they both failed. Mike’s failed because he was visiting relatives in a different timezone with different WiFi credentials, and mine failed because daylight savings time caught me off-guard. In both cases, we hard-coded stuff that could obviously change, but we drew vastly different conclusions.

Mike thought he’d solve his WiFi problem with a fallback to a captive portal, and maybe would have to figure out some web interface for configuring the timezone. A very clean, professional solution. Me? I’ve got good comments in the code, can find the UTC offset (or the WiFi creds) in a few minutes, and flash the new version up simply by fetching a USB cable, for something that happens twice a year. It’s hardly worth the trouble to cobble together a web interface.

We’ve accidentally embodied a quandary that spans both the hardware and software worlds: should flexibility be exposed to the end-user or to the hacker who can peer under the hood or open up the source code? (And what if the end-user is the hacker?) What are the tradeoffs, in project complexity and in ease of use?

And in this, Mike is on the side of right and good, and I’m the heretic. I don’t always write my code to be extensible or re-usable. I sometimes write it to be quickly re-edited and patched whenever I need to. Is it full of magic numbers? Sure! But I know just where they are and how to change them. Heck, most are even well documented in their own header file. You could probably figure it out just about as fast. Would my father-in-law be able to tweak the timezone? Nope! But this ain’t his project anyway.

Dare to code for hackers! Don’t over-generalize or over-abstract. Less is more. Don’t be afraid to edit code. Tweak, compile, and re-flash when the situation changes. After all, that’s how you got the code there in the first place.

And although I’m on the wrong end of history, in this case I was right. You see, before daylight savings time could come around again, and I could have made use of that captive portal that I didn’t bother coding up anyway, my son entered first grade. Everything needs to be changed, from the hardware to the software. Will I code up the next version with flexible time regimes? As flexible as I need it to be, but not more.

The Egg-laying Wool-Milk Pig

September 12, 2020 by Elliot Williams 32 Comments

Last week, I wrote about two recent projects of mine that serve as cautionary tales in keeping projects simple — you probably can’t simplify everything, so it’s worth the time to find out which simplifications have the most bang for the buck. This week, I’d like to share a tale of lack of design focus.

German has the eierlegende Wollmilchsau: a mystical animal that lays eggs, while producing wool, milk, and meat to boot. It’s a little bit like the English “jack of all trades, master of none” except that the eierlegende Wollmilchsau doesn’t do each job badly, it plainly can’t exist. This is obviously a bad way to start a design.

The first surfboard that I made by myself was supposed to be an eierlegende Wollmilchsau. It was going to be a longboard, because we had months with smaller waves that just weren’t all that suitable for shortboarding, but it was also going to turn sharply off the rails like a shortboard. To help it turn, it was going to have tons of camber (bend like a banana), and small fins. And along the way, I thought I’d make it thin to cut through the water.

Of course what I ended up with, not helped by my heavy fiberglassing hand, was a plow that dug into the water, would turn unexpectedly when you managed to get it onto the rails, and couldn’t pick up a small wave to save its life due to the camber and aforementioned plowing. I surfed it anyway, as a matter of pride, but I had no illusions of it being anything but the the worst board I owned. And that’s comparing it to the $30 used rasta-graphic plank that had been taking on water for at least five years, unrepaired, and was rotting out from the inside. At least it had design focus.

My surfboard didn’t suffer from feature creep, where you start piling on features until the project crumbles from overload, but rather from wanting to have my cake and eat it too. Or from failing to realize that certain design goals were necessarily tradeoffs. The “raily” behavior that I wanted when it was in bigger waves was necessarily “diggy” in small waves. Good boards trade off these features, and getting the balance between them is the art of shaping a board.

So when you start up a new project, think about which facets of your design are jointly achievable, and which are necessarily tradeoffs. Ignoring tradeoffs is a recipe for disaster, designing an eierlegende Wollmilchsau. But viewed constructively, it’s exactly these nuanced decisions that separates the simply possible from the truly marvelous. May you identify your trades, and make them well!