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When His Project Was Canceled, an Unemployed Programmer Kept Sneaking Into Apple to Finish the Job

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by Julia Dahl

When an Apple programmer’s project got canceled, he didn’t despair. He just kept sneaking into the office until the program was finished.

Ron Avitzur knew his project was doomed. By the time his bosses cut the cord in August 1993, his team was actually relieved. The graphing calculator program they’d been working on for new mobile devices had finally been shelved, and they could all move on.

Most of his fellow programmers were reassigned to other projects within Apple. The company offered Avitzur a job, too, but it didn’t interest him. Avitzur, then 27, had been freelancing at tech companies since he was a student at Stanford—to him, the work wasn’t worth it if it wasn’t interesting. And what interested him was finishing the graphing calculator program that had just been canceled. But his ambitions were greater than that—Avitzur wanted to make the graphing calculator work on the new PowerPC computer that Apple planned to ship in early 1994.

The young programmer knew the project had merit. Everyone he mentioned it to exclaimed, “I wish I’d had that in school!” If he could just get the program preinstalled on the new computer, teachers across the country could use the tool as an animated blackboard, providing visuals for abstract concepts. The program could simultaneously showcase the speed of the new machine and revolutionize math class. All he needed was access to Apple’s machines and some time.

The Perfect Crime

In 1993, Avitzur had nothing but time. His girlfriend lived in another city, and he’d already spent the previous 18 months working late five or six days a week, sometimes until after midnight. His Apple gig had paid well, and Avitzur lived simply. He could work for almost a year without a paycheck. Plus, Apple had lots of extra offices and computers— who would it hurt if he just kept coming in? It would be the perfect crime.

On the last day of the canceled project, Avitzur’s manager called him into her office to say goodbye. He hadn’t completed the length of his contract, but the company would pay it in full anyway.

“Just submit your final invoice for what’s left,” she told him. That’s when it clicked: If Avitzur didn’t submit the invoice, his contract stayed in the system. And if his contract stayed in the system, his ID badge would keep getting him in the front door.

So Avitzur told his boss that he’d find someone to supervise him while he completed the program. Great, his manager said. Good luck. On the first day Avitzur came to work without a job, everything was pretty much the same. He drove his 1987 Toyota Corolla from the room he rented on the edge of a nature reserve in Palo Alto and parked in the lot outside Infinite Loop, Apple’s fancy new headquarters. He swiped in, went to his old office, and resumed working on the calculator.

Right away, Avitzur found help. His friend Greg Robbins also had an Apple contract that was almost up, so Robbins told his boss he’d start reporting to Avitzur. Robbins wasn’t getting paid either, but it didn’t matter. For the two buddies, it was about the work and the challenge. Plus, it was kind of a kick.

Hiding in Plain Sight

They worked in tandem for about a month. Robbins, the perfectionist, spent days tweaking the grayscale of a single pixel. Avitzur, the big picture guy, was more social. He chatted with fellow engineers, soliciting advice and mulling solutions. Avitzur’s and Robbins’s presence was an open secret; people admired their passion and believed in the project.

Then Avitzur got careless. He told the story to the wrong person—a manager who had come to tell him he needed to move offices.

“You’ll have to leave the building immediately,” said the woman. “I’ll have your badges canceled tomorrow.”

That’s when the real sneaking around began. For the next two months, Avitzur had to find new ways of getting into the building. He kept his canceled badge around his neck and timed his arrival for when he knew there’d be crowds coming through the front door.

“Morning!” he’d say to someone he knew, then he’d follow them past security. Avitzur was a familiar face and still wore his badge, so he looked legit. But he had to keep the badge away from sensors, which would sound alarms.

Avitzur also kept a list of phone numbers of friendly programmers in his pocket. If he couldn’t sneak in the front door, he’d call someone to let him in a side entrance. Inside, he and Robbins set up shop in a couple of empty offices. Though only a few dozen of the new computers were available for testing, friends ensured that Robbins and Avitzur had two of them. And people began pitching in—quality assurance specialists who’d gotten wind of the project would show up to test the software; a 3-D graphics expert devoted his free weekends to perfecting the program.

Still, the threat of being caught was real. Avitzur became adept at slipping into bathrooms and turning quickly down halls when he saw people from the facilities department or the woman who’d canceled his badge walking his way. Yet somehow the work got done.

By November, Avitzur and Robbins were ready to demonstrate the calculator. Engineers who had assisted the pair spread word of the project to their managers, who called Avitzur and Robbins in for a demo. Avitzur was prepared for the worst—ready to be dismissed as a loose cannon who had spent the last three months trespassing—but the demo went perfectly. When the computer came out the next year, Avitzur and Robbins’s graphing calculator program was on it. It has been loaded on more than 20 million machines in the decades since.

“It’s amazing we got away with it,” says Avitzur, who is still designing software, still living in the Bay Area, and still driving his 1987 Corolla. “Even more amazing that we ended up producing something of value.”

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iStock // Ekaterina Minaeva
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Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Nick Briggs/Comic Relief
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What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]

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