Finally, An Open Source Calculator

March 12, 2019 by Brian Benchoff 104 Comments

Microsoft has released the code for the Calculator app. This move is the latest in Microsoft’s efforts to capitalize on the Open Source community. Previous efforts have been the Open Sourcing of an extremely old version of DOS, and shoehorning Linux into Windows somehow in a way that’s marginally more user-friendly than spinning up a VM or popping over to your Linux partition. Oh yeah, Microsoft bought Github. Can’t forget that.

The release of the code for the Calculator app means now you too can truly verify all your calculations are correct. To build the Calculator app, you’ll need a Windows 10 computer and Visual Studio. You might think that this is the same code that’s been shipping for 30 years — it’s a simple calculator, right? Not so: the Calculator for Windows 8 had a strange and odd bug where the square root of 4, minus two, did not equal zero. Floating point is hard, kids.

Of special interest to the community, it’s now possible to disable telemetry sent from the Calculator app to Microsoft servers. Yes, the Calculator app knows you forgot how to divide, and wow man, six times nine, you needed help with that? Fortunately, telemetry can be disabled in developer’s builds by disabling the SEND_TELEMETRY build flag. Now Microsoft won’t know you don’t do math so good.

At the time of this writing, we could not be bothered to contact Microsoft to find out when the pinball game or Ski Free will be updated and Open Sourced.

Stalking Last.fm Streams On Spotify

March 11, 2019 by Lewin Day 8 Comments

Back in the early days of social media and Web 2.0, Last.fm was one of the premier music sites on the internet. With a huge library containing what felt like every song ever, along with an excellent algorithm for recommending new tracks, it quickly gained a large following. Unfortunately, its business model and following changed over the years, but there’s still a diehard userbase. [Hexalyse] was unhappy with Spotify’s algorithms, so built a tool to allow her to shadow what Last.fm users were listening to in real time.

Last.fm’s major feature is that it allows you to tell others what you’re listening to, by “scrobbling” your tracks as you play them. It’s possible to scrape this live data from any user via the Last.fm API, making the project possible. [Hexalyse] whipped up a Python script to query a selected user’s current playing track via Last.fm, before then handing the song data to the Spotify API to play the music locally.

It’s a fun way to find new music, relying on human taste rather than a pile of data center algebra. [Hexalyse] has uploaded the code to Github if you’re eager to try it for yourself. Of course, you get bonus points if you integrate it with Spotify on the Macintosh SE/30.

Engineering For The Long Haul, The NASA Way

March 11, 2019 by Dan Maloney 49 Comments

The popular press was recently abuzz with sad news from the planet Mars: Opportunity, the little rover that could, could do no more. It took an astonishing 15 years for it to give up the ghost, and it took a planet-wide dust storm that blotted out the sun and plunged the rover into apocalyptically dark and cold conditions to finally kill the machine. It lived 37 times longer than its 90-sol design life, producing mountains of data that will take another 15 years or more to fully digest.

Entire careers were unexpectedly built around Opportunity – officially but bloodlessly dubbed “Mars Exploration Rover-B”, or MER-B – as it stubbornly extended its mission and overcame obstacles both figurative and literal. But “Oppy” is far from the only long-duration success that NASA can boast about. Now that Opportunity has sent its last data, it seems only fitting to celebrate the achievement with a look at exactly how machines and missions can survive and thrive so long in the harshest possible conditions.

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Car Alarm Hacks 3 Million Vehicles

March 8, 2019 by Al Williams 30 Comments

Pen testing isn’t about evaluating inks. It is short for penetration testing — someone ensuring a system’s security by trying to break in or otherwise attack it. A company called Pen Test Partners made the news last week by announcing that high-end car alarm systems made by several vendors have a critical security flaw that could make the vehicles less secure. They claim about three million vehicles are affected.

The video below shows how alarms from Viper/Clifford and Pandora have a simple way to hijack the application. Once they have access, they can find the car in real time, control the door locks, and start or stop the car engine. They speculate a hacker could set off the alarm from a nearby chase car. You’d probably pull over if your alarm started going off. They can then lock you in your car, approach, and then force you out of the car.

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Tech Imitates Life: Fireflies Make Better Light Bulbs

March 5, 2019 by Al Williams 46 Comments

While we often think we are clever designers, living things often meet or beat the best human designs. It is easy to forget that nature even has living lightbulbs, among them the firefly. Researchers from Penn State decided to compare how fireflies create light and found that they deal with a problem similar to LEDs. The insight may lead to an increase in efficiency for LEDs, which is currently about 50%.

The problem is that some light generated never gets out of the LED (or the firefly’s body). Some light inevitably reflects back into the device. One known mitigation for this is creating a tiny texture pattern on the LED surface which allows more light to escape. These are typically a V-shaped structure etched into the surface. This isn’t news to the firefly, however, which has similar structures on their lanterns as do some other light-generating animals (apparently glowing cockroaches are a thing). However, the organic structures differ from LED textures in an important way.

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Google Launches AI Platform That Looks Remarkably Like A Raspberry Pi

March 5, 2019 by Brian Benchoff 80 Comments

Google has promised us new hardware products for machine learning at the edge, and now it’s finally out. The thing you’re going to take away from this is that Google built a Raspberry Pi with machine learning. This is Google’s Coral, with an Edge TPU platform, a custom-made ASIC that is designed to run machine learning algorithms ‘at the edge’. Here is the link to the board that looks like a Raspberry Pi.

This new hardware was launched ahead of the TensorFlow Dev Summit, revolving around machine learning and ‘AI’ in embedded applications, specifically power- and computationally-limited environments. This is ‘the edge’ in marketing speak, and already we’ve seen a few products designed from the ground up to run ML algorithms and inference in embedded applications. There are RISC-V microcontrollers with machine learning accelerators available now, and Nvidia has been working on this for years. Now Google is throwing their hat into the ring with a custom-designed ASIC that accelerates TensorFlow. It just so happens that the board looks like a Raspberry Pi.

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Does Tesla’s Autosteer Make Cars Less Safe?

March 4, 2019 by Elliot Williams 77 Comments

In 2016, a Tesla Model S T-boned a tractor trailer at full speed, killing its lone passenger instantly. It was running in Autosteer mode at the time, and neither the driver nor the car’s automatic braking system reacted before the crash. The US National Highway Traffic Safety Administration (NHTSA) investigated the incident, requested data from Tesla related to Autosteer safety, and eventually concluded that there wasn’t a safety-related defect in the vehicle’s design (PDF report).

But the NHTSA report went a step further. Based on the data that Tesla provided them, they noted that since the addition of Autosteer to Tesla’s confusingly named “Autopilot” suite of functions, the rate of crashes severe enough to deploy airbags declined by 40%. That’s a fantastic result.

Because it was so spectacular, a private company with a history of investigating automotive safety wanted to have a look at the data. The NHTSA refused because Tesla claimed that the data was a trade secret, so Quality Control Systems (QCS) filed a Freedom of Information Act lawsuit to get the data on which the report was based. Nearly two years later, QCS eventually won.

Looking into the data, QCS concluded that crashes may have actually increased by as much as 60% on the addition of Autosteer, or maybe not at all. Anyway, the data provided the NHTSA was not sufficient, and had bizarre omissions, and the NHTSA has since retracted their safety claim. How did this NHTSA one-eighty happen? Can we learn anything from the report? And how does this all align with Tesla’s claim of better-than-average safety line up? We’ll dig into the numbers below.

But if nothing else, Tesla’s dramatic reversal of fortune should highlight the need for transparency in the safety numbers of self-driving and other advanced car technologies, something we’ve been calling for for years now.

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