Some people love Amazon, while others think it has become too big and invasive. But you have to admit, they build gigantic and apparently reliable systems. Interestingly, they recently released a library of white papers from their senior staff called the Builder’s Library.
According to their blog post:
The Amazon Builders’ Library is a collection of living articles that take readers under the hood of how Amazon architects, releases, and operates the software underpinning Amazon.com and AWS. The Builders’ Library articles are written by Amazon’s senior technical leaders and engineers, covering topics across architecture, software delivery, and operations. For example, readers can see how Amazon automates software delivery to achieve over 150 million deployments a year or how Amazon’s engineers implement principles such as shuffle sharding to build resilient systems that are highly available and fault tolerant.
The Amazon Builders’ Library will continue to be updated with new content going forward.
Continue reading “Behind Amazon’s Doors Is A Library”
If there’s one thing any cat will work for, it’s food. Usually, this just consists of meowing and/or standing on your chest until you give up the goods. [DynamicallyInvokable] has a beautiful cat, Emma, who meows loudly for food at obscene hours of the morning. As she ages, it’s getting harder and more important to control her weight. Clearly, it was time to build the ultimate automatic cat feeder—one that allows him to get lazy while at the same time getting smart about Emma’s weight.
After a year and a half of work, the feeder is complete. Not only does it deliver the goods several times a day, it sends a heap of data to the cloud about Emma’s eating habits. There’s a scale built into the platform, and another in the food bowl. Together, they provide metrics galore that get automatically uploaded to AWS. Everything is controlled with an ESP32 Arduino, including a rainbow of WS2812s that chases its tail around the base of the feeder. The faster it goes, the closer it is to feeding time.
The best part about this unique feeder is that nearly every piece is 3D printed, including the gears. Be sure to check out the build gallery, where you can watch it come together piece by piece. Oh, and claw your way past the break to see Emma get fed.
Emma doesn’t have to worry about sharing her food. If she did, maybe [DynamicallyInvokable] could use facial recognition to meet the needs of multiple cats.
Continue reading “Cat Feeder Adds Metrics To Meow Mix”
The first step to reducing the energy consumption of your home is figuring out how much you actually use in the first place. After all, you need a baseline to compare against when you start making changes. But fiddling around with high voltage is something a lot of hackers will go out of their way to avoid. Luckily, as [Xavier Decuyper] explains, you can build a very robust DIY energy monitoring system without having to modify your AC wiring.
In the video after the break, [Xavier] goes over the theory of how it all works, but the short version is that you just need to use a Current Transformer (CT) sensor. These little devices clamp over an AC wire and detect how much current is passing through it via induction. In his case, he used a YHDC SCT-013-030 sensor that can measure up to 30 amps and costs about $12 USD. It outputs a voltage between 0 and 1 volts, which makes it extremely easy to read using the ADC of your favorite microcontroller.
Once you’ve got the CT sensor connected to your microcontroller, the rest really just depends on how far you want to take the software side of things. You could just log the current consumption to a plain text file if that’s your style, but [Xavier] wanted to challenge himself to develop a energy monitoring system that rivaled commercial offerings so he took the data and ran with it.
A good chunk of his write-up explains how the used Amazon Web Services (AWS) to process and ultimately display all the data he collects with his ESP32 energy monitor. Every 30 seconds, the hardware reports the current consumption to AWS through MQTT. The readings are stored in a database, and [Xavier] uses GraphQL and Dygraphs to generate visualizations. He even used Ionic to develop a cross-platform mobile application so he can fawn over his professional looking charts and graphs on the go.
We’ve already seen how carefully monitoring energy consumption can uncover some surprising trends, so if you want to go green and don’t have an optically coupled electricity meter, the CT sensor method might be just what you need.
Continue reading “Building A Safe ESP32 Home Energy Monitor”
Anyone with a cat knows that the little purring ball of fluff in your lap is one tiny step away from turning into a bloodthirsty serial killer. Give kitty half a chance and something small and defenseless is going to meet a slow, painful end. And your little killer is as likely as not to show off its handiwork by bringing home its victim – “Look what I did for you, human! Are you not proud?”
As useful as a murder-cat can be, dragging the bodies home for you to deal with can be – inconvenient. To thwart his adorable serial killer [Metric], Amazon engineer [Ben Hamm] turned to an AI system to lock his prey-laden cat out of the house. [Metric] comes and goes as he pleases through a cat flap, which thanks to a solenoid and an Arduino is now lockable. The decision to block entrance to [Metric] is based on an Amazon AWS DeepLens AI camera, which watches the approach to the cat flap. [Ben] trained three models: one to determine if [Metric] was in the scene, one to determine whether he’s coming or going, and one to see if he’s alone or accompanied by a lifeless friend, in which case he’s locked out for 15 minutes and an automatic donation is made to the Audubon Society – that last bit is pure genius. The video below is a brief but hilarious summary of the project for an audience in Seattle that really seems quite amused by the whole thing.
So your cat isn’t quite the murder fiend that [Metric] is? An RFID-based cat door might suit your needs better.
Continue reading “AI Recognizes And Locks Out Murder Cats”
As far as computer architectures go, ARM doesn’t have anything to be ashamed of. Since nearly every mobile device on the planet is powered by some member of the reduced instruction set computer (RISC) family, there’s an excellent chance these words are currently making their way to your eyes courtesy of an ARM chip. A userbase of several billion is certainly nothing to sneeze at, and that’s before we even take into account the myriad of other devices which ARM processors find their way into: from kid’s toys to smart TVs.
ARM is also the de facto architecture for the single-board computers which have dominated the hacking and making scene for the last several years. Raspberry Pi, BeagleBone, ODROID, Tinker Board, etc. If it’s a small computer that runs Linux or Android, it will almost certainly be powered by some ARM variant; another market all but completely dominated.
It would be a fair to say that small devices, from set top boxes down to smartwatches, are today the domain of ARM processors. But if we’re talking about what one might consider “traditional” computers, such as desktops, laptops, or servers, ARM is essentially a non-starter. There are a handful of ARM Chromebooks on the market, but effectively everything else is running on x86 processors built by Intel or AMD. You can’t walk into a store and purchase an ARM desktop, and beyond the hackers who are using Raspberry Pis to host their personal sites, ARM servers are an exceptional rarity.
Or at least, they were until very recently. At the re:Invent 2018 conference, Amazon announced the immediate availability of their own internally developed ARM servers for their Amazon Web Services (AWS) customers. For many developers this will be the first time they’ve written code for a non-x86 processor, and while some growing pains are to be expected, the lower cost of the ARM instances compared to the standard x86 options seems likely to drive adoption. Will this be the push ARM needs to finally break into the server and potentially even desktop markets? Let’s take a look at what ARM is up against.
Continue reading “Amazon Thinks ARM Is Bigger Than Your Phone”
Putting autonomous vehicles on public roads takes major resources beyond most of our means. But we can explore all the same general concepts at a smaller scale by modifying remote-control toy cars, limited only by our individual budgets and skill levels. For those of us whose interest and expertise lie in software, Amazon Web Services just launched AWS DeepRacer: a complete package for exploring machine learning on autonomous vehicles.
At a hardware level, the spec sheet makes it sound like they’ve bolted their AWS DeepLens machine vision computer on an 1/18th scale monster truck chassis. But the hardware is only the tip of the iceberg. The software behind DeepRacer is AWS RoboMaker, a set of tools for applying AWS to robot development. Everything from running digital simulations on AWS to training neural networks on AWS. Don’t know enough about machine learning? No problem! Amazon has also just opened up their internal training curriculum to the world. And to encourage participation, Amazon is running a DeepRacer League with races taking place both digitally online and physically at AWS Summit events around the world. They’ve certainly offered us a full plate at their re:Invent conference this week.
But maybe someone prefers not to use Amazon, or prefer to build their own hardware, or run their own competitions. Fortunately, Amazon is not the only game in town, merely the latest entry in an existing field. The DeepRacer’s League’s predecessor was the Robocar Rally, and the DeepRacer itself follows the Donkey Car. A do-it-yourself autonomous racing platform we first saw at Bay Area Maker Faire 2017, Donkey Car has since built up its documentation and software tools including a simulator. The default Donkey Car code is fairly specific to the car, but builders are certainly free to use something more general like the open source Robot Operating System and Gazebo robot simulator. (Which is what AWS RoboMaker builds on.)
So if the goal is to start racing little autonomous cars, we have options to buy pre-built hardware or enjoy the flexibility of building our own. Either way, it’s just another example of why this is a great time to get into neural networks, with or without companies like Amazon devising ways to earn our money. Of course, this isn’t the only Amazon project trying to build a business around an idea explored by an existing open source project. We had just talked about their AWS Ground Station offering which covers similar ground (sky?) as our 2014 Hackaday Prize winner SatNOGS.
If you have not had children, stop reading now, we implore you. Because before you’ve had kids, you can’t know how supremely important it is that they take care of going to the bathroom by themselves. [David Gouldin] knows how it is. But unlike most of us, he resorted to using an Amazon IoT button and Twilio. No, we are not kidding.
The problem he was trying to solve is when his younger child would need to use the potty in the middle of the night, calling out for assistance would wake the older child. [David] said it best himself:
Behind the smiling emoji facade is an Amazon IoT button, a variant of Amazon’s dash button. When my kid presses this button, it triggers an AWS Lambda function that uses Twilio’s Python Helper Library to call my iPhone from a Twilio number. The Twilio number is stored in my contacts with “emergency bypass” turned on, so even when it’s 2am and I’m on “do not disturb” I still get the call.
Continue reading “IoT Potty Training”