Keep It Close: A Private Git Server Crash Course

June 27, 2018 by Tom Nardi 65 Comments

At this point, everyone has already heard that Microsoft is buying GitHub. Acquisitions of this scale take time, but most expect everything to be official by 2019. The general opinion online seems to be one of unease, and rightfully so. Even if we ignore Microsoft’s history of shady practices, there’s always an element of unease when somebody new takes over something you love. Sometimes it ends up being beneficial, the beginning of a new and better era. But sometimes…

Let’s not dwell on what might become of GitHub. While GitHub is the most popular web-based interface for Git, it’s not the only one. For example GitLab, a fully open source competitor to GitHub, is reporting record numbers of new repositories being created after word of the Microsoft buyout was confirmed. But even GitLab, while certainly worth checking out in these uncertain times, might be more than you strictly need.

Let’s be realistic. Most of the software projects hackers work on don’t need even half the features that GitHub/GitLab offer. Whether you’ve simply got a private project you want to maintain revisions of, or you’re working with a small group collaboratively in a hackerspace setting, you don’t need anything that isn’t already provided by the core Git software.

Let’s take a look at how quickly and easily you can setup a private Git server for you and your colleagues without having to worry about Microsoft (or anyone else) having their fingers around your code.

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Arduino And Pidgin C++

June 23, 2018 by Al Williams 87 Comments

What do you program the Arduino in? C? Actually, the Arduino’s byzantine build processes uses C++. All the features you get from the normal libraries are actually C++ classes. The problem is many people write C and ignore the C++ features other than using object already made for them. Just like traders often used pidgin English as a simplified language to talk to non-English speakers, many Arduino coders use pidgin C++ to effectively code C in a C++ environment. [Bert Hubert] has a two-part post that isn’t about the Arduino in particular, but is about moving from C to a more modern C++.

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Calm Down: It’s Only Assembly Language

June 19, 2018 by Al Williams 43 Comments

Based on [Ben Jojo’s] title — x86 Assembly Doesn’t have to be Scary — we assume that normal programmers fear assembly. Most hackers don’t mind it, but we also don’t often have an excuse to program assembly for desktop computers.

In fact, the post is really well suited for the typical hacker because it focuses the on real mode of an x86 processor after it boots. What makes this tutorial a little more interesting than the usual lecture is that it has interactive areas, where a VM runs your code in the browser after assembling with NASM.

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Using TensorFlow To Recognize Your Own Objects

May 23, 2018 by Steven Dufresne 9 Comments

When the time comes to add an object recognizer to your hack, all you need do is choose from many of the available ones and retrain it for your particular objects of interest. To help with that, [Edje Electronics] has put together a step-by-step guide to using TensorFlow to retrain Google’s Inception object recognizer. He does it for Windows 10 since there’s already plenty of documentation out there for Linux OSes.

You’re not limited to just Inception though. Inception is one of a few which are very accurate but it can take a few seconds to process each image and so is more suited to a fast laptop or desktop machine. MobileNet is an example of one which is less accurate but recognizes faster and so is better for a Raspberry Pi or mobile phone.

You’ll need a few hundred images of your objects. These can either be scraped from an online source like Google’s images or you get take your own photos. If you use the latter approach, make sure to shoot from various angles, rotations, and with different lighting conditions. Fill your background with various other things and even have some things partially obscuring your objects. This may sound like a long, tedious task, but it can be done efficiently. [Edje Electronics] is working on recognizing playing cards so he first sprinkled them around his living room, added some clutter, and walked around, taking pictures using his phone. Once uploaded, some easy-to-use software helped him to label them all in around an hour. Note that he trained on 24 different objects, which are the number of different cards you get in a pinochle deck.

You’ll need to install a lot of software and do some configuration, but he walks you through that too. Ideally, you’d use a computer with a GPU but that’s optional, the difference being between three or twenty-four hours of training. Be sure to both watch his video below and follow the steps on his Github page. The Github page is kept most up-to-date but his video does a more thorough job of walking you through using the software, such as how to use the image labeling program.

Why is he training an object recognizer on playing cards? This is just one more step in making a blackjack playing robot. Previously he’d done an impressive job using OpenCV, even though the algorithm handled non-overlapping cards only. Google’s Inception, however, recognizes partially obscured cards. This is a very interesting project, one which we’ll be keeping an eye on. If you have any ideas for him, leave them in the comments below.

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Machine Learning Crash Course From Google

May 22, 2018 by Al Williams 9 Comments

We’ve been talking a lot about machine learning lately. People are using it for speech generation and recognition, computer vision, and even classifying radio signals. If you’ve yet to climb the learning curve, you might be interested in a new free class from Google using TensorFlow.

Of course, we’ve covered tutorials for TensorFlow before, but this is structured as a 15 hour class with 25 lessons and 40 exercises. Of course, it is also from the horse’s mouth, so to speak. Google says the class will answer questions like:

How does machine learning differ from traditional programming?
What is loss, and how do I measure it?
How does gradient descent work?
How do I determine whether my model is effective?
How do I represent my data so that a program can learn from it?
How do I build a deep neural network?

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Hands-On: Flying Drones With Scratch

May 22, 2018 by Al Williams 8 Comments

I’ll admit it. I have a lot of drones. Sitting at my desk I can count no fewer than ten in various states of flight readiness. There are probably another half dozen in the garage. Some of them cost almost nothing. Some cost the better part of a thousand bucks. But I recently bought a drone for $100 that is both technically interesting and has great potential for motivating kids to learn about programming. The Tello is a small drone from a company you’ve never heard of (Ryze Tech), but it has DJI flight technology onboard and you can program it via an API. What’s more exciting for someone learning to program than using it to fly a quadcopter?

For $100, the Tello drone is a great little flyer. I’d go as far as saying it is the best $100 drone I’ve ever seen. Normally I don’t suggest getting a drone with no GPS since the price on those has come down. But the Tello optical sensor does a great job of keeping the craft stable as long as there is enough light for it to see. In addition, the optical sensor works indoors unlike GPS.

But if that was all there was to it, it probably wouldn’t warrant a Hackaday post. What piqued my interest was that you can program the thing using a PC. In particular, they use Scratch — the language built at MIT for young students. However, the API is usable from other languages with some work.

Information about the programming environment is rather sparse, so I dug in to find out how it all worked.

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Flash And Debug ESP8266 Boards On Android

May 7, 2018 by Tom Nardi 12 Comments

Have an ESP8266 development board such as the NodeMCU or Wemos D1? You’re currently reading Hackaday, so probably. Got an Android device kicking around? Also seems fairly likely. In that case, you should check out ESP8266 Loader by [Bluino Electronics]. This recently released application lets you not only flash new binaries to any ESP8266 board using the FTDI, PL2303, CH34X and CP210X USB chipsets, but also offers a serial monitor for debugging on the go.

You’ll need a USB OTG cable to get your ESP board jacked in to your Android device, but you don’t need root or even to fiddle with the development settings. Here at the Hackaday R&D Dungeon we had somewhat mixed success getting a random selection of Android devices to work fully; all of the ones tried could at least open the serial monitor and read what a pre-programmed ESP was saying, but not all of them could successfully program a board.

Even on the devices where programming worked, it was slow. Just a basic LED blinking Sketch took long enough to write to our test Wemos D1 Mini that we contemplated getting a snack. But still, it shows a lot of promise for managing devices in the field, especially if you don’t have over the air update enabled in your code.

We especially liked that ESP8266 Loader helpfully downloaded a bunch of example binaries, many of which could be of practical use. There are programs for toggling the different GPIO pins on the board, creating Wi-Fi access points, and even a basic web server. With these in hand, you could actually do some testing and diagnostic work right from your mobile device.

This isn’t the first time we’ve seen an ESP8266 team up with a mobile device, but generally speaking, the magic is done over WiFi or Bluetooth.