Zach Archer: Live Coding 500 Watts For ToorCamp

February 28, 2019 by Brian Benchoff 2 Comments

ToorCamp is a five-day open air tech camping event held every two years somewhere around the northwest corner of Washington state. Think of it as something like Burning Man, except you can survive for three hours without water, there aren’t a whole bunch of scenesters and Instagram celebs flying in on private planes, and everyone there can actually build something. Oh, and ToorCamp has delivery drones that will send you creme brulee. These mini creme brulees were probably made with the hot air gun hanging off a soldering station. Don’t worry, you’re getting fresh air that’ll balance out the heavy metal poisoning.

For last year’s ToorCamp, the biggest welcome sign was a 40-foot-long illuminated ToorCamp sign. This was designed, built and coded by Zach Archer, and he was at the 2018 Hackaday Superconference to give us the details on how he made it and how it was coded.

Continue reading “Zach Archer: Live Coding 500 Watts For ToorCamp” →

Ludwig Promises Easy Machine Learning From Uber

February 25, 2019 by Al Williams 3 Comments

Machine learning has brought an old idea — neural networks — to bear on a range of previously difficult problems such as handwriting and speech recognition. Better software and hardware has made it feasible to apply sophisticated machine learning algorithms that would have previously been only possible on giant supercomputers. However, there’s still a learning curve for developing both models and software to use these trained models. Uber — you know, the guys that drive you home when you’ve had a bit too much — have what they are calling a “code-free deep learning toolbox” named Ludwig. The promise is you can create, train, and use models to extract features from data without writing any code. You can find the project itself on GitHub.io.

The toolbox is built over TensorFlow and they claim:

Ludwig is unique in its ability to help make deep learning easier to understand for non-experts and enable faster model improvement iteration cycles for experienced machine learning developers and researchers alike. By using Ludwig, experts and researchers can simplify the prototyping process and streamline data processing so that they can focus on developing deep learning architectures rather than data wrangling.

Continue reading “Ludwig Promises Easy Machine Learning From Uber” →

Foundations For Machine Learning In English (Or Russian)

February 22, 2019 by Al Williams 5 Comments

We are big fans of posts and videos that try to give you a gut-level intuition on technical topics. While [vas3k’s] post “Machine Learning for Everyone” fits the bill, we knew we’d like it from the opening sentences:

Machine Learning is like sex in high school. Everyone is talking about it, a few know what to do, and only your teacher is doing it.”

That sets the tone. What follows is a very comprehensive exposition of machine learning fundamentals. There is no focus on a particular tool, instead this is all the underpinnings. The original post was in Russian, but the English version is easy to read and doesn’t come off as a poor machine translation.

Continue reading “Foundations For Machine Learning In English (Or Russian)” →

Supercon: Ruth Grace Wong And Firmware From The Firehose

February 22, 2019 by Elliot Williams 13 Comments

Firmware and software are both just code, right? How different could the code that runs Internet-scale distributed web stuff be from the code that runs a tiny microcontroller brain inside a personal hydroponics device? Night and day!

Ruth Grace Wong works in the former world, but moonlights as a manufacturing engineer with some friends. Their product had pre-existing firmware that contained (at least) one bug, and Ruth’s job was to find it. The code in question was written by the Chinese PCB engineer, who knew the electronics intimately but who had no software background, providing Ruth an opportunity to jump head-first into the rawest of raw embedded programming. Spoiler alert: she found the bug and learned a lot about firmware along the way. This talk follows her along the adventure.

“The code is very well documented, in Chinese” but the variable names are insanely non-descriptive. Similarly, while the PCB engineer knows full well what a 24C02 is, if you’re a software geek that might as well be Chinese. As you’d expect, web searches came to the rescue on both fronts.

The bug ended up hiding in a logical flaw in the PWM-setting code inside an interrupt service routine, and it kept the fan from ever coming full on. Once found, it was easily fixed. But getting to the point where you understand the codebase deeply enough to know where to look is four-fifths of the battle. Heck, setting up the toolchain alone can take a day or two.

If you’re a fellow software type, Ruth’s talk (embedded below) will give you a quick glimpse into the outer few layers of the onion that is embedded firmware development, from a familiar viewpoint. Give her quick and value-packed talk a watch! Grizzled hardware veterans will nod along, and maybe even gain a little insight into how our code looks to “them”.

Continue reading “Supercon: Ruth Grace Wong And Firmware From The Firehose” →

Drops Of Jupyter Notebooks: How To Keep Notes In The Information Age

February 22, 2019 by Al Williams 32 Comments

Our digital world is so much more interactive than the paper one it has been replacing. That becomes very obvious in the features of Jupyter Notebooks. The point is to make your data beautiful, organized, interactive, and shareable. And you can do all of this with just a bit of simple coding.

We already leveraged computer power by moving from paper spreadsheets to digital spreadsheets, but they are limited. One thing I’ve seen over and over again — and occasionally been guilty of myself — is spreadsheet abuse. That is, using a spreadsheet program to do something I probably ought to write a program to do. For those times that you want something quick but want something more than a spreadsheet, you should check out Jupyter Notebooks. The system is most commonly associated with Python, but it isn’t Python-specific. There are over 100 languages supported — many community-developed. You can even install a C++ interpreter backend for it. Because of the client/server architecture, it is very simple to share notebooks with other users.

You can — in theory — use Jupyter for anything you could use Python for. In practice, it seems to get a lot of workout with people analyzing large data sets, doing machine learning, and similar tasks.

The Good: Simple, Powerful, Extensible

The idea is simple. Think of a Markdown-enabled web page that can connect to a backend (a kernel, in Jupyter-speak). The backend can run on your machine or remotely and will support some kind of language — often Python. The document has cells that line up vertically (like a single wide spreadsheet column). For example, here’s a simple notebook I created to explain how a bunch of sine waves add up to a square wave:

Continue reading “Drops Of Jupyter Notebooks: How To Keep Notes In The Information Age” →

Ask Hackaday: Is There A Legit Use For Operator Precedence?

February 19, 2019 by Mike Szczys 111 Comments

Computing is really all about order. If you can take data, apply an operation to it, and get the same result every single time, then you have a stable and reliable computing system.

So it makes total sense that there is Operator Precedence. This is also called Order of Operations, and it dictates which computations will be performed first, and which will be performed last. To get the same results every time, you must perform addition, multiplication, power functions, bitwise math, and all other calculations in a codified order.

The question I’ve had on my mind lately is, does this matter to us or just the compiler?

Continue reading “Ask Hackaday: Is There A Legit Use For Operator Precedence?” →

A Tiny IDE For Your ATtiny

February 8, 2019 by Tom Nardi 23 Comments

When writing code for the ATtiny family of microcontrollers such as a the ATtiny85 or ATtiny10, people usually use one of two methods: they either add support for the chip in the Arduino IDE, or they crack open their text editor of choice and do everything manually. Plus of course there are the stragglers out there using Eclipse. But [Wayne Holder] thinks there’s a better way.

The project started out as a simple way for [Wayne] to program the ATtiny10 in C under Mac OS, but has since evolved into an open source, cross-platform integrated development environment (IDE) for programming a wide range of ATtiny chips in C, C++, or Assembly. Not only does it integrate the source code editor and programmer, but it even bundles in documentation for common variants of the chips including block diagrams and pinouts; making it a true one-stop-shop for ATtiny hacking.

His IDE runs under Java, including OpenJDK, and [Wayne] provides a stable pre-built executable for those who don’t want to clone the whole GitHub repository. He’s included the GNU/AVR toolchains, though notes that testing so far has been limited to Mac OS, and he’s interested in feedback from Windows and Linux users. Assembly is done either with GNU AVR-AS, or an assembler of his own design, though the latter is currently limited to the ATTiny10.

To actually get the code onto the chip, the IDE supports using the Arduino as a programmer as well as dedicated hardware like the BusPirate or the USBasp. If you go the Arduino route, [Wayne] has even come up with a little adapter board which he’s made available through OSH Park to help wrangle the diminutive chips.

The ATtiny10 might have something of a learning curve, but in exchange this family of tiny microcontrollers offers an incredible amount of capability. When you’re working with what’s essentially a programmable grain of rice, the only limit is your own creativity.