Hack Your Memory

Imagine a fire hydrant being lifted high into the air by a large helium balloon. It goes higher and higher, but suddenly gas starts to leak out of the nozzle, which makes it sound like it’s trying to talk… but with a distinct lisp. A colorful bumblebee then lands on the balloon, licks it, and says “really yum!”  Then the bee takes out its stinger and bores on to the balloon. It pops, causing the fire hydrant to come crashing down. It smashes into a military jeep causing a massive explosion… as if it had been destroyed by a car bomb. Fortunately, the owner of the jeep, a general, was out on his rowing boat at the time. He likes to row his boat at night, and is known as the “night-rowing general” around the base. He was rowing with a bit more exertion than usual, and had to don an oxygen mask to help him breath. But the mask was full of fluoride, which turned his teeth bright neon colors.

You’re probably wondering what the hell you just read. Maybe you’re thinking the author had a stroke. Has the site been hacked? Maybe it’s a prank? What if I told you that you’ve just memorized the first 10 elements of the periodic table.

The Night-Rowing-General via Memorize Academy
  • Fire hydrant – Hydrogen
  • Helium balloon – Helium
  • Lisp – Lithium
  • Bee says “really yum” – Beryllium
  • Bee “Bores on” – Boron
  • Car bomb – Carbon
  • The night-rowing-general – Nitrogen
  • Oxygen mask – Oxygen
  • Fluoride – Florine
  • Neon teeth – Neon

Much of your memory is stored in the form of associations. Encoding things you need to remember into a silly story takes advantage of this fact. The memory of a ‘night-rowing-general’ is already in your head. You can see him in the theater of your mind… rowing his boat under a black sky… the silver stars on his green hat reflecting the moonlight. Associating this visual representation of the night-rowing-general with the term ‘Nitrogen’ is very easy for your brain to do.

You’re probably already familiar with this type of learning. Does “Bad Boys Run Over Yellow Gardenias Behind Victory Garden Walls” ring a bell?  It’s nothing new. In fact, storing memories in the form of mental images was the preferred memorization method of the scholars in ancient times. Today, it has allowed people to perform staggering feats of memorization. Want to know how [Akira Haraguchi] was able to memorize 111,700 digits of Pi?

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The Dark Arts – Remote File Inclusion

In the waning hours of 2010, a hacking group known as Lulzsec ran rampant across the Internet, leaving a path of compromised servers, a trail of defaced home pages, leaked emails, and login information in their wake. They were eventually busted via human error, and the leader of the group becoming an FBI informant. This handful of relatively young hackers had made a huge mess of things. After the digital dust had settled – researches, journalists, and coders began to dissect just how these seemingly harmless group of kids were able to harness so much power and control over the World Wide Web. What they found was not only eye-opening to web masters and coders, but shined a light on just how vulnerable all of our data was for everyone to see. It ushered in an era of renewed focus on security and how to write secure code.

In this Dark Arts series, we have taken a close look at the primary techniques the Luzsec hackers used to gain illegal access to servers. We’ve covered two them – SQL injection (SQLi) and cross-site scripting (XSS). In this article, we’ll go over the final technique called remote file inclusion (RFI).

DISCLAIMER: Fortunately, the surge of security-minded coding practices after the fall of Lulzsec has (for the most part) removed these vulnerabilities from the Internet as a whole. These techniques are very dated and will not work on any server that is maintained and/or behind a decent firewall, and your IP will probably get flagged and logged for trying them out. But feel free to set up a server at home and play around. Continue reading “The Dark Arts – Remote File Inclusion”

You’re the Only One not Playing with Unity

It wasn’t too long ago that one could conjecture that most hackers are not avid video game players. We spend most of our free time taking things apart, tinkering with microcontrollers and reading the latest [Jenny List] article on Hackaday.com. When we do think of video games, our neurons generally fire in the direction of emulating a console on a single board computer, such as a Raspberry Pi or a Beaglebone. Or even emulating the actual console processor on an FPGA. Rarely do we venture off into 3D programs meant to make modern video games. If we can’t export an .STL with it, we’re not interested. It’s just not our bag.

Oculus Rift changed this. The VR headset was originally invented for 3D video games, but quickly became a darling to hackers the world over. Virtual Reality technology is far bigger than just video games, and brings opportunity to many fields such as real estate, construction, product visualization, education, social interaction… the list goes on and on.

The Oculus team got together with the folks over at Unity in the early days to make it easy for video game makers to make content for the Rift. Unity is a game engine designed with a shallow learning curve and is available for free for non-commercial use. The Oculus Rift can be integrated into a Unity environment with the check of a setting and importing a small package, available on the Oculus site. This makes it easy for anyone interested in VR technology to get a Rift and start pumping out content.

Hackers have taken things a step further and have written scripts that allow Unity to communicate with an Arduino. VR is fun. But VR plus physical reality is just down right exciting! In this article, we’re going to walk you through setting up your Oculus Rift and Unity game engine to communicate with the outside world via an Arduino.

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The Neuron – A Hackers Perspective

It’s not too often that you see handkerchiefs around anymore. Today, they’re largely viewed as unsanitary and well… just plain gross. You’ll be quite disappointed to learn that they have absolutely nothing to do with this article other than a couple of similarities they share when compared to your neocortex. If you were to pull the neocortex from your brain and stretch it out on a table, you most likely wouldn’t be able to see that not only is it roughly the size of a large handkerchief; it also shares the same thickness.

The neocortex, or cortex for short, is Latin for “new rind”, or “new bark”, and represents the most recent evolutionary change to the mammalian brain. It envelopes the “old brain” and has several ridges and valleys (called sulci and gyri) that formed from evolution’s mostly successful attempt to stuff as much cortex as possible into our skulls. It has taken on the duties of processing sensory inputs and storing memories, and rightfully so. Draw a one millimeter square on your handkerchief cortex, and it would contain around 100,000 neurons. It has been estimated that the typical human cortex contains some 30 billion total neurons. If we make the conservative guess that each neuron has 1,000 synapses, that would put the total synaptic connections in your cortex at 30 trillion — a number so large that it is literally beyond our ability to comprehend. And apparently enough to store all the memories of a lifetime.

In the theater of your mind, imagine a stretched-out handkerchief lying in front of you. It is… you. It contains everything about you. Every memory that you have is in there. Your best friend’s voice, the smell of your favorite food, the song you heard on the radio this morning, that feeling you get when your kids tell you they love you is all in there. Your cortex, that little insignificant looking handkerchief in front of you, is reading this article at this very moment.

What an amazing machine; a machine that is made possible with a special type of cell – a cell we call a neuron. In this article, we’re going to explore how a neuron works from an electrical vantage point. That is, how electrical signals move from neuron to neuron and create who we are.

A Basic Neuron

Neuron diagram via Enchanted Learning

Despite the amazing feats a human brain performs, the neuron is comparatively simple when observed by itself. Neurons are living cells, however, and have many of the same complexities as other cells – such as a nucleus, mitochondria, ribosomes, and so on. Each one of these cellular parts could be the subject of an entire book. Its simplicity arises from the basic job it does – which is outputting a voltage when the sum of its inputs reaches a certain threshold, which is roughly 55 mV.

Using the image above, let’s examine the three major components of a neuron.


The soma is the cell body and contains the nucleus and other components of a typical cell. There are different types of neurons whose differing characteristics come from the soma. Its size can range from 4 to over 100 micrometers.


Dendrites protrude from the soma and act as the inputs of the neuron. A typical neuron will have thousands of dendrites, with each connecting to an axon of another neuron. The connection is called a synapse but is not a physical one. There is a gap between the ends of the dendrite and axon called a synaptic cleft. Information is relayed through the gap via neural transmitters, which are chemicals such as dopamine and serotonin.


Each neuron has only a single axon that extends from the soma, and acts similar to an electrical wire. Each axon will terminate with terminal fibers, forming synapses with as many as 1,000 other neurons. Axons vary in length and can reach a few meters long. The longest axons in the human body run from the bottom of the foot to the spinal cord.

The basic electrical operation of a neuron is to output a voltage spike from its axon when the sum of its input voltages (via its dendrites) crosses a specific threshold. And since axons are connected to dendrites of other neurons, you end up with this vastly complicated neural network.

Since we’re all a bunch of electronic types here, you might be thinking of these ‘voltage spikes’ as a difference of potential. But that’s not how it works. Not in the brain anyway. Let’s take a closer look at how electricity flows from neuron to neuron.

Action Potentials – The Communication Protocol of the Brain

The axon is covered in a myelin sheet which acts as an insulator. There are small breaks in the sheet along the length of the axon which are named after its discoverer, called Nodes of Ranvier. It’s important to note that these nodes are ion channels. In the spaces just outside and inside of the axon membrane exists a concentration of potassium and sodium ions. The ion channels will open and close, creating a local difference in the concentration of sodium and potassium ions.

Diagram via Washington U.

We all should know that an ion is an atom with a charge. In a resting state, the sodium/potassium ion concentration creates a negative 70 mV difference of potential between the outside and inside of the axon membrane, with there being a higher concentration of sodium ions outside and a higher concentration of potassium ions inside. The soma will create an action potential when -55 mV is reached. When this happens, a sodium ion channel will open. This lets positive sodium ions from outside the axon membrane to leak inside, changing the sodium/potassium ion concentration inside the axon, which in turn changes the difference of potential from -55 mV to around +40 mV. This process in known as depolarization.

Graph via Washington U.

One by one, sodium ion channels open along the entire length of the axon. Each one opens only for a short time, and immediately afterward, potassium ion channels open, allowing positive potassium ions to move from inside the axon membrane to the outside. This changes the concentration of sodium/potassium ions and brings the difference of potential back to its resting place of -70 mV in a process known as repolarization. Fro start to finish, the process takes about five milliseconds to complete. The process causes a 110 mV voltage spike to ride down the length of the entire axon, and is called an action potential. This voltage spike will end up in the soma of another neuron. If that particular neuron gets enough of these spikes, it too will create an action potential. This is the basic process of how electrical patterns propagate throughout the cortex.

The mammalian brain, specifically the cortex, is an incredible machine and capable of far more than even our most powerful computers. Understanding how it works will give us a better insight into building intelligent machines. And now that you know the basic electrical properties of a neuron, you’re in a better position to understand artificial neural networks.


Action Potential in Neurons, via Youtube

On Intelligence, by Jeff Hawkins, ISDN 978-0805078534

Wrap Your Mind Around Neural Networks

Artificial Intelligence is playing an ever increasing role in the lives of civilized nations, though most citizens probably don’t realize it. It’s now commonplace to speak with a computer when calling a business. Facebook is becoming scary accurate at recognizing faces in uploaded photos. Physical interaction with smart phones is becoming a thing of the past… with Apple’s Siri and Google Speech, it’s slowly but surely becoming easier to simply talk to your phone and tell it what to do than typing or touching an icon. Try this if you haven’t before — if you have an Android phone, say “OK Google”, followed by “Lumos”. It’s magic!

Advertisements for products we’re interested in pop up on our social media accounts as if something is reading our minds. Truth is, something is reading our minds… though it’s hard to pin down exactly what that something is. An advertisement might pop up for something that we want, even though we never realized we wanted it until we see it. This is not coincidental, but stems from an AI algorithm.

At the heart of many of these AI applications lies a process known as Deep Learning. There has been a lot of talk about Deep Learning lately, not only here on Hackaday, but all over the interwebs. And like most things related to AI, it can be a bit complicated and difficult to understand without a strong background in computer science.

If you’re familiar with my quantum theory articles, you’ll know that I like to take complicated subjects, strip away the complication the best I can and explain it in a way that anyone can understand. It is the goal of this article to apply a similar approach to this idea of Deep Learning. If neural networks make you cross-eyed and machine learning gives you nightmares, read on. You’ll see that “Deep Learning” sounds like a daunting subject, but is really just a $20 term used to describe something whose underpinnings are relatively simple.

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Keep an Eye on the Sky With rDuinoScope

We’ve all enjoyed looking up at a clear night sky and marveled at the majesty of the stars. Some of us have even pointed telescopes at particular celestial objects to get a closer view. Anyone who’s ever looked at anything beyond Jupiter knows the hassle involved.  It is most unfortunate that the planet we reside on happens to rotate about a fixed axis, which makes it somewhat difficult to keep a celestial object in the view of your scope.

It doesn’t take much to strap a few steppers and some silicon brains to a scope to counter the rotation of earth, and such systems have been available for decades. They are unfortunately quite expensive. So [Dessislav Gouzgounov] took matters into his own hands and developed the rDuinoScope – an open source telescope control system.

Based on the Arduino Due, the systems stores a database of 250 stellar objects. Combined with an RTC and GPS, the rDunioScope can locate and lock on to your favorite nebula and track it, allowing you to view it in peace. Be sure to grab the code and let us know when you have your own rDuinoScope set up!


Hanging 3D Printer Uses Entire Room As Print Bed

There are many things people do with spare rooms. Some make guest rooms, others make baby rooms, while a few even make craft rooms. What do hackers do with spare rooms? Turn them into giant 3D printers of course. [Torbjørn Ludvigsen] is a physics major out of Umea University in Sweden, and built the Hangprinter for only $250 in parts. It follows the RepRap tradition of being completely open source and made mostly from parts that it can print.

The printer is fully functional, proven by printing a five-foot tall model of the Tower of Babel. [Torbjorn] hopes to improve the printer to allow it to print pieces of furniture and other larger household items.

[Torbjorn] hopes that 3D printing will not go down the same road that 2D printing went, where the printers are designed to break after so many prints. Open source is the key to stopping such machines from getting out there.

Thanks to [Jeremy Southard] for the tip!

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