Author: mike

via Dilbert
Dilbert 2017-04-15

By Tom McHale
Tom gives us an introduction to the sport and what it takes getting started in long range shooting.

USA –-(Ammoland.com)- What’s hotter than Captain Gastroplasty at a Texas Chili Cookoff? Long range shooting that’s what!

While we throw around the term ͞Long Range Shooting like it’s a discrete sport, it’s really a collection of multiple gun games of varying styles. Sure, they all have extended distance in common,  but beyond that, the styles diverge. At risk of offending everyone, I might describe long range shooting as two general types of disciplines.

• The methodical and precise ballistic science game. That would be NRA High Power F-Class Shooting.
• Running, gunning, and math-based winging it. That would be PRS or Precision Rifle Series.

So let’s clarify those two very broad and probably unfair generalizations by taking a look at these two long range shooting sports. Just to be clear right up front, both rely on shooter skill and consistency, they’re just different regarding which specific skills are most important

Last weekend I had the opportunity to compete in my very first F-Class match. I’ve done lots of long range shooting in all sorts of flat and mountainous conditions out to about 1,500 yards, so I wasn’t overly concerned about my ability to land at least a couple of rounds in the same zip code as the target.

On the other hand, I expected I’d be facing a bunch of grizzled veterans who know this particular game inside and out. As it turned out, both expectations came true.

Getting Started In Long Range Shooting

The match was held at my local shooting facility, Palmetto Gun Club. Carefully checking the online information in advance, I saw the magic words – “Bring what ya got. Come out and shoot!” You see, like most shooting sports, those grizzled veterans like to win, but most of them like to help rookies get involved even more. I quickly determined that there are two classes for NRA Long Range shooting: Open and F/TR. If you choose F/TR, you have to shoot either .223 Remington / 5.56 NATO caliber, and you’re limited to total rifle weight of 18.15 pounds including optics, bipods, and such. You also have to shoot using a sling and/or bipod – no fancy ground-based shooting support devices allowed.

On the other hand, if you want to just give the sport a try and you don’t have a suitable service caliber rifle handy, you can simply enter Open Class. That allows any caliber up to .35 and you can rest your rifle on just about anything.

I have a loaner rifle in from Masterpiece Arms chambered in 6.5 Creedmoor that will shoot a mosquito off a gnat’s elbow, so I chose to enter Open Class. While I could have used a stationary support rifle rest, I simply added a Caldwell Bipod up front, topped the Masterpiece Arms BA Lite PCR Competition Rifle with a Burris Veracity Riflescope 4-20x50mm and proceeded to load up some ammo. I had a couple of boxes of Sierra’s 130-grain Tipped Matchking (TMK) .264 bullets, so I cranked up my press and made a hundred carefully constructed rounds.

Since I hadn’t shot this particular combination of rifle, projectile, and optic before, I did some advance ballistic planning using Ballistic’s AE smartphone app. After entering some basic information about the projectile type, velocity from the MPA rifle, scope height, zero distance, and atmospheric conditions, I determined that my bullet would drop exactly 14.89 feet over the 800-yard distance set for this day’s competition. That works out to exactly 21.33 minutes of angle. On the Burris scope, 85 clicks brought me pretty darn close.

The elevation or bullet drop part is pretty easy to deal with because gravity is constant. If you put in good numbers, a ballistic program will tell you exactly what adjustments need to be made to get you close to the bullseye, at least vertically. You’ll have to fire a few test shots to account for slight variances in scope precision and such, but that’s fairly easy and unless you have a terrible scope, you’ll be close to start.

The hard part is accounting for the wind. Your ballistic program can also tell you how much your specific bullet will drift based on the wind direction and speed. As an example, the load I used at 800 yards will drift just under 53 inches SIDEWAYS with a 10 mile per hour crosswind. Fortunately, we only had to deal with one to three mile per hour winds coming from the ten to eleven o’clock position that day. As the wind varies shot to shot and can be completely different at any point between shooter and target, wind estimation ability is the skill that separates the rookies from the pros. Even with our very mild conditions, the wind alone could move your shot about three inches give or take. When shooting rifles capable of four-inch groups at 800 yards, that puts the burden on the shooter as much as the equipment. Not only does your sight picture and trigger press technique need to be precise and consistent, you need to make a really good guess at what the wind will do to each and every shot.

So let’s try to sum up the key points of the NRA F-Class long range sport. You can get started with most anything you already have because the Open Class has a lot of leeway regarding rifle and caliber. You can even start with store-bought ammunition. Got sandbags or a bipod? Then you’re good to go from a shooting rest standpoint. Since F-Class deals with known ranges and target size, many repeat competitors quickly start to optimize equipment. To get the most out of your rifle, you’ll soon want to reload your own cartridges. You’ll also want a purpose-built scope that offers somewhere above 20x magnification with great clarity and a fine reticle for precise shot placement. Oh, NRA F-Class doesn’t allow muzzle breaks or suppressors, so be sure to just use a standard thread protector if your rifle has a threaded barrel.

If your rifle has a flash suppressor installed,  I doubt anyone will give you any trouble, but you might find your rifle will shoot better groups if you remove it.

The bottom line getting started In long range shooting is this: If you enjoy precision and tinkering, F-Class will be a great sport for you. While skill is always the ultimate determinant for collecting medals, the right equipment will help you get there too. At your first match, you’ll see all sorts of accuracy enhancing gizmos. If you enjoy playing the equipment game, then F-Class might be for you.

The other major style, Precision Rifle Series, is a bit more like tactical golf with time limits. Competitors have to engage multiple targets at varying distances, and from unusual positions, while the clock is running. You might find yourself shooting from a rooftop or from behind common environmental barriers. While you have to have equipment up to the task and know your ballistics, there’s a lot more emphasis on speed and adaptation to different target scenarios.

We’ll take a closer look at PRS in a future article.

About

Tom McHale is the author of the Insanely Practical Guides book series that guides new and experienced shooters alike in a fun, approachable, and practical way. His books are available in print and eBook format on Amazon. You can also find him on Google+, Facebook, Twitter and Pinterest.

This post Getting Started In Long Range Shooting appeared first on AmmoLand.com Shooting Sports News .

via AmmoLand.com Shooting Sports News
Getting Started In Long Range Shooting

Here is our annual update on Ohio State University salaries.
Some notes on the data:
You can search by employee name, department, title, total pay or year. Searches by dollar amount will return any results at that level and higher. The results column you likely want is Total, which includes pay from all sources. The Regular column, according to OSU, "represents the pay that comes from base salary. It represents standard hours (full-time equivalent) worked plus paid leave taken according to our…

via Columbus Business News – Local Columbus News | Business First of Columbus
Ohio State salary database for year-end 2016

Here is our annual update on Ohio State University salaries.
Some notes on the data:
You can search by employee name, department, title, total pay or year. Searches by dollar amount will return any results at that level and higher. The results column you likely want is Total, which includes pay from all sources. The Regular column, according to OSU, "represents the pay that comes from base salary. It represents standard hours (full-time equivalent) worked plus paid leave taken according to our…

via Columbus Business News – Local Columbus News | Business First of Columbus
Ohio State salary database for year-end 2016

Link

Our first glimpse at Episode VIII picks up right where The Force Awakens left off, with Rey on a mysterious island, and hints at her learning to use her powers much like Luke learned from Yoda. But perhaps her role isn’t to be a Jedi, but to restore order to The Force.

via The Awesomer
Star Wars: The Last Jedi (Teaser)

"Pressing a button on the ornaments sends out a signal syncing nearby pieces; this triggers a performance, including coordinated light and sound. The more ornaments you have, the more dynamic the performance. In total, all seven ornaments can perform five major moments from the movie, and there are an incredible 127 possible story combinations."
via Super Punch
Upcoming Star Wars ornaments are designed to interact with each other

via Gizmodo
Anker’s Smallest Bluetooth Headphones Are Back Down To Their Smallest Price

Ophir Tanz is the CEO of GumGum, an artificial intelligence company with particular expertise in computer vision. GumGum applies its capabilities to a variety of industries, from advertising to professional sports across the globe. Ophir holds a B.S. and a M.S. from Carnegie Mellon University and currently lives in Los Angeles.

More posts by this contributor:

Cambron Carter

Contributor

Cambron Carter leads the image technology team at GumGum, where he designs computer vision and machine learning solutions for a wide variety of applications. Cambron holds B.S. degrees in physics and electrical engineering and an M.Eng. in electrical engineering from the University of Louisville.

If you’ve dug into any articles on artificial intelligence, you’ve almost certainly run into the term “neural network.” Modeled loosely on the human brain, artificial neural networks enable computers to learn from being fed data.

The efficacy of this powerful branch of machine learning, more than anything else, has been responsible for ushering in a new era of artificial intelligence, ending a long-lived “AI Winter.” Simply put, the neural network may well be one of the most fundamentally disruptive technologies in existence today.

This guide to neural networks aims to give you a conversational level of understanding of deep learning. To this end, we’ll avoid delving into the math and instead rely as much as possible on analogies and animations.

Thinking by brute force

One of the early schools of AI taught that if you load up as much information as possible into a powerful computer and give it as many directions as possible to understand that data, it ought to be able to “think.” This was the idea behind chess computers like IBM’s famous Deep Blue: By exhaustively programming every possible chess move into a computer, as well as known strategies, and then giving it sufficient power, IBM programmers created a machine that, in theory, could calculate every possible move and outcome into the future and pick the sequence of subsequent moves to outplay its opponent. This actually works, as chess masters learned in 1997.*

With this sort of computing, the machine relies on fixed rules that have been painstakingly pre-programmed by engineers — if this happens, then that happens; if this happens, do this — and so it isn’t human-style flexible learning as we know it at all. It’s powerful supercomputing, for sure, but not “thinking” per se.

Teaching machines to learn

Over the past decade, scientists have resurrected an old concept that doesn’t rely on a massive encyclopedic memory bank, but instead on a simple and systematic way of analyzing input data that’s loosely modeled after human thinking. Known as deep learning, or neural networks, this technology has been around since the 1940s, but because of today’s exponential proliferation of data — images, videos, voice searches, browsing habits and more — along with supercharged and affordable processors, it is at last able to begin to fulfill its true potential.

Machines — they’re just like us!

An artificial (as opposed to human) neural network (ANN) is an algorithmic construct that enables machines to learn everything from voice commands and playlist curation to music composition and image recognition.The typical ANN consists of thousands of interconnected artificial neurons, which are stacked sequentially in rows that are known as layers, forming millions of connections. In many cases, layers are only interconnected with the layer of neurons before and after them via inputs and outputs. (This is quite different from neurons in a human brain, which are interconnected every which way.)

Source: GumGum

 

This layered ANN is one of the main ways to go about machine learning today, and feeding it vast amounts of labeled data enables it to learn how to interpret that data like (and sometimes better than) a human.

Just as when parents teach their kids to identify apples and oranges in real life, for computers too, practice makes perfect.

Take, for example, image recognition, which relies on a particular type of neural network known as the convolutional neural network (CNN) — so called because it uses a mathematical process known as convolution to be able to analyze images in non-literal ways, such as identifying a partially obscured object or one that is viewable only from certain angles. (There are other types of neural networks, including recurrent neural networks and feed-forward neural networks, but these are less useful for identifying things like images, which is the example we’re going to use below.)

All aboard the network training

So how do neural networks learn? Let’s look at a very simple, yet effective, procedure called supervised learning. Here, we feed the neural network vast amounts of training data, labeled by humans so that a neural network can essentially fact-check itself as it’s learning.

Let’s say this labeled data consists of pictures of apples and oranges, respectively. The pictures are the data; “apple” and “orange” are the labels, depending on the picture. As pictures are fed in, the network breaks them down into their most basic components, i.e. edges, textures and shapes. As the picture propagates through the network, these basic components are combined to form more abstract concepts, i.e. curves and different colors which, when combined further, start to look like a stem, an entire orange, or both green and red apples.

At the end of this process, the network attempts to make a prediction as to what’s in the picture. At first, these predictions will appear as random guesses, as no real learning has taken place yet. If the input image is an apple, but “orange” is predicted, the network’s inner layers will need to be adjusted.

The adjustments are carried out through a process called backpropagation to increase the likelihood of predicting “apple” for that same image the next time around. This happens over and over until the predictions are more or less accurate and don’t seem to be improving. Just as when parents teach their kids to identify apples and oranges in real life, for computers too, practice makes perfect. If, in your head, you just thought “hey, that sounds like learning,” then you may have a career in AI.

So many layers…

Typically, a convolutional neural network has four essential layers of neurons besides the input and output layers:

• Convolution
• Activation
• Pooling
• Fully connected

Convolution

In the initial convolution layer or layers, thousands of neurons act as the first set of filters, scouring every part and pixel in the image, looking for patterns. As more and more images are processed, each neuron gradually learns to filter for specific features, which improves accuracy.

In the case of apples, one filter might be focused on finding the color red, while another might be looking for rounded edges and yet another might be identifying thin, stick-like stems. If you’ve ever had to clean out a cluttered basement to prepare for a garage sale or a big move — or worked with a professional organizer — then you know what it is to go through everything and sort it into different-themed piles (books, toys, electronics, objets d’art, clothes). That’s sort of what a convolutional layer does with an image by breaking it down into different features.

One advantage of neural networks is that they are capable of learning in a nonlinear way.

What’s particularly powerful — and one of the neural network’s main claims to fame — is that unlike earlier AI methods (Deep Blue and its ilk), these filters aren’t hand designed; they learn and refine themselves purely by looking at data.

The convolution layer essentially creates maps — different, broken-down versions of the picture, each dedicated to a different filtered feature — that indicate where its neurons see an instance (however partial) of the color red, stems, curves and the various other elements of, in this case, an apple. But because the convolution layer is fairly liberal in its identifying of features, it needs an extra set of eyes to make sure nothing of value is missed as a picture moves through the network.

Activation

One advantage of neural networks is that they are capable of learning in a nonlinear way, which, in mathless terms, means they are able to spot features in images that aren’t quite as obvious — pictures of apples on trees, some of them under direct sunlight and others in the shade, or piled into a bowl on a kitchen counter. This is all thanks to the activation layer, which serves to more or less highlight the valuable stuff — both the straightforward and harder-to-spot varieties.

In the world of our garage-sale organizer or clutter consultant, imagine that from each of those separated piles of things we’ve cherry-picked a few items — a handful of rare books, some classic t-shirts from our college days to wear ironically — that we might want to keep. We stick these “maybe” items on top of their respective category piles for another consideration later.

Pooling

All this “convolving” across an entire image generates a lot of information, and this can quickly become a computational nightmare. Enter the pooling layer, which shrinks it all into a more general and digestible form. There are many ways to go about this, but one of the most popular is “max pooling,” which edits down each feature map into a Reader’s Digest version of itself, so that only the best examples of redness, stem-ness or curviness are featured.

In the garage spring cleaning example, if we were using famed Japanese clutter consultant Marie Kondo’s principles, our pack rat would have to choose only the things that “spark joy” from the smaller assortment of favorites in each category pile, and sell or toss everything else. So now we still have all our piles categorized by type of item, but only consisting of the items we actually want to keep; everything else gets sold. (And this, by the way, ends our de-cluttering analogy to help describe the filtering and downsizing that goes on inside a neural network.)

At this point, a neural network designer can stack subsequent layered configurations of this sort — convolution, activation, pooling — and continue to filter down images to get higher-level information. In the case of identifying an apple in pictures, the images get filtered down over and over, with initial layers showing just barely discernable parts of an edge, a blip of red or just the tip of a stem, while subsequent, more filtered layers will show entire apples. Either way, when it’s time to start getting results, the fully connected layer comes into play.

Source: GumGum

Fully connected

Now it’s time to start getting answers. In the fully connected layer, each reduced, or “pooled,” feature map is “fully connected” to output nodes (neurons) that represent the items the neural network is learning to identify. If the network is tasked with learning how to spot cats, dogs, guinea pigs and gerbils, then it’ll have four output nodes. In the case of the neural network we’ve been describing, it’ll just have two output nodes: one for “apples” and one for “oranges.”

If the picture that has been fed through the network is of an apple, and the network has already undergone some training and is getting better with its predictions, then it’s likely that a good chunk of the feature maps contain quality instances of apple features. This is where these final output nodes start to fulfill their destiny, with a reverse election of sorts.

Tweaks and adjustments are made to help each neuron better identify the data at every level.

The job (which they’ve learned “on the job”) of both the apple and orange nodes is essentially to “vote” for the feature maps that contain their respective fruits. So, the more the “apple” node thinks a particular feature map contains “apple” features, the more votes it sends to that feature map. Both nodes have to vote on every single feature map, regardless of what it contains. So in this case, the “orange” node won’t send many votes to any of the feature maps, because they don’t really contain any “orange” features. In the end, the node that has sent the most votes out — in this example, the “apple” node — can be considered the network’s “answer,” though it’s not quite that simple.

Because the same network is looking for two different things — apples and oranges — the final output of the network is expressed as percentages. In this case, we’re assuming that the network is already a bit down the road in its training, so the predictions here might be, say, 75 percent “apple” and 25 percent “orange.” Or, if it’s earlier in the training, it might be more inaccurate and determine that it’s 20 percent “apple” and 80 percent “orange.” Oops.

Source: GumGum

If at first you don’t succeed, try, try, try again

So, in its early stages, the neural network spits out a bunch of wrong answers in the form of percentages. The 20 percent “apple” and 80 percent “orange” prediction is clearly wrong, but since this is supervised learning with labeled training data, the network is able to figure out where and how that error occurred through a system of checks and balances known as backpropagation.

Now, this is a mathless explanation, so suffice it to say that backpropagation sends feedback to the previous layer’s nodes about just how far off the answers were. That layer then sends the feedback to the previous layer, and on and on like a game of telephone until it’s back at convolution. Tweaks and adjustments are made to help each neuron better identify the data at every level when subsequent images go through the network.

This process is repeated over and over until the neural network is identifying apples and oranges in images with increasing accuracy, eventually ending up at 100 percent correct predictions — though many engineers consider 85 percent to be acceptable. And when that happens, the neural network is ready for prime time and can start identifying apples in pictures professionally.

*This is different than Google’s AlphaGo which used a self-learned neural net to evaluate board positions and ultimately beat a human at Go, versus Deep Blue, which used a hard-coded function written by a human.

via TechCrunch
Neural networks made easy

An anonymous reader writes: Allegro MicroSystems LLC is suing a former IT employee for sabotaging its database using a "time bomb" that deleted crucial financial data in the first week of the new fiscal year. According to court documents, after resigning from his job, a former sysadmin kept one of two laptops. On January 31, Patel entered the grounds of the Allegro headquarters in Worcester, Massachusetts, just enough to be in range of the factory’s Wi-Fi network. Allegro says that Patel used the second business-use laptop to connect to the company’s network using the credentials of another employee. While connected to the factory’s network on January 31, Allegro claims Patel, who was one of the two people in charge of Oracle programming, uploaded a "time bomb" to the company’s Oracle finance module. The code was designed to execute a few months later, on April 1, 2016, the first week of the new fiscal year, and was meant to "copy certain headers or pointers to data into a separate database table and then to purge those headers from the finance module, thereby rendering the data in the module worthless." The company says that "defendant Patel knew that his sabotage of the finance module on the first week of the new fiscal year had the maximum potential to cause Allegro to suffer damages because it would prevent Allegro from completing the prior year’s fiscal year-end accounting reconciliation and financial reports."

Read more of this story at Slashdot.

via Slashdot
Former Sysadmin Accused of Planting ‘Time Bomb’ In Company’s Database

A Virtual Private Network (VPN) is a great way to add security to your browsing while also preventing snoopers (including your internet service provider), but VPN providers are notoriously sketchy. You could do some research to find a good one. Or you can make your own in about 10 minutes.

As a quick refresher, a VPN encrypts your data before it leaves your device, then that data stays encrypted while it travels through your local network and internet service provider (ISP) until it’s eventually decrypted by the VPN server. In this case, you’ll be installing VPN software onto a web service.

Commercial VPNs are easier to set up and while this project isn’t terribly complicated, you do need to be somewhat technically inclined to do it. Since a poorly set up VPN is useless, I’d recommend sticking with a commercial option from a well-known company, like Private Internet Access, SlickVPN, NordVPN, Hideman, or Tunnelbear if you’re not comfortable setting this up for yourself. For the rest of us comfortable with a little command line usage, let’s get going.

What You Get

Namely, you’ll get a free VPN out of this. That means a secure, encrypted connection between your computer or mobile device and the internet at large. If you’re annoyed that your ISP can see everything you do online or you want a secure connection to the internet when you’re out at coffee shops, then you want to use a VPN.

We’ve highlighted many public VPN providers over the years, including most recently Private Internet Access, but one general catch with any provider is that it’s hard to tell how loyal they’ll be at keeping your private data private. So, the next logical step is to make your own. There are a few different projects out there for doing so, but I settled on Algo because it seems to be the simplest of the bunch. It installs VPN software on one of several different cloud competing services and you can connect to it from any computer you have.

There are a few other options out there, including Streisand, which takes the VPN idea a step further by also integrating a Tor bridge and a few other privacy-focused features. Streisand is great, but it’s overkill for most of us. However, if you’re more interested in the extra privacy and security features in Streisand, the first step of the set up process is nearly identical to Algo, so the first step in this guide will get you through the confusing part of setting up the Amazon EC2 service. After that, follow the Streisand instructions for your operating system.

Finally, before we get started here, Algo does not anonymize your web browsing, nor does it protect you from legal or government entities getting your data. A government could theoretically ask the hosting provider, Amazon in this case, for your billing information. That means your traffic could be traced back to you. They can do this with any commercial VPN providers too, of course. However, at least the VPN portion of this is entirely in your own care. It’s also relatively disposable, so you can set up or destroy this sucker pretty quickly once you get the hang of the process.

Regardless, Algo secures and encrypts your connection, which is plenty for most of us. If you’re uncomfortable shelling out the cash to an anonymous, random VPN provider, this is the best solution.

Step One: Sign Up for an Amazon EC2 Account

You can install Algo on DigitalOcean, Amazon EC2, Google Compute Engine, and Microsoft Azure. If you’re a new user, you can get access to Amazon’s EC2 free tier for an entire year, so we’re going to detail that process here.

There are some limitations here though. First off, you get 750 hours per month, which should be more than enough for one device, but might add up if you have multiple devices. Second, you’re limited to 15GB of bandwidth per month, if you download a lot of large files, this may not be enough. Third, after the year is up, the price switches over to an hourly rate. Still, most people shouldn’t expect to pay more than $10-$11/month.

If this unpredictability is annoying and you’d rather skip the free year in favor of something more reliably priced, I suggest using DigitalOcean’s $5/month tier instead. DigitalOcean’s set up is considerably easier too. Create a new Droplet with the default settings, then click the API tab and generate a new token. You’ll need that number during the Algo installation process. After that, you can just skip down to the Algo step below.

But, we all love free, so let’s continue on with Amazon:

1. Head to the Amazon Web Services site and create a free account. You can link your current Amazon account to your web services account if you want.
2. Once you’re logged in, Click Services > IAM. It’s located under the Security, Identity, & Compliance tab.
3. Click the Users tab on the left.
4. Click Add User.
5. Create a user name, then click the box next to Programmatic Access. Then click Next.
6. Click Attach existing policies directly.
7. Type in “admin” to search through the policies. Find “AdministratorAccess” and click the checkbox next to that. Click Next when you’re done.
8. On the final screen, click the Download CSV button. This file includes a couple numbers and access keys you’ll need during the Algo set up process. Click Close and you’re all set.

Now, your little free tier service is up and running on Amazon. It’s time to install Algo.

Step Two: Download and Install Algo

Next up, we’ll install Algo. You’ll do this using the command line on your Windows, Mac, or Linux computer at home. If you’re on Linux or Mac, go ahead and skip down to part two below. If you’re on Windows, continue on.

Part One: Windows Users (Mac Users Can Skip This Step)

Windows users will need to install the Windows Subsystem for Linux for Algo to work, which is only available on Windows 10. Here’s what you need to do:

1. Open Settings.
2. Click Update & Security, then click For Developers.
3. Set the Developers mode option to enabled.
4. After everything installs, click Control Panel, then select Programs.
5. Click Turn Windows features on or off.
6. Scroll down and then the box next to Windows Subsystem for Linux, then click OK. Windows will install the software, then reboot.

Now, you have the Linux Bash installed. Click the Start menu and type in “Bash.” You’ll be asked a few more questions. Answer those, then Windows will install another set of software. Finally, once that’s complete, you’ll be at the command line. Type this in, then press enter:

sudo apt-get update && sudo apt-get install python-pip python-setuptools build-essential libssl-dev libffi-dev python-dev python-virtualenv git -y

Once that’s complete, type in: git clone && cd algo and press Enter. Once that’s done, skip down to step five on Part Two below.

Part Two: Install Algo

On Mac you can install Algo easily. However, depending on which version of Linux you’re running, you’ll have a different set of commands here. You can figure out which you need for Linux here.

1. One a Mac, download Algo and unzip the file wherever you want on your machine. This creates a folder called algo-master.
2. Open Terminal, then type in cd followed by the “algo-master” directory location. If you’re not sure of this, type in cd, then drag and drop the algo-master directory into Terminal. It’ll auto-fill the location, resulting in something like, cd /Users/jimbojones/Documents/algo-master.
3. Type in python -m ensurepip --user and press Enter.
4. Type in python -m pip install --user --upgrade virtualenv and press Enter.
5. Type in python -m virtualenv env && source env/bin/activate && python -m pip install -r requirements.txt and press Enter. If you haven’t installed the cc command line tools before, you’ll get a prompt to do so. Go ahead and agree.
6. Type in sudo nano config.cfg and press Enter. This opens up a text editor. Under users, type the the name of any users you’d like to create. These are all the different people you want to have access to your VPN, so make a few of them if you’re sharing with friends or on multiple devices. When you’re done, press Ctrl+X to save and exit.
7. Type in ./algo to start the installation process. The installation script asks you a series of questions.
8. For the provider, type in 2 for Amazon EC2 (unless you went with a different provider). Type any name for your VPN and choose the server location (I suggest sticking to the closest available server).
9. Next, you need to grab your AWS Access Key and your AWS Secret Key. Remember that credentials CSV file you downloaded from Amazon in the previous step? That includes both of these numbers. Go ahead and copy/paste each number from that file when you’re asked.
10. Next up, Algo asks you about VPN On Demand. I answered Yes to both questions. This makes it so your Apple devices automatically connect to the VPN. Otherwise, you have to enable them manually each time. I also recommend saying Yes to the security enhancements, HTTP proxy, and local DNS resolver. The rest of the options are entirely up to you, you can say no to everything and your VPN will still work fine.

Finally, after all that, Algo will go off into the world and install itself on your provider, then set up a ton of different services, eventually giving you the go-ahead that it’s complete. Your VPN is now up and running. You need to connect your devices to it in order to use it.

Configure Your Devices for your VPN

In order to connect to your VPN, you need to install a profile or certificate on each device you want to connect to the VPN from. This is more complicated for some operating systems then it is for others. Either way, all the files you need are in that “algo-master” directory inside the “configs” folder.

Set Up Your VPN on Apple Devices

Inside the “configs” folder, you’ll find a .mobileconfig file. On Mac, double-click that file to install the profile on your Mac. To install the profile on an iPhone or iPad, you can either Airdrop that same file from your Mac to your iOS device, email it to yourself, or upload it to cloud service like iCloud or Dropbox and open it from there. You’ll be asked to confirm the profile installation, and from then on, you’ll be connected to that VPN. You can disconnect by simply deleting the profile.

Set Up Your VPN on Android Devices

On Android, you need to first install the strongSwan VPN Client app. Then, copy the P12 file inside the Configs folder over to your Android device and open it in strongSwan. Follow the directions from there to set it up. If you need help, this guide will walk you through each part.

Set Up Your VPN on Windows

This process is rather complicated on Windows, but it’s still doable.

1. Head to the “configs” folder, then copy the PEM, P12, and PS1 files to your Windows machine.
2. Double-click the PEM file to import it to the Trusted Root certificate store.
3. Open the Powershell application, then navigate to the folder with the files you copied in step one a second ago.
4. Type in, Set-ExecutionPolicy Unrestricted -Scope CurrentUser and press Enter.
5. Type in the name of your Powershell script and press Enter. This will be something like windows_$usernameyoumadeup.ps1. Follow the directions on screen.
6. Finally, when that’s complete type in Set-ExecutionPolicy Restricted -Scope CurrentUser and press Enter.

Your VPN should now be set up.

Once you have everything set up, follow our guide to test to make sure your VPN is working properly.

via Gizmodo
How to Set Up Your Own Completely Free VPN In the Cloud