Suddenly this topic seems to be all around me. Although, I’m not sure if it was always here, or if I just wasn’t paying attention. But it’s here in a big way. The meritocracy discussion seems to be mainly about women in tech, but to a lesser extent, minorities.

At first it was an old colleague of mine on twitter. And frankly, I was annoyed to see his many-times-a-day posts about feminism and how the whole world is conspiring to keep his daughter from enjoying science and math. It went on like this for years and I eventually stopped following him because he never talked about anything else and I was tired of his rant.

What I didn’t understand at the time was this was all part of a much larger discussion. Although I did notice the growing trend about a year ago and started to take notice. Looking back at stories from the past few years shows just how much attention this is getting from major internet media outlets: Tech Crunch, The Guardian, Quartz, The Atlantic, Wired, NPR, The Boston Globe. You can even watch the rise of the term ‘meritocracy’ on Google trends as it begins in early 2009, likely in association with its so-called myth.

Recently, I also ran across an indiegogo project called CODE: Debugging the Gender Gap which promises to explore the lack of diversity in the tech world. At this point, it’s fully funded and I’m excited to see it when it’s ready.

Today, the discussion seems to be finding new ground after Microsoft CEO Satya Nadella’s remarks caused a minor internet storm, where he made some regretful remarks about how women should behave regarding salaries and ended up having to take it all back. And that’s great, the man ate his silly words and hopefully we all learned something.

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Through our work on the OptionsHouse API client, we’ve somehow become known as trading algorithm experts. At least once a week, Branded Crate gets a phone call or email from someone who wants to automate trading activity. To even have a thought like this requires some level of sophistication. Even so, many potential clients aren’t aware of what it takes to create and manage a system like this. That’s our area of expertise, so if you’re considering trading automation, read on to learn more about how we do it.

The very heart of any trading algorithm is the actual algorithm, written using instructions a machine can understand (code). This is mainly what clients think about when they talk to us. The idea generally seems simple at first, but complexities emerge as you begin to consider automation. Without even thinking, clients “just know” to do things a certain way as they execute their trading strategies manually. Computers, on the other hand, don’t know anything.

Let’s say a client wants to buy N shares of some stock when the current price of that stock is lower than it was at the same time on the previous trading day and sell when the current stock price is higher than the same time on the previous trading day. This is probably a terrible strategy, but ignore that because it can still serve as an example of how and where complexities emerge.

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As a developer, I’ve long struggled with the problem of how to deploy the applications I create. In an ideal world, I could spend all of my energy doing what I do best (building applications), and none of my energy dealing with operations concerns. That sounds like a good reason to have an operations team. But an operations team has the same problem because ideally, the operations team could spend all their time handling operations concerns, and none of their time worrying about how applications were created.

Deploying an application is largely an exercise in defining (or discovering) the relationship between an application and its environment. This can be a tricky and error-prone job because there is so much variety in applications, environments and the people who create them. If everyone involved could agree on an interface contract, we’d all save a lot of time and energy.

This is what PaaS has tried to do. Solutions like EngineYard, Heroku, Google App Engine, and OpenShift have sprung up to varying degrees of success. Of these, Heroku has had the largest impact on the way we think about software service deployment and what PaaS can do. You can find an entire ecosystem of software packages on GitHub designed to make your applications adhere to the tenets of The Twelve-Factor App. And that’s a good thing because we’re starting to see what life could be like in a world where apps fit neatly into PaaS-shaped boxes.

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This has been plaguing me for years and I finally figured it out. Thanks to eleperte who created ssh-xdg-open, I was finally able to see what to do. Ssh-xdg-open didn’t work for me, but there was enough information available for me to figure out the missing pieces.

Forget about gconftool and you don’t need ssh-xdg-open. If all you want is working ssh://protocol links, then just use xdg-mime to set the default application for handling ssh protocol links and create an application handler with the same name as that application.

xdg-mime default ssh.desktop x-scheme-handler/ssh
cat << EOF > ~/.local/share/applications/ssh.desktop
[Desktop Entry]
Version=1.0
Name=SSH Launcher
Exec=bash -c '(URL="%U" HOST="\${URL:6}"; ssh \$HOST); bash'
Terminal=true
Type=Application
Icon=utilities-terminal
EOF

All this does is launch bash, parse the host from the URL and executes ssh. When ssh exits, it executes bash again so the window stays open. I wrote it this way because you can’t count on everything to work all the time and if you don’t keep the window open, the error messages will vanish into the ether and your sanity with them.

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Like the rest of the world, RightScale has been moving more and more of its application from the server to the client. That means we’ve suddenly found managing larger and larger piles of JavaScript. All that JavaScript needs to be delivered to clients as quickly as possible in order to minimize the time customers spend waiting for web pages to load.

So we created a nice little build tool leveraging Grunt which among other things takes all that JavaScript and compiles it into one big blob for each application. In order to make that big blob as small as possible, we use UglifyJS.

Unfortunately, some of our apps are so big that running the uglify Grunt task can take a long time. Ideally, this task would be fast enough to where it could be run at or just before deploying. Fast enough is a pretty subjective term, but we deploy code all the time to production and various kinds of staging systems, so fast enough becomes however long you want to wait for code deploys in addition to the time it already takes. In my case, three extra minutes is not fast enough.

So I theorized about the virtue of using UglifyJS at all and my reasoning went something like this: Any sane person who’s delivering a lot of JavaScript to clients on the web is going to be using some kind of HTTP compression algorithm like gzip or deflate. And hardcore file size optimizations prior to compression seem like exactly the kind of things that would make regular file compression less efficient. So wouldn’t we be better off with something fast and simple like Douglas Crockford’s good old JSMin? We could just rely more on the file compression than mifification or uglification to reduce file size.

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