Savvy software comes from savvy people.
Maybe you are a senior developer in your role at your place of work. Maybe you happen to have most context on a given tech stack or application. Maybe you’re orienting a new team member, or you’re conducting an interview with a candidate using your code base. In all of these cases, you are in a position to bring your programming pair up to your speed on the project.
Continue reading “Advanced Pair Programming: Enabling Your Pair”
Maybe you work on a distributed team and you want to introduce more pairing into your development cycles. Or maybe you’re used to solving problems with pair programming and you have to be out of town for a few days. Maybe you’re a mentee, and you want to pair with a mentor in another city on your application. In any case, it’s valuable to know how to get the most out of remote pair programming. Here we’ll talk about some of the lessons I have learned pairing remotely with colleagues and clients on entrerprise applications, as well as with some mentors and mentees.
Continue reading “Advanced Pair Programming: Pairing Remotely”
Lately I’ve found myself writing asynchronous network calls on mobile platforms in the reactive style. I want to share how you can test-drive calls like this for iOS. The following example uses SwiftyJSON for JSON deseriaization and FutureKit as the asynchronous framework surrounding the network call. There are several libraries in iOS that do both of these things, but your structure here will look similar with any of them.
This example is updated to Swift 3.
Continue reading “Test-Driven iOS: Testing Asynchronous Network Calls with FutureKit”
When an engineering manager tries to sell you on developing software in iterations, it sounds like a scam waiting to happen. You pay them money to write software for some number of hours, but the developers offer no guarantee that they’ll finish the features you’re asking for. It’s not even like getting your bathroom renovated; sure, the contractors take way longer than they said they would, but at least they don’t charge you more for the privilege of waiting. Why would any business agree to that for software?
This question is more interesting than it looks. It seems like common sense to us not to trust the iteration—but only because we make a common set of assumptions about how we should produce, consume, and pay for software.
And maybe, just maybe, those assumptions are wrong.
*This post originally appeared on the blog of The Digital Dames under one of my pseudonyms.
No. Way.
You just got an offer from that amahhhhhzing company with the $70M venture round and the [insert tech buzzword here].
Maybe the business is super-secretive, or maybe all their glassdoor reviews rave about how fun it is to work there. Beer! And Starcraft!
You show up on your first day, eager to meet all the badass women in leadership.
All zero of them.
Continue reading “A Survival Guide for Female Employees in Male-Dominated Companies”
We have talked before about the intuition behind cost function optimization in machine learning. We took a look at where cost functions come from and what they look like. We also talked about how one might get to the bottom of one with gradient descent.
When you open a machine learning textbook, you’ll see much more math than we used in that introduction. But the math is, in fact, important; the math gives us tools that we can use to quickly find the minima of our cost functions.
We talked about derivatives in the last post, and we’re talking about Taylor series in this post. Both of these tools come from calculus and help us identify where to find the minima on our cost functions.
Continue reading “Machine Learning Intuition: Understanding Taylor Series Approximation”
We have talked before about the intuition behind cost function optimization in machine learning. We took a look at where cost functions come from and what they look like. We also talked about how one might get to the bottom of one with gradient descent.
When you open a machine learning textbook, you’ll see much more math than we used in that introduction. But the math is, in fact, important; the math gives us tools that we can use to quickly find the minima of our cost functions.
We’re going to talk about two of those tools: derivatives (in this post) and Taylor series (in the next post). Both of these tools come from calculus and help us identify where to find the minima on our cost functions.
Continue reading “Machine Learning Intuition: Using Derivatives to Minimize the Cost Function”
Suppose you want to identify and describe the patterns that arise in some data.
How do you look for patterns? How do you measure how closely your descriptions match the data? How can you improve your descriptions of this data? How do you know your description is general enough to describe other data like this data?
Continue reading “Machine Learning Intuition: Cost Function Optimization”
In past posts of test-driven iOS, we have talked about instantiating view controllers with dependencies. What if we want to pass information from one view controller to the next one?
Let’s explore how we would test-drive a storyboarded iOS application that passes information from a master list view controller to a detail view controller.
Continue reading “Test-Driven iOS: Passing Information With Segues”
