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Tech Book Face Off: Breaking Windows Vs. Showstopper!

For this Tech Book Face Off, I felt like expanding my horizons a bit. Instead of reading about programming languages or software development or computer science and engineering, I thought I would take a look at some computer history from the business perspective. There are plenty of reading options out there in this space, but I settled on a couple of books about Microsoft. The first, Breaking Windows: How Bill Gates Fumbled the Future of Microsoft by David Bank, is about Bill Gate's hardball business tactics that won him a monopoly in the PC desktop market, but then nearly destroyed the company in that fateful confrontation with the US Justice Department and caused him to miss the Internet and, later, the mobile revolution. The second, Showstopper! The Breakneck Race to Create Windows NT and the Next Generation at Microsoft by G. Pascal Zachary, has an even longer subtitle that neatly describes the book on its own. Both of these books were written quite a while ago, so let's see how their stories hold up today.

Breaking Windows front coverVS.Showstopper! front cover

Tech Book Face Off: The New Turing Omnibus Vs. Patterns of Software

I'm churning through tech books now, finishing off a bunch that I had started a while back, but couldn't find the time to finish until now. The pair that I'll look at here are a couple of older books that I picked up through recommendations on blog posts. The first one, The New Turing Omnibus: 66 Excursions in Computer Science by A.K. Dewdney, is a survey of 66 topics in a wide range of areas of Computer Science. The second book, Patterns of Software by Richard P. Gabriel, is about advice and experiences on a variety of topics in software development. Whereas NTO is of a strictly technical nature, Patterns of Software has much more of the human aspect of working with computers and software. Let's see how these older books hold up today.

The New Turing Omnibus front coverVS.Patterns of Software front cover

What I've Learned From Programming Languages

I just finished up learning about fourteen new programming languages, and while my head may still be spinning, I've been struck by one thing about learning new languages. Every single new language I learn teaches me something new and valuable about programming. Some languages reveal many new things because they happen to be the first language I've learned based on a new programming paradigm, and other languages may expose only one or two new ideas because they overlap quite a lot with languages I already know. Every language has shown me at least one new thing, though, so I thought I'd take a look back and pick out one thing learned from each language I've encountered. Some of these languages I've used extensively and others I've barely scratched the surface, so I may miss some great insights in the languages less well-known to me, but that's okay. There's still plenty to reflect on.

Word cloud of programming languages

Tech Book Face Off: Seven Languages in Seven Weeks Vs. Seven More Languages in Seven Weeks

Yes, that's right. I learned fourteen programming languages in as many weeks. Actually, it was more like four weeks, but I just couldn't put these books down. I had wanted to work through them ever since I had read Seven Databases in Seven Weeks a few years ago and loved it. Now I finally made the time to read them, and had a blast the whole time. I shouldn't have waited so long to crack these books open. I started off with Seven Languages in Seven Weeks by Bruce Tate, and then quickly moved on to consuming his follow on book, Seven More Languages in Seven Weeks, co-authored with Ian Dees, Frederic Daoud, and Jack Moffitt. It's not as hard as it would seem to learn about so many languages in such a short amount of time, as long as you already have a few under your belt, because really the hardest programming language to learn is the second one. After you overcome the confusion of holding two different languages in your head, it becomes much easier to add another and another and another. The differences get slotted into your brain more readily, and the major hurdle becomes figuring out how to use the new paradigms and features that you run up against. Let's see how these books handled presenting all of these different languages and smoothing the shock of moving from one language to another in rapid succession.

Seven Languages in Seven Weeks front coverVS.Seven More Languages in Seven Weeks front cover

Tech Book Face Off: Confident Ruby Vs. Metaprogramming Ruby 2

I'm always on the lookout for books that will help me improve my programming skills by learning to write cleaner, clearer code. For this Tech Book Face Off I found two relatively short books on how to write better programs in Ruby. The first book is Confident Ruby: 32 Patterns for Joyful Coding by Avdi Grimm, and I paired it up with Metaprogramming Ruby 2: Program Like the Ruby Pros by Paolo Perrotta. I haven't been programming in Ruby too much lately, but I still love the language and think it's a great vehicle for exploring how to write beautiful code. Even if I don't use the things I learn from these books directly in my day-to-day work, plenty of the ideas behind good programming patterns will transfer to any language. The key is to recognize the rationale that makes a particular coding style work well—what are the underlying principles that make it better than the rudimentary way that code is normally written—and adapt that style to the language that you're using. So let's see what these books have to offer, whether you're a Rubyist or not.

Confident Ruby front coverVS.Metaprogramming Ruby 2 front cover

Building a Model for Retirement Savings in Python

It's easy to find investment advice. It's a little less easy to find good investment advice, but still pretty easy. We are awash in advice on saving for retirement, with hundreds of books and hundreds of thousands of articles written on the subject. It is studied relentlessly, and the general consensus is that it's best to start early, make regular contributions, stick it all in low-fee index funds, and ignore it. I'm not going to dispute that, but I do want to better understand why it works so well. As programmers we don't have to simply take these studies at their word. The data is readily available, and we can explore retirement savings strategies ourselves by writing models in code. Let's take a look at how to build up a model in Python to see how much we can save over the course of a career.

Disclaimer: I am not a financial adviser, so this article should not be taken as financial advice. It is merely an exploration of a model of retirement savings for the purpose of learning and understanding how savings could grow over time.

Levels of Understanding

This is going to be a story of how I learn new things and come to understand them well enough to put that knowledge into practice. I imagine these levels of understanding are similar for everyone, but I can't be sure. I'm just going off of my own experience. It's possible that for any given person or any given skill, learning could stop at any point where they've gleaned enough usefulness and the desire to continue learning has been exhausted.

However, if that desire to learn is strong, learning can develop through three main levels of understanding. The first level is the basic what to do to accomplish a task. The second level delves deeper into how things work in context to solve problems in the domain. The third and final level explores why things work the way they do. The answers to those three simple questions: what, how, and why encompass all of the understanding that can be gained from any skill, subject, or domain. To ground this discussion we'll focus on programming to make things more concrete. What is involved in each of these levels of understanding when learning how to program?