Programming

All I Need to Be a Better Programmer I Learned in Kindergarten Sometimes the basics can be boiled down to just a few sentences. Sometimes I think I knew more when I was five than I do now. Of course, that’s a lie, but it’s worth thinking about.

Code Fearlessly I think version control is amazing. I’ve been using Git for a few years now (Subversion before then) and I keep all my writing as well as my code in repositories, backed up to Amazon and a VPS. The great thing about version control is how it lets you make mistakes and try out wild ideas without worrying about how you’ll get back to a working state if you break something.

Health and Fitness

The Creative Brain on Exercise I know, I know. Exercise doesn’t come naturally to most of us spending our days in front of our screens. But given how much of our work is creative in nature, it makes sense to take care of our engines of creation. I think the time spent in exercise will more than pay itself back over the years (in saved medical bills and lost work time if nothing else).

How Getting Buff Can Make You a Better Rubyist. In case you’re wondering about whether any of this exercise and diet stuff actually works or not, here’s some evidence straight from the source. This is worth watching even if you’re not a programmer, but just someone who has a normally sedentary work life.

Tim Ferriss on the 4-Hour Body at the NEXT conference I know that so-called “extreme” advice such as provided by Tim in his book always earns a skeptical look, but I find his idea of minimum effective dose quite interesting. If you’re looking for the most efficient ways to change your body for the better, this is a must-watch.

Eat to Live If you’d rather have advice from a medical doctor who’s also changed the lives of dozens (if not hundreds) of people, this book is your best bet. I tend to think of it as more of a primer on nutrition and health in general rather than just a diet or fitness book. It might take you some time to get through it (though it’s a small book) but again, the investment is definitely worth it.

While I want to improve as a computer science researcher I also want to improve as a developer. For me, the fact that we must take our beautiful algorithms, logics and abstractions and express them in terms understandable to a dumb machine is not something to be despised. In fact, I consider it a pleasant challenge and a source of infinite creative joy. I would like my job a lot less if it didn’t involve a significant amount of programming. That being said, how exactly do we level up as a developer? In fact, what does leveling up even mean for a developer?

For the last few months (years?) I’ve been a growing fan of the idea of deliberate practice – the idea that the best way to improve is to take well-defined measurable steps towards getting better where at each step you get feedback as to what you did wrong and how you can do it better the next time. Deliberate practice has been applied by athletes and writers, can we apply it to programmers? In particular, can we come up with something more detailed than “Read code. Write code. Repeat”? Luckily for all of us, Jason Rudolph took some steps on that path a few months ago.

Remember that deliberate practice requires that we have a list of well-defined, actionable goals on our path to excellence. We must know clearly what the goals are and also be able to unambiguously tell if we’ve achieved them or not. Jason came up with a list of simple yes/no goals that will exercise your programming muscles. What I love about Jason’s list is that it combines a lot of what it means to be a good developer. There are goals for learning tools of the trade (different languages, environments and frameworks), goals for learning core concepts (different paradigms and parts of the software stack) and social goals (open source and community involvement). There are a lot of things on the list, but then again, computer technology is a vast field.

Jason’s list is also necessarily incomplete. I’d argue that it’s practically impossible for one man to know all about the computer technology field today. But the good thing is that we have the technology and the community to take one man’s starting point and extend it for our own purposes. Jason’s list is available as a gist on Github and has already been forked many times by people who are using it as their own deliberate practice guidelines. I have my own fork where I’ve fleshed out sections on uncommon programming languages and more theoretical learning goals.

It might be a bit naive to think that just going through a list of programming challenges will make you level up. However, I think the list is a good head fake. The point isn’t really to go and do everything on the list. The point is everything that comes as a side-effect of completing the list. You’ll definitely put in a few thousand hours and churn out thousands of lines of code in a variety of different languages and environments. You’ll expand your mind by learning about programming styles and tools that you would have missed out on otherwise. As you encounter problems you’ll have to ask around on forums, mailing lists and IRC for help. This is important because deliberate practice is useless if you’re practicing the wrong things. In the absence of programming coaches, the global communities of programmers are your best bet to find mentors and guides. If you release your code to the world you’ll gain some street cred, get valuable feedback and maybe even provide something of lasting value to fellow developers. If you follow through on the social and community goals you’ll gain non-programming, but useful skills and meet a lot more people who can point in new and interesting directions. You’ll discover interesting new problems and come up with applications and solutions you might never have thought about otherwise. Maybe, just maybe, you’ll learn enough to level up as a programmer.

As a parting note, let’s all keep in mind that this will not be easy. It will take time and effort. I’m in graduate school so I might be able to make it part of my day job to do some of these things. But a lot of it will have to happen on my own time and energy, when I could be exploring Ithaca’s gorges or watching infinite Star Trek episodes on Netflix. This is even more true for people who have legitimate day jobs and families. We all need to come up with our own reasons for why we want to invest all this time and effort in deliberate practice. But one thing I keep telling myself is that the time will pass anyway and my energy will be spent somehow. I would rather spend it on writing my own operating system than on Star Trek reruns (no matter how much I love Star Trek).

Society

It’s not gender warfare, it’s math Being a computer science graduate student I’m regularly confronted by the fact that there are not enough women in our field (and that doesn’t seem to be changing any time soon). Here’s a look at why and that needs to change and some work in the right direction.

The Fraying of a Nation’s Decency Sometimes we just need a reminder that we’re all human after all.

Web Technology

10 best @font-face fonts I think embeddable web fonts are one of the best things to have happened to the web in recent years. Think of this article as a good “getting started” guide if you’re trying to figure out what fonts to use for your own projects.

How to make a simple HTML5 Canvas game The canvas element is an even bigger improvement than web fonts. Like the name suggests, it gives you a general purpose drawing element on a web page. Combine that with fast JavaScript engines and you have a pretty decent game engine on your hands.

Video

QuakeCon 2011 John Carmack keynote If you’re interested in gaming engines or high-performance, down-and-dirty programming then you should take the hour and half to listen to John Carmack — the brains behind the Doom and Quake game engines.

First off, let’s get one thing clear: graduate school is not school. If anything, it’s a job with a good amount of freedom. And you take classes for a few semesters. Like Professor Matt Might says: “if acting a certain way would get you fired from a real job, then it’s probably a bad idea to try that in a Ph.D. program too.” I make it a point to treat this gig as a “real” job: I’m getting paid to do actual work, there are deliverables, there are deadlines and requirements, there are people I report to.

That being said, there are a lot of the same challenges that you go through when you end up at school for the first time: you don’t know very many (if any) people, you don’t know where things are (or how to get there), you have to choose classes and you’re constantly on the lookout for free food. Luckily, meeting people is largely a case of being persistent about it (same for finding free food). The social aspect is not to be underestimated: if you’re going to be here for the long haul you might as well make friends with people who are going to be here with you. You never know when you might need a shoulder to cry on. I once read somewhere that in your first week at college you should go to every party you can. I didn’t do that and I’m not sure if I regret it. But it holds for graduate school too (and perhaps any new place you go to). As for free food, there seems to be free lunch almost everyday. Unfortunately most of it is the usual carb-and-sugar heavy stuff you’d expect to find. You probably want to stay away from most of it if you want to eat healthy.

In keeping with my idea of treating graduate school as a job, I’ve been trying to hit the ground running and spend as much time as I can on research. However, this is turning out to be easier said than done. For starters, classes do take up a good amount of time and energy. Accepted wisdom says that for each hour of class you should put in 3-5 hours of work outside of class. But if I plan on getting any actual work done (not to mention having a life) I’m going to have to categorically ignore this formula. Luckily I’m past the point where I get graded on effort and putting in long hours isn’t the same thing as being productive. Though classes aren’t consuming my life (and I’m going to work to keep it that way), it hasn’t been easy to make time for research either.

Though I’ve been power-using iCal sticking to a schedule isn’t easy. As they say, no plan of battle survives contact with the enemy. There are lots of little things that come up during the day (like two fire alarms yesterday) and sometimes estimates are just plain wrong. Since I’m still in the phase where I’m meeting professors and scoping out potential hobbies and activities, my nice time-chunked calendar frequently gets perforated. I’m hoping some of this will subside over time, but this much is clear: any time you want to yourself, whether it be for research, hobbies or just winding down, you have to fiercely defend. I think it’s a good idea to start with a calendar that is all blocked out instead of one that is clear. That way if someone (or some event) wants a slice of your time it’s immediately clear that you’re taking away from something else you could (should?) be doing.

On the topic of research, coming up with good research topics is hard. I have two classes in which I need to do substantial projects and I have very little idea of what to do (except that I want to experiment with some cool technology that could be useful later). Part of this is due to my professional immaturity: I don’t know enough of the field to be able to tell what would make for a good grad-level project. However, becoming more conversant with the state of the field is part of my education and figuring out what to do for a project is a good trial for deciding on a PhD topic in a few years time.

One thing about research that doesn’t get much attention is that it is very draining, both mentally and physically, in terms of energy and time. After a good few hours of reading, thinking and coding it’s hard to get up the energy to do anything else. I can just feel my energy drain away once it gets too late in the afternoon. Personally I have a bunch of side-projects (including this blog) that are distinctly not research-oriented and I’m not sure how I’m going to make time (or energy) for them. One way (again taken from Professor Might) is to keep work and play separate. Since I have a pretty decent office space I want to do all my research at work. I’ve started to move some of my books into my office, I’ve set up a standing desk and generally trying to make it a place I want to work from. Ideally I would like to do all work in the office (again, treating it like a real job) and once I left I’d be done with work for the day.

Finally some logistics. All the new graduate students have been provided with pretty amazing office machines (lots of cores, lots of RAM, fast SSDs). Since we have full root access I can get serious work done on them. It also helps with my plan of not bringing work home. Though I love my office machine, I prefer my Macbook Air for reading and sometimes writing. Also having a mobile work station is nice for the times I want a change of scenery (which can be refreshing). But having two machines also brings up the synchronization problem which is something I really want to avoid. I’ll revisit these issues once I get a better idea of how much time I spend working, where and on what.

So to summarize: grad school is a job, not school. Meeting new people is largely a matter of persistence. There’s lots of free food, but a lot of it is not very healthy. Research and classes burn a lot of time and energy so you have zealously guard what you have left and make sure you spend it on what matters. Keeping a distinction between work and play is a good in theory, the practice remains to be seen. Maintaining multiple machines sucks.

If you have any opinions on graduate school (or just work in general) I’d love to hear.

This idea of gradual, but consistent self-improvement isn’t just applicable to becoming top athletes or musicians. Tim Ferriss’ “The Four Hour Body” including the increasingly popular slow-carb diet applies a similar idea to health and fitness. Cal Newport, a recent MIT doctorate and professor at Georgetown seems to be applying similar ideas to becoming a top academic researcher. My personal goal is to apply it to become the best programmer I can possibly be (as most readers of this blog already know).

For a few weeks now I’ve had a two-fold concern. First, I didn’t know what exactly I was or wanted to be. Was I a programmer? A computer scientist? A software engineer? A technologist? A writer who just happened to use code as well as words? Secondly (and more importantly) I couldn’t see anything resembling a clear path from my current “not totally incompetent code slinger” phase to getting to the point where I could understand the works of the great masters (and maybe create something of similar lasting value). And that really, really bugged me. Combining my current indecision with no idea of how to move forward put me on a path leading straight to madness.

Here I was, a recent college graduate with two rather expensive degrees and a certain amount of knowledge about my field of interest. And yet my four years of education seemed like a piddly little drop compared to the proverbial ocean of knowledge and capabilities ahead of me. Not only could I not cross said ocean, I couldn’t even comprehend it’s boundaries. Sure I could write you a topological sort of some arbitrary graph structure. I could also bit-twiddle my way into a reasonably resource constrained embedded application. I’d even wager that I could build a reasonably stable concurrent system that isn’t too embarassing. But beyond that? I just barely grasped functional programming, my knowledge of type systems was rudimentary at best. I only had the foggiest notion of what a monad was and yet I had signed up for a good few years working on advanced programming languages. What on earth was I thinking?

One of the great dichotomies of our field is that on one hand it is possible to be completely self-taught but on the other there are few systematic guides on going from novice to expert. Most advice on the matter seems to reduce to “Read code. Write code. Think. Repeat.” Wise words to be sure, but not exactly a 12-step program. So what is one to do? What am I to do?

Though Gladwell and his ilk may have shown us that tangible self-improvement is possible, it’s not a quest to be taken lightly for there is no end to it. Until you come up against fundamental physical limits there’s always farther you can go, harder you can push yourself. And yet, after a point I’d argue that a law of diminishing returns kicks in, most victories past that point are Pyrrhic. Which is why it is important that I do what I do for fun, first and foremost. Because I find our field infinitely fascinating, constantly stimulating and there’s nothing else I’d enjoy more (though writing comes a close second).

After the why comes the how. What is the path from point A to point B? Where is point B in the first place? Do I even want to go there? Ostensibly I’m going to graduate school for Computer Science. I have a hard time calling myself a scientist or even a engineer. There’s a formality and heaviness about those terms that seems a little off, in the way that “colonist” has a different connotation than “explorer“. A colonist has a definite, long-term purpose – an unknown world to tame, a new land to cultivate and defend. An explorer is looking around, seeing the way things are, understanding and learning, eventually moving on. I’m content to explore for now. I think I’ll stay “just” a programmer for a while, or maybe even a writer, those two seem the best fit.

Though I do this for fun and computer technology has far-reaching effects on the human race, I’ve grown to see programming as a mind expanding activity in it’s own right, independent of other motivations and effects. I’m looking at our technology as a medium for creative expression. Programming is a way to become familiar with that medium, a way to increase our creative powers. And so the direction to explore is the one which will expand my mind the most, increase my creativity the greatest. It’s easy to stay within my boundaries, to stick to the stateful, imperative programming styles I’m familiar with. To go beyond that, to throw myself into functional programming, to use advanced type systems, to write compilers and virtual machines, to do all that, is hard. It is also a form of “deliberate practice” and essential to becoming better.

For fields and pursuits that aren’t easily quantified, deliberate practice is hard. However one heuristic is to look at how much a potential problem bends your mind. An interesting, worthwhile problem changes the way you think about your field in a general way, but requires you to acquire specific new techniques and skills. Once you solve the problems you’ve increased the size of your toolbox, but you’ve also changed how you will look at and approach problems in the future. With each problem you gain the capabilities to solve a wide range of new problems and inch closer to “expert” status.

A few weeks ago I tried looking for a 12-step program – an organized, decently, reliable path to becoming an expert programmer. I wanted a way to put in my 10,000 hours with a reasonable guarantee of a good payoff. I haven’t found such a plan, but I have found some principles – what I’m doing needs to be fun and must be mind-expanding. I have to keep programming, keeping reading and writing, keep thinking. I’ve reopened my computation theory books, I’m taking baby steps in Haskell, I’m scripting Emacs on a more regular basis. I’m trying to continually expand my mind and abilities, I’m trying to keep getting better. I hope in 10 years I’ll get close to where I want to be. But for right now, I’m drained.

This was Richard Feynman nearing the crest of his powers. At twenty-three … there was no physicist on earth who could match his exuberant command over the native materials of theoretical science. It was not just a facility at mathematics (though it had become clear … that the mathematical machinery emerging from the Wheeler–Feynman collaboration was beyond Wheeler’s own ability). Feynman seemed to possess a frightening ease with the substance behind the equations, like Albert Einstein at the same age, like the Soviet physicist Lev Landau—but few others.

Also, last week I went to a lecture by Jon Kleinberg, Tisch University Professor of Computer Science at Cornell University and winner of a MacArthur Foundation Fellowship (also known as a Genius Award), whose early research formed a large part of Google’s success as a search engine.

Some days we are reminded that we walk among giants, that we live in the presence of gods. On those days, we are humbled and uplifted at the same time.

For most of last semester I had been working on an Actor library in Ruby to do some fun little concurrency experiments. I had been wanting to take my code (which has very rough in the way that only research projects can be) and turn it into a proper Ruby actor library. On Sunday I started down that path.

I had been aware of some prior art in this area, in particular the excellent Revactor library. Until yesterday I hadn’t actally taken the time to dig deep into it. When I did, I was devastated. Revactor is beautiful, well thought-out, flexible powerful, elegantly implemented. With the exception of a few aesthetic details it’s everythng I wanted my own library to be and more. It was devastating because it seemed like all I could do was reinvent the wheel. What’s the point of writing or making something if someone’s already done it before and better? It’s the kind of crushing hopelessness I feel sometimes as a language researcher: it seems like Lisp did everything, 30 years ago, and did it better.

So after banging my head against the table for the better part of an hour (and wondering why the girl sitting next to me had two straws in her iced tea) I decided to go for a walk. At almost 6pm it was still uncomfortably warm but after ten minutes I found myself at the steps of the computer science building (unsurprisingly). I walked in, claimed a couch and started writing some code.

My new project isn’t brilliant research, it’s not scalable and high-performance, it’s not an infinitely reusable library with unit and regression tests. It’s just a little hackish thing I threw together in an hour. It doesn’t do very much yet but offers the promise of many hours of fun hacking ahead. It’s a fun personal project that scratches a little itch and is a lot of fun to code up. I’m writing a combination of C and Ruby, I’m living in Emacs and my terminal and I’m having a lot of fun at it. After a long, long time I’m having fun writing code and remembering why I got into this gig in the first place.

The thing is, at the end of the day, I sling code because I like to. Because it’s fun. If it stops being fun I might as well just give it up and hang up the keyboard (or keep the keyboard and write words instead of code). As Andrew Appel says, not all of us want to be logicians, some of us just want to be hackers. I like math and logic and performance analysis as much as the next guy, but I also like just the pure, raw feeling of code. I remember the allure of the machine as a mysterious black box – a well of infinite potential if only you could figure out how to bend it to your will. I remember a time when we used to be explorers – poking and prodding our systems, seeing how they reacted, how they bent, how we could change them and restore them, how we could make them do what they weren’t meant to do. I remember that it used to be a whole lot of fun. Yesterday I remembered all that and had a lot of fun doing it.

I don’t know why you do what you do, but I hope it’s fun. I hope that when you go to work everyday it’s because you really, really want to, because you can’t see yourself doing anything else that’s as fun. Feel free to change the world, to make it a better place to live in, to support and help the people around you, but please have fun while doing it.

OpenPCR joins a list of recent technological milestones including 3D printing, cheap embedded microcontrollers, ubiquitous computing and broadband Internet connections. The technological scene is supported by social phenomena like the open source movement, coworking and hacker spaces and organizations like Kiva and Kickstarter. The rise of increasingly powerful DIY technology and the surrounding social systems is pushing us toward what can best be described as the Age of the Maker.

Going from idea or innovation to self-sustaining product doesn’t require large factories or upfront investments anymore. As projects like OpenPCR and Coffee Joulies show it’s feasible to create a truly novel, popular product combining nothing more than talented, hard-working creators and willing customers. I’d like to believe that this is the beginning of a new industrial age, one that produces a similar improvement in the quality of human life without many of the bad side-effects of the last one. This revolution focuses on the individual and the small team rather on the factory. Sure, there are businesses and there is manufacturing, but the point of it all is not just profit. Profit is important, but a lot of people and groups I just mentioned are doing it largely because it’s fun and exciting.

Technology and the means of production are becoming increasingly democratic. What can be accomplished by small groups of focussed individuals leveraging modern technology is truly amazing. The software industry has already shown that small groups of people can create products and services that change the world. Today’s generation of makers and hackers are taking that a step further – showing that such world changing innovation doesn’t have to be limited to software.

I’m not an economist, but I’d argue that in many ways we’re seeing a reinvention of capitalism. Financial capital doesn’t have to be concentrated in the hands of a few – it can be widely distributed among the masses – millions of customers around the world. What is needed are people with ideas and skills that can bring that capital together just-in-time to create a product – the makers. And we now have the services required to bring the capital in (the Internet, Kickstarter, Kiva) and the cheap infrastructure needed to get the product out (UPS, FedEx, etc.). With OpenPCR, Arduinos, 3D printers and the we’re democratizing and distributing the means of production.

If you’re someone who likes building cool, interesting things there has never been a better time to be alive. The Industrial Revolution brought about mass production and cheap commoditized goods. But it also decimated independent artisans and craftsmen. Today we’re just getting ready to put all the manufacturing power of modern industrializaton back in the hands of individuals with ideas and skills. With today’s technology Leonardo da Vinci may have been able to build his flying machines.

What have you made today?