I knew in college that some dudes were faster than I was in terms of programming. Since peer programming wasn’t exactly encouraged in college, and at work I did mostly prototyping work, I never really knew how fast other programmers worked.
So when I read Paul Graham (and Joel’s) claim that great hackers are at least ten times as productive as average programmers (too lazy to cite right now), I was kinda shocked. Surely, ten times is an order of magnitude! Something that takes an average programmer a 40 hour week to do the great hacker can do in a 4 hour afternoon?
I wondered about that, since there are times when I get stuck on something, then I just start stabbing around randomly out of frustration. I had assumed that great hackers were faster only because they had either the experience or insight to side-step whatever I was doing wrong.
But lately, I’ve been re-reading everyone’s essays that write about programming productivity. And one thing that caught my eye the second time around was when Paul Graham was talking about bottom up programming and how he didn’t really believe in objects, but rather, he bent the language to his will. He was building new blocks for himself so he could think about the problem at a higher level of abstraction.
This is basic stuff! I mean, that’s the whole point of higher-level programming. When you refactor methods out, you’re creating a vernacular so that you can express the problem in terms of the problem domain, not in terms of general computing. This is much like if you want to drive a car, you’d want to be able to step on the gas, rather than time the firings of the pistons. And if you want to control traffic in a city, you’d rather tell all cars to go to a destination, rather than stepping on the gas and turning the steering wheel for each one.
But taken into the light of bending a language to your will, it makes it more clear for me as to how great hackers are ten times as productive. Great hackers are productive not only because they know what problems to sidestep and can problem solve systematically and quickly, but they also build a set of tools for the problem domain as they go along. They are very good pattern recognizers and will be able to generalize a particular pattern of code, so that they can use it again. But not only that, great hackers will create an implicit understanding attached to the abstraction, ie. what we might call common sense.
A case in point. Before Ruby, I’d used for loops over and over again, never really thinking that I could abstract a for loop. It wasn’t until they were taken away in Ruby did I realize that map, inject, and their cousins are all abstractions of the for loop. When I see “map” I know that it performs a transformation on every element. But I also know that the array I get back will be the same size, that each element operation doesn’t affect other elements, among other things. These are implicitly stated, and they allow for shorter code.
When that happens, you can simply read “map”, and get all the connotations it comes with, and hence it comes with meaning. It also becomes easier to remember, since it’s a generalized concept that you can apply in different places in the code. The more times you use it, the easier it is to remember, instead of having specialized cases of the same kind of code where the behavior is different in different parts of the code.
A great hacker will take the initial time upfront to create this generalized code, and will save in the long run being able to use it. Done over and over again, it all adds up. So it’s not that for any given problem, a great hacker will be done in 4 hours what it takes an average programmer 40 hours, but that over time, a great hacker will invest the time to create a tools and vocabulary that lets him express things easier. That leads to substantial savings in time over the long haul.
I hesitated writing about it, as it’s nothing I (nor you) haven’t heard before. But I noticed that until recently, I almost never lifted my level abstraction beyond what the library gave me. I would always be programming at the level of the framework, not at the level of the domain. It wasn’t until I started writing plugins for rails extracted from my own work and reading the Paul Graham article that a light went off for me. It was easier to plug things like act_as_votable together, rather than to still mess around with associations (at the level of the framework). I still believe you should know how things work underneath the hood, but afterwards, but all means, go up to the level of abstraction appropriate for the problem domain.
DSLs (Domain specific languages) are really just tool-making and vernacular creation taken to the level of bending the language itself. It’s especially powerful if you can add implicit meaning to the vernacular in your DSL. It’s not only a way of giving your client power in their expression, but it’s also a refactoring tool so that you can better express your problem in the language of the problem domain. Instead of only adding methods to your vernacular, you can change how the language works. It was with this in mind that I did a talk on DSLs this past weekend at the local Ruby meetup. First part is on Dwemthy’s Array, and the second is using pattern matching to parse logo. Both seemed pretty neat when I first read about it. Enjoy!