Intellect, the ability to focus in on a problem and sheer time committed to the craft of programming are critical and pretty obvious elements that make a programmer good. Having these things on your side is partly luck and partly an expensive time commitment. However, I believe there are further traits that can be developed through habit-forming practice that make a programmer excellent.

Some programmers transcend being merely good; they are highly effective. This often becomes apparent when you see them becoming the team’s de facto problem solver, or when they reliably design and implement excellent-fit solutions, topping their previous attempts.

In the teams I’ve participated in and built I have found three traits that recur in highly effective programmers. When I find even one of them they often go on to live up to great promise. Any one of them is a strong tell, and more is a sign of a programmer with serious potential to be impactful.

The first trait is intellectual curiosity. When you find someone who tinkers because they are curious about new results you are engaging someone who has internalized the impetus for pioneering solutions. Internalization of curiosity is key because it is the surest driver of tangential exploration. A programmer who has exercised solutions to problems they dreamt up themselves out of pure interest in discovery has strengthened their abilities in excess of the rigours of standard professional performance. Professional programming makes you strong enough to stand tall in full gravity. Intellectual curiosity exceeds that; it is like training with a weight belt on.

The second trait is tenacity. Tenacity is the sworn enemy of “Cool, it works! We’re done!”. Those who internalize this trait never spitball their way to a final solution. If multiplying by negative one solves the problem but they don’t know why, they remove it and figure out why the sign inversion makes everything seemingly work. Inherent to this behaviour is the inclination to traverse underneath abstractions. Making it work is no longer the quest; the search is for a deeper understanding, one that makes the answer readily apparent. Illuminate the problem with a hard-earned understanding of the facts and the rest is small muscle movements.

An example of tenacity is spending three weeks tracking down a memory leak in ostensibly mature system libraries. Working through source, compiling it yourself, pouring over machine code, examining the compiler, and then reading your processor instruction manual. Rewriting portions of libc to verify results. Thermal imaging in your data center. Whatever it takes.

The final trait is a willingness to self criticize. Most programmers eventually have the experience of looking at code from a few years back and cringing. While syntax choices evolve, the cringe truly comes from a looking-in view of a naive problem solver doing their best and missing their mark. When a priori derived solutions are mismatched with the present understanding of a problem, personal growth is felt at a gut level.

An unprompted individual who consistently criticizes their own solutions is going to blossom quickly. Any valuable solution space is enormous, and the ability to criticize from a positive vantage point is the natural habitat of an always improving programmer.

Those are the three traits I’ve seen that suggest a programmer is going to be promising and impactful. Next time I am going to ponder the question that affects your effectiveness more than anything else: How do you decide what to work on?

Between the cloud, VMs, Docker and cheap laptops, I run into more unconfigured shell environments than I ever did before. In the simple old days you used to get a computer and configure it. You reaped the productivity of that configuration for years. Nowadays environments are ridiculously disposable. The tyranny of the default has become incredibly powerful.

I decided to take the power back and create a self-installing bootstrap script which I could use to configure any new system with a Bash shell. This ended up being a one-day hack that has made my life a lot more sane. My requirements were:

1. It must self-install and self-configure without needing to type any commands.

2. It must be accessible everywhere from an easy-to-remember URL so I don’t need to copy/paste.

3. It must optionally let me choose how each system is configured.

4. It must run anywhere — inside stripped down Docker containers, etc.

To build this, I decided on using Bash scripting. Perl, Python and Ruby are not always available. Bash, while not quite as ubitquious as /bin/sh and Busybox, is close enough.

Makeself creates self extracting archives. You can download a shell script that unarchives to a tempdir, running a setup script with access to a hierarchy of files. This is exactly what we needed.

In order to centrally host the bootstrap script, I used Amazon S3. S3 buckets have notoriously long names, but Amazon gives you the ability to use a CNAME for a subdomain that you own. This means I could use a subdomain like https://bootstrap.frogtoss.com that is backed by S3, guaranteeing the bootstrap is accessible virtually anywhere in the free world.

What remained is a long day of enjoyable hacking that produced a set of very personal dotfiles, emacs tweaks and sed manipulations that converted a basic install into something as usable as my most tweaked workstation.

Now I have a chained command that is similar to the following which highly configures any Linux instance:

rm -f bootstrap.sh;
wget http://bootstrap.frogtoss.com/bootstrap.sh;
chmod +x bootstrap.sh ; sudo ./bootstrap.sh

If you are distributing a library, you have a responsibility to make it as trouble-free as possible to build and use. To do otherwise litters the Internet with a project that frustrates and consumes the time of potentially hundreds of programmers. (And when have you met a programmer with extra time?).

This presents an interesting building challenge: how do you create the most compatible build system while simultaneously burdening the user as little as possible?

The Alternatives

Evidently, a lot of people turn to GNU Make. GNU Make, however, calls out to bash on Windows and uses Unix path slashes. Good luck getting that to work seamlessly with Visual Studio. The standalone GNU Make executable that the FSF hosts is over ten years old and is riddled with bugs. It hangs on fork() in Windows 10. Do not tell your users to download this. Do not tell your users to download msys2 or Cygwin so they can build your code if you respect their time.

Another option is CMake. Sadly, its sole installer download is 55MB, bundles QT and a full documentation set, and forces your users to parse syntax that they likely don’t understand. CMake generates projects with hardcoded paths which means the generated files can’t be reused.

SCons requires Python 2 to be present. We live in a Python 3 world; asking users to install a second Python implementation side by side is just rude.

Even Better Than Running Premake

In light of these options, Premake is a breath of fresh air. It’s not perfect, but you can download a one 1MB self-contained binary which consists of a small C-based engine and bundled Lua scripts. Premake generates build scripts with relative paths and gets you up and running in most popular environments very quickly.

But, wait! You can do even better. You can run Premake for your users, checking the generated projects into the repository. Nothing generated by Premake is specific to your computer’s setup. This removes two steps from building for your users: downloading Premake and running it. Awesome!

Automating Premake

Premake has a nifty action feature, making it straightforward to script new actions, such as running itself multiple times to generate projects.

Doing so has a couple of subleties. Firstly, Premake is limited to generating project files in the directory that the premake5.lua script resides in. One directory with multiple builds in it is a mess. One option is to create a subdirectory per build type, then copy the premake5.lua script in there, run it, and then delete it. This sounds hacky, but it actually works without giving you any trouble.

Another thing to keep in mind when generating all of your Makefiles at once is that the Makefiles are operating system-specific. A Windows-based makefile will link different libraries, for instance. You need to generate build scripts-per platform.

In order to build from a subdirectory, you need to specify your paths relative from the generated subdirectory. This can be resolved succintly at the top of your premake5.lua script:

-- assumes the premake5.lua is in a subdirectory called "build" under
-- the project root.
local root_dir = path.join(path.getdirectory(_SCRIPT),"../../")
local build_dir = path.join(root_dir,"build/")

Example Code

My library, Native File Dialog, has a Premake 5 Script action called “dist” which does exactly this. If I need to update the build scripts, I type premake5 dist and they are all refreshed. My library users need only run the generated builds. Feel free to copy the code for yourself.

Zero dependency building for users and a one meg standalone dependency for the package maintainer! This is the smallest price to pay for cross platform building I’ve ever come across.

Git-svn is the bridge between Git and SVN. It is more dangerous than descending a ladder that into a pitch black bottomless pit. With the ladder, you would use the tactile response of your foot hitting thin air as a prompt to stop descending. With Git-Svn, you just sort of slip into the pit, and short of being Batman, you’re not getting back up and out.

There are plenty of pages on the Internet talking about how to use Git-svn, but not a lot of them explain when you should avoid it. Here are the major gotchas:

Be extremely careful when further cloning your git-svn repo

If you clone your Git-svn repo, say to another machine, know that it will be hopelessly out of sync once you run git svn dcommit. Running dcommit reorders all of the changes in the git repo, rewriting hashes in the process.

When pushing or pulling changes from the clone, Git will not be able to match hashes. This warning saves you from a late-stage manual re-commit of all your changes from the cloned machine.

Git rebase is destructive, and you’re going to use it.

Rebasing is systematic cherry picking. All of your pending changes are reapplied to the head of the git repo. In real world scenarios, this creates conflicts which must be resolved by manually merging.

Any time there is a manual merge, the integrity of the codebase is subject to the accuracy of your merge. People make mistakes — conflict resolution can bring bugs and make teams need to re-test the integrity of the build.

svn dcommit fails if changes conflict

This might seem obvious, but think of this in context with the previous admonishment. If developers are committing to SVN as you perform time consuming rebases, you are racing to finish a rebase so you can commit before you are out of date.

Getting in a rebase, dcommit, fail, rebase loop is a risk. Don’t hold on to too many changes, as continuously rebasing calls on you to manually re-merge.

When Git-SVN Works

Here are a handful of scenarios where git-svn comes in handy and sidesteps these problems:

• When you are running a quick experiment and don’t intend to dcommit back to SVN.
• When you aren’t extensively cloning, or branching the code once it’s in Git.
• When you can guarantee that SVN will sit idle while you work in Git.