Hiring the Best

A commitment to hiring the best requires a significant departure from typical hiring practices, because of the very nature of the beast: the best are rare by definition, and sought-after by nature. That means that when they’re available, you have to hire them. This sounds straightforward enough, but it’s harder to put into practice.

In order to have the best, you have to always be hiring, whether or not you need people. I’m not suggesting you staff up to a thousand employees when you only need ten, but you should be open to the idea of hiring people now up to the number of people you think you’ll need a year or two from now. Allowing moderate over-staffing gives you the flexibility to make an excellent hire when the candidate is available, so that when you really do need them, you aren’t forced to accept less than the best because you’re in a time crunch.

Which brings us to the correlary: when you’re hiring for a specific opening, you have to commit to not hiring people who don’t meet your standards, no matter how badly you need to fill the position. That may mean that while you’re searching for the perfect person, you have to outsource, hire contractors, or hire temporary workers. That may mean that your current employees have to go a little outside their comfort zone and pick up the slack until you can find someone who is a good fit. Just don’t buckle: every time you lower your standards to fill a position, you’re lowering the average quality for your entire operation. Don’t do it.

Both of these ideas – hiring when you don’t need to, and not hiring when you do need to – are somewhat counter-intuitive, can be very difficult to stick to in practice, and can be even more difficult (if not impossible) to convince management of. But if you want to have the best, the only way to get there is by hiring the best when you can, and refusing to hire anyone that doesn’t meet your standards.

It’s also important to understand that candidates who don’t look good on paper may turn out to be rock stars of only you knew you should hire them. If you can possibly manage it, try setting up an apprenticeship program. Take on candidates that seem promising despite being apparently unqualified. Give the paperboy the chance to prove he can write code even though he never went to college. Give the mechanic the opportunity to show he’s a diamond in the rough for your sales department.

The purpose of your apprenticeship program should be genuine apprenticeship – a learning experience for the prospect, and for your team as well. Don’t just treat it as a temp-to-hire situation. Find out how far this person can go if you really back them up and give them the tools to succeed. If it doesn’t work out, cut them loose. If it does work out, guess what? You just picked up an excellent employee that no resume search our recruiter world ever have found, and they’ll likely show more loyalty to your company than an industry veteran ever would.

Truly Agile Software Development

Truly agile software development has to, by nature, allow for experimentation. In order to quickly assess the best option among a number of choices, the most effective method is empirical evidence: build a proof of concept for each option and use the experience of creating the proof, as well as the results, to determine which option is the best for the given situation.

While unit tests are valuable for regression testing, a test harness that supports progression testing is at least as useful. Agile development methodologies tend to focus on the idea of iterating continuously toward a goal along a known path; but what happens when there’s a fork in the road? Is it up to the architect to choose a path? There’s no reason to do so when you can take both roads and decide afterward which you prefer.

Any large development project should always start with a proof of concept: a bare-bones, quick-and-dirty working implementation of the key functionality using the proposed backing technologies. It doesn’t need to be pretty, or scaleable, or extensible, or even maintainable. It just has to work.

Write it, demo it, document what you’ve learned, and then throw the code away. Then you can write the real thing.

It may seem like a waste of time and effort at first.  You’ll be tempted to over-engineer, you’ll be tempted to refactor, you’ll be tempted to keep some or all of the code. Resist the urge.

Why would you do such a thing? If you’re practicing agile development, you might think your regular development is fast enough that you don’t need a proof. But that’s not the point; the point is to learn as much as you can about what you’re proposing to do before you go all-in and build an architecture that doesn’t fit and that will be a pain to refactor later.

Even if it takes you longer to build the proof,it’s still worth it – for one thing, it probably took longer because of the learning curve and mistakes made along the way that can be avoided in the final version, and second because again, you’ve learned what you really need and how the architecture should work so that when you make the production version you can do it right the first time, with greater awareness of the situation.

This approach allows much greater confidence in the solutions chosen, requiring less abstraction to be built in to the application, which allows for leaner, cleaner code, and in less time. Add to that the value of building a framework that is flexible enough to allow for progression testing, and you’ve got the kind of flexibility that Agile is really all about.

Note: Yes, I understand that Scrum calls prototypes “spikes”. I think this is rather silly – there are already terms for prototypes, namely, “prototype” or “proof of concept”. I’m all for new terms for things that don’t have names, but giving new names to things that already have well-known names just seems unnecessary.

Assumptions

We all make assumptions. It’s the only way we can get anything done. If every time you found a bug you started at the iron – testing the CPU to make sure every operation returns an expected result – it’d take you months to troubleshoot the simplest issue. So we make assumptions to save us time, when we know that the likelihood of something being the cause of a problem is far less than the time it would take to verify it.

We also make assumptions out of sheer bloody-mindedness. You can spot these assumptions by phrases like “that couldn’t possibly be it” or “it’s never been a problem before” or “I wrote that code, I know it works”. These are the kinds of assumptions that can get us into trouble, and they’re the exact reason why it’s important to have developers from different backgrounds, with different perspectives, who make different assumptions.

Since we all make assumptions, the best way to challenge those assumptions is to have someone who makes different assumptions look at the issue. They’ll bring their perspective and experience to the matter, challenge your assumptions where they don’t make sense, and make you prove those assumptions to be accurate or not. This stands as a strong incentive to hire a team with diverse backgrounds and areas of expertise. They bring not just talent to your team, but a different perspective.

It’s also a good reason to invest the time in learning different technologies, languages, and development philosophies. Getting outside of your comfort zone can open your eyes to things you might not otherwise have considered, and help you to gain new perspective on your work – helping you to challenge your own assumptions.

The State of PC Upgrades

I’m not the first to point this out, but PCs have really reached the point of diminishing returns recently in many respects. While technological progress marches on, there’s not a tremendous subjective difference between this year’s hottest CPU and a mid-range part from two years ago. In particularly intensive applications, sure, you’ll notice it; but for the majority of users, there’s not much incentive to upgrade. For the rest, there’s likely to be one or two parts that will really get you a big benefit, while you’ll be happy with the rest of the system being 2-3 years old, and those parts will likely satisfy you at least 2-3 years more.

I used to operate on a two-year upgrade path: every other year I’d build a new machine, and in the years between, I’d make some individual upgrades (additional RAM, additional disks, faster GPU). Now I’m looking more at a 5-year path, with individual upgrades every year or two between. I really think that, at this point, anyone with a machine built in the last 3 years has little to benefit from a total overhaul. There are a few areas where everyone is likely to see real improvements:

  • Operating system: if you don’t have Windows 7, get it, along with any upgrades required to meet the minimum specs. I can’t recommend Windows 8 for any user for any purpose at the current time.
  • RAM: if you have a 32-bit system (unlikely if it’s less than 3 years old), you should have 4GB of RAM. If you have a 64-bit system, you should have 8GB; possibly 16GB for computer audio, video, or graphics professionals, or users running intensive virtual machines.
  • SSD: you should have an SSD. Honestly. If you don’t have one, get one. They’re getting cheaper by the day, and will give you a real, noticeable performance improvement across the board. Your SSD should host your OS and applications, at the least. Let Windows 7 handle optimizing system configuration for the SSD; just do a clean install onto the SSD and let it do the rest. Ignore all the “SSD tuning tips” that require changing OS settings or disabling services. 99% of them are wrong, and the other 1% are debatable.
  • Display: IPS displays are a world away from your typical bargain LCD, and they’re getting cheaper constantly. You can now get a name-brand, 24″ IPS display for under $300. If you’re a computer professional, you probably want at least two.
This is, of course, a generalization, and depending on how you use a PC, you will have different needs. Audio professionals will obviously see benefits from discreet audio hardware. Imaging and video professionals will want a fast CPU. 3D graphics professionals will want a fast CPU with as many cores as they can get, as well as a fast GPU. 3D gamers will want the fastest GPU they can get – possibly upgrading GPU every year to 18 months.
My system is two years old now. So far I’ve upgraded from 4GB of RAM to 8GB, and I’ve added a 256GB SSD drive. I’m planning on replacing my single 23″ TN LCD with dual 24″ IPS LCDs soon. In another year or two I’ll probably upgrade the GPU, and in three years or so I might be in the market for a total replacement – or I might not. I wouldn’t be shocked to find that, three years from now, brand-new hardware doesn’t put enough distance between itself and what I’ve already got to make it worth the money. Only time will tell.

Video Game Business Models

I see an opportunity, particularly for indie game developers, in developing new business models for sellings games. There are currently three predominant business models in the gaming industry:

  1. The major retail model: release a game for $60 in major retail outlets, with a huge marketing push, looking for a big launch week payout. Steadily lower the retail price by $5 or $10 a couple of times a year as it ages, until it eventually ends up in the $10 bargain bin. In the meantime, release DLC or expansions to try to get more money out of existing players, and raise the total cost for those buying the game late for $20 at retail up to or above the original $60 price tag.
  2. The subscription model: the game itself is cheap or free, but players must pay a monthly fee (usually around $15) to play the game. This is most common in the MMO genre, but can be seen elsewhere as well.
  3. The “freemium” model: the game itself is free, but players pay for in-game items, bonuses, avatars, skins, or other unlockable content, on a per-item basis. This is most commonly done with a points system, where players buy points with cash, and then spend the points on in-game items. This is particularly popular with mobile games, but is fairly widespread in general.
All three have found great success with the big game publishing houses, and the last one has found a good deal of success for indie game developers. But that last option doesn’t work with all game types, and has two possible outcomes: either all the purchasable content is purely aesthetic, and doesn’t seem worth paying for, or it offers real in-game advantages, and gives players the option to “pay to win”, leaving those who can’t or don’t pay feeling unfairly handicapped.
I think there’s another option waiting in the wings, however; I call it the value model, for lack of a better term, and it works something like this: release a game at a very low price point, and do the exact opposite of the major retail model. Players can purchase the game at any time and gain access to all content, past, present, and future. As content is added through updates and expansions, the price goes up accordingly with value. This has several effects on the sales dynamic:
  • For indie developers, releasing at an initial low price point can help to boost sales when a large marketing budget is unavailable, and help to fund further development. It’s also easier to sell a game at a lower price point before it gets popular, and easier to set a higher price point as popularity increases.
  • For players, it helps to avoid feeling like they’re being swindled, or continuously squeezed for more money; they know up front what they’re paying, they know what they’re getting right away, and if it’s worth it, then whatever content (which is free for them) is a welcome bonus.
  • From a marketing perspective, it gives the opportunity for a reverse discount: if you announce ahead of time that new content will be released (and therefor the price will be going up), it can push people to make the purchase (to lock in the lower price while guaranteeing the upcoming content) the same way a true discount would, without actually having to lower the price. The price is effectively reduced because prospective buyers are aware that the price is about to increase.
Does anyone know of any examples of such a model being used for games? I’ve seen it occasionally in game content (e.g. Unity assets and the like), but I don’t think I’ve seen it for a public game release. I’d be happy to hear thoughts on the subject in the comments!

Convenience Languages

I’ve come to see the uncertainty of untyped and interpreted languages as something of a curse. In a strongly typed, compiled language, you know ahead of time that the code is at least trying to do what you want it to; you know you didn’t typo any variable, function, method, or class names. You know you didn’t misuse or misunderstand a function, passing one type when another is required. Sanitizing inputs and returns is a matter of checking bounds, not types. Type-safe comparison is a non-issue.

After working with PHP and JavaScript extensively, as well as dabbling in Perl, Python, and Ruby, I miss the basic assurance you get from a language like C/C++, C#, or Java that if it compiles, nothing is completely wrong. Even in HTML, you can validate the markup. But in PHP or JavaScript, you probably don’t know about even a major, simple error until run-time testing (unit or functional).

To me, that’s a nightmare. I miss knowing. I miss that little bit of certainty and stability. With an untyped interpreted language, you may never be 100% certain that you’ve not made a silly but fatal mistake somewhere that your tests just didn’t happen to catch.

These are languages of convenience: easy to learn, quick to implement small tasks, ubiquitous. But they just aren’t professional-grade equipment.

Developing software is both an art and a science. I make an effort every day not to just be a coder, but to be a code poet. That’s hard to do on the unsure footing of a dynamic language. I won’t argue that these languages let you do some neat tricks; on the other hand, I also won’t discuss the performance issues. My concern is purely quality.

Is it possible to write quality code in a dynamic language? Absolutely. Unfortunately, it’s harder, and far more rare – not just because it’s challenging. It’s mainly temptation. Why would the language offer global variables if you weren’t supposed to use them? Why have dynamic typing at all if you aren’t going to have variables and function return values that could have various types depending on the context? Even with the best intentions, you can commit these Crimea against code accidentally, without even knowing it until you finally track down that pesky bug 6 months down the road.

Using (and abusing) these sorts of language features makes for messy, sloppy, confusing, unreadable code that can be an extraordinary challenge to debug. Add to that the fact IDEs are severely handicapped with these languages, unable to offer much – if any – information on variables and functions, and unable to detect even the simplest of errors. That’s because variable types and associated errors only exist at runtime; and while an IDE can rapidly attempt to compile a source file and use that to detect errors, it can’t possibly execute every possible code path in order to determine what type(s) a variable might contain, or function might return.

I know most of this has been said before, and every new language will inspire a new holy war. I’m writing this more because all of the above leads me to wonder about the growing popularity of dynamic languages like Python, Ruby and JavaScript, and the continued popularity of PHP. Anyone care to shed some light on the subject in the comments?

Simplicity, Flexibility, and Agility

Agile programming is supposed to be about flexibility in the face of changing requirements. It’s supposed to be about rapid development and iteration. But all too often it ends up being like classical methodologies in many ways. Many agile methodologies drown developers in process, taking time away from development. Test-driven development is a brilliant concept, but it puts more time between planning and iteration, making it more difficult to deal with changing requirements, not easier, and increasing the burden of change and the cost of refactoring.


Every developer wants carefully, precisely defined requirements. Developers often try to handle changing requirements by developing for flexibility, but flexibility often comes at the cost of added complexity. Trying to write-in endless flexibility to allow for changing requirements is very much akin to premature optimization. You end up doing a whole lot of work to make some code “better” – more flexible in this case, versus more performant in the case of optimization – when you don’t yet know which code really needs it and which code doesn’t.

“There are two ways of constructing a software design: One way is to make it so simple that there are obviously no deficiencies, and the other way is to make it so complicated that there are no obvious deficiencies.” –C. A. R. Hoare 

Often the best way to maintain flexibility is through simplicity. A program that meets its requirements with the simplest possible implementation is one that will be naturally flexible, maintainable, stable, and manageable.Of course, intelligent development plays a major role; making appropriate use of design patterns can do a lot to improve both flexibility and simplicity. Key design tenets like DRY, YAGNI, separation of concerns, and avoiding premature optimization (including overarchitecting for flexibility) help keep complexity down and productivity up.

What if requirements change? What if the simplest possible solutions isn’t as extensible? Good news: having invested in the simplest possible solution, you’ve lost little in development. You haven’t built anything that wasn’t strictly necessary. The new solution will still aim for simplicity, still reap the same rewards. By keeping things simple, you’ve kept down the cost of change.

Software development is a learning process by nature; you’re building something that’s never been done before, or at least building something in a way that’s never been done before. Innovation is at its heart, and learning is the personal experience of innovation. That being the case, every iteration has value in what’s learned, even if the code is later removed or replaced. The experience of writing it adds value to the team, and the team defines the value of the end product.


Many readers may think of targeting simplicity as a given, but it truly isn’t; while simplicity is often a goal, all too frequently it takes a back seat to other concerns, or is abandoned altogether because simplicity becomes far more difficult to achieve with many of the popular frameworks and libraries available today. Frameworks have to aim for maximum flexibility in order to be successful; in order to be general-purpose, they can’t make many assumptions, and they have to account for a whole host of different usage scenarios and edge cases. This increases the complexity of the framework, and accordingly, the complexity of any implementation using the framework. The fewer assumptions the framework can make, the more effort a developer has to put in just telling the framework what she’s trying to accomplish.


I can’t count how many implementations I’ve seen that are drastically more complex than necessary, simply because they have been forced to apply the conventions required by their chosen framework; and even following those conventions, they’re still left managing arcane configuration files, and tracking down bugs becomes an epic undertaking requiring delving deep into the inner workings of the framework and libraries. All too often, the quick-start “hello world” app is far more complex with a framework than without it, and adding functionality only makes the situation more bleak.


So, what does all this add up to? Here’s the bullet-point version:

  • If you’re aiming for agility – the ability to adapt quickly to changing requirements – don’t invest too much time on nailing down requirements. Get as much detail as you can, and start iterating. If you’re planning for requirements to change, go all the way – assume that your initial requirements are wrong, and think of each iteration as an opportunity to refine the requirements. All code is wrong until proven right by user acceptance.
  • Use interface mockups (for software with a UI) or API documentation (for libraries and services) as a tool to give stakeholders a chance to revise requirements while looking at a proposed solution and thinking about using it in real-world scenarios.
  • Don’t choose flexibility or modularity over simplicity. Choose a framework that won’t get in your way; if there isn’t one, then don’t use a framework at all. Don’t write what you don’t need. Don’t turn a piece of code into a general-purpose API just because you might need to use it again. If you need it in multiple places now, separate it out; otherwise, you can refactor it when it’s appropriate.
  • Think about separation of concerns early in the game, but don’t sacrifice simplicity for the sake of compartmentalization. Simple code is easier to refactor later if refactoring turns out to be necessary. Overarchitecting is the same sin as premature optimization, it’s just wearing a nicer suit.
  • The simplest solution isn’t always the easiest. The simplest solution often requires a lot of thought and little code. Don’t be a code mason, laying layer after layer of brick after brick; be a code poet, making every line count. If a change could be implemented by increasing the complexity of existing code, or by refactoring the existing code to maintain simplicity, always take the latter route; you’ll end up spending the same amount of time either way, but will reap far more benefits by maintaining simplicity as a priority.
  • Simplicity carries a great many implicit benefits. Simpler code is very often faster (and easier to optimize), more stable (and easier to debug), cleaner (and easier to refactor), and clearer to read and comprehend. This reduces development, operational, and support costs across the board.
  • Simplicity doesn’t just mean “less code”, it means better code. SLOC counts don’t correlate directly to complexity.
  • Don’t reinvent the wheel – unless you need to. All wheels aren’t created equal; there’s a reason cars don’t use the same wheels as bicycles.
What are your experiences? What ways have you found to keep hold of simplicity in the face of other pressures? Feedback is welcome in the comments!