How Growing Software Changes The Engineering Problem

The first version of the TrailStudio timeline did something simple: it loaded every item onto the canvas and let PIXI.js render whatever was there. Small projects, that approach worked perfectly. A handful of clips, a couple of tracks, nothing to think twice about. The implementation was easy to write, easy to reason about, and, for a while, completely correct for what the editor actually needed to do.

Then projects got bigger. More clips, more tracks, more overlays, more elements sitting on the timeline at once. And the same approach that had been effortless at small scale started costing real performance at larger scale, not because anything was implemented incorrectly, but because the assumption underneath it, that loading everything into memory was fine, stopped being true.

That’s the shift this post is actually about. Not a bug, not a mistake. A piece of engineering that was completely reasonable at one size and increasingly wrong at another.

Small software and large software are different engineering problems

Early in a project, most of the engineering problem is just making things work at all. Can this feature exist? Can this interaction feel right? Can the whole thing hang together into something usable? At that stage, writing the simplest implementation that solves the immediate problem is usually the correct move, not a shortcut you’re getting away with.

Growth changes the question. It’s no longer just “does this work,” it becomes “does this keep working as more gets thrown at it.” Those are genuinely different engineering problems, and code that answers the first one well doesn’t automatically answer the second one at all. The TrailStudio timeline wasn’t badly engineered for a small project. It was engineered exactly right for one. It just stopped being the right engineering once the input it needed to handle grew past what the original design anticipated.

The cost of growth is often invisible until it isn’t

What made this particular problem interesting to live through was how gradual it was. There wasn’t a single moment where the timeline broke. Performance degraded slowly, project by project, as timelines got a little more complex than the last one. For a long stretch, nothing about the code was obviously wrong. It was just quietly getting more expensive to run, one added element at a time.

This is one of the less obvious costs of growing software. It’s rarely a single dramatic failure. It’s usually a gradual tax that accumulates so slowly it’s genuinely hard to notice from the inside, especially if you’re the person who built the original implementation and already has a mental model of why it should be fine.

Refactoring isn’t always about clean code

There’s a version of this story where the fix is framed as “the code needed to be cleaner.” That’s not really what happened. The original timeline implementation wasn’t messy. It was direct, understandable, and did exactly what it was supposed to do when it was written. The problem wasn’t code quality in the abstract sense. It was that the underlying architecture, loading everything into memory rather than managing what actually needed to be rendered, was built around an assumption about scale that the product had since outgrown.

That’s a genuinely different kind of refactoring problem than tidying up messy code. It’s not about making something more readable. It’s about recognizing that a decision which was architecturally sound at one scale needs to be rethought at another, and that rethinking often means changing how the system fundamentally manages its own data, not just how it’s organized on the page.

Growth turns implicit assumptions into explicit constraints

When you write software for a small, controlled case, you make a lot of assumptions without ever writing them down. Loading all the timeline data into memory wasn’t a decision I consciously weighed against alternatives when TrailStudio was young. It was just the obvious way to build a timeline, because at that scale, obvious and correct were the same thing.

Growth is what forces those unwritten assumptions to surface. Once the timeline started getting genuinely large, “load everything into memory” stopped being an invisible implementation detail and became a real constraint I had to actively design around, decide whether to only render what’s visible, manage what stays loaded versus what gets released, and rethink how the system tracks state as the amount of data on the timeline scales upward.

None of that thinking was necessary in the beginning. All of it became necessary as the product grew. That’s the essential shift growing software creates: problems that used to be implicit design choices become explicit engineering decisions that actually have to be made on purpose.

Developer productivity quietly depends on this

There’s a real, practical cost to letting growth-driven problems go unaddressed, and it isn’t just performance. It’s how much harder every future change becomes.

Once a system is built around an assumption that no longer holds, every new feature built on top of it inherits that same weakness. Adding a new timeline capability on top of an architecture that already struggles with scale means either working around the underlying problem again, or building on top of something that’s already strained. Either way, development gets slower, not because anyone is doing anything wrong, but because the foundation is quietly fighting against every new thing built on it.

This is part of why addressing an architectural problem like this earlier tends to be worth it, even though it doesn’t produce a visible new feature. Fixing how the timeline manages its data wasn’t something a user could see directly. But it changed how quickly I could build the next feature on top of it, and that compounding effect is exactly the kind of thing that’s easy to underweight because it never shows up as a single, attributable win.

The lesson generalizes past this one system

The specific fix, moving toward managing what actually needs to be rendered instead of holding everything in memory at once, is fairly specific to how timelines and canvas-based rendering work. But the underlying pattern isn’t specific to video editors at all.

Almost every growing piece of software eventually runs into some version of this: an approach that was completely correct for the scale it was built at quietly becomes the wrong approach as that scale changes, and nobody necessarily notices until the cost becomes visible in performance, in development speed, or in how hard the system has become to extend. Software architecture isn’t a decision you make once and finish. It’s a decision that has to be revisited as the thing you’re building keeps changing size underneath you.

The version of TrailStudio’s timeline that exists today isn’t better because I got smarter about writing code. It’s better because the product grew into a shape the original implementation was never designed to hold, and that growth is exactly what surfaced the problem worth solving.