Dev reflection - January 24, 2026

Hey, it’s Paul. January 24th, 2026.

So here’s something I’ve been sitting with lately. I spent the last couple days working across a bunch of different projects, and I noticed something strange. In almost every single one, the most important work wasn’t building new things. It was figuring out what not to build. Or more precisely—stepping back and asking whether the thing I was building was even shaped right.

And that got me thinking about what it actually means for a system to evolve. Not in the abstract sense, but in the practical, day-to-day sense of using something you built, discovering where it breaks, and deciding what to do about it.

Let me start with a specific example. I’ve got this system that sends emails—different types of emails for different stages of a process. And for years, there was this massive decision tree in the code. You know the kind. If it’s this type, do this. If it’s that type, do that. Twenty-four lines of branching logic, repeated in three different places. Every time I wanted to add a new type of email, I had to go surgery on the code.

So I refactored it. Made it configuration-driven. Now the structure lives in a file that says “here are the stages, here are their fields.” The code just reads that and does what it’s told.

But here’s the interesting part. There was one special case I deliberately kept as code. One type of email that formats things differently—includes extra details about what it’s responding to. And I could have made that configurable too. I could have invented some little expression language in the configuration file to handle it. But that would have been insane. I would have been rebuilding a programming language inside a configuration file.

So I left it as code. Explicit. Visible. Honest about the fact that this one thing is different.

And I think that’s the real insight here. The goal isn’t “eliminate all special cases.” The goal is “make the special cases visible.” When you hide complexity behind abstraction, you’re not eliminating it—you’re just making it harder to find. The best systems I’ve built are the ones that honestly admit where the abstraction stops and the specific domain logic begins.

I saw the same pattern show up in a completely different project. I’m working on this game modification—doesn’t matter what game—and there’s a tool that generates content. Events that can happen during gameplay. And this tool was doing something clever: whenever it generated two events with the same name, it would automatically add a number to the end. Event one, event two, event three. Technically correct. Every file could be saved. No errors.

But it was hiding the real problem. The real problem was: why is the system generating duplicates in the first place? That’s a content quality issue, not a file naming issue. The clever collision handling was papering over something that should have been surfaced as an error.

So I deleted twenty-one duplicate files and changed the tool to show the AI its previous work before generating new stuff. Now duplicates don’t happen in the first place. And if they do, it’s a signal that something’s wrong—not something to quietly handle.

Both of these examples point at the same principle. Automation should handle mechanical variations—the boring stuff that’s always the same. But it shouldn’t paper over conceptual problems. When your system is technically correct but hiding real issues, you’ve optimized for the wrong thing.

Here’s another thing I’ve been noticing. Tool selection keeps coming down to one question: what will you iterate on most?

I spent a day building these beautiful templates for one-page service descriptions. Clean markup, good structure, everything parameterized so I could swap in different content. And they worked. They absolutely worked.

Then I threw them away.

Not because they failed. Because I realized I was optimizing for the wrong constraint. The templates were great for content changes—swap this paragraph for that one, update this bullet point. But what I actually needed to iterate on was the visual design. The layout. The typography. And for that, the templates were terrible. Every design change meant mucking around in style definitions, rebuilding, checking the output.

So I switched to a different tool. One that’s basically the inverse—harder to do content variations, but you can tweak the design in real time and see what you’re getting.

The work I’d done wasn’t wasted, exactly. It clarified what the content should be. It proved what the real constraints were. But continuing down that path would have been sunk-cost fallacy. “I already built this, so I should use it” is a trap. The question is always: does this tool match how I’ll actually work?

There’s a related pattern I keep running into, and it’s about the gap between doing work and recording that you did it.

I’ve got project management systems. Task lists. Boards with cards that move from column to column. And in theory, they show what’s done and what’s not done. In practice, they drift. You get into flow, you ship three features, and you don’t stop to update the board. Why would you? You’re making progress. The board can wait.

Then a week later you look at the board and it shows seventeen active tasks. But you know half of them are done. So now you have to do this reconciliation exercise—cross-referencing against what actually happened to figure out what the board should say.

Here’s the thing. When your task list shows completed items as pending, you have to mentally filter “what’s actually done?” before you can answer “what should I do next?” That cognitive tax compounds over time. Either you stop trusting the system—which defeats its purpose—or you spend cycles reconciling it, which is time that could go to shipping.

I don’t have a great answer here. Maybe it’s accepting that some drift is inevitable and scheduling regular reconciliation. Maybe it’s keeping the task list in the same place as the work itself, so updates happen naturally. But the pattern is clear: execution moves faster than administration. Always.

There’s a related problem with documentation, and it’s subtle. The issue isn’t missing information—it’s missing why.

I discovered this week that a field in one of my systems serves two completely different purposes. It’s the description for a podcast episode in the RSS feed, and it’s also the meta description for search engine optimization on the web page. Same field, two uses. And this was never documented anywhere. You only learn it by asking the right question at the right time.

Now, the field works. People use it. The system functions correctly. But the architectural knowledge—why does this field exist? what depends on it?—that stays implicit. It lives in someone’s head, or in a conversation that happened months ago, or nowhere at all.

The problem isn’t “write better documentation.” The problem is that implementation knowledge is easy to capture—how to set a field, what values it accepts. But architectural knowledge requires understanding the whole system. And the people who understand the whole system are busy building it, not documenting it.

Let me tell you about a bug that taught me something.

I have this game mod, and I tested it during early game. Everything worked. Events fired, content appeared, players could interact with it. Great. Ship it.

Then someone loaded the mod in the middle of a game, hours in, and half the content didn’t appear at all. Certain types of events just… never happened.

The issue wasn’t in the code logic. The code was correct. The issue was in my assumptions about the game state. One type of event required unowned star systems to spawn in. Early game? Plenty of those. Mid game, after players have expanded? They don’t exist anymore. The spawning condition was technically correct but practically impossible.

Another type of event had fourteen possible variations competing for the same spawn slot, while a different type had only two. So the first type was statistically drowning out the second. Again—technically correct, but experientially broken.

Both bugs existed from day one. They were latent, invisible, until someone tested under different conditions. And that’s the real lesson about testing. It doesn’t just verify that things work. It reveals what you assumed.

Every test that passes is confirming an assumption you made. Every test that fails is discovering an assumption you didn’t know you had. The tests that matter aren’t the ones that pass—they’re the ones that surface what you thought was true but isn’t.

So what does all this add up to?

I think it challenges the textbook version of how software evolves. The textbook says: gather requirements, design architecture, implement, test, ship. The reality is messier. Build something. Use it. Discover where it breaks under real conditions. Admit what was wrong. Rebuild the part that matters.

That email refactor didn’t start with “design a perfectly flexible system.” It started with “we have these massive decision trees and adding new types is painful.” The fix made structure configurable but kept formatting as code, because that’s where the actual boundary is.

The template pivot didn’t start with “choose the right tool.” It started with “build templates because they’re fast,” then discovering that speed on content doesn’t help when the constraint is design.

Good architecture doesn’t come from upfront design. It comes from honest confrontation with where the current design fails. And that means being willing to admit when something you built isn’t right—even when it works.

The code that survives isn’t the code that was perfectly designed. It’s the code that can admit what it doesn’t handle and evolve accordingly.

I kept that email special case visible instead of hiding it in configuration. I deleted those duplicate files instead of making the collision handler smarter. I abandoned working templates when they optimized for the wrong thing.

In each case, honesty about limitations mattered more than technical completeness.

So where does this leave me?

I think the work right now is making implicit architectural knowledge explicit. Not comprehensive documentation—nobody reads that anyway. But capturing the “why” decisions. Why this thing is code instead of configuration. Why that tool instead of this one. Why duplicates get rejected instead of auto-numbered.

These decisions shape what’s possible in each system. But right now they only exist in work logs and conversations that will be forgotten.

And the deeper question—the one I’m still sitting with—is about testing. If testing’s real value is discovering assumptions, not just verifying correctness, then how do you design tests that surface assumptions earlier? Before the player loads your mod mid-game. Before the production system crashes. Before the drift between reality and your mental model gets too wide to bridge.

I don’t have an answer yet. But I think that’s the right question.

Alright. That’s the reflection for today. Talk to you tomorrow.