Dev reflection - February 05, 2026

Hey, it’s Paul. Wednesday, February 5th, 2026.

I want to talk about something I keep running into: the moment when you realize the outside of something no longer matches the inside. And what that actually costs.

Here’s what I mean. I found a wrong Amazon link today. Not a broken link—a working link that pointed to the wrong book. It had been sitting there, looking perfectly valid, copied into fourteen different places. Four templates, six content files, six social media posts queued up and ready to go. The URL structure was fine. The path looked plausible. Nobody clicked through to verify because why would you? It looked right.

This is a particular kind of failure that I think we underestimate. It’s not a syntax error. It’s not a crash. It’s semantic drift, when the structure is correct but the meaning is wrong. And the terrifying thing about semantic drift is that it propagates silently. Every time someone touches that content, they copy the error forward. It spreads through your system like a slow infection because nothing flags it as a problem.

Now, this showed up in my code today, but the pattern is everywhere. Think about the last time you inherited a process at work. The documentation looked complete. The workflow diagram made sense. But when you actually tried to use it, you discovered that three of those steps hadn’t been done that way in eighteen months. The shell stayed, but the guts changed. Nobody updated the map because the territory kept working, until someone new tried to navigate by the map.

Organizations are full of this. Org charts that don’t reflect actual decision-making authority. Job descriptions that describe roles from two reorgs ago. Onboarding documents that reference tools you’ve since replaced. The interface looks right, so nobody questions it. The error propagates every time someone makes a decision based on what they see instead of what’s actually happening.

The question this raises is uncomfortable: What catches semantic drift? Type systems catch syntax. Code review catches logic errors. But when the structure is valid and the meaning is wrong, you only find out when someone verifies the end-to-end flow. When someone actually clicks the link. When someone actually tries to follow the process. When someone asks the obvious question that everyone assumed had already been answered.

Second thing I’ve been thinking about: what it means when infrastructure work just… ends.

I had a project today that ran out of broken things. Three days of migrations, validation fixes, authorization tests, and then the TODO list got shorter and shorter until there was nothing left to fix. Seventy-eight passing tests. Deployment pipeline working. And no celebration moment. No clear signal that said “foundation complete, now build features.”

This is strange, right? We have rituals for starting things. Kickoffs, planning sessions, roadmap presentations. But completion, especially infrastructure completion, just sort of happens. You realize it in retrospect. The work changed what’s possible, but there’s no ceremony marking the transition.

I think this matters more than it seems. Because the decision about what to build next is strategic, not technical. And if you don’t notice the moment when infrastructure becomes sufficient, you keep building infrastructure. You gold-plate. You optimize things that don’t need optimizing. Or worse, you start the next feature while the foundation is still half-done, and you pay for it later.

I saw the inverse of this in another project. Someone marked a migration as complete. Task done, checked off. But the command that actually sends the output still expected the old format. The migration was half-finished. This happens constantly: someone redesigns the process, declares victory, and downstream systems still expect the old way.

Here’s the thing about completion: it’s not about changing the thing. It’s about changing everything that touches the thing. And most of our tracking systems, task lists, project boards, status updates, they track the thing. They don’t track the dependencies. So you get false completion signals. The task is done but the system isn’t updated. The infrastructure works but nobody knows to stop building it.

What would it look like to have better completion signals? Not just “tests pass” but “nothing downstream is broken by the change”? Not just “migration done” but “all consumers updated”? I don’t have a clean answer, but I think the question matters. Because the gap between “task complete” and “actually complete” is where a lot of organizational dysfunction lives.

Third thing, and this one’s been nagging at me: the difference between tools that work for you and products that work for others.

I spent time today extracting a piece of software that’s been running fine for months. Personal tool, does its job, no complaints. But turning it into something someone else could use required writing documentation for decisions I’d never articulated. Why this database instead of that one? Why this AI model instead of the more powerful option? Why does the learning system only add patterns, never remove them?

These were all decisions I made in the moment. They turned out to be defensible. I could explain each one when forced to. But they were never written down. They lived in my head as tacit knowledge. And tacit knowledge is fine when you’re the only user. You remember why you made the choice. You know which parts are fragile. You don’t click the link because you know it’s wrong.

Products require making everything explicit. Documentation, error handling, positioning clarity. The extraction process doesn’t just move code. It surfaces what was always implicit. And some of what surfaces is technical debt you didn’t know you had. Some of it is design gaps you’d been working around. Some of it is brittleness that only worked because you knew not to touch it.

This is true beyond software. Every time you try to hand off a process, delegate a responsibility, or scale something that used to be just you, you hit this wall. The thing that worked in your hands doesn’t transfer cleanly because half of what made it work was knowledge you didn’t know you were applying.

The question I’m sitting with: At what point does the extraction work become the actual work? When do you stop building the thing and start building the explanation of the thing? Because they’re not the same skill. And the people who are great at making things work are often terrible at making things transferable. That’s not a character flaw. It’s a different kind of labor that we don’t always recognize as labor.

So here’s what I’m left with. Semantic drift propagates silently until someone checks the actual behavior. Completion doesn’t announce itself, and our systems track tasks instead of dependencies. Extraction reveals what was always tacit, and tacit knowledge doesn’t transfer.

The thread connecting all of this: the gap between how things look and how things work. Interfaces that no longer match implementations. Tasks marked done while downstream systems break. Tools that work for you but can’t explain themselves to anyone else.

I don’t think the answer is more documentation or better tracking systems, though those help. I think the answer is building in verification moments. Points where someone actually clicks the link. Actually follows the process. Actually tries to use the thing without the tribal knowledge.

What would that look like in your work? Where are the shells that no longer match the guts?