Where Are the Maps for Code?

When we return to a codebase after a few months, a lot of the work is not writing code at all, but rebuilding context. We reopen files, trace relationships again, reread docs, search logs, and try to reconstruct the same mental map we had before.

That feels normal only because we are used to it.

We already map code, just informally

Every time we explore a project, some form of mapping starts happening.

We follow calls, move between modules, try to understand what talks to what, and gradually assemble a picture of the system. Sometimes that picture stays in our heads. Sometimes it ends up on paper or in a diagram.

So the mapping itself is not optional. We already do it. The problem is that the map is usually temporary.

Code helps, docs help, but neither fully solves this

Code is the source of truth, but it is not always the easiest way to regain context quickly. There is often too much of it, and not every codebase is clean enough to explain itself on its own.

Documentation helps too, but only when it exists, is good, and is still current. In real projects, that combination is fragile.

So there is a gap between implementation and understanding.

Some maps already exist, but they solve different problems

To be fair, software development is not completely mapless.

In Flutter, for example, Flutter Inspector already gives us a very useful view of widget hierarchy. Other tools can generate UML diagrams or database diagrams. Those are all valuable, but they do not really answer the same question:

What is happening in the app right now, across its modules and flows, and how is that behavior moving through the system?

That is a different kind of map.

Logs are closer to the answer than they seem

Logs already describe runtime behavior. They tell us that something happened, usually when it happened, and often where it happened.

That is a strong starting point.

The limitation is that a plain log stream is linear: it scrolls away, competes for attention, and can easily hide something important before we even notice it. Watching values over time often means staring at logs or building temporary debug UI. Reconstructing event chains from raw messages is possible, but often painfully manual.

At some point, the problem stops looking like “we need better logs” and starts looking more like “we need a better representation of runtime behavior.”

What I think a better logging layer should represent

For me, a better logging system should not just record messages. It should help represent:

• system elements
• connections between them
• hierarchy
• events flowing through that structure
• tags and filters
• counters and alerts
• parameters and live values
• event chains across multiple steps

That starts to look less like traditional logging and more like a runtime map.

Why I built Uyava

That line of thought is what led me to build Uyava.

Today, Uyava is a tool for Flutter apps. The goal is simple: make runtime behavior easier to see, follow, and understand as a system, not just as a stream of text. The longer-term ideas around it include a Local API mode and MCP support, but the current focus is still the core problem: making app behavior easier to inspect in a structured, visual way.

The full article, with the complete argument and visuals, is here:

Where Are the Maps for Code?

Feedback, ideas, and real-world use cases are very welcome.