AI coding agents have changed how we build software.
Things that used to take hours can now take minutes. Features can be generated almost instantly. Entire parts of an application can be implemented by an agent while you work on something else.
That's incredible. But I've noticed something else happening.
The faster the code gets generated, the harder it can become to maintain a mental model of the system. That's why I decided to create a tool to solve this problem. Heimdall
Not long ago, I wrote most of the code myself. When I opened a codebase, I usually had a rough mental model of what was happening.
With AI coding agents, that relationship changes.
I can ask an agent to implement a feature, refactor a module, add an integration, or create an entire component. And it can do it incredibly quickly.
But when I come back to the code, I sometimes have the feeling that I'm looking at a system I didn't actually build.
The code works. But my mental model is incomplete.
When you inherit a large codebase, the obvious thing to do is start reading files.
That works for small projects. It becomes much harder when the system contains hundreds or thousands of files. The problem isn't that the information isn't there. It's that the information is there but you never wrote it, it was AI.
Source code tells you what the system does. But before diving into the details, I often want to know:
I wanted to see the system before reading every line of code.
That's why I built Heimdall. Heimdall turns a codebase into an interactive architecture map. Instead of starting with thousands of files, you can start with the big picture or map the system from any specific file or type in your codebase.
You can explore:
The goal isn't to replace your code editor.
It's to give you a better starting point for understanding the code.
This part was important to me.
Heimdall does not use an LLM or a coding agent to analyze your code.
There are:
The analysis runs locally on your machine.
100% offline. 100% private.
Your source code stays where it belongs: with you.
I deliberately wanted the architecture analysis itself to be deterministic and local.
You shouldn't need to send your entire codebase to an AI service just to understand its structure.
I don't think AI coding is going away. Quite the opposite!
AI will probably continue to write more and more of our code. And I think that's the point where Heimdall helps... When machines write more of the implementation, humans need better ways to understand what was built.
The bottleneck may shift from:
"How do I write this code?"
to:
"How do I understand this system?"
That's the problem I'm interested in.
Heimdall currently supports 16 languages and frameworks, including:
Rust · Java · Python · Kotlin · TypeScript · Swift · C# · JavaScript · Go · C · C++ · PHP · Ruby · Scala · React · Jetpack Compose
It's available as a desktop application, and there's also a CLI for headless analysis and integrations.
That means you can use it not only when exploring a project manually, but also as part of development workflows and AI-assisted workflows.
This is ultimately a developer experience problem. AI made writing software dramatically faster. But speed isn't the same thing as understanding. I don't want AI to stop writing code for me. I want better tools for understanding the code it writes. That's what I'm trying to build with Heimdall.
If you're also finding it harder to maintain a mental model of your codebase as AI writes more of it, I'd love to hear how you're dealing with it. You can check out Heimdall here:
There is a free trial for you available already and a 40%-off promotion to celebrate the first public beta release! I hope you find it useful.
Heimdall - Map the system, master the code.