How do you build a mental model of a large unfamiliar codebase? I tried something different.
Posted by DocsReader@reddit | programming | View on Reddit | 6 comments
For most programmers, building a mental model of unfamiliar source code, especially large codebases, is still a slow and often painful process.
After years of working with large systems and reading open-source codebases (usually without anyone asking for help), I kept coming back to the same question: Is there a way to make junior developers ramp up like seniors?
That question resurfaced today when I revisited some of my older projects to see how modern LLMs would approach them especially from UI/UX point of view as this always has been a place to improve for me as full-stack developer.
And honestly, it was both exciting and unsettling. The truth is clear: LLMs are incredibly powerful in hands of people who know what they are doing.
So instead of resisting that reality, this experiment embraces it.
The idea is to transform an entire codebase into an interactive network graph, designed to dramatically reduce the time it takes to understand unfamiliar code and build a reliable mental model.
I'm sharing an early demo to gather feedback, find early adopters, and potentially grow this into an open-source project.
You will find Discord community I created for this in the YT video description.
mushgev@reddit
The idea is right — the dependency graph is the most reliable way to bootstrap a mental model because it's derived from the code, not someone's memory of what the code does.
One important caveat: rendering everything at once produces an unreadable hairball. The more useful approach is hierarchical — start at the service/module level, then drill into specific subsystems. That's the difference between a useful tool and a wall of Graphviz nodes.
TrueCourse (https://github.com/truecourse-ai/truecourse) takes this approach — interactive map where you can navigate architecture at different levels. If you're building your own, the level-of-detail problem is worth solving early.
DocsReader@reddit (OP)
Hey, thank you for your recommendation. The first version is 99% complete, and after talking to a few developers, yes, all of them said, "Please no (hairballs)."
The engine intelligently mashes Vim DX with VS Code. Users can open the first node anywhere via keyboard shortcut command or mouse clicking, and then they can expand the graph incrementally.
In addition to the level of detail you mentioned, scalability is very important; there is very large source code out there, and the tool by design should take this into account, structura does this with complete event-driven architecture frontend and backend with everything has been thought off.
I have been extremely busy lately, and so far the project is stuck at 99%, and I plan to deliver the 1% this week; it's just wiring everything together and shipping something that works.
The first version of the project works only with TS/JS/JSX/TSX; hopefully in the future, with community adoption, we can expand it to other programming languages.
I have just been very busy, and I am struggling to find time to ship this project.
mushgev@reddit
The incremental expand approach is the right call, starting from one node and expanding on demand keeps the graph readable regardless of codebase size. Good luck shipping the last 1%, that final wiring step always takes longer than expected but you are clearly close.
DocsReader@reddit (OP)
Yes, thanks for your feedback. No, it should have taken me just a few hours; I just didn't find time for it. Today I already wired things up clearly, and the 1% is almost like 0.5%. I should be posting soon about it here in Reddit. Looking forward to your feedback.
OkSadMathematician@reddit
This is genuinely interesting. The codebase-to-graph approach is clever—visualization does accelerate understanding way faster than line-by-line reading.
One thing to consider (and I'd be curious if your tool handles this): context depth. A graph of everything can get visually overwhelming fast. The real value is usually in: - Call flow from entry points (where does execution actually start?) - Dependency boundaries (what talks to what, and where are the walls?) - Data flow (how does information move through the system?)
LLMs are good at extracting this structure, but they struggle with: - Implicit dependencies (circular imports, side effects, module initialization order) - Performance-critical paths (what matters for latency/throughput?) - Historical decisions (why is it this way, not that way?)
For junior devs especially, annotating the graph with "why did they design it this way?" context is worth more than perfect topology.
Curious: are you planning open-source? The onboarding problem is real and unsolved. Something that let teams quickly generate "this is our architecture" diagrams would be genuinely valuable.
DocsReader@reddit (OP)
Appreciate this, you hit several real pain points here, and I think this is exactly the discussion worth having publicly so others can feel encouraged and add their perspective too.
Onboarding is one outcome, but the core goal is faster coebase understanding and new way to navigate large source code. Visualization is just the medium I thought of.
You are right about LLMs blind spots: implicit dependencies, circualr imports and side effects during module initalization as well hidden runtime coupling. That's why the foundation is here is not LLMS magic alone. The plan is to rely heavily on AST-level anlaysis, which most languages provide utilties for, to extract structural facts the model would otherwise miss, with multiple layers added on such as pattern detection we can catch LLMs blind spots.
AST will serve static gurantees regarding runtime stuff, it's not a failure it's truth boundary. AST can flag module level side effects that's alone is huge. Regarding more run-time challenging problems like meta-programming for example, we can follow layered approach, statically with AST marking as dynamic, conditional, side-effect annotations. Another way is pattern recognition without execution. I think the greatest help would be here flagging risk zone e.g. " runtime mutation , structure modified dynamically ".
O design decisdions , agreed. Topology without intent is shallow. One idea is to per-node docs via config + markdown ( mkDocs-style ) that lives on GitHub for example, so the graph becomes both navigation layer and a livign documentation surface.
Yes the current direction is opensource first with really good design, so I am trying to talk with other people like you before implementing anything let's say I am still in the anlaysis stage. Auto-generation with LLms is likely a later layer. I want to see where the tool provides the most value before baking in assumptions. A plugin architecture ( possibly integrating with tools like CodeRabbit ) feels like the right direction.
I hope, I answered all your questions, would love to take this further with you here on in the discord channel.