What problem PViz is solving
Large codebases become hard to reason about because structure is implicit
Most onboarding approaches operate at the wrong level: reading files sequentially, grepping for symbols, asking teammates, or pasting snippets into AI. You accumulate details without first establishing context—where the complexity lives, what depends on what, and where execution begins.
PViz extracts structural signals directly from the repository to restore orientation: central vs peripheral modules, dependency paths, coupling hotspots, and cycles.
What PViz does (and does not do)
Narrow scope by design
- ✓Map dependency relationships
- ✓Surface coupling hotspots
- ✓Detect circular dependencies
- ✓Identify entry points
- ✓Group code into zones
- ✓Export machine-readable artifacts
- ✓Infer business intent
- ✓Explain domain logic
- ✓Write documentation for you
- ✓Claim architectural correctness
- ✓Replace engineering judgment
- ✓Guess missing context
PViz produces evidence about structure. How you act on it is intentionally left to you.
Who built it
A pragmatic origin story (no résumé cosplay)
PViz was built by a solo builder who is not a programmer by trade.
It started with a one-time problem: dealing with a program that got large enough that normal “read files and figure it out” approaches stopped working. The issue wasn’t syntax or missing documentation—it was not having a reliable way to see how the system fit together.
AI tools were used heavily during development, and PViz is designed for AI-assisted analysis as well. The output artifacts are meant to let large language models answer higher-level questions using repository-specific structure (instead of guessing based on patterns).
The guiding principle has been consistent: make implicit structure explicit, then let understanding follow.
Intended use
Where PViz tends to help the most
- ✓Joining a new team or inheriting a legacy system
- ✓Evaluating open-source projects or third-party libraries
- ✓Planning refactors and understanding change impact
- ✓Identifying coupling hotspots and cycles
- ✓Supporting AI-assisted analysis with grounded context
Privacy and safety
Designed to minimize risk when analyzing code
- ✓Analysis runs in isolated job environments
- ✓Private repo access uses user-provided credentials only for the job
- ✓Artifacts are generated per job and per user
- ✓No cross-user sharing of repositories or outputs
If you’re evaluating PViz for sensitive code, review the privacy/terms pages and use a limited-scope repo first.
Want the map before you dive into the code?
Start with a small repo, run an analysis, and inspect the graph + outputs. If it saves you time, scale up from there.