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LegacyLens
Visualize GitHub repos as a Code City. Find tech debt.
LegacyLens analyzes your GitHub repo and visualizes it as an isometric Code City — folders are districts, files are buildings. Height = complexity, color = risk. Click any building to open the Inspector: full metrics for that file plus AI analysis — what's risky, what might break, what to refactor first. Supports Python, JavaScript, TypeScript, Vue. Paste a GitHub URL — analysis takes ~20 seconds.
LegacyLens started from a frustration I kept running into while exploring large repositories.
Most code analysis tools give you metrics, static reports, or endless tables, but they rarely help you *see* where architectural risk actually lives.
I wanted something more visual and explainable.
So I started experimenting with the idea of turning repositories into an interactive “Code City”, where:
- folders become districts
- files become buildings
- height reflects size + complexity
- color reflects risk
Under the hood, the project combines AST-based analysis, weighted risk scoring, aggregated folder analytics, and optional AI-generated insights.
One of the hardest parts wasn’t parsing code — it was making the system understandable.
Balancing metrics, visualization, UX, and explainability together turned out to be much more difficult than I expected.
Another interesting challenge was scaling:
large repositories quickly become visual noise, so I had to introduce adaptive aggregation and logical grouping strategies for monorepos and high-volume projects.
The project is still evolving, and I’d genuinely love feedback, especially from engineers working with large or legacy codebases.
About LegacyLens on Product Hunt
“Visualize GitHub repos as a Code City. Find tech debt.”
LegacyLens was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #155 on the daily leaderboard. LegacyLens analyzes your GitHub repo and visualizes it as an isometric Code City — folders are districts, files are buildings. Height = complexity, color = risk. Click any building to open the Inspector: full metrics for that file plus AI analysis — what's risky, what might break, what to refactor first. Supports Python, JavaScript, TypeScript, Vue. Paste a GitHub URL — analysis takes ~20 seconds.
On the analytics side, LegacyLens competes within Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how LegacyLens performed against the three products that launched closest to it on the same day.
Who hunted LegacyLens?
LegacyLens was hunted by Iryna Hnatovska. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of LegacyLens including community comment highlights and product details, visit the product overview.
Iryna Hnatovska