Desktop product design
OmniDex
Compare assets without losing your place.
An Unreal asset library built around one question: what do I already own that fits this game?
My contribution. I framed the problem from my game-development workflow, chose the visible comparison fields and directed AI-assisted implementation. I tested the library in my own use.
- My role
- Product direction, interface design & AI-assisted development
- Period
- 2025–ongoing
- Built with
- Electron · React · TypeScript · SQLite FTS5 · Rust / napi-rs
Keep comparison in one view.
I used to collect favorite Unreal assets in an Obsidian vault: find the asset, take a screenshot, copy its metadata, repeat. Preparing a game could take days of manual curation.
I chose to keep the thumbnail, title, category, creator, rating and review count visible. Selecting an asset opens fuller metadata alongside the collection, so browsing does not require a new page for every candidate.
Enough information to make the next choice.
The compact card is the comparison layer. Rating and review count help me decide what deserves a closer look; they are signals for my own selection, not an automated quality score.
This crop comes from the same app capture above. The selected card has an orange border and check mark, while the collection stays visible.
Defer detail, preserve context.
The inspector holds the larger gallery and the Details, Description and Technical tabs. The grid answers which asset; the inspector answers whether it will work for the project.
That division is the main tradeoff: the overview stays compact, while fuller information takes an explicit selection. The capture also exposes unfinished library-quality cases, including repeated titles and missing previews.
Make uncertain matches a decision.
Local asset names do not always map cleanly to marketplace listings. The implemented review queue places local facts beside a suggested match and distinguishes rejecting that candidate from dismissing the pack.
Those decisions persist across reloads and folder renames. This is an implementation detail supporting review; the screenshot above shows library browsing, not a validation study of the matching workflow.
Choose a stack I could iterate with.
I considered Svelte and an earlier Rust/Tauri approach. At the time, the AI tools I used were more effective with the Electron workflow, and this personal project prioritized fast interface iteration and easier QA.
The benefit I observed was being able to patch, inspect and retest the frontend more easily. I did not measure a development speedup.
A personal workflow benefit.
Sorting assets I already owned by rating and review count gave me an overview without looking up each listing manually. I recall saving hours compared with the Obsidian curation process.
The project has no verified public release or teammate installations. Its asset index informed a six-person game team’s production plan; that is separate from other people using the application.
Outcome & next test
In my own use, sorting by rating and review count saved hours of manual asset curation. This is a personal observation, without a timed study or external adoption claim.
The next usability test is whether another creator can compare a mixed-quality library without confusing duplicate titles or missing previews.
Have a project in mind?
I’m open to product engineering roles and contract work.