Case study · 2025–present

Movie Watchlist

A fully deployed web app that dynamically tracks movies and TV shows - with synopses, ratings, and filtering by type and watch status. Built with a deliberate focus on both user experience and data integrity, including duplicate detection and title validation, the app reflects an ability to think through a product from design to functionality.

Year2025–present RoleDesign + build PlatformBase44 Base44 No-code UI Design
Movie Watchlist app showing a search bar, filters for All/Movies/Shows/To Watch/Watched, and a list of movies with ratings.

01 The situation

Every year I watch the Academy Awards and walk away with a long list of movies I want to see, on top of the shows I've already discovered and added to my list. I was tracking everything manually, but the list was static - no summaries, no ratings, nothing to help me decide what to actually watch first when I had free time.

02 The task

I wanted to build a dynamic app that would not only store my watchlist but give me enough context about each title to make an informed decision about how to spend my time.

I also wanted to focus as much on the user experience - the aesthetics, color scheme, and overall design - as on the data itself. This was a distinctly different challenge from a back-end tool like my flight tracker, and I wanted to push myself in that direction.

03 The action

I built a web app called My Watchlist using Base44 after evaluating several platforms including Manus and Google Firebase. I chose Base44 because it offered the best UI concept for what I had in mind. It’s important to note that I did not reference any existing apps with this build, I wanted to employ an unbiased approach and build something that truly worked for me. I spent significant time refining the visual design, layout, and interaction patterns alongside the core functionality.

"Design and data integrity, treated as the same problem."

The app records and categorizes movies and shows with synopses and ratings, and lets you filter by title type and watch status. I also built in data quality features:

  • flags duplicate entries before they enter the list,
  • validates that a title actually exists as entered to prevent inaccurate data,
  • and requires explicit confirmation before any deletion.

I deployed it as a live application and continue to add features as new ideas come up.

Delete confirmation dialog: 'Delete Project Hail Mary? This will permanently remove this title from your watchlist.'
fig. 02 — Delete actions require explicit confirmation.
Inline duplicate warning when re-adding a title already in the watchlist.
fig. 03 — Duplicate detection prevents redundant entries and hallucinations when the input is vague.

04 The result

I now have a personal tool I actively use that replaced a static list with a dynamic, intelligent system. The project gave me hands-on experience evaluating and selecting the right platform for a use case, building with a focus on data integrity, iterating on a live product, and thinking through front-end design and user experience - a well-rounded skill set directly transferable to AI development work.

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