I am a full-stack developer in the Netherlands, a bit over ten years in. For the last year my evenings have gone into one side project: I Like Movies, an Android app for tracking what you watch and deciding what to watch next. It went live on Google Play this summer. This is the honest version of how it got built, what the stack looks like, and the three or four decisions that mattered more than the rest.
The problem was never finding a film
Every movie app I tried was built for one person keeping one list. My actual problem was two people on one sofa, each with a watchlist, neither remembering which of us had saved the film worth watching. Picking something to watch with someone else is genuinely harder than picking alone, and no amount of better search fixes it, because search is not the bottleneck. Deciding is.
So the app is organised around that. A household shares one library: one watchlist, one watched history, visible to everyone who lives with you. Add a film on your phone in the supermarket and it is on your partner's phone before you are home. That one feature is why the app exists, and it shaped almost every backend decision that followed.
The stack, and why it is boring on purpose
The backend is Go, GraphQL via gqlgen, and Postgres. The app is React Native with Expo. Film and TV metadata comes from TMDB. That is close to the most conservative stack you could pick in 2026, and that is the point. A solo project dies when the maintenance load exceeds one person's evenings, so every technology had to be something I could debug at 11pm without a second opinion.
Go earned its place. The whole backend is one binary with no framework magic, and the type system plus gqlgen's generated resolvers mean a schema change breaks loudly at compile time instead of quietly in production. Postgres does everything: data, full-text search support, import staging. No microservices, no queue, no Redis. A single process and a single database will carry a consumer app much further than the architecture content industry admits.
GraphQL was the decision I second-guessed most and regret least. A tracking app's screens are wildly different shapes of the same data: a film detail page, a friend's profile, a shared watchlist grouped by member, a stats dashboard. With REST I would have ended up with either forty endpoints or one endpoint returning everything. With one schema, the mobile app asks for exactly the shape each screen needs, and the generated types keep both repos honest with each other.
Expo, and specifically over-the-air updates, is what makes solo mobile development survivable. A JS-level fix ships to every installed phone in minutes without a store review. When a real user hits a real bug, the loop from report to fixed-on-their-device can be an evening. Native releases still go through Play review, but they are rare.
The AI part, without the hype
The headline feature is a chat assistant you can ask for something to watch in plain language. By mood, by half-remembered plot, by "something short and funny for a Tuesday". Under the hood it is an LLM with tool calling, and the tools are where all the actual work lives.
The model never answers from its own memory of cinema, because that is how you recommend a film that does not exist. Every title it mentions is resolved against TMDB through tools, checked against your watch history so it never suggests something you have already seen, and checked against streaming availability in your country so the answer is watchable tonight, not in theory. The model does the language understanding. Deterministic code does everything that has a right answer.
Two lessons from running an LLM feature in production as one person paying the bill:
First, guardrails are a launch feature, not a hardening phase. Rate limits, per-user caps and input constraints went in before the public launch, because a public chat endpoint attached to your API key is an invitation.
Second, the best AI features are the small weird ones. The assistant accepts a photo. Point it at a shelf of discs, or a screenshot of someone's list of recommendations, and it reads the titles and offers to add them to your collection, asking for confirmation first. That took a fraction of the chat feature's effort and it is the thing people show other people.
What tracking apps get right, so I stole it
I did not pretend the category has no history. People who track their watching are particular about it. Episode-level TV progress, ratings out of five stars, private notes, custom lists, and imports so nobody has to retype years of history. The import path reads a Letterboxd export or an IMDb CSV, stages it, matches titles against TMDB, and works out on its own whether a CSV is a watchlist or a custom list, asking only when the file is genuinely ambiguous.
Imports taught me a rule I now apply everywhere: the feature is not the parser, the feature is the trust. Someone uploading ten years of film history needs to believe nothing gets mangled. Most of the import code is not parsing at all. It is matching, deduplication, and refusing to guess.
Shipping solo, honestly
Some things that kept this alive for a year of evenings:
A defect pipeline beats motivation. The app has shake-to-report built in. A user shakes their phone, a report lands in a database table with a screenshot, their route, and the last API operation. My weekend routine is triage, fix, ship OTA. It keeps the work honest, because the queue is real user pain and not my roadmap fantasies.
The first users must be people you cannot disappoint. My household uses the app daily. There is no faster code review than someone next to you on the sofa hitting your bug in real time.
Cut features in code, not in conversation. Several built features are dark behind flags or deleted entirely because they did not earn their screen space. Deleting a feature you built alone at midnight hurts. Shipping an app that feels focused is worth it.
The app is free, on Android now, with an iPhone version in development. If you have opinions on what a movie tracker should do, I genuinely want them. I built this because I use it, and it gets better fastest when someone tells me where it is wrong.
I Like Movies is at ilikemovies.app, and on Google Play.
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