This is a submission for the DEV Weekend Challenge: Community
The Community
I built this for the anime community. Anime fans often face a specific problem: managing long-running shows. When a series like Bleach or My Hero Academia takes a long break, or when a viewer simply falls behind, it is hard to remember exactly where they stopped watching. It is also difficult to figure out if a show is worth continuing. Past solutions mostly involve reading through long lists of episode summaries or asking questions on forums like Reddit.
What I Built
I created Animu, a web app designed to solve two big problems for returning or new anime viewers.
First, the app has a cross-season Arc Search. A user might only remember that they stopped watching around the time "Killua met Biski." They can type that phrase into the search bar. The app pulls data for the entire anime franchise and ranks the episodes by relevance. This helps them find the exact episode they need.
Second, the app includes a Sentiment Map. This feature uses AI to read the synopsis of every episode in a series and grades the emotional tone. The app then graphs these scores over the course of the show. If a viewer wants to know if an anime stays lighthearted or eventually turns dark, they can see the overall emotional arc at a glance before they decide to start watching.
Demo
You can try it live here: https://animu.net
Code
The project is completely open source:
Animu
Animu is an open-source web application built for the anime community to solve the problem of managing and exploring long-running shows.
You can try it live here: https://animu.net
Features
Arc Search (Franchise-Wide)
When an anime takes a long break or you fall behind, it is hard to remember exactly where you stopped watching. Searching for specific events across hundreds of episodes is painful.
Animu allows you to search for vague plot points (e.g., "Killua met Biski"). The Arc Search pulls data for the entire anime franchise automatically—crawling related seasons, prequels, and sequels—and ranks the episodes by relevance to help you find the exact episode you need.
Sentiment Map
It is often difficult to figure out if a new show is worth continuing or if its tone matches what you want to watch.
The Sentiment Map feature uses AI to read the synopsis of every episode in a series and…
How I Built It
The frontend uses React and TypeScript with Tailwind CSS for styling. The application is hosted on Vercel.
For the data side, the app relies on two different APIs. It queries the Jikan API to build the franchise tree and find all related seasons. It then merges that information with the Kitsu API to get the episode synopses. Because Jikan separates every season into a different ID, I wrote a recursive search function that crawls the relations endpoint to group the whole show together.
The Sentiment Map and the fallback Deep Search use the Google Gemini API (gemini-2.5-flash). To keep the API keys secure, I moved these calls off the client. I built a Vercel Serverless proxy backend to handle the requests. The frontend uses the Recharts library to draw the mood graph in the browser.
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