I made a Stremio addon to sync your IMDB Watchlist

Hacker News

I built Stremlist to solve my own frustration with having to constantly switch between IMDb and Stremio to find content I wanted to watch. After one week of development, I'm releasing it for free to the community. What it does: Stremlist creates a dynamic catalog in Stremio that pulls directly from your IMDb watchlist. It runs a JavaScript scraper to fetch your saved movies and TV shows, then converts them to a Stremio-compatible format. Technical details: - Automatic background syncing of watchlists while Stremio is running - In-memory caching with configurable TTL for improved performance - Redis integration for persistent storage and horizontal scaling - No server-side storage of user data (privacy-focused) - Built with Node.js with Docker support Installation is straightforward - visit stremlist.com and follow the instructions. I'd appreciate any feedback on the implementation or feature suggestions. Note: When installed, new Stremio addons appear at the end of content lists by default (Stremio's fault).

Visit product View on Hacker NewsMar 23, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, dock, new · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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