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Nextflick.xyz – Watch a random movie trailer

Hacker News

Nextflick.xyz – Watch a random movie trailer

I want to introduce Nextflick to you, a website I made showing you a random movie trailer, so you can find out what to watch next. Technically, Nextflick is a rather simple website. Everything is rendered on the server-side. Movies are scraped from the TMDB API and dumped into a NoSQL database. When you request a page, a random movie is selected and it shows you a YouTube embed with a respective trailer. Even though this may seem like such a simple tool, I am immensely proud of it. It is my first project I ever actually shipped, and I had this idea in my head for over 5 years. Sadly I am suffering from a bad load of perfectionism, which always got in the way of finishing anything, until today. I deliberately chose to create something that is small and straightforward. I chose Go as a language because it helps me enormously to "get sh t done". I don't have to think about too many language constructs or idiomatic ways of how to achieve something. I can simply write down my thoughts. I love the error handling as well, it makes me worry less about unexpected errors because the API pretty much tells me exactly where they happen and I can handle them right then and there. NoSQL helped me even more to keep things simple. Since there are no write operations happening on the website itself, it can be used in a readonly mode. If I want to download a new set of movies from TMDB, I simply write everything into a new NoSQL file and then swap the old against the new. I consciously did not implement any sort of filters. I wanted the experience to mimic that of going to your local cinema: you see a set of random trailers, some of them you won't like, but maybe one will stick. You might end up watching a movie you never thought to look for in the first place. There are currently 5.546 movies in the database. This number seems a bit low to me, given that TMDB has over 1.000.000 movies stored. However, I applied some criteria to the movies I imported in order to achieve a certain amount of quality: They needed to have a trailer * They needed not to be flagged as "adult" * They needed to have an average TMDB user rating of over 6.5 to filter out low quality results * I realise that there are people who are interested in "crappy" B-movies. Pending feedback, I might be willing to reconsider this. * I excluded certain genres like live music or documentaries * They needed to be released, so you can actually go and watch them * They needed to have a runtime of at least 80m, I didn't want to include short films for now

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new · Missing: mac, agents, macos
83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released
60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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.
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 · Strong signals: introduce · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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