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nextflick.tv – watch random movie trailers

Hacker News

nextflick.tv – watch random movie trailers

I wanted to have a fun way of discovering movies to watch. I always thought the experience of watching a bunch of trailers, like in the cinema, works best for me. So I sat down and replicated that. I already coded and launched a version of this 2 years ago (see https://news.ycombinator.com/item?id=35361279 ) but resonance wasn't very good and I got discouraged. I let the domain expire and - to my surprise - I got quite a few people reaching out to me asking what happened to it. So I sat down and wrote the whole thing from scratch. Because: why not... Out of curiosity, this time I decided to write everything in PHP. It’s the scripting language I grew up with and I wanted to get back into that “dirty hacking” mindset I had as a kid. Last time I wrote the whole thing in Go. As I suspected, I was remarkably productive. No framework to think about, no unit tests to write. I simply hacked out line after line. For a small project like this, this seems perfectly fine to me. I took me around 4-5 hours to write the code. In its final shape, it consists of 217 lines (excl. styling): 163 for the import job and 54 for the actual website. My data source is The Movie Database (TMDB). They have a nice API that I scraped. As a database I chose SQLite. Since I have literally no write operations after I imported the TMDB, I thought it would be a smart choice. It performs really well so far. Another couple of hours went into refining the filters that I chose to decide whether I import a movie or not. I changed them quite a bit from the initial launch, in order to come up with an even better experience. This is what I ended up with: - English (for now) - Released after 1965 - Have at least HD trailers (720p) - Rated at least 6.0 on TMDB - No adult movies - No short movies - No documentaries - No concerts This gave me around 5,000 movie trailers in my database. There are a couple of known issues, mostly around trailers on YouTube not being available or not embeddable. I have plans to work around this by checking YouTube's API for the trailers before importing them. But I haven’t gotten around to implementing this yet. I'm very happy to share this new version with the world. I'm still proud of having built something - even though it might be considered quite small. But it works and I have people who use it and are happy. What more can I ask for?

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4points
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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
95%95% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released
79%79% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, code · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, way · Missing: mobile apps, personal, entrepreneurs
40%40% 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
37%37% 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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · 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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