A social media site for reviewing albums, movies, books, and podcasts

Hacker News

I’ve been working on a social media web app for reviewing works of entertainment with the hopes to compete against Goodreads and Letterboxd. I started working on this two and a half years ago and it has taught me a lot about full stack development, from Vue, and REST APIs, to hosting with AWS. It’s still very much so a work in progress, but it’s functional and I’d love any feedback I can get. I know other options exist, but I couldn’t find one that I like that had all of the different mediums, and I’d rather have them all in one place. Likewise didn’t give me enough freedom with rating, and seemed more focused on algorithm generated suggestions. Usage : A lot of functionality requires a login, so I created this guest account for you guys to use: username: showhnguest password: hackernews23 If you wind up liking it enough to create an account, feel free to invite your friends.

Visit product View on Hacker NewsAug 8, 2023

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Launch Intel predictions

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

85%85% 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, new, apis · Missing: mac, agents, macos

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, io · Missing: https docs, excited, just released

58%58% 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 · Missing: mobile apps, ios, personal

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

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

35%35% 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

21%21% 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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