Game industry news with AI summaries and author scoring

Hacker News

I’ve always been a tiny bit frustrated over having to visit multiple websites to get my gaming news, and since my twitter followees have mostly abandoned the platform, my feed there isn’t working out for this either. Obviously, instead of just using an rss reader, I built a web app because it was kinda fun. The site aggregates article links from a handful or so gaming websites and optionally allows people to up/down vote articles. This score goes towards the author of the article though, not the story itself. I’m thinking if this gets any traction maybe there’s a semi-interesting “author quality” metric that might appear from this. I also run the content through an AI model to extract a summary, which turned out surprisingly better than I expected.

Visit product View on Hacker NewsApr 10, 2023

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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.

best fitHighest predicted score across all platforms for this description.

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

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% 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 · Strong signals: platform · Missing: plus, intuitive, reviews

29%29% 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

17%17% 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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