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Easily filter your Steam Deck library by HLTB, ProtonDB and more

Hacker News

Easily filter your Steam Deck library by HLTB, ProtonDB and more

Hey, Felix here! I recently built a mobile app for Steam Deck lovers overwhelmed by their game collections, like myself. It's called DeckFilter’s and it's helping you find your next game, without the guesswork. You can filter your steam library by HLTB/HowLongToBeat, ProtonDB, Deck Verified status, unplayed games and more. And let's you view those at a glance. Would love to see what you think and get some feedback. Thank you !

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Actual performance

2points
Did not reach leaderboard

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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
32%32% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
30%30% 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
28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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
12%12% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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