Void Scourge – a browser CCG auto-battler where every card is unique

Hacker News

I built a collectible-card auto-battler you can play in the browser. Every card is unique: we procedurally generate the card’s stats/bonuses/name and its illustration (via Stable Diffusion prompts derived from attributes). Decks are 6 cards, fights are auto-resolved and typically finish in < 20s. Progression is free (daily booster + quests), with Legacy / ~6-month Seasons / weekly Draft modes, a Catalyst fusion (5 same-level → stronger card, with a chance to produce an “Abnormality”), and fair matchmaking with multiple leaderboards. No downloads. Free to play. I’d love feedback on: – Proc-gen balance (bonus ranges & rarity odds), – Combat readability (order = attack order; Taunt targeting), – Draft reward structure and matchmaking. Happy to answer technical questions (proc-gen pipeline, image prompting, leaderboard snapshots/Hall-of-Fame).

Visit product View on Hacker NewsAug 29, 2025

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

71%71% 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.

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

46%46% 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.

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

36%36% 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 HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

31%31% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: reward · Missing: web3, chat, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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