Reecova

Indie Hackers

Stop losing 5-9% of MRR to failed Stripe payments.

SaaS quietly loses 5-9% of MRR to failed Stripe payments. Reecova detects them in real time and recovers the revenue with adaptive retries and dunning. Live on the Stripe App Marketplace. Free scan: reecova.io/scanner

Visit product View on Indie HackersMar 1, 2026commitment-full-time employees-0 founders-code-yes

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Actual performance

Did not reach leaderboard

Launch Intel predictions

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

59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

BetaListSuited for BetaList early-adopters · Strong signals: real time · Missing: web3, chat, crypto

58%58% predicted probability of success on BetaList, based on ML models trained on real launch data.

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

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

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

Acquire.comPre-revenue stage for this audience · Strong signals: mrr, revenue, saas · Missing: arr, profit, profitable

47%47% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

41%41% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

20%20% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Correct prediction on native model

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