Steal My Spot

TrustMRR

Steal My Spot has launched as a public leaderboard that lets people purchase placement for a link or social media handle. Users choose a whole dollar bid, and the platform ranks entries by bid amount. Higher bids move higher on the board, while equal bids preserve the earlier entry’s position. The service does not require accounts and does not use advertising or subscriptions. Instead of engagement-driven algorithms, placement is determined by a visible price.

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Actual performance

Did not reach leaderboard

Traction signals

Domain Rating1

MRR growth 30d-100.0%

Launch Intel predictions

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

78%78% 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 HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos

32%32% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Missing: https docs, just released, open source

32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: reviews, intuitive, videos

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

Acquire.comPre-revenue stage for this audience · Strong signals: subscription, subscriptions · Missing: arr, mrr, revenue

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: api, 000, profitable

9%9% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Missing: web3, just like, small businesses

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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Tempah – Reserve a Spot in Supermarket40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

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