Launch Intel
Holdoff: Impulse Buy Blocker

Holdoff: Impulse Buy Blocker

TrustMRR

Holdoff is an impulse-buying blocker designed to help users avoid unnecessary purchases. It encourages users to pause before buying, track items they want to purchase, and reconsider whether they real

Holdoff is an impulse-buying blocker designed to help users avoid unnecessary purchases. It encourages users to pause before buying, track items they want to purchase, and reconsider whether they really need them. By adding a waiting period before making a purchase, Holdoff helps users reduce impulsive spending, save money, and build better spending habits.

Visit productView on TrustMRRJun 14, 2026ProductivityMobile AppB2C
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Actual performance

2customers
$8MRR/mo
Did not reach leaderboard

Traction signals

MRR growth 30d+200.0%

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
69%69% 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.
TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos
42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
34%34% predicted probability of success on Hacker News, 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 · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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