Launch Intel
QuickClip

QuickClip

TrustMRR

QuickClip is a universal clipboard for people who switch between phone and laptop all day. Copy text or images on one device, paste instantly on another. No emailing yourself links. No WhatsApp drafts

QuickClip is a universal clipboard for people who switch between phone and laptop all day. Copy text or images on one device, paste instantly on another. No emailing yourself links. No WhatsApp drafts. No random notes you forget. It just stays in sync and gets out of the way.

Visit productView on TrustMRRJan 27, 2026ProductivityOtherB2C
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Actual performance

1customers
$1MRR/mo
Did not reach leaderboard

Traction signals

Domain Rating4

Launch Intel predictions

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TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: email, notes · Missing: mac, agents, macos
55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
23%23% 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
8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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