WA

WACopyMate – Copy Numbers to Clipboard on WhatsApp Web in 1 Click

Hacker News

WACopyMate – Copy Numbers to Clipboard on WhatsApp Web in 1 Click

WACopyMate is a dead simple Chrome/Firefox add-on to copy contact numbers from chats on WhatsApp Web in 1 click. With this, you will not have to manually copy numbers, remove spaces, and remove parenthesis. Just tap on the ad-don's icon in the toolbar to copy number of the contact in currently opened chat. It is my first attempt at creating browser extensions.

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Actual performance

2points
2comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
53%53% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos
32%32% 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
27%27% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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