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Watson – email finder with waterfall enrichment
Hacker NewsWatson – email finder with waterfall enrichment
Hey friends, I built a B2B email finder tool with waterfall enrichment. That means you can upload a list of prospects (name + company) and the tool searches through hunter.io, apollo, dropcontact and other email finder tools to find their email address. That way you can be sure the email is found (or cannot be found in a GDPR compliant way). Quick demo: https://youtu.be/K9efP0G3bR8?si=mWf7e0lZltRyJr80 Let me know your thoughts! :)
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Product HuntOn track for Day 1 leaderboard · Strong signals: email · Missing: mac, agents, macos67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released64%64% 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 · Strong signals: way · Missing: mobile apps, ios, personal48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting35%35% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue14%14% 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, crypto7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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