I

I made email validation tool

Hacker News

I made email validation tool

Hey HN, I’m a solopreneur and have been in the SaaS space for the past 3 years. My last 3 startups failed, and a common challenge in all of them was figuring out how to reach my audience. Recently I started with cold outreach (its a struggle, since I dont have much experience with marketing). I am finding leads mainly using exa.ai websets (web vector search, extremelly good for multiple use cases, recommend checking them out) and then sending them through smartlead. I ran the campaign for the past 30 days and 12-15% emails always bounced. This wasted my email credits & chances of reaching the right people. So we made a tool that checks emails before we send them (mx, timeouts, domain check, disposable emails, catch-all emails,..). Now the deliverability is close to 99% :) https://verifyemailai.com I am using it myself, lets see how market responds :)

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Actual performance

7points
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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
90%90% 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 HuntOn track for Day 1 leaderboard · Strong signals: email, using · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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
45%45% 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, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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