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A more accurate email validation tool for catch-all domains

Hacker News

A more accurate email validation tool for catch-all domains

Hey HN, I've been working on an email validation service that aims to provide more precise results, particularly for catch-all/accept-all domains, at a fraction of the usual cost. Traditional solutions often struggle with these edge cases, leading to more bounces, a drop in domain reputation, and overall deliverability issues. Key points: • Higher accuracy on catch-all domains: Our engine uses a combination of verification layers and heuristics to significantly improve accuracy. • Cost-effective: Priced to be about 10X to 20X more affordable than many established competitors. • Fast and scalable: Built for performance, so you can run large batches of validations without bottlenecks. We're also launching on Product Hunt today: https://www.producthunt.com/posts/emailawesome . If you find it interesting, any feedback there would be helpful. As a token of appreciation for your support, here's a coupon code for 1000 extra credits: NFFY5Agh6NVp6vP (valid until December 31, 2025), if you're the kind of heavy user who really wants to push the platform to its limits, just let me know and I'll hook you up with more credits. I'd love any thoughts on the approach, accuracy claims, or ideas for further improvements. Link: https://emailawesome.com Thanks for taking a look!

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Actual performance

4points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
96%96% 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: user, email, code · Missing: mac, agents, macos
65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000, io · Missing: https docs, excited, just released
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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