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I built an API to detect and verify disposable or temp email addresses

Hacker News

I built an API to detect and verify disposable or temp email addresses

Hi HN, I'm excited to share a project I've been working on: UnwrapEmail( https://unwrap.email ), an API designed to detect and verify disposable or temporary email addresses. Features: - Detect disposable email addresses - Extract labels from email addresses (e.g., Gmail) - Normalize addresses by removing labels or special characters (e.g., Gmail) - Validate MX records of domains - Check whether it’s safe to send to a given email to reduce bounces and maintain sender reputation Why I Built This: As the owner of an AI wrapper app, I've seen decent traffic thanks to SEO efforts + ads, and I've been able to convert visitors into users with free plans. However, some users exploit these free resources by using disposable emails or Gmail’s "+" trick to create multiple accounts. This abuse costs me money, as I have to pay third-party AI API providers from my limited profits. I couldn’t ignore this behavior, as it directly impacts my expenses. However, I also didn’t want to put my products behind a paywall without offering a free trial option. To address this, I developed this API initially for internal use, and it proved to be effective that I decided to turn it into a dedicated service. Try it now: https://unwrap.email Note: I understand the need for temporary/disposable addresses, but I can't afford to cover the costs for those exploiting the system. This API is designed to help app owners make informed decisions based on the responses. Thanks for reading! I’d love to hear your feedback.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
91%91% 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, using · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
63%63% 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: users · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: profit · 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.

Incorrect prediction on native model

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