Mailfrom.dev – a cheaper MailTrap alternative

Hacker News

Hi HN, Two months ago, we launched MailFrom.dev, a fake SMTP server for developers who want to test email sending without hitting real inboxes – think of it like a simpler, cheaper alternative to Mailtrap or Mailhog with a focus on ease of use. Since then: - We’ve had 55 developers sign up with 4 paid subscription active and 821 test emails sent - Improved stability, speed, and small features our early users needed - Worked hard to add roles (wich will be released later next week) What's improved: - Better UI to inspect headers, bodies, and attachments - Faster inbox refresh and sync between team members, now near real-time Lessons learned: - Developers do care about price and simplicity over enterprise features (almost all came from MailTrap) - People love tools that “just work” with minimal config What’s next: - Roles (coming next week) - Webhooks (to test what happens after the email lands) - Test by sending a real mail to a MailFrom.dev team address (something like a shared inbox) - A blog to help spread the word If you’ve used tools like Mailtrap, Papercut, or Mailhog before — we’d love for you to give MailFrom.dev a try and tell us what you think. Link: https://www.mailfrom.dev Happy to answer any questions or feedback here. Thanks again!

Visit product View on Hacker NewsJun 20, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, inbox, email · Missing: mac, agents, macos

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

60%60% 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: month, users · Missing: mobile apps, ios, personal

48%48% 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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription, active · Missing: arr, mrr, revenue

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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