EmaiLens

TrustMRR

Emailens is an email QA and compatibility testing platform built for developers, marketing teams, designers and growth teams. It catches rendering bugs, broken layouts, and HTML/CSS quirks across major email clients before campaigns go out. Powered by @emailens/engine, it lets teams automate email validation, preview dynamic templates instantly, and protect inbox deliverability. A modern, developer-first tool replacing outdated testing suites with fast, reliable pre-send quality assurance.

Visit product View on TrustMRRMar 2, 2026Developer Tools Other B2B

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Actual performance

3customers

$37MRR/mo

Did not reach leaderboard

Traction signals

Domain Rating3

MRR growth 30d+247.2%

Launch Intel predictions

Analyze your own launch →

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

79%79% 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

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, 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

49%49% 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

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

25%25% 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

14%14% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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