I made a simplified mail merge tool for sending mass personalized email

Hacker News

Hey HN. About a month ago, I was sending a lot of emails for one of my products. I needed to personalize a lot emails and I realized that the processes was time consuming. I started searching for a solution and discovered the mail merge method. I tried it with a few platforms, and realized it wasn't really user friendly. I also noticed that the platforms I was using was limited in the amount of emails you could send unless you get a subscription. I created this product out of frustration with existing mail merge platforms that lacked intuitive usability and imposed subscription models. As a user myself, I found the process cumbersome and costly, especially for occasional users or small-scale projects. I wanted a solution that offered straightforward, pay-as-you-go pricing, where users pay only for what they use without being tied down by recurring fees. The project's ui is still work in progress but it is functional.

Visit product View on Hacker NewsJul 24, 2024

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

82%82% 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: model, user, models · Missing: mac, agents, macos

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

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

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: platform, intuitive, friendly · Missing: plus, reviews, host

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: personal, month, users · Missing: mobile apps, ios, entrepreneurs

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

17%17% 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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