Fastmail Extension for Email Campaigns

Hacker News

I'm one of those who moved from Gmail to Fastmail and the only thing I really miss is bulk sending, click tracking and other features GMass used to provide. So me and my team figured out Fastmails API (how the frontend works plus JMAP) and implemented the "Mass Send" button straight into the Fastmail UI. We've got many features still to come like auto-followup, mail merge open/click-tracking. So if you're using Fastmail give it a go! Chrome: https://chrome.google.com/webstore/detail/massfastmail/gbdfagckfacojiaicjhngpgepjofchhi Firefox: https://addons.mozilla.org/en-US/firefox/addon/massfastmail/

Visit product View on Hacker NewsJul 14, 2020

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

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best fitHighest predicted score across all platforms for this description.

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

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

18%18% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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