pi

pigeonal.com, a read-only email app

Hacker News

pigeonal.com, a read-only email app

Appcoast is a small collective of entrepreneurs and engineers, working on extensions for eBay and bespoke work for Mozilla by day, and working on The Next Big Thing by night. This week we launched our first commercial iOS app. Pigeonal is an email client for iPhone. Its purpose is to quickly archive (and occasionally mark for follow-up) emails which only need a casual glance, or a quick check, before you file them away (examples: receipts, notifications, reminders). The app scans your inbox for (currently) 207 different services and displays the results using icons in pigeonholes (a wood texture cupboard). Q: Why only Gmail and MobileMe/iCloud? A: We'll roll out general support for IMAP, but it takes a lot of testing. IMAP is hairy. Q: Can I flag/star an email for follow-up? A: That will be possible in the next version, flagged email is currently moved to a Follow-Up folder (which also works great, both in Gmail and MobileMe, but some people prefer flagging/starring). Q: Are you crazy? Asking money for a read-only email client? A: The read-only feature is a deliberate design choice: it prevents you from being sucked into an email conversation. We ask money for the app because we think it provides value for some people. Q: Are you storing my email credentials on your servers? A: No, your email account credentials are only stored on your phone using iOS keychain and are only sent to Gmail or MobileMe/iCloud. http://www.pigeonal.com/

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
92%92% 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: inbox, email, using · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios, entrepreneurs, way · Missing: mobile apps, personal, apps
53%53% predicted probability of success on TrustMRR, 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
41%41% predicted probability of success on Hacker News, 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: occasional · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
20%20% 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.

Correct prediction on native model

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