A personal Gmail

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A personal Gmail

Hey guys, so I've been building my own Gmail for the past few weeks and would like your feedback. Find a preview here: http://i.imgur.com/UDz2y.png (Mouse cursor is over the "REPLY" button, to show the hover-effect) The app you see in the screenshot is what I've been using in the past week and it starts to feel better than the gmail interface. Just a quick explanation: - It's conversation-based like Gmail. - Conversations with new emails are on the left. - Conversations may be labeled ... based on predefined filters or manually. - Every conversation stays in the left sidebar until marked "Done" or "Pinned". - Done emails are accessed through the "check"-button next to the "MailApp"-logo. - Pinned emails are moved to the right sidebar. This is basically a to-do list or for future reference. - Both sidebars may be filtered by attributes or labels. - The "list"-icon, next to the filter in the left sidebar, gives you the classic gmail list in the center-panel. Some technical background: - It's build with Rails and PostgreSQL. - E-Mail sending/receiving is based on the MailGun infrastructure. - Turbolinks (https://github.com/rails/turbolinks) and Memcache for a speedy UX. - All mail data is saved in a multi-tenant PostgreSQL db and for backup purposes in a IMAP mailbox on MailGun. - Hosted on heroku. I'm quite happy with the app by now and only go back to Gmail for older emails. So my BIG QUESTION right now: Would you even consider using the app, in case I'd make a commercial product out of it? Would you pay for it? Searching HN yielded that quite a few people are looking each month to leave Gmail (for various reasons), but of course there are quite a few gotchas with my approach, especially considering trust-issues. Let me hear what you think.

Visit product View on Hacker NewsNov 19, 2012

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

80%80% 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

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

51%51% 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: host, interface · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, month · Missing: mobile apps, ios, entrepreneurs

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · 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.

Correct prediction on native model

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