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Mboxreader – easily view your Gmail Takeouts

Hacker News

Mboxreader – easily view your Gmail Takeouts

Hey HN, Did you know you can download all your emails (and attachments) from GMail into a .mbox format using Google Takeout ( https://takeout.google.com )? I did this and for the longest time my .mbox backup just sat around. I built a web app that can take an .mbox file and load it into a UI that looks like GMail: threaded conversations, original labels and categories, contacts, attachments, and full-text search. The mail never leaves your machine: the upload and parsing happens in a Web Worker in your own browser, and the archive is stored in browser storage on your device. Close the tab and it's wiped. There's no account required to try it out, and if you still don't trust it there's a built-in demo mailbox — no file needed. Try it out at https://mboxreader.com . Let me know if you have any questions or any constructive feedback. Thanks in advance! Pre-emptive FAQs: 1. "If 'no account required' why is there sign up?": I'm curious if there is a "power user" use case that needs support for larger backups and advanced features. But again, there's no need to sign up to try out the app. 2. "Why make it a web app instead of something I can run locally and privately?": There's less friction to try it out as a web app and local setups don't eliminate privacy concerns either.

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Actual performance

7points
Made the leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% 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: mac, google, user · Missing: agents, macos, agent
77%77% predicted probability of success on Product Hunt, 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
49%49% 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 · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal
27%27% 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
13%13% 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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