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Bloben – Self-hosted web CalDAV calendar client

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Bloben – Self-hosted web CalDAV calendar client

Hello all, I have been working on a self hosted CalDAV calendar client Bloben https://github.com/nibdo/bloben-app This is mostly an alternative to other web clients like great AgenDAV with some additional features I wanted to use like email invites, alarms, webcalendars, etc. You can play with demo (limited to read access) here: https://demo.bloben.com/api/v1/users/login-demo?username=dem... In the end it should provide separate clients also for CalDAV tasks and notes. One thing I would maybe like to discuss more is adding encryption, which is obviously quite problematic when using standards like CalDAV. Initially I just added non-compatible fully encrypted calendars, but realised this might not be useful for many people using different clients. Something halfway is adding an option to protect only some, say sensitive, events by encrypting fields like title, description or location. That way events will still be displayed via standard mobile apps and decrypted only in Bloben client (with link to open from description). As part of this project I published also React calendar component https://github.com/nibdo/kalend Thank you, if you are interested and feel free to ask me anything.

Visit productView on Hacker NewsApr 6, 2022
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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, 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 · Strong signals: para, compatible · Missing: supports, reddit linkedin, podcasting
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: mobile apps, apps, users · Missing: ios, personal, entrepreneurs
57%57% predicted probability of success on TrustMRR, 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
56%56% 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, users · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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.

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