Frogtab – Private, peaceful task management

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Hi HN! I'm excited to share the task manager that I've been building and relying on for the past 9 months I started developing Frogtab after a particularly busy period at work, where a large number of ad-hoc tasks would need my attention each day. What I Initially tried was a single (long!) checklist of outstanding tasks, but I found that too overwhelming to look at all day long. Then I tried scheduling tasks based on when I expected to complete them, but that also required too much cognitive load - I only had the capacity to bucket tasks into "today" and "not today" And so Frogtab was born. Originally as a Google doc, then later as a rudimentary web app with some simple automation. The automation was there to nudge me towards good habits, e.g., grouping all my outstanding tasks at the beginning of each day and requiring me to consciously choose which tasks to tackle that day. I subsequently refined the system a little, but the core mechanic has been serving me well ever since! More than the benefits I've gotten from using Frogtab at work, it's been really fun to develop Frogtab into a form that is usable - dare I say enjoyable - by other people too. Thanks to feedback from the awesome community at Bear (bearblog.dev) and also from sharing an early version here on HN, I've been able to make a whole host of improvements to Frogtab Highlights include: - Support for a weekly routine, aka recurring tasks - Data export and import, including auto-backup in supported browsers - Ability to receive tasks sent from any device - Dark theme - Keyboard shortcuts - Full docs at frogtab.com/help As I've been developing Frogtab, one of my primary concerns has been data privacy. Frogtab stores data in your bowser and doesn't require an account. Also, Frogtab does not use cookies, collect PII, serve ads, or track you in any way. It's entirely free to use and fully open source - the code is at github.com/dwilding/frogtab If you try Frogtab, I hope that you have a great experience! I would love to hear any feedback/suggestions, thank you :)

Visit product View on Hacker NewsDec 5, 2023

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

94%94% 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: started, including · Missing: supports, reddit linkedin, podcasting

92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

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

16%16% 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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