DateTime Utilities

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DateTime Utilities

I built this tool to replace all other web based ad-laden tools I use to work with timestamps in my day job. All of these things exist on the web already, but I put them all in one place and built a workflow around them to make working with them easier. - It takes natural language input (yesterday at 3pm) - It can convert a timestamp into different timezones - It can convert a timestamp into various formats (how many of us have looked up "epoch converter" on google?) - It can calculate the time difference between two timestamps - It can add or subtract a time duration to a given timestamp - It can round a given timestamp to the nearest minute, hour, day, month, year, etc. I also added a few QoL features - History section that stores the results on your browser - A button to copy a result in the history to input (Copy to Input). This allows you to chain multiple operations together - Last used timezones and formats will persist on your browser across sessions to make common conversions quick and easy Here's a demo video to show how it all works: https://www.youtube.com/watch?v=pcBwIcqBLk8 I apologize for the potato video quality. I used free trial software and this was the first time for me to record or edit anything. Happy to take questions and feedback!

Visit product View on Hacker NewsAug 25, 2025

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86%86% 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: google · Missing: mac, agents, macos

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video, month, google · Missing: mobile apps, ios, personal

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, ide, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

22%22% 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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