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Datetime Utilities

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Datetime Utilities

I have been frequently frustrated when debugging issues as a backend engineer, by having to manually fiddle with timestamps - doing mental math to account for timezone differences, different timestamp formats between tools, calculating the difference between two timestamps, etc. I built a tool to do all these things in a single place. Demo video: 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. I also wanted to learn frontend development, and this felt like a good project for dipping my toes into it. I wrote a lot of code using AI, but quickly ran into trouble and had to take the reins. I still used AI to write tightly scoped functions and hooks, but kept most of the architecture in my head. I used AI to debug issues as well, and found it to be very useful at detecting issues that a new frontend engineer might make. Everything runs on your browser (except for format inference for unrecognized timestamp formats using gemini-flash-lite). History and recently used timezones and formats are stored in IndexedDB in your browser. Everything is hosted on free tier infrastructure - Cloudflare workers and the free tier of gemini-flash-lite. It felt incredible to be able to put something out there completely for free, without even having to enter my credit card anywhere. If you find bugs, please report them here: https://github.com/viraniaman94/datetime-utils-issues/issues... I didn't want to open-source it because I am not sure if this can take off in a significant way. If it does, I'd like to be able to make some money off of it if possible. If it doesn't, I'll open source it. Happy to take questions and feedback!

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, single, gemini · Missing: mac, agents, macos
92%92% 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: gemini · Missing: supports, reddit linkedin, podcasting
64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released
39%39% 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 · Missing: plus, platform, intuitive
32%32% 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
15%15% 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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