Temporal – Enhancing Date and Time Processing in Go

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Hi HN, I've developed Temporal, a Go library aimed at making date and time operations more accessible and efficient. It's not a replacement for Go's standard time package but an enhancement, with features that are practical in real-world scenarios. Key aspects: * Simplifies date parsing/formatting to user-friendly formats. * Handles relative time and date ranges effortlessly. * Adds useful utility functions missing in the standard package. I'm eager to hear your thoughts, suggestions, or potential use cases. Here's the link to the GitHub repo: [ https://github.com/maniartech/temporal ] Looking forward to your feedback!

Visit product View on Hacker NewsJan 30, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

66%66% 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.

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

54%54% 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: friendly, efficient · Missing: plus, platform, intuitive

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos

43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

31%31% 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

10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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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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