A simple, beautiful and interactive wrapper of curl

Hacker News

Hello HN! I built a small programming tool called Ribbot, which is a simple and interactive wrapper of the command curl. It started off as just a small toy that I built while playing with gum[0], but now has grown into a pretty polished app. Right now it is in a pretty much complete state and ready to use. As this is the first time I have written something this big in shell script, there might be some mistakes here and there, so I would love hearing your feedbacks and comments on the project! [0]: https://github.com/charmbracelet/gum

Visit product View on Hacker NewsAug 2, 2022

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2points

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

73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

63%63% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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.

Incorrect prediction on native model

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