A simple API/CLI for scheduling HTTP requests

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Hi HN! This is something I've been tinkering on for the past couple months. It's basically just an API/CLI for scheduling delayed or recurring jobs as HTTP requests. I initially built it as a personal tool to save myself a bit of time on little side projects where I've needed scheduled/recurring alerts, but decided it could be a good opportunity to practice building out a nice landing page [0] and documentation [1]. And who knows, maybe someone else will find it useful ¯\_(ツ)_/¯ The tool relies heavily on Elixir's Oban [2] library for managing jobs, and Mintlify [3] for documentation. I also shamelessly stole most of the frontend design from Resend [4] because I'm a fan of the aesthetic and thought it would be good for my design chops to use their design as a guide. I also discovered Radix [5] UI while working on this, which ended up being immensely helpful for moving quickly on the frontend. Anyways, I almost certainly spent a bit too much time on small UX details that are most likely utterly inconsequential, but it was a fun exercise in polish :) All feedback is welcome! [0] https://www.booper.dev/ [1] https://docs.booper.dev/ [2] https://github.com/sorentwo/oban [3] https://mintlify.com/ [4] https://resend.com/ [5] https://www.radix-ui.com/

Visit product View on Hacker NewsSep 27, 2023

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

76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

43%43% predicted probability of success on TrustMRR, 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.

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