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Boop – tiny, self-hosted push notifications for your apps built in Go

Hacker News

Boop – tiny, self-hosted push notifications for your apps built in Go

Hi HN, I built Boop because I wanted application events sent directly to my phone without paying for another service or routing everything through Slack or Telegram. Boop is a small, open-source server paired with an open-source iOS app. The server currently uses around 8 MB of memory on my machine. It’s deliberately not an observability platform or a replacement for Sentry. It receives events from your applications, sends native push notifications, and keeps them in a purpose-built mobile inbox. I use Elixir, so I built an integration for ErrorTracker. ErrorTracker still captures and stores the errors; the plugin sends them to Boop so I can see them on my phone: https://github.com/chrisgreg/boop_error_tracker There’s also a general Elixir client: https://github.com/chrisgreg/boop_ex And a Node client: https://github.com/chrisgreg/boop-node The iOS app can be built and installed locally. I’ve included instructions for configuring private push notifications for your own device, so you don’t need to release anything through the App Store. Although errors were my original use case, Boop can receive any event worth knowing about: failed jobs, deployments, signups, payments, low disk space, or anything else your application can send. Built in Go because I wanted to save memory on my self-hosted machine so Go and Svelte felt like a good combo. Everything is free and open source. It’s early, so I’d especially appreciate feedback on the architecture, setup process, and which integrations would make it useful to others.

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Actual performance

6points
3comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
80%80% 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: mac, slack, apps · Missing: agents, macos, agent
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: ios, apps · Missing: mobile apps, personal, entrepreneurs
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
40%40% 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
21%21% 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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