MP

MPDPilot – MPD client for iPhone and iPad

Hacker News

MPDPilot – MPD client for iPhone and iPad

Hi, HN. I'm one of the developers of MPDPilot. During the 2020 pandemic, I found myself relying heavily on MPD (Music Player Daemon) but couldn't find a good iPhone app to control it. So, I decided to build one myself. MPDPilot is a Swift-based app with a user interface built entirely with SwiftUI. It supports multiple MPD connections, local network server discovery, HTTP streaming playback, cover art display, song search, and more. For local playback, it uses a custom cross-compiled version of libmpv that supports several popular audio codecs. The project started as a quick solution for my own needs but has grown with continuous development over the years. If you use MPD and have an iPhone or iPad, I hope you find it useful too. Looking forward to your feedback.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, started · Missing: reddit linkedin, podcasting, created
92%92% 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: user, code · Missing: mac, agents, macos
64%64% 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
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
41%41% 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: interface · Missing: plus, platform, intuitive
36%36% 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
10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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