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Lifeboat, a distributed YouTube radio

Hacker News

Lifeboat, a distributed YouTube radio

A couple years ago I made some of my first online friends in a radio station on grooveshark. When it got shut down, we scrambled to find each other and a new home. After a few false starts, I decided to make a new home for us. https://github.com/swimmadude66/YTRadio became that new home. Its just a simple MEAN (with mysql and Angular2) app, but its worked for us for a little bit. It was written to be distributed, with one "room" per instance, including its own self contained chat. We have our own instance running at https://lifeboatradio.com Its still very much a work in progress, but I'd love some more feedback/contributors! P.S. The name "Lifeboat" came as a joke that this would keep us afloat until we found a real replacement for grooveshark. At this point, its kind of a lifeship, since we don't do a lot of looking for other services anymore

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
77%77% 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: ide, io, including · Missing: https docs, excited, just released
70%70% 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 · Strong signals: new · Missing: mac, agents, macos
58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
41%41% 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
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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