Automatically tweet (X) the music you're enjoying on Spotify

Hacker News

Hey HN! Excited to show a project called Bopping that makes it effortless to share the music your enjoying on X (Twitter). Bopping connects to both your Spotify and X (Twitter) accounts, tracks your listening activity, and automatically tweets out your top 3 tracks every day (timeframe adjustable in settings). When it knows an artists handle, it will tag that artist in the tweet, which has resulted in some nice artist/fan interactions. I often get artists retweeting or liking my tweets which feels very cool. It's been fun to see the tweets from the small group of people using it on my timeline. Would love for you to give it a try. Excited to hear feedback and see what tracks everyone’s vibing to. You can see some other bops here: https://x.com/search?q=I%27m%20%40bopping_to&src=typed_query... All bops are also added into a (rather eclectic) playlist: https://open.spotify.com/playlist/5bXlqMaUBEcwN8lP5CEZbN?si=...

Visit product View on Hacker NewsNov 11, 2024

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: activity, using, open · Missing: mac, agents, macos

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

67%67% 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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, io · Missing: https docs, just released, exist

38%38% 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 · Missing: mobile apps, ios, personal

30%30% 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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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