Stop Babysitting Lovable

Hacker News

I got sick of staring at Lovable like it was gonna finish faster if I believed hard enough. So I built a Chrome extension with a friend (@jason zook) to fix it. It’s called Lovable Buddy, and it does one thing: lets you STOP babysitting AI. It’s a lil floating widget that: - Tells you when Lovable is still working - Tells you when it’s done Yes, it works in background tabs. Yes, it gives you desktop notifications too. Yes, you can move it wherever you want. Yes, it makes multitasking WAY less stupid. And yes, we are charging $9 for it, because your time (and our time to create it) is worth wayyy more than that. Learn: http://lovablebuddy.com Get it: http://bit.ly/lovablebuddy And yes, we use it ourselves. Constantly. That’s why we built it.

Visit product View on Hacker NewsMay 28, 2025

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

87%87% 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.

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

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% 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

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.

Correct prediction on native model

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