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Hi HN, I built a quick app using Lovable AI and Nebius—took me about 10-15 minutes. I was just messing around with AI tools to see how well they can handle coding and honestly, I’m impressed. Lovable is surprisingly good at handling basic CRUD operations. I didn’t have to write a single line of code for this app. The idea behind it was to create something like Google Lens, but powered by AI—a way for users to instantly learn interesting facts about anything, anywhere. Kind of like a basic version of Duolingo for real-world learning. Right now, it’s super simple—no auth or anything, just to keep it lightweight. But it could easily be upgraded with features like learning streaks and user profiles to make it more interactive. Would love to hear your thoughts or ideas for improvements!

Visit product View on Hacker NewsJan 20, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

93%93% 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

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

38%38% predicted probability of success on AppSumo, 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

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

11%11% 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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