Discover most reviewed things.in/city

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Hi HN, When I used to travel to new cities, I relied on place ratings, but I noticed that sometimes, 5-star reviews were left by the owner and their family, which skewed the results. I started trusting the number of reviews instead of the rating itself, but I found it difficult to find platforms that let me sort places this way. I created this travel guide website to solve this issue. It features attractions, restaurants, and stays in various cities, all sorted by the number of reviews. Additionally, the website offers simple guidebooks for each city to help travelers plan their trips. Here are examples for two cities: San Francisco: https://things.in/sf Dubai: https://things.in/dubai The site received a lot of love when I first launched it on Reddit: https://www.reddit.com/r/InternetIsBeautiful/comments/1fec8p... I built this over a weekend without writing a single line of code using the Cursor editor. This is my third product built with Cursor. You can follow my product journey on X: x.com/shyjal. Please give it a try and let me know what you think!

Visit product View on Hacker NewsSep 17, 2024

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Did not reach leaderboard

Launch Intel predictions

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

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

60%60% 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: cursor, new, single · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform, reviews · Missing: plus, intuitive, host

54%54% 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

38%38% 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

15%15% 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.

Incorrect prediction on native model

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