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I built FindCity on my spare time after my 9 to 5

Hacker News

I built FindCity on my spare time after my 9 to 5

Hello Hacker News! As a side hustler with a 9-to-5, I’ve been developing FindCity, a platform born from my deep interests in travel and data. Balancing this project with my day job has been challenging, but also rewarding. When I was planning my move to Norway, I noticed a lack of comprehensive tools that could offer detailed insights into various city metrics. This inspired me to create FindCity, which is a more visually appealing and AI-driven alternative to existing platforms like Numbeo. We aim to offer a comprehensive view of life in different cities, beyond just providing data. Our goal is to present these insights in a visually engaging and user-friendly manner, taking into account quality of life, community atmosphere, and personal growth opportunities. We’re also planning to introduce AI-driven features, including a personalized trip planner to enhance the user experience even further. For those of you working on your own projects after hours, I hope FindCity serves as inspiration. Take a look, explore the capabilities, and let me know what you think. Your feedback is very valuable as we continue to refine and expand. Thanks for sharing this journey with me!

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Actual performance

2points
5comments
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
97%97% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, visual · Missing: mac, agents, macos
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, lua, existing · Missing: https docs, excited, just released
52%52% 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 · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, friendly · Missing: plus, intuitive, reviews
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue
7%7% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: reward, introduce · 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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