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Hometowns – Map the area codes in your contacts list

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Hometowns – Map the area codes in your contacts list

I've always been interested in area codes because we all tend to keep our first phone number and it tells a little bit of your life story. I probably only know 5 area codes that really excite me because I'm familiar with the area, but I've wondered how many others are from areas I'm familiar with, but I'm just not familiar with the area code. So in full transparency, this project came together because I finally threw an LLM at it. I've had this project written down for probably 10 years, and the initial commit to this repo was 5 years ago. It parses your contacts, and shows you a map of where they are from (North America only). You can also choose to share it with someone else and see the overlap of your maps which I found really fascinating too. I recognize it's a big ask to enter names and phone numbers into a random site, but this is published to github pages and the source code is right there. It is all fully client-side, no data collection, no network requests or anything. You can run it in airplane mode / disable networking on the tab once it's loaded too. Hopefully others find it fun too!

Visit productView on Hacker NewsSep 16, 2026
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
79%79% 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
68%68% 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
62%62% 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: way · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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