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Coverage Critic Map – Combined Coverage Map for US Cell Networks

Hacker News

Coverage Critic Map – Combined Coverage Map for US Cell Networks

Hi HN! I built a map that shows cell coverage from the major US networks. It draws primarily on data from the FCC, but that's supplemented/calibrated with a bunch of crowdsourced data from the DIMO project (dimo.zone). My goal is to make comparisons between networks super easy--one click to toggle between networks or technologies. I'm never sure how much to trust networks' self-published maps. I'm still in the early days of incorporating the crowdsourced data, but I'm hoping all the on-the-ground data can lead to a map that's more reliable than network operators' maps (those are largely based on RF modeling). Couple notes: -Toggle options from the settings menu, like adding Dish & US Cellular or switching to exclusively crowdsourced data -Map shows native (non-roaming) coverage -Coverage info is most detailed while zoomed in Would love any feedback! Cheers, Chris

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
71%71% 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: model, notes · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: exclusive · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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36%36% 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
6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

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