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Google Maps Email Lead Scraper (Real-Time)

Hacker News

Google Maps Email Lead Scraper (Real-Time)

Yo. Basically: Google Places API handles the search layer. Verified businesses with addresses, phone numbers, websites, ratings. From there I scrape homepages, /contact, and /about pages in parallel looking for actual email addresses. The biggest lever turned out to be query expansion. A basic search like "electricians in Michigan" tops out around 60 Google results. But if you auto-expand into every major city in the state, try synonyms (electrical contractor, electrical service, etc.), and use an LLM to generate more variations when you're still short, you can pull 500+ unique businesses from the same starting search. Email validation was way more painful than I expected. Without it you end up with phone numbers glued to emails, URL-encoded junk, placeholder addresses like example[@]mysite.com. Took something like 15 different validation rules before the output was consistently clean. I appreciate any feedback.

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
86%86% 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: google, email, code · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, way, para · Missing: mobile apps, ios, personal
43%43% 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
37%37% 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
31%31% 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 · 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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