A directory of work-friendly cafes powered by Google Reviews analysis

Hacker News

I built awifi.place to solve the problem of finding cafes where working is actually allowed. Instead of manual curation, it uses Google Reviews analysis to identify suitable places. How it works: - Analyzes Google Reviews for keywords like "work-friendly" and "wifi" - Automatically excludes cafes where reviews mention working is not allowed - Extracts and displays relevant review quotes about wifi quality and working conditions - Cafe owners can opt-out anytime (ethical consideration) Technical stack: - Next.js 15 with TypeScript - Supabase for database - Google Maps Location API for address/hours and outscraper for reviews - OpenAI for generating descriptions - AI-generated city illustrations (using recraft-ai/recraft-v3) Currently covering multiple cities worldwide, but I am looking for feedback on the approach and suggestions for additional features before I continue with the next city as its quite pricey to retrieve the reviews.

Visit product View on Hacker NewsFeb 28, 2025

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best fitHighest predicted score across all platforms for this description.

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AppSumoMay struggle as an AppSumo deal · Strong signals: reviews, friendly · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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42%42% 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: google · Missing: mobile apps, ios, personal

35%35% 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

12%12% 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.

Correct prediction on native model

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