Built a tool that lets anyone create cinematic aerial shots with AI

Hacker News

DreamFlow is an AI-powered platform for generating cinematic aerial footage. Simply upload a Google Maps screenshot or pick a location from the built-in map, and DreamFlow will create stunning drone-style videos within seconds. Choose from different seasons, weather conditions, times of day, and camera movements—no drone, travel, or filming skills required.

Visit product View on Hacker NewsAug 23, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: google · Missing: mac, agents, macos

70%70% 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.

TrustMRRFits verified-revenue profile · Strong signals: video, google · Missing: mobile apps, ios, personal

67%67% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

41%41% 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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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