Startup Ideas Vault – 1500 app/site ideas from Google Autocompletions

Hacker News

I built a tool that systematically pulls Google autocomplete suggestions for "an app to/for/that" across the entire alphabet and surfaces 500+ real app ideas that people are actually searching for. Unlike brainstormed lists or AI-generated ideas, these come directly from Google's autocomplete data – meaning real people typed these phrases when looking for solutions. Stuff like "an app to find parking spots," "an app for tracking plant watering," "an app that translates dog barks" (yes, really). Built this in about 7 hours using the existing Google Autocomplete A-Z API I built. The data updates daily and I flag anything that appeared in the last 7 days as "new" to catch trending ideas. What I'm still working on: Right now it's just a clean list of keywords, which feels a bit dry. I'm still looking for ways to surface the most interesting/viable ideas and trying to add context. If anyone has creative ideas for presenting this kind of data, I'm all ears. Also curious: What other categories would be interesting to explore with this? I focused on app and website ideas first, but the same technique works for any seed phrases. What would be most valuable to the HN community? And for those building tools or doing research – this runs on the Google Autocomplete API if you want to experiment with your own keyword categories (free with rate limits).

Visit product View on Hacker NewsAug 29, 2025

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

90%90% 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

69%69% 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: exist, lua, existing · Missing: https docs, excited, just released

44%44% 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, way · Missing: mobile apps, ios, personal

38%38% 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

34%34% 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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real people · 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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