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Grably's accessing private data to fuel AI growth

Hacker News

Grably's accessing private data to fuel AI growth

Did you know only 4% of the data is publicly available for the whole LLM market and other AI products? Grably's here to help AI companies instantly access accurate, diverse, unique and specific datasets sourced from individuals who turned their galleries and private files into digital assets, allowing them to monetize their data. Our team previously founded and grew a company from $180k ARR to $1.6M ARR within just 5 months. we faced issues with the data we needed... Just weren’t able to find the dataset anywhere. Spent two years to collecting it manually. We believe Grably will accelerate AI growth and innovation by simplifying access to rare high-quality datasets. Join our beta program and start monetizing your files today: https://grably.us/turn-gallery-into-assets Beta is simple. It allows you to share the album (iOs & Android) or a folder (Dropbox & Google Drive). We receive the notification to our corporate email and then process the files. It happens manually for now, but don’t worry, we’re going to make it on-device without downloading files to any server. Please, share what kind of concerns do you have?

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
93%93% 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 · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% 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
46%46% 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: ios, month, google · Missing: mobile apps, personal, entrepreneurs
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, growth · Missing: mrr, revenue, profit
35%35% 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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