Data for AI and AI for Data

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Hey all, We’ve applied to YC three times and got rejected each time. we’ll get there eventually! The main reasons for rejection were: being a solo founder (no co-founder), having no team, an MVP that wasn’t ready, or no traction in place. --- My expertise lies in building software products for startups, unicorn companies, and even governments. Throughout my years in software, I’ve had plenty of exposure to data products. I’ve faced recurring challenges like the lack of data, uncertainty around data handling, or even generating too much data and not knowing how to best utilize it to generate revenue. Many times, I turned to data marketplaces, but my experience with them was nightmare-ish and frustrating. Every time I tried to use data marketplace, I was redirected to a data vendor. This meant spending 7–14 days on phone calls or emails, trying to explain my data needs, only to hear responses like, “Let me talk to data supplier and see if we can produce this dataset.” On top of that, navigating the top data marketplaces felt like you needed a master’s degree. Tasks like to create an account or view data listings and samples were always behind "Contact Us" walls. In one of my previous startups, I built a data storage solution and spent an insane amount of time trying to sell it to B2B businesses. Most of my calls ended with, “Cool stuff, but this isn’t the problem we have.” From there, conversations often shifted toward data monetization and acquisition, with companies describing the same pain points and challenges they had with top-tier data marketplaces. That’s when I realized it wasn’t just me—the actual problem was much larger than it appeared. I decided to continue as a solo founder and managed to attract 13 experienced professionals to the team. Together, we’ve built Opendatabay, an AI-powered data marketplace that makes data discovery, listing, and access as simple as shopping on Amazon. The product is live, we are getting traction, and I’d love to hear your thoughts on its feel, look, and message we trying to deliver. • Would you say it’s easy to use? • Do you understand what we do? • What, in your opinion, is missing? I appreciate your feedback! Thanks, Justin (Founder)

Visit product View on Hacker NewsJan 16, 2025

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

97%97% 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: email, tasks, open · Missing: mac, agents, macos

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

53%53% 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: way · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: revenue, recurring · Missing: arr, mrr, profit

19%19% 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.

Incorrect prediction on native model

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