TL;DR: Web3 wallets are evolving from simple storage tools into data monetization platforms. ONTO Wallet allows users to earn passive income by providing verified, consented data for AI training, without compromising privacy. The concept of a Web3 wallet is changing rapidly. In the past, wallets like MetaMask were simply tools to hold tokens and interact with dApps. Today, the narrative is shifting towards data sovereignty and monetization. With the explosion of AI, the demand for high-quality, human-verified data has skyrocketed, creating a new opportunity for crypto users. ONTO Wallet is at the forefront of this shift. Unlike traditional wallets, ONTO integrates decentralized identity (ONT ID) to verify that you are a real human. This verification makes your data incredibly valuable to AI companies, who are currently struggling with "model collapse" caused by training AI on synthetic or bot-generated data [1]. By opting in, you can allow ONTO to securely share your metadata with these companies, earning you passive income in the form of crypto rewards. Q: Is my personal data safe? A: Yes. ONTO uses zero-knowledge proofs (zkTLS) to verify your data without actually revealing the underlying sensitive information. You remain in complete control of what is shared. Q: How much can I earn? A: Earnings depend on the type and amount of data you choose to share, as well as the current market demand from AI developers. However, because verified human data is at a premium, the rewards are generally higher than traditional Web2 data-sharing programs. Q: Do I need to actively do anything to earn? A: No. Once you set up your ONT ID and opt into the data monetization program, the process is entirely passive. References [1] "The AI Data Crisis: Why Human Data is the New Oil," TechCrunch, 2026. submitted by /u/Rc7xn [link] [Kommentare]

Sharing a project I have been working on called Third Eye. It does visual geolocation. Given a video, it figures out where it was filmed using only the image content, and draws the route on a map. Pipeline in short: per frame place recognition against a street imagery index a trajectory search that stitches the frames into one coherent path a geometric verification step to catch false matches per frame confidence so weak frames are flagged, not faked I ran it on real dashcam footage and it traced the route quite well. Cross domain matching like this is genuinely hard, so a fair amount of the work went into making it honest about uncertainty. Keen to hear feedback on the matching and trajectory side. Video Demo: https://youtu.be/U3sItFlvq6E?si=-KJrwb0gSlk-GxVH The Index was covering a 12KM2 Area around NYC. submitted by /u/Ok-Apricot956 [link] [Kommentare]

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