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A LITTLE CRAZY 🤪 This might sound a little crazy, but I've completely lost myself in @IThinkItsArt . I've gone through every single image, and I'm genuinely obsessed with the art, creativity, and vision behind this project. I'd absolutely love to be part of ITIA and contribute to something this incredible. Let me be one of you! 🖤 @Iv_ITIA @kimiloo @DL_ITIA and more TEAM....

👀Something is comming We are still here Magnitude is Key @SeismicSys

Doma just made something important easier to track: whether your domain tokens are actually profitable. Not every meaningful Web3 product update needs to introduce a new feature category. Sometimes, better visibility into existing positions is what matters. Doma Protocol recently introduced improved Profit & Loss (P&L) tracking across two areas: 1. Portfolio Overview Users can now monitor individual domain token performance directly from their portfolio, including profit/loss amounts and percentages alongside their holdings. 2. Individual Token Trading The Trading tab provides a more detailed breakdown of each position, including average entry price, total spent, shares held, and fees earned. But one detail stands out to me. Doma confirms that these calculations account for the cost basis of both vested and purchased tokens. Why is this important? When tokens are acquired through different mechanisms, market prices alone don't accurately explain an investor's position. Understanding the original acquisition cost provides a clearer picture of actual performance. My perspective: This update addresses a practical challenge in tokenized asset markets: financial transparency at the user level. Tokenizing domains creates tradable digital assets, but making those assets understandable requires more than ownership records and price charts. Of course, visible profits don't guarantee liquidity or favorable execution prices. Still, better portfolio analytics reduce friction and help participants make more informed decisions. For Doma, that's a meaningful step toward improving the everyday experience of trading tokenized domains. @domaprotocol @D3inc

👀Welcome to Chapter II of zkArgos 0x7642BD67466Eb420a94E46d6e341Fc6751113AFb @zkArgoseth

👀Wailist Wanderforest alpha here👉 wanderforest.com/waitlist?re… xitter.cf/playwanderfores/status…

The real difference is migration debt. Ethereum must upgrade an ecosystem already in use. QMS took another path: signature-free block production and post-quantum finality by design. Its EVM accounts still need migration, though. A head start, not full protection. @QMSNetwork #QMS1002

Why is everyone talking about .agent? And where does Doma Protocol fit in? I've been following the recent discussion around .agent, and I think there's something more interesting here than another domain extension. On October 7, ICANN revealed applications for new top-level domains. .agent attracted 13 applicants, including OpenAI, Google, and Meta, making it the most contested extension in the 2026 round. But why does this matter? As AI agents become capable of executing transactions, interacting with services, and acting independently, a fundamental question emerges: How do we verify an agent's identity, ownership, and authority? A domain could serve as a recognizable identity layer, connecting an agent to its operator and providing a foundation for discovery and verification. This is where I find Doma Protocol particularly relevant. Doma already focuses on bringing traditional DNS domains onchain, enabling programmable ownership, transfers, and integration with blockchain applications. My interpretation is that these capabilities could eventually support an internet where both humans and autonomous agents interact with domain-based assets. However, an important distinction: Doma has not been confirmed as the operator of .agent. The extension itself remains subject to ICANN approval and contention resolution. And domain ownership alone cannot guarantee an agent is trustworthy. To me, the interesting question isn't who wins .agent. It's whether existing internet naming infrastructure can evolve into a practical identity and ownership layer for an agent-driven web. @domaprotocol @D3inc

Dinner with candles? ❌ Dinner with Seismic? ✅ All I need is a blockchain that understands that not everything needs to be public. 🫡 Privacy is my love language. ❤️🔐 @SeismicSys @xealistt @NoxxW3

It's crazy that agents can now borrow, lend, trade and hedge with options!

What Does It Mean to Build a Verifiable Data Foundation for AI ? As you probably know, AI today doesn't just need more training data. What matters even more is that the data has a clear origin, transparent usage rights, and a fair reward distribution mechanism for those who contribute it. That's why the combination of DATA Network, Sui, Walrus, and Trace is worth paying attention to. Don't worry, I'll break it down in detail 👇 TRACE – Verifying Data Provenance To put it simply, Trace records where contributed data comes from, information about the contributor's consent, licensing conditions, and an auditable history. In short, according to DATA Foundation, Trace has already recorded more than 250 million data contributions. WALRUS – Data Storage As part of the integration, Walrus is intended to handle the storage of encrypted reference data files and allow them to be verified against previously recorded hashes. In simple terms, this helps detect whether data has been modified without publicly exposing sensitive datasets. SUI – Coordination and Execution Thanks to Sui object centric architecture, datasets, licenses, and access permissions can be represented as programmable assets. In short, this allows data related transactions to be managed throughout the entire data lifecycle. So, when these three components work together, they aim to connect data contribution, verification, licensing, and reward distribution within a more transparent system. TL;DR, My Personal Perspective: What I find truly valuable is the ability to establish a verifiable connection between data, ownership claims, and the scope of permitted usage. However, the transition is still being implemented in phases. Recording information onchain does not automatically guarantee that the data is high-quality, that it was shared with the owner's consent, or that it fully complies with legal requirements. And ultimately, the real question is whether AI developers can reliably access properly licensed datasets while contributors can also track how their data is being used. This is the infrastructure challenge that I believe is truly worth keeping an eye on. @datafdn @SuiNetwork #DATAonSuiQuest

👀Hint 🔒🔒 Give me the key to open it. @D3inc @domaprotocol

✅Milestone 1 : Done ✖️Milestone 2 ✖️Milestone 3 ✖️Milestone 4 .................. Now the time to find at least one Bot for yourself. Create original content, don’t copy, stay true to yourself, and don’t let worthwhile alpha pass you by. @conso_xyz

You can read the details on this page👇 conso.xyz/bots