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Documents are a critical missing layer in trustless RWA tokenization. jean paoli of Docugami explained to JP Mullin (🕉, 🏘️) how DGML structures real estate data, gives AI agents a verifiable way to check it, then immutably anchors that proof on NVNM Chain. Open source at

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MANTRA | The EVM L1 for RWAs

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18,749 просмотров • 18 дней назад •via X (Twitter)

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DGML makes onchain document verification more efficient: hash only the clause you need, then anchor that proof on NVNM Chain. Explained by jean paoli of Docugami with JP Mullin (🕉, 🏘️).
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DGML makes onchain document verification more efficient: hash only the clause you need, then anchor that proof on NVNM Chain. Explained by jean paoli of Docugami with JP Mullin (🕉, 🏘️).

MANTRA | The EVM L1 for RWAs

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a top 20 token that slumped 90% in four hours : $OM what went down in the founder's office? keeping it real : - otc deals - large token movements to CEX - price manipulation allegations - liquidation cascade JP Mullin (🕉, 🏘️), founder of MANTRA | The EVM L1 for RWAs on @cryptotownpod 🐦‍🔥
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You don't need to be a developer to install AI agents and other open-source software. OpenCode + Venice gives you a private system administrator that runs on natural language—no coding, no data stored Use it to safely experiment with autonomous agents inside Docker containers:
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Robotics and AI need a steady flow of reliable data. As these systems grow more advanced, the stakes rise. They make real decisions in real settings, and they need information they can trust. XYO Layer One gives them that structure. It keeps data verifiable, organized, and available at the scale modern automation depends on.

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🔍How do we make sense of messy, real-world documents? We (the EPIC Lab at UC Berkeley) are building DocETL, an open-source system for LLM-native data processing. We've started to create a showcase of demos. Here's our first: an analysis of public feedback on US AI strategy. 🇺🇸

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🚨 Introducing Hash Network - World's Provenance Chain. Misinformation is at an all-time high. AI-generated content, deepfakes, and manipulated media make it impossible to tell what’s real. This crisis of authenticity undermines our ability to rely on the information we consume, leaving us questioning the integrity of everything from images and videos to news we consume. This has given rise to critical questions: How can we trust what we see? Who created this piece of content? How was it modified? The answer lies in provenance, it is the ability to trace the origin, history, and modifications of any content. Simply put, provenance provides the much needed transparency to verify authenticity and understand the steps taken to reach a content’s current state. However, the way provenance is currently stored presents a critical issue. Currently, provenance data is written into the metadata of the content itself. This approach leaves it vulnerable to being erased or tampered with. To truly address the issue, provenance must be tamper-proof, transparent, and universally accessible. This is where Hash Network comes in. Building on C2PA standards and leveraging Content Authenticity Initiative tools, Hash Network provides a tamper-proof layer for content authenticity. By embedding every action whether it’s a content edit or a transformation on-chain, Hash Network ensures that provenance is not just traceable but also tamper proof. It is built as a Layer 3 Arbitrum orbit chain that settles on Base . This infrastructure ensures that the provenance data is protected from tampering and remains accessible, creating a global provenance chain.

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AI is concentrating, in a few countries, a few companies, a few chips. Open-source AI is the only real check on that. And it only works if the infrastructure to run it actually exists. Today, we're announcing our Series A, $20 M, to make this a reality.

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My Hermes agent can now create LIVE datasets on the fly. Just added a real-time data layer to Hermes using the open-source TinyFish BigSet. First it builds the dataset. Then it finds the signal. This is WHAT agent-native research looks like in 2026.

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