Tuansaha

@origin_trail $TRAC has the best utility in AI ✅ Solves real AI problems (hallucinations, misinformation, bias) ✅ Has a real, working product with many integrations ✅ Fully decentralized, permissionless, open source and compliant ✅ Positive earnings teapawt.net/otnoderunner/status/18…

🇪🇺We welcome Europe's commitment to mobilize €200 billion for AI investments! @origin_trail has been implemented in over a dozen of EU funded solutions in construction, aerospace, supply chains, privacy protection etc. Here are some of the initiatives supported by the EU👇

🎙️ Podcast Reminder for the Crypto & Tech Community 🎙️ Don’t miss this upcoming episode featuring key voices from the worlds of venture capital, technology, and regulation: ▫️ Chris Rynning – Economist and investment professional with decades of experience in venture capital, M&A, and global markets. Currently the Managing Partner at the Piëch-Porsche family office AMYP Ventures. ▫️ Toni Piëch – Serial entrepreneur and 4th-generation member of the Piëch-Porsche family, focused on building a trusted technology ecosystem through investments and sustainability initiatives. ▫️ Fady Mansour – Lawyer and partner at Friedman Mansour LLP, Managing Partner at Ethical Capital Partners, bringing expertise in regulatory matters and online data protection. Tune in for thoughtful insights 📅 Don’t forget to watch! 🎥 youtube.com/watch?v=eLgf5Iih…

Simplified explanation for @origin_trail's Decentralized Knowledge Graph (DKG): (Shorter summary in the comments available) Overview: 👉Imagine a Library with the world's most precious knowledge (books, texts, content, science papers, etc.) 👉Anyone can add a book for a small fee; everyone has free access to read. 👉Visitors don't read books directly; they ask an LLM questions and receive response based on all available information. 👉Book contributors can create separate sections (Paranets) by topic or curated list, e.g., science papers on Longevity. 👉Books can be publicly accessible or private, which can be monetized through LLM (without readers getting access to the actual content). The Network: 👉Library content is stored on a decentralized network of nodes; anyone can run a node. 👉Each book is replicated on three nodes for redundancy. 👉Each book gets a "library card" tracking content hash, owner, and duration in the Library. 👉Changes to the library card are permanent and recorded on blockchains like Ethereum, Base, @NeuroWebAI, Gnosis, with more to come. Payments: 👉Book uploaders set payments for node operators for a certain length (i.e. several months, years or permanent, etc.) in a smart contract, paid every 60 days. 👉The Library checks node reliability; if a node fails, books are replicated to a new one, and payments follow. 👉Payments are in $TRAC; stakers earn 90-99%, node operators get 1-10%. 👉About 15% of $TRAC is staked, yielding 15-20% annually, with no premine or inflation. Why It's Needed: 👉Knowledge is fragmented across the internet, controlled by corporations like Google, Meta, etc. 👉Most knowledge monetization benefits corporations, not content creators. 👉Corporations can skew or hide information. 👉Even archives like the Internet Archive can lose data. 👉Web3 is all about decentralized, user centric, unfiltered access to information; the OriginTrail's DKG is crucial to fulfil this vision. In my next post, I will discuss current and potential use cases. Follow for more content!

The Porsche family decided to invest in @origin_trail $TRAC A family with a net worth of $55 billion chose to support the Decentralized Knowledge Graph, and you still have doubts?

But Jim.. couldn't someone like @Google or @amazon just build their own Decentralised knowledge graph to compete with @origin_trail 🤔 Credit to @wjlivingston for the eloquent explanation 🗣️ 'Other companies have tried to create similar solutions. IBM tried to create an information sharing network that could be used for global supply chain logistics. The problem was that other companies were unwilling to upload private, sensitive data to a network that was controlled by somebody else. It created all sorts of legal issues as to who owns the data once it is on the network. The solution is to make it decentralized. Nobody owns the DKG, meaning companies can put their data on it without worrying about a competitor trying to claim ownership to it. The DKG is a neutral ground where companies can upload all of their previously siloed data and share it with other companies in whatever way they choose in a way that is completely 'trustless'. The records of that data are also forever recorded on the blockchain and a permanent record made of every movement or change of that data. Also, the DKG has been developed to comply with the most widely used business and data standards in conjunction and cooperation with the creators of those standards (BSI, GS1, etc.) It is ready to accept just about any private, siloed database and instantly update a company’s ability to share data with whomever they please under any terms they chose while maintaining ownership of their data. This ability has never existed before. Google could create a decentralized network but what would be the point? Especially when one already exists. If it is truly decentralized, they cannot own or control it, but they could benefit from the one that exists in innumerable ways. This is a very complex project and your questions are valid. Luckily, a lot of us have had years to follow and learn more about it. Adding AI into the mix and the ability for companies to create their own private section of the DKG (paranets) and use any AI of their choosing to analyse that data in whatever way they choose is a development that I don’t think anybody saw coming in the early years of this project and has turned OT into a whole new beast. This project keeps evolving into something bigger and badder. It is a ride you will want to stay on. One last point: creating a knowledge graph is expensive and labour intensive. The DKG can be used by anybody, meaning a company that previously could not justify the cost, now can. Plus, they will have access to whatever other data is uploaded on the DKG that has been made public, or can purchase data from other entities on the DKG as well as sell their own. It creates a massive knowledge marketplace where anybody can buy or sell data as needed. Add in AI agents acting as automatic brokers and you have a massive data superhighway. I think it is safe to say we are only beginning to understand its potential. I can only compare it to what it must have felt like trying to comprehend why the internet would be valuable in the early 90s.' Big 🧠s know $TRAC Also check his latest thread making @origin_trail easy to understand: teapawt.net/wjlivingston/status/18… (Give him a follow)

The @origintrail Decentralized Knowledge Graph (DKG) for dummies (like me): What is it? It is the world’s first DKG. At its core, it is a decentralized (ownerless and open-protocol) network of nodes (just like the internet) that sits atop blockchain. 🧵1/7

Differences Between Normal Knowledge Graph and Decentralized Knowledge Graph (DKG) 1. Structure: - Knowledge Graph: Typically centralized, organized in a structured format to represent relationships between entities. It is often managed by a single organization or entity. - DKG: Decentralized and permissionless, consisting of interconnected Knowledge Assets managed by multiple stakeholders. It allows for broader participation and contribution. 2. Data Ownership: - Knowledge Graph: Ownership and control of data are held by the organization that manages the graph, often leading to concerns about data access and intellectual property. - DKG: Each Knowledge Asset has identifiable owners, ensuring clear rights management and access control. Ownership is transparent and verifiable. 3. Data Verification: - Knowledge Graph: Verification of data authenticity may rely on the organization’s internal processes, which can vary in rigor. - DKG: Utilizes cryptographic proofs and immutable records to ensure the integrity and authenticity of the information, making it tamper-proof. 4. Interoperability: - Knowledge Graph: May be limited in its ability to integrate with other systems and data sources, depending on the organization’s policies. - DKG: Designed for seamless interoperability across various platforms, facilitating collaboration and knowledge sharing among different stakeholders. 5. Incentivization: - Knowledge Graph: Generally does not provide incentives for contributing data or knowledge. - DKG: Encourages contributors by rewarding them for sharing valuable knowledge, promoting a collaborative ecosystem. 6. Use Cases: - Knowledge Graph: Commonly used for applications like search engines, recommendation systems, and enterprise data management. - DKG: Focused on decentralized applications, verifiable data retrieval, and collaborative knowledge management, particularly in AI and blockchain contexts. In essence, while both Knowledge Graphs and DKGs serve to organize and represent knowledge, the DKG offers enhanced decentralization, ownership clarity, data verification, interoperability, and incentivization, making it more suitable for collaborative and trust-based applications. @ChatDKG @origin_trail @opus_genesis

And thus the @origin_trail Decentralized Knowledge Graph has in mere few days become the main substrate driving collective memory for AI agents. @TopHat_One on $trac(k) 🤝

$TRAC holding strong at $0.77 💠 With @origin_trail's proven track record of enabling trusted data exchange, this is just the beginning. Supply chain, AI, and Web3 infrastructure powered by $TRAC are laying the foundation for a decentralized future. 🎯 Aiming for 1.5$ in the next leg up.

🎯 Knowledge graphs are a big deal for making #AIAgents smarter! But decentralized knowledge graph #DKG @origin_trail is even bigger deal. Supercharge AI agent memory, taking everything great about regular knowledge graphs and making them even more powerful. Here’s how: ✅ Stronger and Can Grow Bigger Centralized systems can crash or hit limits when too many people use them. A DKG spreads the work across many systems, so it doesn’t break and can handle way more data. AI agents always get fresh, reliable info to work with. ✅ Everyone Can Help Build Them Anyone people or systems can add and update info. This makes DKG grow faster and smarter, constantly improving as more contribute. ✅ Trustworthy Info With a DKG, you can check where the info comes from and whether it’s legit. That means AI agents work with reliable, easy-to-find facts. ✅ Works With Everything DKG connect to data from all kinds of sources no need for a single company to control it. For #AI, this means accessing info from all over the world, without limits. ✅ More Personal but Still Private People keep control of their data. This lets AI learn about you without invading your privacy, delivering smarter, more personal results while respecting boundaries. 🚀 Knowledge graphs are awesome, but #DKG is the future. They help AI agents stay smarter, adapt faster, and work with better info in a way that’s fair and open for everyone. Number of knowledge assets exploded on #DKG in last 3 weeks: dkg.origintrail.io/ #TRAC #AIAgents #DeSci #AI

You can now launch a Knowledge Graph enabled ElizaOS AI agent in minutes with the new @origin_trail DKG plugin. Agents can use the DKG as neuro-symbolic AI memory, such as to store conversations, facts, predictions etc and build out collective swarm memory together. All verifiable and on chain Try talking with the @ChatDKG agent on X, and test out the experimental Eliza DKG plugin yourself 👇