Vi foreslår enklere digitale regler, en strategi for en EU-dataunion og europæiske digitale virksomhedstegnebøger. Det betyder, at virksomhederne årligt kan spare op til 37,3 mia. kr. Innovation vækst = klar til fremtiden📱 Læs mere: denmark.representation.ec.eu… #eudk @DigitalEU

🧠 Since launch, @infinityg_ai has been on a mission — forming high-impact partnerships that give the platform an edge in scalability, transparency, and open-source innovation. Each alliance pushes community-owned AI closer to becoming the new global standard. Here’s a breakdown of the key collaborations driving the movement 👇 --- 1️⃣ Partnership Highlights 1. Polygon Labs (ZK-Rollup Team) > Powering scalable, low-cost AI inference via Polygon zkEVM. Infinity G AI jobs now run faster, cheaper, and verifiable on-chain through ZK-proofs. 2. EleutherAI > Tapping into open-source GPT-NeoX datasets and pipelines. Infinity G AI builds from transparent checkpoints — 100 % auditable, fork-ready, and community-maintained. 3. Chainlink > Decentralized oracle compute backbone. Real-world data (price feeds, weather, news) now fuels AI agents, while VRF ensures fair randomness in governance and model selection. @X1_EcoChain 4. MIT Media Lab (DCI) > Research alliance for privacy-preserving AI and token-based governance. Joint studies on incentive design, federated learning, and decentralized AI policy frameworks. 5. OpenAI-Compatible Wrapper (Community Devs) > Drop-in compatibility for GPT-3/ChatGPT integrations. Developers can migrate to Infinity G AI with zero code change. The bridge to adoption is now seamless. 6. DataUnion > Decentralized data marketplace where contributors earn tokens for quality datasets. Builds a community-driven supply chain for model training. 7. Astra Labs > Hardware accelerator startup designing custom transformer ASICs. On-chain inference is now lightning-fast — real-time AI for DeFi bots, gaming, and NFTs. 8. Filecoin > Censorship-resistant storage for model weights and datasets. Guarantees that Infinity G AI’s open-source models stay accessible forever. --- 2️⃣ Why It Matters 🔗 Network Effects & Interoperability Cross-chain integrations (Polygon, Chainlink, Filecoin) prevent silos. Users can deploy AI across chains based on cost and speed — always verifiable, always open. ⚙️ Scalability & Cost-Efficiency ZK-rollups compress data; Astra’s ASICs reduce latency to milliseconds — making large-scale AI accessible to every builder. 🧩 Open-Source Integrity EleutherAI OpenAI-compatible wrapper = true transparency. Infinity G AI is the community’s answer to closed AI ecosystems. 💰 Crypto-Economic Flywheel Chainlink oracles DataUnion marketplaces create “Proof-of-Training” loops — rewarding every contributor in the ecosystem. 🎓 Academic & Governance Depth MIT’s DCI partnership brings peer-reviewed rigor to decentralized AI governance and tokenomics design. --- Infinity G AI isn’t just building models — it’s building the framework for the next era of open, verifiable intelligence. Every partnership adds another layer to that decentralized vision. #InfinityGAI #AI #DeAI #Web3 #AuraPoints

Since the launch of @infinityg_ai , the team has been on a partnership‑hustle, locking in strategic collaborations that give the platform a competitive edge, broaden its ecosystem, and put community‑owned AI at the center of the next technological wave. Below is a deep‑dive into the marquee alliances secured so far, and why each one is a game‑changer for the project, its users, and the broader open‑source AI movement. 1️⃣ Partnership Highlights 1. Polygon Labs (ZK‑Rollup Team) :- Scalable, low‑cost, zero‑knowledge rollup infrastructure & ZK‑proof expertise. Deploys Infinity G AI’s compute‑intensive inference jobs on Polygon zkEVM, slashing gas fees and enabling on‑chain verification of AI model outputs. | 2. EleutherAI :- Massive open‑source language‑model datasets & model‑training pipelines. Integrates EleutherAI’s GPT‑NeoX‑style weights as base checkpoints, allowing community forks that stay 100 % open‑source and audit‑friendly. 3. Chainlink (Decentralized Oracle Network) :- Secure, tamper‑proof off‑chain data feeds & Compute‑to‑Earn (C2E) primitives. Feeds real‑world data (price feeds, weather, news) directly into AI agents, while Chainlink VRF powers truly random model‑selection for decentralized governance. 4. MIT Media Lab :– Digital Currency Initiative (DCI). Academic research on cryptoeconomic incentives, privacy‑preserving AI, and AI‑governance frameworks. Co‑author white‑papers on AI‑driven tokenomics and pilots on privacy‑preserving federated learning across the ecosystem. 5. OpenAI‑Compatible API Wrapper (Open‑Source Community) :- Compatibility layer for existing OpenAI‑style endpoints. Lets developers port existing GPT‑3/ChatGPT integrations to Infinity G AI with zero code changes, dramatically lowering adoption friction. 6. DataUnion (Decentralized Data Marketplace) :- Peer‑to‑peer data exchange powered by token incentives. Enables data providers to sell high‑quality training sets directly to Infinity G AI models, decentralizing the data supply chain. 7. Astra Labs (Hardware Accelerator Startup) :- Custom ASICs for transformer inference with sub‑millisecond latency. Deploys on‑chain inference nodes that can execute massive language‑model calls at a fraction of the cost of traditional GPUs. 8. Filecoin (Decentralized Storage) :- Robust, incentives‑driven storage for massive model weights & datasets. Stores trained checkpoints on a verifiable, censorship‑resistant network, ensuring perpetual accessibility. 2️⃣ Why These Partnerships Matter :- Network Effects & Interoperability :- - Cross‑chain Compatibility :- (Polygon, Filecoin, Chainlink) ensures Infinity G AI isn’t siloed on a single L1. Users can spin up AI jobs on whichever chain offers the best throughput/price ratio, while still keeping the model and data verifiable on‑chain. Scalability & Cost‑Efficiency :- - Zero‑knowledge rollups :- compress transaction data, making large‑batch inference cheap enough for everyday developers. - Astra Labs ASICs cut inference latency to the millisecond level, unlocking real‑time AI for DeFi bots, NFT minting assistants, and gaming NPCs. Open‑Source Credibility :- - By integrating EleutherAI checkpoints and the OpenAI‑compatible wrapper, Infinity G AI positions itself as a true alternative to closed‑source giants. This deepens trust among privacy‑concerned users and regulators alike. Crypto‑Economic Incentives :- - Chainlink’s VRF & oracles bring trust‑less randomness and reliable off‑chain data, enabling token‑driven incentives for model contributors (e.g., Proof‑of‑Training). - DataUnion and Gitcoin Grants :- create a virtuous loop where data providers, model trainers, and token holders all profit, reinforcing a community‑owned economy. Academic Rigor & Governance :- - Collaboration with MIT DCI injects peer‑reviewed research into the roadmap, guaranteeing that governance proposals (e.g., AI‑driven tokenomics) are backed by solid theory.

🚀 $BIOME – dự án khai thác tiềm năng Microbiome & @BioProtocol 🧬 Cùng nhìn qua Tokenomics: Supply: 1,000,000 Core Team: 20% Service Providers: 7.9% (BIO & DataUnion) Community Incentives: 3% VCs: 10% Community Auction: 15% Treasury: 41.1%

We've talked about getting paid for data. But what is the second-order effect of a system like Openledger AI? Imagine the rise of "Data Unions." Groups of people could pool their specialized data (e.g., medical data from patients with a rare disease) to collectively license it for research—ensuring it's used ethically and that they share in the profits of any resulting breakthroughs. It’s not just a new economy; it's a new form of collective action. #FutureTech #DataUnion #Web3 #SocietalImpact @OpenledgerHQ

#Sapien 是一个去中心化数据代工厂项目，与其他类似项目相比，它在多个方面展现出独特的特点和优势。 以下是对比 Sapien 与其他去中心化数据代工厂项目（如 Ocean Protocol、SingularityNET、DataUnion 和 Streamr）的主要不同之处： 1.专注领域 Sapien：专注于为 AI 模型提供高质量的训练数据，特别针对需要人类专业知识和多样性数据的领域，例如自动驾驶、医疗研究和教育。 其核心目标是提升 AI 训练数据的质量和多样性。 其他项目： Ocean Protocol：侧重于通用的去中心化数据交换，允许数据提供者安全地发布和消费数据。 奇点网：一个去中心化的 AI 服务市场，主要用于交换 AI 算法和服务，而非专注于训练数据生产。 数据联盟：强调用户创建数据合作社，共享和货币化个人数据。 流：专注于实时数据流的发布、订阅和交易。 差异：Sapien 的专注领域更加垂直和具体，针对 AI 训练数据的生产，而其他项目覆盖更广泛的数据共享或 AI 服务场景。 2.激励机制 Sapien：通过链上激励和游戏化机制确保数据质量。 链上激励利用区块链技术防止作弊（如合成数据生成机器人），而游戏化设计使数据标注过程更具吸引力，提升工人参与度和数据准确性。 其他项目： Ocean Protocol 和 SingularityNET：也使用代币激励，但主要用于促进数据共享和 AI 服务交易，而非专门优化数据质量。 DataUnion 和 Streamr：激励机制更倾向于鼓励数据共享和交易，缺乏游戏化元素。 差异：Sapien 的游戏化方法和针对数据质量的链上激励设计是其独特之处，为数据生产提供了创新的质量控制方式。 3.工人网络 Sapien：拥有一个覆盖 70 个国家的全球 AI 工人网络，包括医生、工程师等专业人士，以及普通用户如高中生。 这种多样化的工人群体有助于减少数据偏见，并为 AI 模型提供更丰富的训练数据。 其他项目： DataUnion 和 Streamr：用户群体主要是数据拥有者或普通用户，而非专门为 AI 训练提供标注服务的工人。 Ocean Protocol 和 SingularityNET：更倾向于连接数据提供者或 AI 开发者，缺乏类似 Sapien 的全球专业工人网络。 差异：Sapien 的工人网络在专业性、全球覆盖范围和多样性上具有明显优势。 4.商业化成功 Sapien：已筹集 1050 万美元的种子资金，并服务于 17 家全球企业客户，包括财富 500 强企业。 其在自动驾驶、教育、医疗研究和机构 DeFi 等领域的应用表明其商业化能力强劲。 其他项目：Ocean Protocol 和 SingularityNET 等项目也吸引了资金和关注，但 Sapien 在短时间内获得的企业客户数量和行业多样性显示出其在商业化方面的快速进展。 差异：Sapien 在商业化成功和市场认可度上表现突出，展现了更强的应用落地能力。 5.技术创新 Sapien：正在开发 AI-20，这是一个去中心化训练数据的互操作性标准，旨在提高数据在不同系统间的兼容性和使用效率。 其他项目： Ocean Protocol：专注于数据交换协议的开发。 奇点网：致力于 AI 服务的去中心化市场。 流：强调实时数据网络的技术。 差异：Sapien 的 AI-20 标准为其在数据互操作性方面带来了新的技术创新，与其他项目的方向有所不同。 总结 Sapien 在去中心化数据代工厂领域中脱颖而出，其主要不同点在于： 专注的领域：聚焦于 AI 训练数据生产。 创新的激励机制：结合链上激励和游戏化提升数据质量。 全球化的专业工人网络：覆盖广泛且多样化的工人群体。 显著的商业化成功：已获得资金支持并服务于多家企业客户。 技术创新：推出 AI-20 互操作性标准。 这些特点使 Sapien 在竞争中占据独特优势，为 AI 和数据领域的未来发展提供了有前景的解决方案。 @JoinSapienkie @cookiedotfun #sapien #cookie

@OpenledgerHQ Analysis: The New Infrastructure for Decentralized AI 1. Vision & Value Proposition OpenLedger positions itself as the decentralized “proof layer” for AI. It aims to fix a real issue: AI models using data without attribution. Their solution? A protocol that tracks, verifies, and rewards the use of content turning data into assets with transparent ownership. This is huge in the age of generative AI 2. SLMs Over LLMs Instead of focusing on general-purpose Large Language Models (LLMs), OpenLedger doubles down on Specialized Language Models (SLMs). Why it matters: SLMs offer domain precision, which is critical in fields like healthcare, finance, legal, and education. It’s not about replacing ChatGPT it’s about powering AI that’s accurate, trusted, and useful in real-world verticals. 3. Attribution Marketplace (Future Use Case) OpenLedger’s roadmap includes a marketplace where: •Verified datasets •Research •Content libraries •Live data feeds can be licensed, tracked, and monetized. This builds an ecosystem where creators are stakeholders, not invisible contributors 4. Community & Ecosystem Play Partnerships with Theoriq, Ambios Network, and LiveArt show they’re serious about cross-sector utility blending AI, art, data, and infrastructure. Their testnet is community-driven with daily points, roles, and gamified rewards via the “gOcto” ecosystem. 5. Risk Factors •The name “OpenLedger” carries legacy baggage from the now-defunct DEX (2018–2020), which may confuse some users. •Adoption relies on trust from creators and AI devs it needs critical mass to thrive. •Competition in AI attribution (e.g. from Proof of Humanity, DataUnion, or even big players building in-house) could affect growth. 6. Overall Take OpenLedger isn’t trying to out-scale OpenAI—it’s building infrastructure that supports fairness and traceability in the AI age. It’s still early, but if the team delivers on attribution, marketplace economics, and developer adoption… It could become foundational to Web3 x AI

Is this a @streamr @dataunions @DIMO_Network video data #DataUnion in the making…???🤔 @Matt3rai

No doubt on the role of the #AutomotiveAftermarket and #IAM in digital transformation in EU with huge opportunities in a data driven economy #DataUnion #DataAct

🎨 Artists are standing up for their rights, and we are all for it!🦾 A U.S. court just passed a lawsuit against AI companies like Stability AI for using artists’ work without consent. With #AdaMoments, content creators can take control of their digital assets and stop AI from exploiting their creations. #AI #DigitalOwnership #DataUnion #Blockchain #NFTs #ArtWorld news.artnet.com/art-world/ar…

“the data generated from a user’s car is recorded off-chain. This includes the speed, location, and other information generated over time. Users are then able to share that data with @DIMO_Network if they choose to do so, earning tokens as an incentive” #DataUnion $DIMO $DATA

ℹ️ What makes a ‘good’ #dataunion? 🗣️@ikkibop emphasized on #interoperability in the @DataUnion_ERC at the 13th #EDEN Event 🇪🇺 📊 ❗️

Every @DIMO_Network user who connects a @Tesla car can create their own stream on @streamr. That way a Tesla driver can own and monetize all the data that the car produces Streams from multiple cars combined can create a #DataUnion with the DU framework on the #Streamr network🤯

With #SwashApp, adoption = 🗝️ 💯 Imagine the world’s first #DataUnion with millions of users 😎📈 The browsing data market is in our hands🤝. Let’s reach the billions$$$.💰💰💰 Start referring now🚀 ➡️ swashapp.io/referral/334 @swashapp $SWASH #EarnOnline

Earn points, redeem gift cards, cash & crypto with @swashapp 💰💰💰 ➡️ swashapp.io/referral/334 #SwashApp #DataUnion $SWASH

Nice posts from my friend @ankush_web3 👏 See example below👇 Follow him for more. Supporting @swashapp for many years now. 💪👍💚 #SwashApp $SWASH #DataUnion

“Chirp will integrate @Streamr’s Data Union Framework to enable its #community to #monetize and generate #revenue from IoT data.” 🚀🚀 #DataUnion #Web3 #DePIN

This is incredibly exciting news (for Stanford and the people in the Bay Area who care about getting more, better, more shareable data)!! #DataUnion @alex_pentland ---> #Stanford

“We envision a future where data producers can monetize their data by contributing it to a #dataunion which developers use to unlock value for companies and users - sharing revenue with whoever contributes data.” - @HEALENetwork ❔Partnership with @streamr #DataUnions?👀

“In this [#DePIN] model, more users voluntarily provide data, breaking data monopolies. The project can use this data to develop more comprehensive services…” - @PowerPod_People #DataUnion #Web3 #NEV 👇