📡 Data access may determine which autonomous agents can... actually function onchain at scale. Lithosphere explores why continuous access to reliable data, coordination layers, and execution context is becoming essential infrastructure for AI-native systems. Read more 🔍 #Lithosphere #Web4 #AutonomousAgents #AIInfrastructure #OnchainDatashow more

🚀 Strategic access is opening ahead of LITHO’s planned... TGE. Lithosphere is broadening ecosystem participation around AI-native infrastructure for autonomous execution, cross-chain coordination, identity, and verifiable Web4 systems. Read more 🔍 #Lithosphere #LITHO #Web4 #AIInfrastructure #AutonomousAgentsshow more

🌐 Web4 agent economies require infrastructure built for continuous... execution, coordination, and interoperability. Lithosphere is positioning its AI-native architecture as an execution layer for autonomous systems operating across decentralized environments. Read more 🔍 #Lithosphere #Web4 #AutonomousAgents #AIInfrastructure #Interoperabilityshow more

🌐 Autonomous agents require more than liquidity access —... they require infrastructure that can coordinate execution across decentralized environments. KaJ Labs explores why AI-native systems depend on interoperable liquidity infrastructure built for routing, verification, and persistent onchain coordination. Read more 🔍 #KaJLabs #Web4 #AutonomousAgents #LiquidityInfrastructure #AIInfrastructureshow more

🧬 Web4 infrastructure works better when core systems operate... together. Lithosphere connects identity, naming, AI-native execution, and cross-chain coordination into one stack for users, applications, and autonomous agent activity. #Lithosphere #Web4 #AIInfrastructure #Interoperability #AutonomousAgentsshow more

🔗 Utility becomes meaningful when it maps to real... network activity. Lithosphere highlights how LITHO supports execution, coordination, verification, cross-chain interaction, and agent operations across AI-native Web4 infrastructure. Learn here 🔍 Read more 🔍 #Lithosphere #LITHO #Web4 #AIInfrastructure #AutonomousAgentsshow more

🛡️ Trust between agents needs more than identity. Lithosphere... advances an agent reputation framework for Web4 coordination, helping autonomous systems evaluate reliability through behavior, completed tasks, and verifiable interaction history. #Lithosphere #Web4 #AutonomousAgents #AIInfrastructure #DigitalIdentityshow more

🧭 Web4 wallets are starting to look less like... storage and more like coordination layers. Lithosphere introduces Thanos as a multi-chain agentic wallet for users and autonomous agents, connecting asset access, dApp interaction, and Web4 participation in one environment. #ThanosWallet #Lithosphere #Web4 #AutonomousAgents #DigitalAssetsshow more

AI needs data to grow. But accessing high-quality datasets... is becoming harder than ever. · Data collection is increasingly centralized in the hands of a few large players. · Platforms are locking content behind expensive APIs and private data deals. · Specialized datasets (medical, legal, code) can cost 20–40× more than general web data. · And while 94% of companies are exploring Gen-AI, only 12% have reliable data channels. The result? A growing data bottleneck for AI development. The future of AI won’t just depend on better models. It will depend on who can access and activate quality data.show more

In 2025, the AgentFlayer exploit highlighted a new category... of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.show more

Prediction: By 2030, global data center workloads are projected... to split into: • 50% traditional workloads • 37% AI inference • 13% AI training Inference is set to become nearly 3× larger than training. As more AI applications move into production, builders will need infrastructure that can support lower-cost inference, reliable execution, faster routing and private workloads at scale. This is where FAR AI enters the market. Source: JLL Research, 2025show more

. Han Jin / Bluwhale has been recognized as... a Silver Winner at the 2026 Globee® Awards for Artificial Intelligence 🏆 Awarded in the “AI Data Privacy Advocacy Achievement” category for work around AI, privacy, and bringing Zero Knowledge Proofs into financial systems ⚔️🤖 As AI agents become more autonomous, privacy and trusted financial infrastructure matter more than ever. More to come 👀 👇show more

China just took AI infrastructure underwater. The country has... reportedly launched a wind-powered underwater data center near Shanghai, designed to cool AI servers using seawater instead of traditional cooling systems 🌊🤖 Around 2,000 servers are said to be sealed inside submarine-grade modules, helping reduce land use, water consumption, and cooling energy. The project is powered by offshore wind and built for AI workloads, big data, and large-scale computing. Why it matters? AI is creating massive demand for electricity, cooling, and infrastructure. If underwater data centers prove reliable, they could become a new way to scale AI while using less energy. But the challenge is huge: corrosion, maintenance, sea conditions, and long-term reliability still need to be proven.show more

Exciting news from HYPE3.cool! We’re teaming up with Chainbase... (💜,💛), the world’s largest omnichain data network to supercharge AI agents with robust on-chain data. Very soon, users will be able to integrate real-time blockchain insights when configuring their agents—making them smarter and more capable than ever! What this means: - Real-time on-chain data for enhanced decision-making - Greater agent intelligence powered by advanced data analytics - Seamless blockchain integration backed by Chainbase (💜,💛)’s decentralized infrastructure About Chainbase: Chainbase is the world’s largest omnichain data network built to power the AI economy with high-quality on-chain data. Through its innovative four-layer, dual-consensus architecture, Chainbase’s network, anchored by Chainbase AVS, provides secure, interoperable data for AI and Web3. Your AI agents can now tap into rich, reliable on-chain insights—paving the way for more dynamic and intelligent interactions. This is just the start of making AI agents truly Web3-native. Stay tuned as we build the future of intelligent property in partnership with Chainbase (💜,💛)! #Web3 #AI #Blockchainshow more

How can AI agents gain true autonomy? CARV ID... (ERC-7231) is transforming AI and blockchain integration by creating trustless, sovereign AI agents. This identity protocol aggregates cross-chain and off-chain data, enabling AI Beings to operate with verified identity, privacy, and autonomy. By unifying fragmented data and enriching it with actionable metrics, CARV ID powers the D.A.T.A Framework, giving AI agents access to contextualized insights while preserving user data sovereignty. This means developers can build AI systems that learn, evolve, and act independently within a secure, decentralized ecosystem, free from centralized control. For businesses and researchers, this opens a new frontier where AI-driven decisions are transparent, verifiable, and privacy-preserved. CARV's integration of Solana scalability and Ethereum security ensures these trustless AI agents perform with speed at scale. The future is clear: AI Beings will no longer be mere tools, they will be sovereign participants in digital economies. What implications do you see for AI autonomy in blockchain?show more

Tokenization is quickly becoming one of the most talked-about... ideas in crypto. This year we’re aiming much higher. We don’t just want to tokenize miner revenue; we want to bring entire productive ecosystems on-chain. That means tokenizing baskets of assets, real energy infrastructure, AI agents, compute power, data centres, and even the cooling systems that keep high-performance hardware running at scale. All of it connected and interoperable with DeFi. Thats what we have been building throughout. A single on-chain framework that unlocks something genuinely powerful: real ownership, real yield, and real coordination between digital and physical infrastructure. From backend systems and front-end interfaces to smart contracts, AI, physical infrastructure, oracles, and autonomous agents, the stack keeps expanding and compounding. It’s about building the foundation for how value will be owned, deployed, and earned in the next generation of the on-chain economy. Simplified by PinLink.show more

Introducing Grayscale Space and Time Trust $SXT 🪐 ,... backed by M12 - Microsoft's Venture Fund and integrated with Microsoft Fabric*, connects the power of decentralized blockchains with the speed and flexibility of traditional databases through: 🏢 Enterprise-Readiness: Backed by Microsoft and integrated into Microsoft Fabric*, allowing developers to easily access data from networks like #Bitcoin, Sui, and #Ethereum. 🖊️ Proof of SQL: Verifies queries on tamperproof data using zero-knowledge proofs without needing to rerun or reveal underlying data. ⏳ Decentralized + Scalable: Blends blockchain security with traditional database speed and accessibility for reliable, real-time data access. ✅ Verifiable Data: As we enter the new age of AI, transparency is paramount. Space and Time enables verifiable data, helping ensure we can trust the datasets that power AI systems and smart contract applications. See important disclosures and learn more about the Grayscale Space and Time Trust: *Source: Space and Time press release, May 20, 2025.show more

.Sentient has just integrated Messari by Blockworks 's data... and research into its AI-powered search platform, Sentient Chat. This partnership basically allows users to access Messari’s research directly through the Agent Hub in Sentient Chat where they can get instant answers and insights from Messari reports. The integration is done via Messari Copilot, which means users can now easily get to Messari’s crypto data without having to dig through extensive reports themselves. Messari's data and research now feeds into Sentient’s Agentic Perplexity, here users can access this in the Agent Hub for all their crypto related questions. Integrating Messari’s research now helps provide an open & community-driven platform for AI-powered search, ensuring that users have access to the best crypto data and insights in real time, while also expanding the functionality of Sentient’s Agent Hub, where users can find and use a growing library of AI agents for various tasks. Now I don't know about you but I know where I'll be getting my stats from moving forward.show more

We’re launching the Latch MCP and announcing its availability... within Claude Science, Anthropic’s new AI workbench for scientists. AI for biology requires agent-native infrastructure: systems where agents can store, process, and visualize large molecular datasets from the interfaces scientists already use. Biological analysis workflows often require substantial compute. Retries or incorrect long-running tool calls can quickly inflate workflow time and cost, especially when analyses take hours or days to complete. These tools should also be curated with appropriate parameters and agent-readable documentation, ideally provided by the original assay developer, to support correct use across many complex scientific contexts. At Latch, we’ve seen customers of our Solution Provider partners, including TakaraBio, Vizgen, and AtlasXOmics, use both the Latch Agent and external harnesses like Claude Code and Cursor to accelerate analysis of their data. The Latch MCP is a remote MCP server that securely connects agent harnesses to the Latch platform, giving agents access to verified bioinformatics tools built and maintained by kit and instrument providers. Agents can navigate data on Latch and launch existing deployments of validated bioinformatics workflows to analyze that data.show more

🚨 THE BIGGEST BOTTLENECK IN AI ISN'T COMPUTING POWER... ANYMORE IT'S MOVING DATA. Instead of laying new cables, Chinese researchers have upgraded existing fiber infrastructure by doing two things at once: Using three wavelength bands (C + L + S) instead of the usual two. Using four cores inside each fiber instead of one. Each core acts like an independent highway, and each band acts like an extra lane on that highway. Together, they’ve reportedly increased transmission capacity per core by nearly 50% and overall data throughput by up to 5×. This matters enormously for AI. Modern AI clusters move terabits of data per second between thousands of GPUs. The biggest bottleneck is often not the chips themselves, but moving data fast enough between them. If you can push 5× more data through the same physical cables, you can train bigger models faster and reduce network congestion. Why this is significant: • It shows multi-core + extended spectrum technology moving from labs into real-world commercial use • The system has already run over 35 km of existing telecom network • It could be especially useful for submarine cables and large-scale data center interconnects • China is also eyeing it for its “Eastern Data, Western Computing” project The deeper implication: We’re reaching the physical limits of how much data we can push through single-core fibers using traditional methods. By combining spatial multiplexing (multiple cores) with spectral multiplexing (more wavelength bands), engineers are finding new ways to keep scaling bandwidth without having to dig up the planet to lay new cables. This kind of breakthrough is quiet but foundational it’s the kind of infrastructure upgrade that will determine how fast AI and cloud computing can actually grow in the coming years. The future of data movement might not require more cables. It might just require smarter ones. How important do you think multi-core and multi-band fiber will be for keeping up with AI’s exploding data demands? Follow for more frontier networking, photonics, and infrastructure technology.show more

Google just wired DeepMind and Earth Engine directly into... the biggest geospatial dataset on the planet. For two decades, millions of people used Google Earth to scale the Himalayas or zoom in on their childhood neighbourhoods. In 2026, Google is basically trying to shift the entire platform toward professional execution. They turned a massive digital twin of the world into an agentic AI engine for global infrastructure. The technical foundation is (obviously) all about data. Google integrated 20-metre and 40-metre elevation contours globally. Engineers and urban planners now have instant access to the exact topographic context required for site planning anywhere on Earth. The data catalogue updates continuously to maintain the freshest imagery possible. Collaboration used to kill geospatial projects. Teams would lose momentum through stale materials or bad handoffs. Google fixed this by building frictionless data import systems. You can now drop KML, KMZ, and GeoJSON files directly onto the global map. Entire departments can align on a single source of truth, moving from a raw question to a definitive answer instantly. The biggest upgrade is the introduction of agentic geospatial intelligence. Users can open 'Ask Google Earth' and search massive satellite and Street View databases using natural language. You type a command, and the AI handles the manual data wrangling. It identifies new site locations and analyses infrastructure before you even open a spreadsheet.show more