1️⃣ @unibase_ai enabling verifiable & gasless payments for AI Agents via x402 Facilitator Service, and enabling ERC-8004 & x402-native AI Agents to launch, stake and interoperate via @bitagent_io

Traditional online payment systems today are slow, expensive and human-dependent. x402 changes that, allowing developers and AI agents to pay for APIs, software and services directly with stablecoins over HTTP. Builders like @unibase_ai, @pieverse_io, @AEON_Community &

🤔 Thinking out loud...#x402 lets agents pay autonomously @Unibase_AI gives agents memoryWhat if we combined these?Imagine: Agent stores memory → x402 pays storage fees Agent sells knowledge → x402 handles payments Agents collaborate → x402 auto-splits revenue Memory layer

AI is starting to reshape systems research, long regarded as a domain with a high entry barrier. https://t.co/a3910PkHDj

Unibase provides a native solution for this next-generation infrastructure, enabling seamless agent discovery and dynamic addressing across the network.

This raises a fundamental question: how do these agents find and communicate with each other in such a fluid environment?

The traditional internet is built on entities with fixed addresses — primarily IPs and MACs. However, in the emerging Agent Internet, billions of autonomous agents will need to connect and interact dynamically.

Editing a calendar is easy. Editing memory—like how a star is remembered across thousands of movies/images—is the real challenge. That’s where research meets reality.

An agent’s memory could span books, figures, and videos. The key is managing these intentionally—understanding, organizing, and updating. For instance, it should track your schedule, remind you of meetings, and adjust when plans shift.

Agents still don’t have memory like organic beings. They live in the digital, binary world—built on the existing computer stack. Our world stores data in countless formats… but how do they make use of them?

We know human memory is stored in neurons. But what does 'memory' look like for an AI agent? Not cells, not synapses—something else entirely. What do you think?

The next leap: Memory → AI that remembers past interactions & adapts to you Internet → AI that connects to real-time knowledge & tools Together, they create agents that evolve over time—moving closer to true digital collaborators.

The evolution of AI: From static tools → dynamic agents From short Q&A → multi-turn dialogue From single-use apps → context-aware systems that adapt over time LLMs aren’t just smarter—they’re transforming how we work with AI.

😊what’s coming next for #Unibase? Introducing @bitagent_io — A Decentralized Multi-Agent Collaboration Platform, powered by $UB. Immortal AI Agents with on-chain identity, permanent memory, and cross-platform collaboration are here. 🧵 👉 Stay tuned — the first Agent is

$UB is now available on top CEXs for spot and perpetual futures trading, including: @binance Alpha @kucoincom @Gate @LBank_Exchange @MEXC_Official Perps: @BinanceFutures @Bybit_Official @bitgetglobal With more exchanges coming soon! 😊

Unibase's #AIP (Agent Interoperability Protocol) is live! #Unibase introduces the first Web3-native multi-agent communication standard, designed for: ✅ Seamless cross-agent collaboration ✅ Cross-platform interoperability (MCP/gRPC compatible) ✅ Decentralized persistent

「Unibase Research - 3: Architectures for LLM Memory Systems」 1⃣ Introduction In our previous article, we explored the foundations of agent memory: short-term, working, and long-term layers. But how do we actually implement these in practice? Designing memory for LLM agents

「Unibase Research: Memory as the Missing Layer for LLM Agents」 1⃣ Introduction Large Language Models (LLMs) have taken the world by storm, powering chatbots, copilots, and autonomous agents. But beneath their apparent intelligence lies a fundamental limitation: they are