Workshop

This week, the crypto market experienced fluctuations, with the sentiment index dropping to 10%. The DeFi zone's TVL saw a slight rise, while the market capitalization of AI projects pulled back. Solana and Tron performed outstandingly, and the market is following the Fed meeting and upcoming changes in crypto policy. It is expected that opportunities in the market will increase after February.

Most infra optimizes for speed. INFINIT optimizes for alignment. Here's where the system stalled: 🧵 For the last decade, we’ve obsessed over execution layers. Cheaper swaps. Faster confirmations. Parallel threads. But the next 100x won’t come from latency. It’ll come from orchestration. The real problem isn’t speed. It’s misalignment between; agents, capital, and time. → The Cost of Uncoordinated Capital DeFi today is a loose mesh of autonomous actors: • Vaults operate in isolation • Agents farm local yield curves • DAOs rebalance on calendar time, not market conditions • Strategies fight each other for the same alpha There’s no shared intent. No adaptive memory. No incentive to coordinate. So value leaks slowly, constantly. Yield gets lost during transitions. Opportunities get missed across silos. Capital gets stuck chasing yesterday’s playbook. → How @Infinit_Labs Aligns the Stack? INFINIT doesn’t just optimize execution. It solves alignment. The system runs a coordination-first compute layer: • Agents sync memory states • Swarms optimize cross-primitive flows • Actions are compounding, not conflicting Each agent adapts to the rest of the network. Each move considers what others are doing, not just itself. This isn't copy trading. It's convergence of intent. → Coordination Is the Yield Multiplier Coordination isn’t fluffy. It directly affects ROI: • Less lag between vault exit and perp entry • Fewer redundant trades across agents • Better capital efficiency from synchronized intent Compounding happens when systems reduce internal friction. That’s the real unlock. → The Architecture of Intelligence Execution infra got us here. Coordination infra takes us forward. @Infinit_Labs treats collective intelligence as a computational design problem. Its goal isn’t faster swaps. It’s higher ROI per action across the whole network. Because in systems of autonomous agents, intelligence isn’t local. It’s emergent. And emergence requires alignment.

Web3 used to be: buy token read roadmap cope Now @TheoriqAI drops swarms of AI agents modular, onchain, collaborating for you. It’s not just automation. It’s DeFAi: routing liquidity, running DAOs, managing yield, no dashboards, no micromanage Set intent, let agents work. Self-organizing. Transparent. Fully unlocked $THQ at TGE. $78M+ pledged, wild conviction this is agentic finance in motion You want InfoFi? Here’s the stack: logic, identity, incentives all built modular. Not just bots, but actual intelligence networked together When Theoriq moves, it’s not hype. It’s a movement

In recent years, the Crypto Assets market has remained sluggish, especially with significant falls in AI-related and celebrity coin projects. The market's follow for these projects has decreased, mainly due to a lack of viable applications and sustainable profit models. Investors need to seek out projects with real returns to enhance market competitiveness and maintain participants' confidence.

The development of AI Agents in the Web3 field: exploring from Manus to MCP. Recently, a global first universal AI Agent product named Manus has attracted widespread attention. As an AI tool capable of independent thinking, planning, and executing complex tasks, Manus demonstrates unprecedented versatility and execution ability, providing valuable product ideas and design inspiration for the development of AI Agents. An AI Agent is a computer program capable of autonomously making decisions and executing tasks based on the environment, inputs, and predefined goals. Its core components include large language models (LLM), observation and perception mechanisms, reasoning processes, action execution, as well as memory and retrieval functions. Currently, there are two main development paths for the design patterns of AI Agents: one emphasizes planning capabilities, while the other emphasizes reflective capabilities. In the Web3 industry, AI