SpaceX's IPO Filing Reveals Musk's Symbolic Salary, CEO's Total Compensation Reaches $85.8 million

Binance announced on April 21 that it will delist and cease trading for the following spot trading pairs at 03:00 UTC on April 24, 2026: 1INCH/BTC, WIF/BTC, and XRP/MXN.

April 21: At the "Decoding Web 4.0: When AI Agents Take Over On-Chain Authority" roundtable, Kelvin Tan, Associate VP at the Hong Kong University of Science and Technology (HKUST), discussed the "Agent Moat." He emphasized that different AI agents rely on distinct model training trajectories and technical systems, leading to significant gaps in real-world user experience. Recently, some new models and tools have delivered stronger generation quality, faster execution efficiency, and a higher ceiling for development output. However, Tan noted these differences haven’t yet formed a decisive lead—they lean more toward "efficiency gains" than "paradigm shifts." In short, AI agent competition remains in a fast-evolving stage, with no stable, insurmountable tech barriers on the horizon. Current AI agents and large models are evolving at a blistering pace: new products or features launch almost weekly, fueling ongoing industry progress. But from the lens of real-world use and busines

April 21st: Crypto analytics platform Cryptoonchain shared social media data showing the 50-day Simple Moving Average (SMA) of Ethereum’s Taker Buy/Sell Ratio on Binance has climbed to 1.018—its highest level since early 2023. A ratio above 1 means active buy volume is far outpacing active sell volume, underscoring aggressive demand in the derivatives market and a sharp resurgence of bullish sentiment. Should this momentum hold, it would lay a solid fundamental foundation for a medium-term price rally.

April 21st — At the "Decoding Web 4.0: When AI Agents Take Over On-Chain Authority" panel discussion, Christian, founder of Infini, introduced the "Agent Moat" concept. He emphasized that from a product and growth standpoint, AI’s core competitiveness still hinges on large language models’ (LLMs) language processing capabilities—specifically, the **human-like quality of their content output**. He noted that unlike early models’ mechanized, template-driven responses, today’s users prioritize whether AI can deliver expressions and content that mirror real human conversations. This directly impacts its practical value in external communication and team collaboration. Christian added that as LLM capabilities evolve, basic information processing is becoming increasingly commoditized. When LLMs can handle roughly 90% of standardized tasks, relying solely on the model itself makes it hard to build long-term competitive advantages. AI agent competition is shifting away from raw model

April 21st – Cobo AI Growth Lead Brad Bao spoke at the offline event “Decoding Web 4.0: When AI Agents Take Over On-Chain Permissions,” where he revealed Cobo is restructuring its Agent fund control logic via a structured permission system. In its product, the user-Agent relationship is no longer simple wallet authorization but defined by an executable “Agreement” that sets clear permission boundaries. This Agreement includes at least four core components: Intent, Path, Rules, and Termination. It shifts the original “all-or-nothing” permission control to a negotiable, auditable, and revocable fine-grained governance mechanism. On the technical front, Cobo integrates MPC (Secure Multi-Party Computation) to ensure funds remain under user control even amid Agent anomalies, system attacks, or mnemonic injection risks—no single entity can bypass constraints to transfer assets independently. Brad Bao emphasized that under this architecture, Agents have sufficient autonomy to complet

On April 21, Brad Bao, Growth Lead at Cobo AI, spoke at an offline event titled “Decoding Web 4.0: When AI Agents Take Over On-Chain Authority.” He shared that AI agents’ involvement in fund management and asset allocation has evolved from concept to reality—so the question of whether AI agents will manage funds is no longer up for debate. As more agents directly connect to wallets to execute transactions, the industry has seen instances of funds not reaching their intended destinations as expected. In response, the team is continuously logging the issues and risks AI agents encounter in real-world transactions within its experimental system, driving improvements to relevant mechanisms. Bao noted the industry’s core tension right now: If an AI agent lacks autonomy, it’s no more valuable than traditional automated trading tools. But if it lacks verifiable determinism, humans won’t feel confident entrusting their funds to these agents. This “autonomy vs. determinism” tension is eme