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As of April 21, according to the Islamic Republic of Iran Broadcasting (IRIB), Iran has not sent any diplomatic delegation to Islamabad, Pakistan, whether it be an advance team or a follow-up group, nor for preliminary missions or subsequent delegations.

[Iran Has Not Sent Any Diplomatic Delegation to Islamabad, Pakistan]As of April 21, according to the Islamic Republic of Iran Broadcasting (IRIB), Iran has not sent any diplomatic delegation to Islamabad, Pakistan, whether it be an advance team or a follow-up group, nor for preliminary missions or subsequent delegations.

Iran Has Not Sent Any Diplomatic Delegation to Islamabad, Pakistan

On April 21, Ripple announced a multi-phase post-quantum roadmap for the XRP Ledger (XRPL), aimed at equipping it to handle future quantum threats, with a plan for full readiness by 2028. The roadmap is divided into four phases: the first phase focuses on post-quantum recovery (Q-Day preparation), emphasizing the development of contingency plans to ensure safe migration of funds when classical cryptographic standards are compromised; the second phase, scheduled for the first half of 2026, will involve proactive planning and experimentation to assess quantum risks and collaborate with Project Eleven to test NIST-recommended algorithms; the third phase is expected to take place in the second half of 2026, integrating post-quantum signature schemes into Devnet and exploring privacy primitives; the fourth phase aims for 2028, proposing new amendments to the ecosystem for a comprehensive transition to native post-quantum encryption. Ripple stated that the existing key rotation and seed-based key generation features of XRPL lay the groundwork for this migration, providing a native protocol advantage in addressing quantum threats compared to networks like Ethereum.

[Ripple Releases Post-Quantum Roadmap for XRPL, Aiming for Full Transition by 2028]On April 21, Ripple announced a multi-phase post-quantum roadmap for the XRP Ledger (XRPL), aimed at equipping it to handle future quantum threats, with a plan for full readiness by 2028. The roadmap is divided into four phases: the first phase focuses on post-quantum recovery (Q-Day preparation), emphasizing the development of contingency plans to ensure safe migration of funds when classical cryptographic standards are compromised; the second phase, scheduled for the first half of 2026, will involve proactive planning and experimentation to assess quantum risks and collaborate with Project Eleven to test NIST-recommended algorithms; the third phase is expected to take place in the second half of 2026, integrating post-quantum signature schemes into Devnet and exploring privacy primitives; the fourth phase aims for 2028, proposing new amendments to the ecosystem for a comprehensive transition to native post-quantum encryption. Ripple stated that the existing key rotation and seed-based key generation features of XRPL lay the groundwork for this migration, providing a native protocol advantage in addressing quantum threats compared to networks like Ethereum.

Ripple Releases Post-Quantum Roadmap for XRPL, Aiming for Full Transition by 2028

On April 21, PeckShield reported that the KelpDAO attacker has transferred 75,700 ETH to two new addresses.

[PeckShield: KelpDAO Attacker Transfers 75,700 ETH to Two New Addresses]On April 21, PeckShield reported that the KelpDAO attacker has transferred 75,700 ETH to two new addresses.

PeckShield: KelpDAO Attacker Transfers 75,700 ETH to Two New Addresses

On April 21, during the roundtable discussion 'Decoding Web 4.0: When AI Agents Take Over On-Chain Permissions', MuleRun CTO Shu Junliang discussed the topic of 'Agent Moat'. He stated that the traditional AI technology moat is being rapidly weakened, primarily due to the accelerated convergence of model capabilities and exponential improvements in development efficiency. He pointed out that the performance gap between mainstream large models is narrowing quickly, especially over the past year, where the capability gap between domestic and international models has significantly converged. Additionally, with the explosive enhancement of code generation capabilities, software development efficiency has greatly increased—features that previously took weeks or even months to complete can now be achieved in just a few days. This means that both Agent frameworks and specific functional modules can be quickly reused or replicated through open-source solutions, making the 'functional moat' at the product level increasingly fragile. In this context, Shu Junliang believes that the core competitiveness of Agents in the future will mainly be reflected in two aspects: first, the ability for continuous high-frequency iteration, meaning whether the team can maintain a leading product update speed over the long term; and second, data advantages, including exclusive data resources and user accumulated data. On one hand, platforms with unique data acquisition capabilities (such as data from specific industries or regions) will form natural barriers; on the other hand, the behavioral and memory data accumulated from long-term user engagement on the platform will become key assets that are difficult to migrate, further enhancing user stickiness and product competitiveness. He concluded that as the trend of 'equalization' in models and technologies continues, the Agent moat is shifting from 'technical capability' to a comprehensive competition of 'data assets and execution efficiency'.

[MuleRun CTO Shu Junliang: Model Gaps Rapidly Converging, Agent Moat Shifts to 'Speed + Data']On April 21, during the roundtable discussion 'Decoding Web 4.0: When AI Agents Take Over On-Chain Permissions', MuleRun CTO Shu Junliang discussed the topic of 'Agent Moat'. He stated that the traditional AI technology moat is being rapidly weakened, primarily due to the accelerated convergence of model capabilities and exponential improvements in development efficiency. He pointed out that the performance gap between mainstream large models is narrowing quickly, especially over the past year, where the capability gap between domestic and international models has significantly converged. Additionally, with the explosive enhancement of code generation capabilities, software development efficiency has greatly increased—features that previously took weeks or even months to complete can now be achieved in just a few days. This means that both Agent frameworks and specific functional modules can be quickly reused or replicated through open-source solutions, making the 'functional moat' at the product level increasingly fragile. In this context, Shu Junliang believes that the core competitiveness of Agents in the future will mainly be reflected in two aspects: first, the ability for continuous high-frequency iteration, meaning whether the team can maintain a leading product update speed over the long term; and second, data advantages, including exclusive data resources and user accumulated data. On one hand, platforms with unique data acquisition capabilities (such as data from specific industries or regions) will form natural barriers; on the other hand, the behavioral and memory data accumulated from long-term user engagement on the platform will become key assets that are difficult to migrate, further enhancing user stickiness and product competitiveness. He concluded that as the trend of 'equalization' in models and technologies continues, the Agent moat is shifting from 'technical capability' to a comprehensive competition of 'data assets and execution efficiency'.

MuleRun CTO Shu Junliang: Model Gaps Rapidly Converging, Agent Moat Shifts to 'Speed + Data'

On April 21, during the roundtable discussion "Decoding Web 4.0: When AI Agents Take Over On-Chain Permissions," Xu Jialong, Associate Vice President of Hong Kong University of Science and Technology, commented on the "moat of AI agents." He noted that there are differences in the model training paths and technical systems behind different AI agents, leading to significant variations in actual user experience. Recently, some new models and tools have shown superior performance in generation quality and execution efficiency, even demonstrating higher ceilings in development output. However, he pointed out that at the current stage, these differences have not yet formed a decisive generational gap, being closer to "efficiency improvement" rather than a "paradigm breakthrough." In other words, competition among agents is still in a rapid evolution phase, with no stable and insurmountable technical barriers emerging yet. The iteration pace of current AI agents and large models is extremely fast, with new products or capabilities appearing almost weekly, driving the industry forward. However, from the perspective of practical use and business decision-making, whether there is a need to continuously and frequently follow these changes still requires careful evaluation.

[HKUST's Xu Jialong: The Moat of AI Agents Is Not Yet Solidified, Model Differences Reflect Efficiency Rather Than Disruptive Breakthroughs]On April 21, during the roundtable discussion "Decoding Web 4.0: When AI Agents Take Over On-Chain Permissions," Xu Jialong, Associate Vice President of Hong Kong University of Science and Technology, commented on the "moat of AI agents." He noted that there are differences in the model training paths and technical systems behind different AI agents, leading to significant variations in actual user experience. Recently, some new models and tools have shown superior performance in generation quality and execution efficiency, even demonstrating higher ceilings in development output. However, he pointed out that at the current stage, these differences have not yet formed a decisive generational gap, being closer to "efficiency improvement" rather than a "paradigm breakthrough." In other words, competition among agents is still in a rapid evolution phase, with no stable and insurmountable technical barriers emerging yet. The iteration pace of current AI agents and large models is extremely fast, with new products or capabilities appearing almost weekly, driving the industry forward. However, from the perspective of practical use and business decision-making, whether there is a need to continuously and frequently follow these changes still requires careful evaluation.

HKUST's Xu Jialong: The Moat of AI Agents Is Not Yet Solidified, Model Differences Reflect Efficiency Rather Than Disruptive Breakthroughs

Crypto analyst Ai disclosed data on platform X, revealing that in the first quarter of 2026, AI companies raised approximately $242 billion, accounting for about 80% of the total global venture capital, setting a historical record. However, the funding is highly concentrated in a few 'super funding rounds,' such as OpenAI's $122 billion, Anthropic's $30 billion, xAI's $20 billion, and Waymo's $16 billion. Meanwhile, enterprise-level AI spending continues to rise. Gartner predicts that global AI spending will reach $2.52 trillion in 2026, a year-on-year increase of 44%, with funds rapidly shifting from application layers to infrastructure and computing power construction.

[Analyst: AI Companies Raise $242 Billion in Q1, Setting Historical Record]Crypto analyst Ai disclosed data on platform X, revealing that in the first quarter of 2026, AI companies raised approximately $242 billion, accounting for about 80% of the total global venture capital, setting a historical record. However, the funding is highly concentrated in a few 'super funding rounds,' such as OpenAI's $122 billion, Anthropic's $30 billion, xAI's $20 billion, and Waymo's $16 billion. Meanwhile, enterprise-level AI spending continues to rise. Gartner predicts that global AI spending will reach $2.52 trillion in 2026, a year-on-year increase of 44%, with funds rapidly shifting from application layers to infrastructure and computing power construction.

Analyst: AI Companies Raise $242 Billion in Q1, Setting Historical Record

On April 21, during the roundtable discussion 'Decoding Web 4.0: When AI Agents Take Over On-Chain Permissions', Box from the Monad Foundation's Greater China Developer Relations addressed the topic of 'Agent Moats'. He stated that the current technical threshold for AI Agents is relatively low, and their so-called 'moat' does not primarily come from the products themselves, but rather depends more on the founders' or teams' understanding of AI, their control capabilities, and external influence. He pointed out that many Agent products at this stage essentially rely on open-source code and existing tools to be quickly assembled, significantly compressing development cycles, with the phenomenon of 'building a product in just a few hours' not being uncommon. However, this also means that product forms are easily replicable; once cloud vendors or large model vendors get involved, they can often replicate or even surpass these products in a very short time, causing early-stage startups to rapidly lose their competitive edge. Box further emphasized that in this environment, the survival logic of Agent projects is changing: on one hand, they need to maintain a lead through high-frequency iterations, and on the other hand, they must expand market influence and user awareness as much as possible to establish a first-mover advantage before the industry fully matures. He believes that if the product update speed is insufficient and lacks dissemination capability, once large companies take notice and quickly follow up, its lifecycle could be significantly compressed or even lead to immediate exit.

[Monad Foundation's Box: The Competitive Edge of Agents Lies in 'Influence + Iteration Speed', Products Easily Replicable]On April 21, during the roundtable discussion 'Decoding Web 4.0: When AI Agents Take Over On-Chain Permissions', Box from the Monad Foundation's Greater China Developer Relations addressed the topic of 'Agent Moats'. He stated that the current technical threshold for AI Agents is relatively low, and their so-called 'moat' does not primarily come from the products themselves, but rather depends more on the founders' or teams' understanding of AI, their control capabilities, and external influence. He pointed out that many Agent products at this stage essentially rely on open-source code and existing tools to be quickly assembled, significantly compressing development cycles, with the phenomenon of 'building a product in just a few hours' not being uncommon. However, this also means that product forms are easily replicable; once cloud vendors or large model vendors get involved, they can often replicate or even surpass these products in a very short time, causing early-stage startups to rapidly lose their competitive edge. Box further emphasized that in this environment, the survival logic of Agent projects is changing: on one hand, they need to maintain a lead through high-frequency iterations, and on the other hand, they must expand market influence and user awareness as much as possible to establish a first-mover advantage before the industry fully matures. He believes that if the product update speed is insufficient and lacks dissemination capability, once large companies take notice and quickly follow up, its lifecycle could be significantly compressed or even lead to immediate exit.

Monad Foundation's Box: The Competitive Edge of Agents Lies in 'Influence + Iteration Speed', Products Easily Replicable

On April 21, according to The Information: Musk significantly increased his stake in SpaceX last year, spending $1.4 billion.

[Musk Acquired $1.4 Billion in SpaceX Stock Last Year]On April 21, according to The Information: Musk significantly increased his stake in SpaceX last year, spending $1.4 billion.

Musk Acquired $1.4 Billion in SpaceX Stock Last Year

On April 21, according to Nikkei News: The Bank of Japan will maintain interest rates unchanged in April.

[Bank of Japan to Maintain Interest Rates in April]On April 21, according to Nikkei News: The Bank of Japan will maintain interest rates unchanged in April.

Bank of Japan to Maintain Interest Rates in April

South Korea's prosecution service is taking a bold step to combat international crypto laundering by acquiring cutting-edge blockchain analytics software. This technology is expected to enable authorities to apprehend individuals involved in illicit activities involving cryptocurrencies across borders.

[South Korea to Acquire Blockchain Analytics Software to Combat International Crypto Laundering]South Korea's prosecution service is taking a bold step to combat international crypto laundering by acquiring cutting-edge blockchain analytics software. This technology is expected to enable authorities to apprehend individuals involved in illicit activities involving cryptocurrencies across borders.

South Korea to Acquire Blockchain Analytics Software to Combat International Crypto Laundering

Bitcoin Created By The CIA? Chinese Professor Jiang Xueqin Makes Bold Claim

BIT Research: Five Signals Flash Simultaneously, Has the Bitcoin Bear Market Ended?

Bitcoin Recovery Fails To Lift Market Sentiment From Extreme Fear

Nic Carter Says Bitcoin Has 3 Ways To Handle Satoshi’s Coins

Crypto And Financial Industry Giants Reveal What X Money Launch Means

Analyst Exposes Bitcoin Market Maker Buy Strategy, Shows What Happens When Accumulation Ends

Conversation with Bitwise Advisor: From K-Shaped Economy to AI Taking Jobs, How Can Bitcoin Save the Younger Generation?

Analyst Reveals The Chances Of Bitcoin Price Crashing Again

Bitcoin Miners Selling Nears Exhaustion – What Comes Next

Crypto Regulation: Polish Parliament Fails To Overturn Presidential Veto Again

Article

JPYC Completes Additional Financing of Approximately 2.8 Billion Yen with Participation from Metaplanet

South Korea to Acquire Blockchain Analytics Software to Combat International Crypto Laundering

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