Claude Code is a pixel-perfect copy of Cursor, not even bothering to rename its Projects feature.

Prominent trader Bonk Guy stated in a post that meme coin USELESS, which has a market capitalization of roughly $270 million, has surpassed all other meme coins on Coinbase in trading volume, with the only exception being DOGE. For comparison, DOGE, SHIB, PEPE, and TRUMP have respective market caps of approximately $13 billion, $3.16 billion, $1.54 billion, and $556 million. USELESS’s trading volume on Coinbase has long been higher than that of meme coins including SHIB, PEPE, and TRUMP. Meanwhile, its number of token holding addresses and open interest in Hyperliquid perpetual contracts have both hit all-time highs; spot and perpetual contract trading volumes remain elevated, market attention on the coin is sustained, and it has been listed on leading crypto exchanges. He believes that USELESS is forming the liquidity and adoption traits required for a major meme coin expansion, and a strong rally to catch up with DOGE’s valuation is not out of the question, though this assessment is his personal view.

Decentralized exchange platform ANT.FUN announced today that it has secured its second Canadian Money Services Business (MSB) license. On the same day, the ANTFUN mobile app launched on Google Play across 38 countries. The Canadian MSB license ranks among the most prestigious compliance credentials in the global digital asset sector, overseen by Canada’s Financial Transactions and Reports Analysis Centre (FINTRAC). Licensed entities must adhere to strict anti-money laundering (AML) and counter-terrorist financing (CTF) mandates, so the MSB approval confirms the platform has reached international standards in risk management systems, secure operations, and compliance infrastructure.

Beating AI Express News Brief: Alibaba’s Qwen launches native multimodal model Qwen3.8-Omni-Flash, supporting text, images, audio, video, and a 1M context window. This generation significantly enhances audio and video agent capabilities. The model can independently extract information from videos, plan tasks, and call external tools to complete editing, dubbing, and rendering. The official demonstrated use cases including long meeting processing, short drama translation, movie commentary, MV production, and video-to-text-and-image note conversion. For hours-long videos, it does not need to process the entire content from start to finish; instead, it first identifies where the relevant answer may lie, then gradually pinpoints key segments. On OmniVideoBench, accuracy has risen from 63.4 to 67.8, while token consumption dropped from 145,736 to 79,117, a 45.7% reduction. Qwen stated that Qwen3.8-Omni-Flash’s average score across 30 benchmarks is over 26% higher than Qwen3.5-Omni-Plus. Its audio and video capabilities are close to Gemini 3.8 Flash, and its overall audio performance surpasses the latter. Audio input costs have fallen by more than 98%, and audio-video input costs by over 93%. Currently, the model weights of Qwen3.8-Omni-Flash have not been released; only its API is available. Qwen also open-sourced Qwen-MM-Plugins, which can be integrated into agents like Claude Code, Codex, Gemini CLI, and Qwen Code, adding image, audio, long video processing, and video editing capabilities to these tools. Another component, Qwen-Live Harness, acts more like a real-time scheduling layer. Users can directly interact with the real-time multimodal model, then assign complex tasks to backend agents such as Gemini CLI, Claude Code, and Codex for continuous execution. It tracks task progress and proactively informs users of results once completed.

Bankless co-founder David Hoffman has published an article stating that NEAR can be viewed as a generalized version of ZEC. ZEC primarily provides privacy for funds, while NEAR focuses on delivering privacy for commercial activities. He noted that ZEC held in its privacy pool does not protect users' other aspects of life, whereas NEAR can be applied to privacy scenarios beyond financial use cases.

According to monitoring by Farside Investors, U.S. spot Bitcoin ETFs recorded a net inflow of $159.5 million yesterday, with IBIT seeing a net inflow of $183.7 million. Ethereum ETFs, meanwhile, posted a net outflow of $39.3 million, among which ETHA had a net outflow of $42.9 million.

Insight: Beating AI News Flash – Carter Leffen, a product developer at Bloomberg, used GPT-6 Astra to crack a 1941 German Wehrmacht Enigma ciphertext. Leffen is not a cryptographer. The 82-letter ciphertext had long been marked as unsolved by cryptography history archive CryptoCellar. Astra Extra High took roughly 10 hours to derive the solution. The key clue came from another already decrypted telegram sent the same day: Astra noticed the place name “ROSENOW” appearing twice in a row, then hypothesized the same word was hidden in the target ciphertext. It then built an Enigma simulator and search program, tested different keys in parallel, and verified results against the original telegram header and remaining text. The final decrypted message was simple: “Please inform the marching route. I am in Rosenow. Please reply immediately via radio.” That said, the achievement was not solely dependent on Astra: Leffen set the investigation goal and led the process, while Astra handled most of the research, coding, search runs, and verification tasks.