Genius.fun announces it will add the "6666" contract suffix to its platform-issued tokens as a brand mark.

According to GMGN market data, the USELESS token has rebounded over 35% today, with its market cap temporarily standing at $344 million. On the news front, Bonk Guy returned to Twitter yesterday and has been continuously bullish on the USELESS token’s performance. Additionally, per TradingBeats monitoring, a wallet address (7LK2oT) created a new wallet 7 hours ago and spent 2.28 million USDC to purchase 6.64 million USELESS at a price of $0.34 per token.

According to GMGN market data, MONITOR, a stock-linked meme coin on Robinhood Chain, has seen its gains widen, with its market cap breaking through $13 million and currently standing at $12.48 million, marking a 1,070% increase in the past 24 hours. Earlier reports noted that this morning, Joe Lonsdale, co-founder of Palantir, followed MONITOR’s Twitter account, sparking FOMO hype around the token. Palantir is a U.S. AI defense contractor whose use of AI technology for "mass surveillance" of U.S. citizens has sparked widespread controversy. The stock-linked meme coin MONITOR was launched on the long.xyz platform, pairing tokenized U.S. stock Palantir as its liquidity pool and leveraging stock trading platform Robinhood.

Someone created a new wallet (7LK2oT) and spent 2.28M $USDC to buy 6.64M $USELESS at $0.34 7 hours ago.

According to TradingBeats' monitoring, a new wallet address tracked yesterday withdrew a total of approximately 8,100 ZEC in three transactions from Binance and OKX, at an average withdrawal price of around $1,523.20, before transferring the funds to the same aggregation wallet. Combined with previous withdrawals and fund flows, six associated addresses hold a total of 34,510.73 ZEC, valued at roughly $55.71 million. Based on current balances and after deducting transferred funds, the overall average entry price is approximately $1,474.53, generating an unrealized profit of around $4.8265 million since the transfer, a gain of about 9.48%. Address: t1NZ1N2Hvd9R6vQDJAR4VXim8uA5sAHrJDx. Yesterday's news: A whale withdrew around 14,000 ZEC from Binance, and may now hold a total of $42.5 million in spot ZEC.

Beating AI News Flash: Chinese AI startup StepFun has open-sourced its AI coding tool Step Code v0.1.0 under the MIT license. The tool runs directly in the terminal, supporting code reading and writing, test execution, and development task handling, with compatibility for MCP, Agent Skills, and Claude Code plugins. Its built-in StepPage feature allows users to publish local static websites online with a single command. Step Code’s core highlight is low token consumption. In StepFun’s self-conducted Terminal-Bench 2.1 benchmark, it completed 72 out of 89 tasks, scoring 80.9% – tying DeepSeek Harness for the highest score while using fewer tokens. In StepFun’s in-house Multi-Frame long-task test (150 questions total), Step Code finished 110 tasks for a 73.3% success rate, averaging 5.09 million tokens per task. Among the 6 tested harnesses, it ranked first for both highest task completion rate and lowest token usage. However, StepFun did not disclose the specific underlying model used for the benchmark, nor did it detail how token consumption was reduced. The company only noted that Step Code was optimized for token efficiency alongside its Step series models. Numerous token-saving designs are visible in the source code: file reads and command outputs are length-limited, search results are narrowed as much as possible, long conversations default to context compression, and the system prompt prioritizes direct tool calls over spawning additional agents. There is also a more aggressive contextProjection feature that trims old tool outputs, duplicate results, and historical reasoning, though it is disabled by default. Overall, Step Code’s token advantage comes from multiple optimizations to both the model and the test harness.

Ethereum co-founder Vitalik Buterin delivered a keynote speech at the 12th Global Blockchain Summit, stating that while early blockchain boasted features like openness, neutrality, and security, it faced critical flaws including poor scalability, full transparency, and inadequate privacy protection. Zero-knowledge proofs— which enable private transactions and scalable verification— have moved from planning to actual Ethereum Improvement Proposals (EIPs), with technologies such as fully homomorphic encryption also seeing initial applications. Indistinguishability obfuscation may find on-chain use in the future, he added. Parallel block construction and verification can deliver higher scalability, and the Web3 industry’s focus has expanded from digital assets to both digital assets and information, covering the full workflow spanning user ends, transaction mempools, and on-chain processes. With AI advancement, feeding cryptographic rules to AI for automated formal verification can boost verification efficiency.