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GOOGL $343.44 +1.19%
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ath

ATH is the abbreviation for "All-Time High," referring to the highest market price that an asset has reached in its trading history. ATH is an important indicator for investors and traders, often used to assess the market performance and investment potential of an asset. When the asset price reaches or surpasses the ATH, it may trigger widespread attention and trading activity in the market.
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first_img BitMart announced the preliminary redemption plan, and users can choose from three exit paths

The cryptocurrency exchange BitMart has released a preliminary intention plan, stating that it will review its assets and liabilities and formulate response arrangements starting from August 2026. The plan was formed during the initial due diligence conducted by Alvarez & Marsal and White & Case; users can choose front-end allocation or potentially higher recoveries from sources that may be recovered and liquidated later. The management team stated that they will continue to cooperate.BitMart stated that the total market value of the cryptocurrency market decreased in 2026, with volume manipulation groups profiting from commission and zero slippage policies in contract business, while fee-based services turned into losses; a hacker attack in December 2021 resulted in a balance sheet gap of approximately $319.5 million based on the value as of December 4, 2021. Starting from May 2026, social media attacks triggered panic withdrawals, and related personnel faced risks of personal information leakage. After evaluating a $10 million liquidity proposal from an investor, the management deemed it insufficient to cover operational pressures and gaps.The plan proposes to convert outstanding account balances into dollars based on the weighted average price from July 26, 2026, to the time of recording, to be verified by an independent party appointed by the court. Users can receive a proportional front-end allocation of fiat currency, USDC, PYUSD, USDT, and liquid assets such as BTC, ETH, SOL, or participate in the recovery of stolen assets by exchanging for recovery tokens at a rate of 1 unit per $1, or exchange for continuation tokens that can be traded on decentralized exchanges, corresponding to investment rights, non-standard asset liquidation, and a portion of distributable profits after business restart. In the next three to four weeks, feedback will be solicited from the top 50 users, and a report on the expected recovery ratios for each option will be compiled.

X-Agent Hackathon Emerges: The Prototype of Agent Economy - AI Begins to Independently Accept Orders, Refuse Transactions, and Purchase Models

The ongoing X-Agent AI MCP Hackathon has received nearly 40 projects, with some public works turning work decisions, capital management, and inter-machine trading into runnable products. X-Agent introduces three cases: BountyProof checks whether tasks are open, claimed, have relevant submissions, and the authenticity of rewards before the Agent accepts GitHub bounty tasks, with the core question being "Is this work worth doing?"Abstain empowers the Agent with the "do not trade" capability, returning execution, abandonment, or no trade based on preset rules before order execution. sumplus helps the Agent choose suitable model service solutions based on task context, output scale, model capability, and invocation costs, making the Agent an autonomous buyer of models, computing power, data, and API services.The three projects correspond to the foundational economic behaviors of the Agent: accepting work, utilizing funds, and purchasing services, pointing to work, capital, and trading primitives. X-Agent believes that a true Agent economy requires a complete cycle of "building, deploying, operating, discovering, invoking, paying, earning revenue, and distributing," positioning itself as the application layer of the Agent economy. This hackathon is still in the review stage, and the mentioned projects are only for public case reference, not representing shortlisted or award results.

first_img Chief Legal Advisor of the U.S. SEC's Cryptocurrency Working Group Elaborates on the Path for Cryptocurrency Custody Rules

According to CoinDesk, Taylor Lindman, the Chief Legal Counsel of the U.S. Securities and Exchange Commission (SEC) Crypto Working Group, stated at the CoinDesk Policy & Regulation event held in Washington that the SEC is advancing rules for the custody of crypto assets. The relevant proposal has been submitted to the Office of Management and Budget (OMB) for review, covering investment companies and broker-dealers. She indicated that the rule aims to inform the market about how to hold non-securities crypto assets within broker-dealers without special registration and clarifies that investment advisors can store client assets in institutions such as state-chartered trusts.Once the proposal passes the review by the Office of Management and Budget, the SEC will formally present it and seek feedback from the industry and the public. Lindman also mentioned that the SEC will issue an employee statement in December 2025 as a transitional arrangement, guiding broker-dealers on handling crypto custody matters before the rules are implemented, and will allow investment advisors to store client assets in state-chartered trusts as qualified crypto custodians starting in September 2025.Lindman described the SEC's recent work as "laying the groundwork," including previously proposed rules allowing crypto issuance and exemptions for tokenized securities. She stated that the SEC is working to ensure that existing securities intermediaries and market participants can confidently use blockchain to hold and trade crypto assets. Previously, the SEC's attempts at custody rules under Gary Gensler in 2023 were abandoned, and a leadership supportive of crypto was appointed after the Trump administration took office.

first_img OpenAI claims to have solved over 100 long-standing open problems in the majority of mathematical fields

OpenAI announced the establishment of an independent advisory group on mathematics and artificial intelligence in collaboration with mathematicians, hosted by the Institute for Advanced Study in Princeton. OpenAI stated that its new internal model, which began training on August 28, has solved the Navier-Stokes Millennium Prize Problem and addressed over 100 long-standing open problems in most areas of mathematics, with progress that surprised its internal mathematicians.The group will provide advice on the review and dissemination of emerging results, academic and professional standards in mathematical research, and how OpenAI tools can support mathematical research and learning. Initial members include François Charles, Camillo De Lellis, Timothy Gowers, Martin Hairer, Nikhil Srivastava, Ulrike Tillmann, Ravi Vakil, Edward Witten, and Melanie Matchett Wood.The group operates independently of OpenAI, and members are not compensated by OpenAI; they can proactively provide advice, comment on its impact on mathematics, and make public recommendations. Previously, mathematicians expressed concerns in an open letter titled "A Severe Misalignment of AI in Mathematics" regarding the use of solving open problems as a benchmark for AI. The group is not responsible for providing advice on the pace of OpenAI's internal mathematical progress.

first_img Chamath: The open-source weighted model is about four months away from the best closed-source frontier model

Social Capital founder Chamath Palihapitiya released an in-depth research report stating that open-source weight models are about four months away from matching the best closed-source frontier models in public evaluations, with increasing fluctuations in the gap. If open-source models allow companies more control over data, infrastructure, and customization while approaching frontier performance, the value corresponding to companies still paying for frontier laboratories becomes a business issue. Openness exists on a spectrum, from fully open-source models that can be downloaded and freely modified to open-source weight models with various restrictions, while closed-source models keep weights proprietary.Palantir CEO Alex Karp warned that companies might hand over differentiated proprietary knowledge and processes to frontier model providers. Microsoft CEO Satya Nadella stated that companies are effectively paying for intelligence twice: once in money and again in the more valuable proprietary knowledge that must be disclosed to make the intelligence useful. Despite concerns, companies are still willing to pay for frontier performance, even if the best open-source weight models are only months behind, with frontier laboratory revenues continuing to accelerate.Leading companies use both types of models, leveraging open-source models for control and customization while utilizing closed-source frontier models for maximum capability, with some cases reporting up to 12 times engineering efficiency and over 20 times cost savings. Some vendors adopt a dual-track approach, with Google offering both Gemini and Gemma, and Meta providing both Muse Spark and Llama. The 99-page report also discusses the costs of maintaining a lead for frontier laboratories, five factors of model competition, model operating locations, and investments in open-source weights by NVIDIA and Samsung.
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