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TRX $0.3384 -1.54%
DOGE $0.0951 +2.17%
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ZEC $1,556.88 +3.57%
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AMZN $250.16 +0.50%
GOOGL $342.76 +0.77%
MSFT $497.05 -0.20%
META $777.52 +5.63%
NVDA $224.57 -0.09%
TSLA $379.25 +0.29%
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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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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.

first_img Justin Sun: Establish the Justin Sun Prize to reward mathematically verified proofs

The founder of TRON, Justin Sun, announced the establishment of the "Justin Sun Award," which currently focuses only on mathematics. He stated that the award follows the problem rather than the individual, with no annual meeting, nominations, or age restrictions; each problem has two columns for signatures, one for the prover and one for the person who formalizes the proof and writes it into the machine. Both can be the same person, or they can be a person, AI, or a person using AI. The condition for triggering the prize is that the machine verifies the proof from the first line to the last line without error. Once verification is passed, the names in the two columns become the prize owners for that problem.The rules also include: before machine verification, if the mathematical community has accepted a human proof, it can be listed first, with the prize remaining unchanged, and the status being "Proven, awaiting formalization." The prize in the proof column is only awarded to problems that are solved after being listed; for problems that were already solved at the time of listing, the prover is still credited, but the prize is only awarded to the person who completes the formalization, not retroactively for the history of mathematics. The list of problems is determined by Justin Sun's signature for the listed problems and pricing; once listed, the price can only increase, and money can only be exchanged, not refunded. Mistakes in the problems will still be paid, and they will be re-listed afterward; beyond pricing, verification, payment, and naming, he does not participate in voting.Justin Sun stated that the focus of public welfare will shift to this award, with the initial prize pool already deposited into the blockchain, with the address and balance made public. He only retains the right to continue injecting funds; the award proof and prize distribution will also be made public on the chain. He mentioned that naming it after a person is to avoid name changes due to institutional changes and expressed that wealth comes from mathematics, hoping to "come from mathematics and return to mathematics."
2026-09-16
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