CZ releases crypto asset custody guide, advises choosing custody methods based on asset size and technical capabilities.

According to a post by MTS, OpenAI’s artificial intelligence model has proven half of one of the million-dollar Millennium Prize Problems in physics, with support from a review panel led by a Nobel Prize laureate. MTS identifies the problem as the Yang-Mills problem. OpenAI first unveiled the proof on October 26, noting its model completed the work in September. Per MTS, the review panel is chaired by Nobel Prize winner David Gross, and includes Arthur Jaffe, who co-authored the official description of the Millennium Prize Problem. The proof has also been formalized in Lean, a computer language enabling line-by-line verification. The Yang-Mills problem is one of the seven Millennium Prize Problems established in 2000, each carrying a $1 million reward; to date, the only solved problem was completed by Grigori Perelman, who declined to accept the prize. MTS points out that to win the award, a mass gap proof is still required, alongside compliance with the Clay Mathematics Institute’s rules on publication and a two-year validation period. X user Theo Jaffee noted that physicists have been able to work with quantum field theory and quantum Yang-Mills theory empirically, but have long lacked a mathematical formalization explaining why these theories are valid.

US brokerage Baird has downgraded Nvidia’s stock rating from “Outperform” to “Neutral,” slashing its price target by 60% from $500 to $200. The move has drawn attention on Wall Street, as Nvidia has long been favored by the vast majority of analysts, making this downgrade a relatively rare bearish signal. Baird analyst Tristan Gerra outlined the key reasons for the cut in a report: · AI spending growth may slow: Hyperscalers’ AI capital expenditure growth is projected to drop to around 40% or lower by 2027, driven primarily by supply constraints, with early signs of softening demand already emerging. · Risks of China export restrictions: Concerns over potential further U.S. curbs on chip exports to China. Analysts note that if H200 chips are banned from being exported to China, related inventory in Asian warehouses could face an impairment risk of up to $5 billion; additionally, roughly 10% to 30% of orders through Southeast Asian channels may be canceled due to anticipated restrictions. · Leadership uncertainty: CEO Jensen Huang’s tenure faces uncertainty amid an investigation into his alleged extramarital affair and suspected national security issues. Baird estimates the likelihood of a CEO transition before 2027 is rising, with an external candidate seen as the more likely successor. The report also notes visibility into Nvidia’s data center business by 2027 has declined, with supply constraints remaining a key headwind, though H300 series chips are still in high demand. Analysts project Nvidia’s stock will likely trade in a range in the near term until the investigation outcomes, policy impacts, and financial effects become clear.

According to Lookonchain monitoring, the whale that previously held heavy long positions in NVIDIA (NVDA) and SNDK has closed out its SNDK long positions and shifted to full-scale long positions in NVIDIA. The whale currently holds 190,952 long shares of NVIDIA, valued at $30.67 million, with an unrealized loss exceeding $2.6 million.

China’s largest flash memory manufacturer Yangtze Memory Technologies (YMTC) was reported last week to possibly delay its listing, though the company remains on track with its initial public offering (IPO) plan, initially targeting a debut on the Shanghai Stock Exchange’s STAR Market as early as the first quarter of next year. Citing people familiar with the matter, Bloomberg reported on Wednesday (November 11) that Wuhan-based YMTC is still advancing its listing plan, aiming for an earliest launch on the STAR Market in the first quarter of 2027. Earlier, Reuters had cited an internal memo and insiders on November 4 that YMTC’s listing plan could be delayed by several months, with an expected debut in the first half of next year, the specific timing of which would depend on market conditions. However, the latest Bloomberg update shows relevant work is still ongoing. YMTC entered the next stage of the listing review process in September this year, with plans to raise 33 billion yuan (approximately 6.3 billion Singapore dollars). The Shanghai Stock Exchange sent its first-round review inquiry to the company in early September, officially kicking off the listing review procedure.

The whale who went heavily long on $NVDA and $SNDK has closed his $SNDK long and gone all-in on $NVDA! He now holds a long on 190,952 $NVDA ($30.67M), with over $2.6M in unrealized losses.

Dongcha Beating AI News Flash: Google has launched its first decision-making model, Gemini Decision 1, which supports mixed inputs of text, images, audio, video, and PDF, with a 256,000-token context window. Compared to Jev and OpenAI’s Decisions API, Google’s model can directly process a wider range of materials. For example, it can take a customer service recording, order details, and product photos together to simultaneously assess a customer’s request, issue severity, and need for human intervention. It can evaluate up to 1,000 queries per run; multiple queries sharing the same input material only incur a single input fee. The official end-to-end response time is 150 to 250 milliseconds. For inputs under 16,000 tokens, the model’s processing time remains nearly unchanged even when the number of queries increases from 1 to 100. In Google’s internal tests, Gemini Decision 1 scored 92.2% on JevBench, outperforming Jev 1.14 (90.0%) and OpenAI’s Decisions API (90.9%). However, it lagged Jev (82.0%) with a 79.9% score on the tool calling benchmark When2Call. In third-party evaluator Artificial Analysis’s Decision Index v2.1, Gemini Decision 1 posted an overall score of 65.98, ranking 9th among 18 models—nearly on par with Jev’s 66.23, but trailing top-ranked Drex Max v1.1’s 72.62. Google charges $0.1 per million input tokens, with free output. Calculated using Artificial Analysis’s standardized task set, completing 1 million tasks costs roughly $345 for Google, $156 for Jev, and about $103 for Drex Max. Google’s average task latency is 211 milliseconds, compared to 92 milliseconds for Jev. Google has deployed the new model for model routing in Gemini Notebook, reducing latency for some daily requests by up to 40%. The API is now available, with a free trial quota offered.