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Flash

Pons Founder: The PONS new repurchase mechanism will distribute funds every 7 days and complete the repurchase and destruction in the following 7 days

The founder of Pons, Ozzy, posted on platform X in response to community concerns about the PONS buyback and burn mechanism, stating that the current burn rate has indeed not been adjusted, and the "Claim" process has not yet achieved complete decentralization.He mentioned that an on-chain contract upgrade is currently underway, with a new buyback mechanism planned to execute a Claim every 7 days. Subsequently, all funds received in the following 7 days will be used for buyback and destruction of PONS, and this process will repeat to establish a more sustainable buyback and burn mechanism. Currently, all buyback and burn operations have been automated. Anyone can trigger this bot and receive a small reward for this action.He further stated that the previously set buyback and burn rate is 2e per hour. Combined with the current funding scale of approximately 950,000 USD in the Splitter (fund diversion contract), this rate aligns with the 7-day buyback cycle. Funds will also be automatically executed according to the 7-day cycle after being claimed, so the buyback funds will be displayed in two different sections: one is the current active buyback fund pool (Active Buyback Vault), and the other is reserved for the buyback funds for the next week.Previously, crypto analyst yyy posted on platform X stating that Pons has not replenished funds to the buyback allocator for over 5 days, with approximately 440,000 USD in the escrow account awaiting claim. He believes that the untimely claiming of funds has led to a low burn rate of PONS recently and calls for promoting the decentralization of fund claims from the escrow account.

NEAR co-creation sharing ecological application construction direction and distribution suggestions, covering AI, cross-chain payment, and privacy transactions, among other directions

Illia Polosukhin, co-founder of NEAR, stated that recently the activity of developers within the ecosystem has increased and the development threshold has further lowered, focusing on what kind of applications to build and how to establish distribution channels.In this regard, he proposed a series of potential construction directions, covering AI electronic pets that combine NEAR AI on-chain verifiable reasoning with non-fungible tokens (NFTs), a universal checkout component that supports full-chain intent payments, a decentralized encyclopedia that combines prediction markets with AI-generated content, a privacy off-chain trading (OTC) market based on NEAR Intents and management of anti-liquidation lending and perpetual contracts, a cross-chain activity registration custody tool in the form of a staking deposit, privacy code review agents, enterprise equity structure management tools, Delta neutral strategy management, privacy tracking single transactions based on view keys, end-to-end encrypted medical auxiliary diagnostic systems, privacy voice transcription desktop applications, and AI benchmarking based on private test sets.Regarding product distribution, he suggested combining NEARLegion with community channels like X, achieving multi-chain wallet compatibility through Aurora Intent Connect to lower cross-chain thresholds, and actively expanding across communities and platforms like Product Hunt.

first_img Ming-Chi Kuo: NVIDIA tests fiberglass-free copper-clad laminates

Tianfeng International Securities analyst Guo Mingqi published that his latest supply chain check shows that NVIDIA has begun testing fiberglass-free copper-clad laminates primarily made of hydrocarbon resin to replace the previously tested fiberglass-free PTFE copper-clad laminates, along with fiberglass-free hydrocarbon prepregs. The test aims to confirm whether this combination can meet the requirements of the Rubin Ultra NVL576 exchange tray PCB. NVL576 is an eight-rack interconnection solution expected to enter mass production in the second half of 2027.Guo Mingqi stated that preliminary tests conducted to enhance PCB manufacturability show that the fiberglass-free hydrocarbon copper-clad laminate has met the high-frequency electrical requirements of the exchange tray. Its electrical performance lags behind the original PTFE solution but is better than the M9 level solution and exceeds the M10 published specifications. This result does not represent the final material selection, indicating that NVIDIA is still seeking higher PCB manufacturing yields and production efficiency while meeting electrical requirements.Guo Mingqi mentioned that Shengyi Technology is currently the leading supplier for the NVL576 exchange tray material evaluation, with related materials including fiberglass-free PTFE copper-clad laminate SG5300N, fiberglass-free hydrocarbon copper-clad laminate SG1030N, and fiberglass-free hydrocarbon prepreg SIF09. SG1030N and SIF09 still contain a small amount of PTFE. He also stated that once specifications are confirmed and mass production begins, Shengyi Technology is expected to accelerate procurement from Chinese hydrocarbon material suppliers such as Dongcai, Shengquan, and potential supplier GCH Technology.

first_img Alibaba ModelScope competes with MoArk for the domestic open-source model platform

According to Rest of World, Alibaba launched the open-source model platform ModelScope in 2022, and OSChina launched MoArk in 2023, providing model hosting, testing, and customization services for Chinese developers behind firewalls. In 2023, Chinese regulators blocked Hugging Face without disclosing specific reasons; reports indicate that the government simultaneously allowed artificial intelligence laboratories to bypass the blockade to share Chinese models globally. ModelScope currently hosts over 170,000 models and stated in March that it has 170,000 models and 250 million users; MoArk hosts about 20,000 commonly used models, both still below Hugging Face, which hosts over 3 million open models.OSChina CEO Xu Yong stated that not everyone can always use a VPN, and China needs to establish an independent artificial intelligence ecosystem that serves Chinese users, claiming that China is developing an independent ecosystem faster than during the internet era. He also mentioned that MoArk has deployed engineering teams to ensure that models can run on different types of Chinese chips, making it their mission to ensure mainstream open models are compatible with Chinese chips. Both platforms offer some services for free, with additional computing power and premium features charged; ModelScope also hosts university hackathons and has opened co-working and event spaces for AI entrepreneurs in Hangzhou.Headquartered in New York, Hugging Face is a major platform for sharing open models and datasets. In September, Nvidia announced its acquisition of the company for $12.9 billion and noted in regulatory filings that regulators might prohibit sharing Chinese models on the site.

first_img DeepSeek open-source Ascend basic components, covering compilation, computation, and communication libraries

The artificial intelligence company DeepSeek has officially open-sourced infrastructure components for the Huawei Ascend computing platform, covering the TileLang high-level language compilation tool, computing libraries, and distributed communication libraries, corresponding to the previously open-sourced components for the NVIDIA platform. TileLang aims to provide a general-purpose, simpler programming language that can achieve the hardware performance limits, improving development efficiency and simplifying logic compared to CUDA, while its programming model can also leverage chip features.The TileLang route was first validated on the NVIDIA platform and has already supported the implementation of most operators in the training of the DeepSeek V4 series models. The open-sourced Ascend version encapsulates the underlying instructions of the Ascend C, providing a high-level programming approach without sacrificing hardware performance. Currently, every TileLang operator used in DeepSeek's training has a corresponding high-performance implementation on Ascend.The components open-sourced at the same time also include DeepGEMM, DeepEP, TileKernels, FlashMLA, and DeepSelect, which are used for general matrix operations, large-scale cross-device communication, conventional vector calculations and memory access, long-context sparse attention, and data filtering, respectively. DeepSeek claims that in multiple key tests, the related computing and communication performance has approached hardware limits; during the R&D process, the Huawei team provided support, and both parties collaborated to advance the 128-card supernode solution based on Ascend 950, with deep optimizations made for computing and communication.
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