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Flash

first_img Micron Technology's Taoyuan Union in Taiwan has received authorization for a strike, and the bonus dispute has escalated

According to a report by Reuters, reprinted by Nikkei Asia, the union representing employees at Micron Technology's Taoyuan plant in Taiwan announced on Wednesday that it has obtained authorization for a strike, with the focus of the dispute being the company's bonus plan. The union stated that 1,994 members voted in favor of the strike, accounting for 99% of the voters, while details such as the timing of the strike are still under discussion. Micron has approximately 15,000 employees in Taiwan, with independent unions in Taoyuan and Taichung, and the two unions together represent over 80% of Micron's employees in Taiwan. Micron Taiwan is the company's largest manufacturing base.Taiwan is a key production site for Micron's DRAM and high-bandwidth memory, with related chips being important components for AI servers. Mediation with the Taoyuan union did not reach an agreement in September, and the Taichung union is still in negotiations. In September, Micron announced that it would distribute bonuses for the fiscal year 2026 to over 60,000 employees worldwide, stating that the company had experienced an "extraordinary year," with Taiwanese employees receiving a cash bonus of 1 million New Taiwan Dollars, approximately 31,400 US dollars. The Taoyuan union stated that the plan did not address the request for a permanent profit-sharing mechanism and criticized Micron for announcing the bonuses without first reaching an agreement with the union during the mediation period.

Vitalik Buterin: AI may weaken cryptography within two years and put ECDSA at risk, suggesting a priority on hash cryptography

Ethereum co-founder Vitalik Buterin posted on the X platform, stating that AI-accelerated mathematical research may pose new security threats to existing cryptographic systems, especially lattice-based cryptographic schemes, including ML-DSA and fully homomorphic encryption (FHE). He believes that advancements in AI in the field of mathematics over the next two years could significantly undermine the practical security of lattice cryptography, and ECDSA may face the risk of being compromised sooner than expected.Vitalik pointed out that over the past year, the Ethereum Lean roadmap has gradually shifted towards pure hash cryptographic schemes, avoiding the use of lattice-based signatures and related zero-knowledge proof structures. He suggested prioritizing the use of hash cryptography whenever feasible and adopting more conservative security parameters for lattice cryptography; privacy protocols should avoid storing encrypted information directly on-chain, and multi-signature wallets should prioritize off-chain signature confirmations. Additionally, Vitalik advised users, when operationally convenient, to store funds in addresses that have not initiated transactions to reduce potential risks, but he does not recommend users immediately migrate assets on a large scale. He emphasized that the migration process itself also carries risks, and his own losses due to migration errors have even exceeded the losses from past hacking attacks.

first_img Sui off-chain channel pressure test peak reached 40.6 million TPS, setting a new throughput record

Layer 1 blockchain Sui announced that its programmable off-chain channels processed over 40 million transactions per second during a real-time stress test aimed at high-frequency activities for AI agents, breaking the record set in July of this year. The test was conducted on Wednesday at the Sui Basecamp in Singapore, peaking at 40,614,180 TPS, surpassing the approximately 6.1 million TPS level of similar tests in July.During the test, transactions were processed through Sui's off-chain channels, which settle to the Sui mainnet when closed. Sui stated that over 10,000 channels were opened on the mainnet during the test, covering scenarios such as payments, gaming, and chat applications. These channels are designed to handle high-frequency activities, including interactions between AI agents, without requiring each transaction to be processed directly on-chain.Adeniyi Abiodun, co-founder and Chief Product Officer of Sui's original contributor Mysten Labs, stated that Sui is built for the moment when millions of AI agents transact daily, noting that no market in the world needs 6 million transactions per second, let alone 40 million; humans cannot move that fast, but agents can. Security company CertiK served as an independent auditor during the demonstration, reviewing the test data, with a report expected to be released in the coming days.

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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