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first_img Licheng, Jingyuan Electronics, and Silergy experienced a double increase in revenue in August, and Licheng's panel-level packaging capacity has been fully booked

Licheng, Jingyuan Electronics, and Silergy all reported year-on-year and month-on-month revenue growth in August, with Licheng and Silergy setting new historical highs for two consecutive months. Licheng's consolidated revenue in August was 8.558 billion New Taiwan Dollars, a month-on-month increase of 3.48% and a year-on-year increase of 24.46%. After breaking the 8 billion mark for the first time in July and setting a historical high, August saw another record. The cumulative revenue for the first eight months reached 61.258 billion New Taiwan Dollars, a year-on-year increase of 30.83%. Jingyuan Electronics reported revenue of 4.08 billion New Taiwan Dollars in August, a month-on-month increase of 2.23% and a year-on-year increase of 31.58%. The cumulative revenue for the first eight months was 29.404 billion New Taiwan Dollars, a year-on-year increase of 35.53%. Silergy's revenue in August was 2.07 billion New Taiwan Dollars, with a month-on-month increase of nearly 3% and a year-on-year increase of nearly 29%. The cumulative revenue for the first eight months reached 15.26 billion New Taiwan Dollars, a year-on-year increase of over 20%.Licheng's main source of growth this year remains the rebound in demand for memory packaging and testing. Licheng previously anticipated that revenue in the third quarter would show single-digit quarter-on-quarter growth, with the fourth quarter having the potential to exceed the third quarter. The momentum for memory orders is expected to continue until the end of the year, with a positive market outlook at the beginning of next year. In addition to its core memory business, advanced packaging has become a key focus for the next phase of development. The panel-level packaging has already been adopted by American clients, and the production capacity of the P11 plant has been secured by clients, with plans to enter the mass production phase of integrated ASIC and HBM AI chips by mid-2027.Jingyuan Electronics has been actively expanding its high-end testing capacity in recent years, with AI-related products becoming an important driving force for its operations. The testing demand for AI GPUs, ASICs, and high-performance computing chips remains its main source of growth. Silergy pointed out that the demand for AI and high-speed interconnects is strong, including an increase in demand for high-performance computing chips such as CPUs, GPUs, ASICs, and AI accelerators. After completing the cleanroom construction at the newly acquired Hukou plant in February, Silergy began mass production in July, currently achieving 40% of the total capacity of the plant and gradually taking on new demand from overseas clients.

Galaxy Research: Coldcard attackers continue to transfer funds, approximately 45% of the stolen assets have entered mixing or cross-chain pathways

Galaxy Research published that the attackers in the Coldcard "Wave 3" attack are still continuously transferring the stolen funds. During this phase, the attackers created 293 2-of-2 multi-signature wallets for each victim's assets. The first batch of funds was transferred across chains to Ethereum via THORChain; the latest round of transfers has begun entering the CoinJoin mixing process.Currently, the Wave 3 attackers are processing the largest amounts of stolen funds in order of the stolen amount, having sequentially transferred the funds from wallets ranked 1 to 11. The next 10 wallets that have not yet been transferred hold a total of 30.81 BTC, while wallets ranked 61 to 293 hold a total of 33.77 BTC. So far, the attackers have transferred about 45% of the stolen assets from this exploit, with funds flowing to Ethereum (via THORChain) or entering CoinJoin mixing transactions. Additionally, this fund transfer has revealed a previously unknown wallet: 58 addresses jointly spent in a 2-of-2 multi-signature format identical to that of Wave 3, and these were further transferred by the Wave 3 attackers to a jump address that funds CoinJoin.The on-chain analysis team currently marks this wallet as "cause = open," but believes it likely also belongs to Coldcard victims, which means the number of wallets involved in Wave 3 may increase to 294, raising the previously reported total amount stolen from the Coldcard vulnerability to approximately 1806 BTC. Currently, about 82% of the stolen BTC remains in addresses initially controlled by the attackers, while about 18% has been transferred, with the flow of funds indicating that it may be undergoing laundering processes.

first_img OpenAI Chief Scientist says AI may continue to rise rapidly to recursive self-improvement

OpenAI Chief Scientist Jakub Pachocki published an article titled "An Alien Mind" on September 6, 2026. The article reviews the results of the RLSlow research project, which emerged in mid-2023, demonstrating the first scalable training of reasoning models. It states that three years later, reasoning language models have become part of rapid economic growth, beginning to push scientific boundaries, capable of operating computers and graphical interfaces, collaborating with humans and other AIs on research projects, while also changing the landscape of computer security and introducing new dangers.The author anticipates that the current pace of progress may continue towards recursive self-improvement based on internal results. If AI development continues along the current path, systems may experience equivalent or greater leaps in capability in the coming years, increasingly driving their own development. The author calls for extreme caution, believing that no one is prepared to deal with the consequences of the rapid rise of machine intelligence. OpenAI will continue to seek technical solutions for alignment and monitoring, build defensive systems, and unilaterally halt further scaling when necessary, but believes broader intervention is needed.The article states that machine intelligence is primarily driven by increased computational power, with AI growing more than being designed. In terms of alignment, it distinguishes between goal alignment and value alignment, with the core challenge being generalization. GPT-6 Astra shows significantly better alignment than GPT-5.6 Sol, but more progress is still needed. In monitoring, the focus is mainly on chain-of-thought monitoring, with assessments showing that reliance on capabilities is gradually weakening. In terms of scalable defense, models are becoming superhuman in breaking into computer systems, currently in a narrow window where using the best available models significantly enhances the security of critical systems.
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