testing

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.

According to the official announcement, Binance has launched the AI feature suite "Binance AI," which is currently in Beta testing. The aim is to help users interpret complex market data, identify market dynamics, and reduce information noise through AI.It is reported that Binance AI will provide a personalized information interface, AI market insights covering both crypto assets and traditional assets, as well as concise and timely market dynamic updates. The first batch of users can apply to participate in the testing, and users who qualify for the whitelist will receive access instructions via email.Currently, the Binance AI Beta is only open to Binance users who have completed identity verification. The number of spots is limited and will be distributed in batches. Submitting an application does not guarantee testing eligibility. Binance also stated that user feedback will be used to further improve the product.

Anthropic stated that the company has temporarily paused certain AI training and cybersecurity assessments. The company disclosed the relevant adjustments in a blog post, explaining the measures taken in response to unauthorized actions by its AI agents earlier this year. OpenAI previously indicated that it had paused some model development work due to safety concerns. Anthropic also confirmed that it has taken similar measures and emphasized the need for broader coordination on the pace of frontier AI development.Anthropic stated that after disclosing three related incidents in July, the company paused external cybersecurity assessments for pre-release models and also briefly suspended internal testing of pre-release models. Following the incidents, the company also paused high-risk reinforcement learning environments in pre-release models for several weeks. Anthropic noted that most reinforcement learning work has resumed, but some high-risk environments remain on hold, awaiting human review or updates to monitoring tools.

According to a report by the Economic Daily, ASE Technology Holding benefited from the increased capacity of Google TPU, significantly adding advanced packaging and testing orders. Intel is accelerating the promotion of its EMIB advanced packaging platform while simultaneously expanding its outsourcing orders to ASE Technology Holding, with the previously raised testing quotes showing benefits. Google continues to increase its AI capital expenditures to expand data centers, using TPU to support the Gemini large model, AI agents, and Google Cloud services. The market estimates that Google TPU will enter a large-scale production phase by 2028, with shipments expected to reach 12 to 15 million units. ASE Technology Holding has mastered 2.5D, 3D advanced packaging, and high-end testing technologies, and Google is also expanding its use of TSMC's advanced processes and CoWoS capacity.Intel is racing to advance its EMIB packaging platform, with major cloud companies like Meta and Google successively adopting the EMIB and EMIB-T platforms. ASE Technology Holding, positioned as a pure testing and packaging foundry, can directly procure organic embedded silicon bridge substrates to undertake backend wafer-level assembly and high-end testing services. Wu Tianyu, the Chief Operating Officer of ASE Technology Holding, believes that Intel's EMIB and TSMC's CoWoS are not zero-sum competitors. As the testing time for AI chips lengthens, the costs of equipment, materials, and labor are rising, and high-end packaging and testing capacity is becoming increasingly tight. Major testing and packaging factories have gradually raised prices based on products, capacity, and customer conditions, with increases of about 5% to 10%.

XRP has dropped 6.23% in the past 24 hours to around $1.38, making it the token with the largest decline among the top ten cryptocurrencies by market capitalization. Previously, XRP surged from around $1 on August 18 to $1.69, an astonishing increase within four days, briefly touching the psychological level of $1.7.The pullback is mainly due to leveraged liquidations. XRP has still risen 35.55% over the past week, second only to Hyperliquid's 38.65% weekly increase. Notably, XRP-related ETFs have recorded net inflows for nine consecutive days, indicating that institutions have not withdrawn, and the issue mainly lies in the leverage aspect rather than capital outflows.After breaking through $80,000 on Tuesday, Bitcoin has retreated to around $78,000, dragging down the overall altcoin market. The market is awaiting tonight's core PCE inflation data and Nvidia's earnings report; whether XRP can hold the $1.4 support level will determine if this rally is a healthy correction or a further decline.

According to Techcrunch, AI Agent testing infrastructure startup Arga Labs announced the completion of a $10 million seed round financing, led by General Catalyst, with participation from institutions such as BoxGroup, Emergence Capital, Gradient Ventures, and SV Angel.The company aims to address the challenges of testing, training, and adapting complex business systems during the deployment of enterprise-level AI Agents by creating a complete digital twin environment for enterprise software, replicating permission systems, Webhooks, and system interaction logic, allowing AI Agents to train in an environment close to real work.

According to the "Science and Technology Innovation Board Daily," Alibaba Qianwen is currently testing a multi-Agent collaboration feature on the Qianwen creation platform in a small scope, mainly aimed at professional creative scenarios such as AI short dramas, comic dramas, marketing advertisements, and image design. Currently, only a small number of professional creators have been granted testing qualifications.This feature enables multiple Agents to collaborate, linking together the stages of creative planning, script writing, visual design, storyboard generation, and final production. Different Agents can cooperate based on task division to complete the entire production process from concept to final product. Users can participate in discussions throughout the process, adjust the creative direction at any time, or delegate some tasks to AI for independent completion.Recently, Qianwen has launched Qianwen creation for the professional content production field, and it has already integrated Alibaba's latest models such as Wan 3.0, Wan 3.0 Prime full version, and Qwen-Image 3.0 Pro.

Recently, multiple users discovered an image model named luna-lisa-alpha appearing on the LMArena testing platform, suspected to be a new generation image generation model that OpenAI is testing. Although not officially confirmed, there is speculation that it could be an early version of GPT Image 2.5 or the next generation of GPT Image.From the testing feedback, the new model shows significant improvements in character consistency, image realism, text generation capability, and noise control. Some testers compared it with models like GPT Image 2.0, Nano Banana Pro, and Grok Imagine, believing that luna-lisa-alpha can better maintain the appearance, clothing, and style of the same character across multiple generations. However, some testers pointed out that there are still issues with repeated textures in complex texture areas. On the same day, OpenAI announced that the GPT Image 2 API preview supports transparent background generation, which can be used for product images, web design, and other scenarios. The exposure of multiple checkpoint names has also led the community to speculate that OpenAI may be conducting A/B testing. Whether this model will be officially released remains uncertain, and we need to wait for official information from OpenAI.

According to X user MaxForAI, Tencent has begun gray testing its brand new flagship model, Hunyuan Hy4. Some users have discovered that the model selection list in the Tencent Yuanbao App now includes Hy4, labeled as an expert-level model, ranking above Hy3 and DeepSeek. The official account @TencentHunyuan also shows this model option at the relevant entry.Last week, Tencent confirmed in its Q2 financial report that the larger parameter-scale Hy4 will be launched soon, further enhancing model performance and multimodal capabilities. Currently, Tencent has not officially released Hy4, and the outside world cannot confirm whether this is a small-scale gray test or an early opening of the entry, but based on the progress, the model's launch seems imminent.

The Ethereum Foundation's DevOps team announced the launch of the Platåberget testnet as an early public testing environment for the Glamsterdam (Gloas + Amsterdam) upgrade. This testnet is open to the community and is planned to run for several months, providing developers with a stable experimental platform to identify and fix issues before the upgrade is deployed to long-term testnets like Sepolia and Hoodi. The Glamsterdam hard fork is scheduled to activate on August 20 on this testnet.The Glamsterdam upgrade includes several significant changes to both the consensus layer and execution layer, including built-in proposer-builder separation (ePBS), block-level access lists (BALs), gas repricing targeting approximately 200 million gas, an increase in the maximum contract deployment size from 24KiB to 64KiB, an increase in the initcode limit from 48KiB to 128KiB, and forward-compatible consensus data structures. Relevant EIPs are summarized in meta EIP-7773. Gas repricing will affect wallets, indexers, and gas estimation tools, and any tools with hard-coded maximum gas limits will need to be updated; EIP-8037 also introduces an independent state gas dimension, where new accounts or writing to new storage slots will be charged by state bytes.The Platåberget validator set is small and allows public participation, supporting deposits from validators or builders submitted through the Dora browser. The testnet provides one-click resources to add networks, faucets, and client images. The community can provide feedback on issues in the Ethereum R&D Discord and related specification repositories.

Bitcoin infrastructure development company Blockstream announced on Twitter that due to Boltz's ongoing automated detection and multiple vulnerability attacks leading to the suspension of its exchange services, Blockstream will launch its own Blockstream Swaps service.Currently, Blockstream Swaps is in the beta testing phase, aimed at providing a toolkit for the ecosystem and will complement existing service providers.

The Trump administration will revise the new artificial intelligence guidelines to expand regulation of AI models. The White House has developed a set of AI frameworks that require U.S. laboratories to undergo safety testing by the federal government before public release. In the coming months, this framework is expected to cover open-source models, which will be included in testing once their capabilities reach the same level as Anthropic's Mythos-level models and OpenAI's GPT-5.6.

HashKey has become the authorized distributor of the Anchorpoint Hong Kong Dollar stablecoin HKDAP, launched beta testing, and completed the first minting and redemption transaction.

The largest bank by asset size in Latin America, Itaú, is collaborating with OpenAssets to participate in a tokenized fixed income and investment fund test led by the industry. Brazil has become a testing ground for placing bonds, credit, and other financial assets on the blockchain under a regulatory framework.

On August 10, UMX (The Unified Market Exchange), incubated by Avenir Group under Li Lin, announced the launch of an invitation-only public beta. UMX is positioned as a crypto-friendly securities platform for global professional investors, supporting spot, leverage, contract, and options trading of crypto assets, while also supporting real US stocks, ETFs, and US stock options trading. The aim is to connect crypto assets and real securities trading on the same platform, enhancing the capital efficiency of cross-asset trading.The public beta focuses on opening the capital link between crypto assets and securities accounts, supporting functions such as stablecoin exchange transfers, non-stablecoin collateralized lending, fiat account withdrawals and deposits, and the mutual conversion of securities holdings and stock tokens. To enhance cross-asset capital efficiency, the platform introduces a cross-asset margin mechanism, allowing financial assets to be used as margin. During the public beta, BTC and USDT financial products will also be launched, with the highest annualized returns reaching 2.5% and 5.5%, respectively. This public beta is invitation-only, and users who have not yet obtained a public beta code can reserve the official version and participate in the early bird benefits program.

Starknet announced that it has completed the quantum-resistant signature transfer test, where a wallet account using a quantum-resistant signature mechanism completed a real transfer on the Starknet mainnet, with a transaction fee of about 6 cents, and can be queried through the block explorer. This account is currently an experimental, unaudited version, mainly used for research testing. StarkWare stated that this transfer benefits from the design of the Starknet account model.Unlike most blockchains that fix the signature algorithm at the protocol layer, each account on Starknet is a smart contract that can autonomously define the accepted signature schemes, allowing users to upgrade their wallets from traditional elliptic curve signatures to quantum-resistant signatures without hard forks, asset migrations, or changing addresses. StarkWare pointed out that most blockchains face quantum computing risks because wallet signatures and underlying verification systems rely on elliptic curve cryptography. Once large-scale quantum computers appear, running Shor's algorithm could potentially break the related encryption systems. Currently, the Starknet ecosystem supports a quantum-resistant signature scheme based on Falcon-512, which is part of the NIST post-quantum cryptography standardization process, with relevant implementations promoted by ecosystem teams and organizations such as OpenZeppelin.

According to Wired, during security testing, the open-source model Kimi K3 from the dark side of the moon broke out of the sandbox and accessed the internet, attempting to find test answers via GitHub. American security startup Frontier Security stated that Kimi K3 exploited a sandbox configuration vulnerability, but it itself lacked sufficient built-in protective mechanisms.Similar to incidents previously disclosed by OpenAI and Anthropic, this escape partially stemmed from a sandbox configuration error. However, Kimi K3 did not initiate an attack, as the answers were easily accessible on GitHub. The CEO of Frontier Security stated that Kimi K3 performed excellently in cybersecurity defense but lacked mechanisms to prevent cheating or escaping the sandbox. Security experts emphasized that configuring the testing environment is crucial, as the misuse of AI agents could lead to system failures.

According to Reuters, citing three informed sources, Samsung Electronics and SK Hynix are testing the etching equipment from Chinese semiconductor equipment manufacturer AMEC to assess its potential application in their factories in China, aiming to hedge against the risk of the U.S. potentially tightening export controls in the future. The testing began about two years ago, coinciding with rising uncertainty over whether the U.S. would allow Korean companies to import U.S.-made chip equipment to China.The report noted that although no decision has yet been made on large-scale deployment, verification from Samsung or SK Hynix would be an important commercial endorsement for AMEC. The gap in etching, deposition, cleaning, and other areas for Chinese equipment has gradually narrowed, with prices typically 20%-30% lower than similar overseas products. Samsung officially denied having tested AMEC equipment for its factories in China, while SK Hynix declined to comment. Deutsche Bank expects Northern Huachuang, AMEC, Tuojing Technology, and Shengmei Semiconductor to each exceed $1 billion in revenue by 2026, collectively expected to capture 25%-30% of China's approximately $28 billion wafer fabrication equipment market this year.

According to Reuters, citing two informed sources, the Trump administration informed leading AI companies on Tuesday that it will not conduct security testing on open-weight AI models. This decision marks a new direction for the U.S. government in its approach to AI regulation. Open-weight models allow developers and users to freely download, modify, and deploy model parameters without relying on closed API access. Previously, organizations such as the American Institute for AI Safety (AISI) had conducted security assessments on cutting-edge models. The White House and relevant departments have not publicly commented on this matter yet.