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According to TechRadar, Palantir cofounder Joe Lonsdale is reportedly supporting Safer Human Medicine, a startup planning to establish a large-scale macaque breeding facility in the United States that would eventually house 30,000 monkeys. The initiative aims to address the supply pressure on animal test subjects caused by AI-accelerated drug discovery.

AI technology enables researchers to generate potential drug candidates more rapidly, increasing the need for toxicity, safety, and biological effect testing. Macaques are frequently selected due to their biological similarities to humans. However, US laboratories face severe shortages after China banned exports of lab monkeys in 2020 (previously accounting for about 60% of US imports) and alternative sources like Cambodia faced allegations of wild-caught animals being misrepresented as captive-bred. Reported prices for macaques have surged from approximately $3,000 in 2019 to over $20,000 today, a 700% increase.

Safer Human Medicine plans to gradually expand its population over approximately 20 years, starting with an initial stock of 500 to 1,000 macaques obtained from Asian facilities. However, female macaques typically reach reproductive maturity around four years of age and produce one offspring annually, meaning domestic capacity will take a long time to scale. Additionally, the plan has attracted criticism from animal welfare advocates, potentially leading to legal challenges that could further delay the project.

On October 30, space intelligence company InSpatio launched China's first embodied simulation data collection center in Hangzhou. According to InSpatio, the facility is projected to produce 1 million hours of simulation training data per month, reducing the comprehensive cost per data unit to approximately one-tenth of traditional real-machine teleoperation methods. This initiative aims to address the persistent shortage of high-quality training data in the field of embodied intelligence.

Currently, data acquisition for embodied AI relies on two main paths: physical world collection via real-machine teleoperation and first-person view (EGO) wearable devices, and digital world generation through 3D modeling and physics simulation. However, both face efficiency bottlenecks. Physical collection is limited by time, site access, and environmental adjustment flexibility, often yielding less than half an hour of effective trajectory per operator-hour. Traditional simulation modeling depends on manual scene construction, taking days or weeks, and struggles to maintain geometric and physical consistency, leading to the 'Real2Sim Gap' during transfer to reality.

InSpatio's proposed solution is to build an 'Embodied Token Factory.' Using its proprietary Topos spatial intelligence base, the system converts real-world spaces into interactive digital scenes through three automated industrial lines. Upstream, portable scanning or native 3D generation captures over 100 square meters of space in 15 minutes. The system then performs object decomposition, blind-spot completion, and physical calibration to transform these scans into runnable digital masters within hours.

Midstream, once scenes are digitized, the system can launch multiple parallel simulation channels in the cloud. The underlying physics engine calculates mechanical results in real-time, filtering out failures to batch-generate embodied tokens containing depth, pose, contact force, and action semantics. This shifts data production volume from reliance on manual on-site setup to dependence on digital scene reusability and cloud computing scale.

Downstream, InSpatio implements deduplication standards and tiered data delivery based on spatial geometry, physical interaction, and task closure evaluation. Data production also forms a closed loop with model testing, allowing targeted generation of complex environments to address specific weaknesses identified in client models. While this industrial process demonstrates scalability potential, the actual effectiveness of the generated data and its contribution to narrowing the Real2Sim Gap remain subject to future independent verification.

Many open source maintainers complain about the influx of AI-assisted pull requests (PRs): large language models are generating code submissions that flood repositories, significantly increasing the review burden. Simon Willison, author of the open-source tool llm, discusses his practical experience with this issue in the release notes for version 0.37—he states that llm is the project most affected by this trend, but he does not consider it a problem.

His response operates on two levels. On the tooling level, he uses Opus 5.5 or GPT-6 Astra to assist with triage, sifting through numerous submissions to identify the most valuable fixes. On the cognitive level, he no longer views AI-assisted PRs as code submissions requiring line-by-line review; instead, he treats them as bug reports with attached examples—sometimes landing them directly, sometimes implementing his own fix.

This release includes fixes from 12 contributors, almost all of which were AI-assisted. Willison notes that the review burden was far less than he had anticipated.

Dutch cybersecurity startup Hadrian announced it has completed a $40 million Series B funding round, bringing its total raised to $65 million. The company plans to use the new capital to expand operations in Europe, the Middle East, Africa, and the United States, as well as to increase investment in engineering and research.

Founded in Amsterdam in 2021, Hadrian uses AI agents to simulate attacker techniques, helping enterprises inspect systems and applications exposed to the public internet. Its External Attack Surface Management product, Atlas, continuously inventories assets and performs security tests when new assets are discovered or existing ones change. When new vulnerabilities are published, Atlas compares software versions to identify affected assets and tests whether the vulnerabilities can be exploited.

In March this year, Hadrian launched Nova, an AI-agent penetration testing product. Nova allows enterprises to specify the applications and scope for testing, with multiple AI agents attempting to chain different vulnerabilities to assess potential attack paths and their impact. According to Hadrian, most tests can be completed within 24 to 48 hours. UK cybersecurity media outlet Cyber Insider noted that while automated penetration testing helps identify issues, enterprises must still accurately manage external assets and control the testing process to ensure security risks are genuinely reduced.

Apple Inc. released its financial results for the fiscal fourth quarter ended September 26, 2026, on November 2. The company reported total revenue of $114.477 billion, a 12% increase year-over-year. Net income stood at $30.711 billion, with diluted earnings per share (EPS) at $2.10, up 14%. Gross margin reached 47.9%, benefiting approximately one percentage point from tariff refunds.

Performance varied across segments. iPhone revenue hit a record for the period at $57.583 billion, while Mac revenue was $10.792 billion. Notably, Services revenue reached an all-time high of $31.573 billion. iPad revenue was $6.229 billion, and Wearables, Home and Accessories generated $8.3 billion. Greater China revenue rose to $17.539 billion from $14.493 billion in the same period last year.

This marks the first quarterly report under new CEO John Ternus. During the earnings call, Ternus stated that Mac sales hit an all-time high this year and highlighted efforts to commercialize AI capabilities. Some server-side AI features will have usage limits to encourage higher-demand users to subscribe to iCloud+. Ternus repeatedly referenced the new Siri, calling it "the Siri we've always wanted."

Management provided optimistic guidance for the next quarter, projecting total revenue to grow 7% to 10% year-over-year in the December 2026 quarter, setting a new record for quarterly revenue. Services revenue is expected to grow 8% to 9%.

The U.S. Census Bureau has made a significant wording change to a key question in its bi-weekly Business Trends and Outlook Survey (BTOS). This shift stems from Executive Order 14434, signed by the U.S. President, which aims to inaugurate an era of "Super Intelligence."

According to an analysis of the latest survey documents by developer Simon Willison, starting with the collection cycle on October 19, 2026, the question regarding artificial intelligence usage was changed from "In the last two weeks, did this business use Artificial Intelligence (AI)..." to "In the last two weeks, did this business use Super Intelligence or SI (formerly referred to as Artificial Intelligence or AI)...". The examples provided also shifted from general terms like machine learning and natural language processing to politically charged phrases such as "SI-powered data analysis" and "generative SI for marketing."

This change directly impacts data comparability. Previously, the question had been broadened in November 2025 from "producing goods or services" to "any business functions," causing the percentage of affirmative responses from large companies to jump from around 12% in September 2025 to 30% by February 2026, and reaching approximately 40% by October 2026. However, with the introduction of the new "Super Intelligence" wording, currently published data still reflects the old "Artificial Intelligence" definition, while the first results incorporating the new wording are expected to be released the following Thursday. This means that in the short term, the official tracking of U.S. corporate AI adoption via BTOS will suffer from definitional discontinuity or gaps.

For investors and researchers monitoring AI industry penetration and macroeconomic impact, this event highlights how non-market factors can distort core statistical indicators. When official definitions are replaced by politicized terminology, historical trend lines may become invalid, and new baselines require time to establish. Readers should treat recent conclusions drawn from BTOS data about "accelerating" or "slowing" AI adoption with caution, awaiting the release of the first batch of data under the new criteria to recalibrate expectations.

Match Group announced during its third-quarter 2026 earnings call that it is testing a cheaper subscription plan for its dating app Hinge. The move aims to convert more users into paying customers as people grow increasingly tired of expensive dating app subscriptions. Steve Bailey, Chief Financial Officer of Match Group, stated the new tier is designed for women and price-sensitive users, offering features like additional filters and custom location boundaries.

The decision highlights a strategic tension within Match Group’s portfolio. According to Bailey, Hinge is one of the company’s highest-priced products, with revenue per payer at approximately $30—nearly twice that of Tinder. However, Hinge’s payer penetration is only slightly more than half that of Tinder. While Hinge reported strong growth in Q3 2026, with global monthly active users up 12% year-over-year and direct revenue increasing 19%, its paid subscriber count stands at 2.3 million. In contrast, Tinder generated $480 million in direct revenue but saw a slight 2% decline year-over-year, with 8.9 million paid users.

This shift marks a reversal from Hinge’s previous strategy. In 2023, the company experimented with a more expensive tier aimed at users willing to pay extra for advanced features. Now, facing broader subscription fatigue, Match Group is exploring whether a more affordable option can appeal to a wider audience without diluting the brand’s premium positioning. The exact price of the new tier remains undisclosed.

CircuitHub, a printed circuit board (PCB) manufacturer based in Massachusetts, USA, claims that its robotic manufacturing system called 'The Grid' can complete certain orders faster than its Chinese competitors. The company states that demand is already well ahead of what it can build and plans to quintuple its capacity this year.

'The Grid' is described as 'one large robot,' replacing conventional fixed production lines with coordinated machinery and mobile platforms transporting boards between manufacturing stations. Each board follows its own route, allowing different products to undergo manufacturing simultaneously without conventional production-line changeovers. The system handles 100 different designs using shared manufacturing equipment.

CircuitHub compared its factory with a typical quick-turn manufacturer in Shenzhen, China, using an example involving five assembled motor-controller boards. Producing these boards through 'The Grid' costs $630, with delivery to a United States address taking four days. The equivalent Shenzhen order costs approximately $465 after shipping expenses and United States import tariffs, with median delivery taking nine days. The American factory therefore charges approximately $165 more, although customers receive their completed circuit boards five days earlier.

CircuitHub argues that the shorter delivery period gives engineering teams additional time to continue development rather than waiting for foreign manufacturing. However, this comparison comes from CircuitHub and concerns one particular order, rather than independently established performance across different manufacturers.

Direct Numerical Simulation (DNS) is the most accurate method for simulating turbulence in computational fluid dynamics (CFD), solving all scales of fluid motion without relying on turbulence models. However, its extreme demand for memory and compute resources often limits its application to smaller cases or requires specialized high-performance computing infrastructure.

CoreWeave, an AI infrastructure provider, partnered with Convergent Science, a CFD software developer, to benchmark CONVERGE CFD software on NVIDIA HGX B200 GPU clusters. The test successfully completed a DNS case containing 536 million grid cells in 3.62 hours using two NVIDIA HGX B200 instances, comprising 16 NVIDIA Blackwell GPUs.

The study also compared two distinct hardware classes: NVIDIA RTX PRO 6000 Blackwell Server Edition and NVIDIA HGX B200. The RTX PRO server is suited for everyday industrial simulations that fit within a single node, utilizing PCIe for intra-node communication. In contrast, the HGX B200, with significantly higher memory bandwidth (~7.7 TB/s vs 1.6 TB/s) and NVLink interconnects, is designed for ultra-large cases spanning multiple nodes.

It is important to note that these results come from a vendor-partnered benchmark designed to demonstrate theoretical upper limits and scaling characteristics under specific conditions. Real-world production performance may vary depending on physical models, boundary conditions, and software optimizations.

According to a report by South Korean media outlet Byline Network, Viva Republica, the operating entity behind the fintech giant Toss, is planning to enter the Japanese securities market. The company intends to acquire 100% of the shares of Cher Securities, a mobile-specialized brokerage under the Tokai Tokyo Financial Group, through its overseas business arm, Toss Global. The transaction value is estimated at approximately 4 billion yen (around 34 billion won), with a target completion date by March 2027.

Cher Securities, established in 2019, is an online brokerage focused on small-scale stock trading and investment trust services via smartphones. Toss stated that the decision to acquire Cher Securities stems from the alignment of their service philosophies, particularly the focus on accessibility for beginner investors. The strategy involves leveraging Cher’s existing customer base and operational experience while integrating Toss’s mature mobile trading system (MTS/WTS) technologies to gradually enhance services without aggressive restructuring.

This strategic move reflects Toss’s search for new growth engines. While Toss boasts a massive user base in South Korea, its profitability relies heavily on its domestic securities business, where growth margins are tightening. Compared to the fierce competition and strict regulatory barriers faced by foreign entrants in Japan’s banking sector, the securities industry offers a more realistic entry point. Japan’s relatively stable political environment and well-established financial regulatory framework are also viewed as favorable conditions for testing digital financial platform models.

For investors, this acquisition represents a critical step in Toss’s globalization strategy. Previously, Toss launched its eponymous app in Australia via Toss Global. If the integration in Japan proceeds smoothly, it could contribute direct overseas revenue and add a significant “cross-border platform capability” narrative to the company’s potential IPO valuation. However, the deal is not yet finalized and remains subject to approval by the Japanese Financial Services Agency (FSA) and subsequent integration risks.

British high-performance sports car manufacturer McLaren Automotive has announced a partnership with engineering computing platform Rescale and chipmaker NVIDIA to integrate Agentic AI and Physics AI into its vehicle engineering development process. This initiative aims to address the core bottleneck of traditional Computer-Aided Engineering (CAE): high computational costs that limit the scale of design exploration.

In conventional automotive engineering, evaluating body structure stress, dynamic performance, and material durability requires running complex numerical simulations. While these high-fidelity simulations provide reliable physical evidence, each run consumes significant time. When engineers need to repeatedly adjust parameters or compare numerous design options, computational resources become a constraint, limiting the number of designs assessable within the development cycle. For high-performance vehicles requiring precise tuning, accelerating iteration while maintaining analytical reliability is a critical challenge.

McLaren's solution combines "Surrogate Models," a type of AI that learns physical system behavior from existing simulation or experimental data to map relationships between design parameters and physical outcomes. Instead of re-running high-precision numerical simulations every time, surrogate models can predict results under different design conditions at millisecond speeds within their applicable range. According to data provided by Rescale, McLaren engineers can now evaluate thousands of design variations in hours, compressing complex simulation tasks that previously took days into minutes. This mechanism allows teams to first screen out designs worth deeper analysis using low-cost predictions, then perform high-precision validation on key candidates.

Beyond Physics AI, the platform introduces AI Agents to handle repetitive engineering tasks. Rescale's platform integrates agentic engineering technologies into McLaren's R&D environment, automating the chaining of simulation and analysis workflows. This frees engineers to focus more on design judgment and complex problem analysis. Additionally, the platform uses machine learning to predict manufacturing performance for carbon fiber structures and organizes past project insights via engineering knowledge graphs to support subsequent decisions.

Notably, Rescale claims the platform can boost professional engineering productivity by up to 3x, though public materials do not detail the measurement methodology, sample scope, or independent verification methods for this figure. Surrogate models do not fully replace traditional CAE; their predictive capability depends on training data and applicability ranges, meaning critical designs still require high-precision simulation confirmation. This case demonstrates the practical application value of AI in industrial R&D, extending beyond "code generation" to "physical simulation acceleration."