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Anthropic, the company behind Claude, has addressed user complaints about its performance decline. The issue at hand is not that the model itself has become less capable, but rather that users have been misinterpreting how to use it effectively. This misunderstanding has led many developers to upgrade to more expensive models in an attempt to improve their AI’s intelligence, only to find that this trick does not always work as expected.

In fact, switching to a larger model is often not the solution to improving performance. According to Anthropic, users have been confusing two options in Claude Code: Model selection and Effort level. The default setting for Effort level was recently changed from high to medium, which has led many users to believe that their AI’s capabilities are declining.

The root cause of this issue is not a problem with the model itself but rather a misunderstanding about how to use it effectively. Many developers have been upgrading to more expensive models in an attempt to improve performance without realizing that the Effort level setting plays a crucial role in determining the AI’s behavior and output.

In March, many users reported issues with their Claude Code, including errors such as failing to read required files, skipping scheduled tests, and abandoning tasks halfway. The head of AI at AMD, Stella Laurenzo, conducted an investigation into these issues and found that Claude’s thinking volume had dropped by 67% compared to before February.

The issue was not with the model itself but rather a result of the default Effort level being changed from high to medium in March. This change was noted in the official update log, but many users did not notice it until later when they realized that their AI’s performance had declined.

After bearing the backlash for a month, Anthropic restored the default Effort level on April 7 and reset the usage quota for all subscription users. It was then that most users realized this switch had been right beside them all along, secretly determining whether the AI would devote its full capacity to work for them.

Anthropic’s official breakdown of how Claude works can be summed up in one simple sentence: Model determines capability, Effort determines dedication. The model defines the underlying capability and is backed by a set of ‘frozen weights’ that are permanently fixed during training. These weights cannot be altered or modified during inference, meaning that users can guide the AI but not train it.

Switching models essentially means replacing the entire set of weights to take over your task, solving the problem of whether the AI is capable of doing it. However, this does not necessarily mean that a larger model will always outperform a smaller one. In fact, Anthropic has found that a small model running at high Effort can absolutely outperform a large model running at low Effort on many tasks.

The division of responsibilities between Model and Effort is crucial to understanding how Claude works. The official judgment framework becomes extremely useful once users understand this concept. When Claude makes a mistake, the first step is always to check the context: is the prompt clear? Are all required tools provided? Is the CLAUDE.md file configured correctly?

Most so-called ‘AI getting dumber’ issues are rooted in incorrect configuration or insufficient dedication rather than an issue with the model itself. If the context is indeed correct but the AI still makes mistakes, users should ask themselves: is it incapable of doing the task, or is it just not trying hard enough? Not trying hard enough is easy to identify: it skips files that should be read, fails to run tests, and abandons a refactoring task halfway to ask for help.

If it’s genuinely ‘incapable’, then no amount of increased Effort will help. The issue lies with the model itself, and users need to switch to a more powerful model. Anthropic has given an analogy to explain this concept: Sonnet is like a versatile all-rounder who has an entire afternoon available to work; Opus is an expert who only gives you 5 minutes.

Each model has its strengths and weaknesses, and users should not treat it as a rigid token budget limit. The Effort setting in Claude Code determines how thoroughly and confidently the task needs to be completed, governing text responses, tool calls, and extended thinking all at once. For example, for the exact same prompt, Claude at high Effort can generate roughly 7 times more tokens than at low Effort.

The official team has also released an illustration showing that a small model running at high Effort can genuinely outperform a large model running at low Effort on many tasks. This is because a small model paired with sufficient context and high dedication can handle far more work than expected.

In the past, users would simply pick the most powerful model without considering other factors such as Effort level. However, this approach has become outdated, and users now need to act like project managers, assigning different roles and dedication levels to different models. This requires a new skillset: orchestrating AI agents rather than just relying on more powerful models.

The newly added ‘ultracode’ option in Claude Code’s Effort menu brings this orchestration mechanism directly into the product. When selected, Claude gets the xhigh Effort level, plus an authorization to decide on its own whether to spin up a team of AI agents, split the task, and run it in parallel.

The era of only looking at model rankings is passing, and orchestrating models is becoming a core skill. Whoever learns to assign tasks to AI first will get a head start, using a Claude that truly goes all out for them.

Digital bank Monzo has launched an automated debt consolidation service, streamlining the process of paying off existing debts for its customers. The new offering is made possible through a partnership with credit score provider ClearScore, which brings its Clearer technology to bear on the task.

The service works by bringing together all of a customer’s borrowing into a single loan, and then automatically finding and settling their outstanding debt directly with lenders. This means that customers no longer need to contact lenders or check balances themselves, freeing up time and reducing stress.

According to Monzo Head of Borrowing Luke Enock, the new service is designed to make it easier for customers to manage their finances and get on top of their debts. ‘For customers, this means less admin and confidence that the loan is being used to pay down what they already owe,’ he explained in a LinkedIn post.

The automated debt consolidation experience requires a Monzo current account, and is available to UK residents aged 18 and over who meet certain eligibility criteria. The service also comes with its own set of terms and conditions, which customers will need to review before applying.

Monzo’s representative APR for loans up to £10,000 is 21.8%, while the rate drops to 10.2% for loans between £10,001 and £35,000. These rates are in line with industry standards, but may vary depending on individual circumstances.

The partnership with ClearScore marks a significant development in Monzo’s mission to make money work for everyone. ‘This is another example of our commitment to using technology to simplify financial services and improve people’s lives,’ Enock said.

Clearer, the automated debt repayment technology developed by ClearScore, ensures that when a customer takes out a consolidation loan, their existing debts are automatically paid off rather than being used for other purposes. This reduces the risk of default and makes it easier for lenders to assess creditworthiness.

The new service is part of Monzo’s ongoing efforts to expand its offerings and attract new customers. The bank has recently increased spending on its ‘refer a friend’ program, which saw payouts rise by almost 40% in the year leading up to March.

At the recent Automate 2026 exhibition, Festo showcased its solutions to address the growing demands on original equipment manufacturers and system integrators. The company presented initiatives and technologies aimed at solving deployment and integration challenges in automation projects.

The complexity of machines is putting pressure on OEMs and system integrators. Machines are becoming increasingly complex with more devices, networks, and flexible production requirements. This increases the burden on engineering teams to work within limited technical resources and keep projects moving forward.

Festo’s exhibits at Automate 2026 highlighted its focus on addressing these challenges through various technologies and initiatives. The showcase included motion control, handling systems, connectivity, engineering tools, and workforce development solutions designed to improve startup visibility and reduce integration effort.

One of the key products showcased by Festo was its next-generation VTUX valve terminal platform. This IP67-rated platform integrates pneumatic control, vacuum generation, and I/O into a lightweight modular design that can be mounted directly near the application.

The benefits of this integration include reduced tubing runs, cabinet space, installation effort, and the number of separate automation components required on the machine. Festo’s VTUX terminal also features integrated vacuum monitoring, which improves troubleshooting visibility while helping to reduce compressed air consumption.

This compact design makes it ideal for end-of-arm tooling applications. Festo has further enhanced its VTUX platform with the introduction of a new CTED multi-protocol interface allowing direct connection to Industrial Ethernet networks.

The CTED module supports multiple control environments, including EtherNet/IP, EtherCAT, Modbus TCP, PROFIBUS, and CC-Link. This flexibility enables manufacturers to adapt their systems more easily to changing production requirements by eliminating the need for designing automation architectures around a single standard.

Another critical aspect of robotic performance in high-mix production environments is end-of-arm gripping systems. Festo showcased adaptive gripping and vacuum technologies that improve handling reliability while simplifying end-of-arm integration.

The company’s HPSX adaptive gripper uses food-safe silicone fingers to handle delicate and irregularly shaped products without requiring tool changes. This makes the HPSX well-suited for applications in the food, pharmaceutical, and cosmetic industries.

Festo also demonstrated its Simplified Motion Series of electric actuators which combines motor and drive into a single integrated motion package commissioned directly on the device without specialized software. This design simplifies commissioning, reduces complexity, and is more cost-effective compared to traditional electric automation systems.

The SMS series includes various configurations such as toothed belt axes, spindle axes, mini slides, electric cylinders, and rotary drives. These actuators support digital I/O and IO-Link connectivity enabling remote parameterization, backup functions, and process monitoring while reducing commissioning effort and engineering overhead.

Festo also showcased its engineering tools designed to automate workflows for designers working on component sizing, compatibility verification, accessory selection, documentation, and ordering across multiple systems. These tools compress these workflows from hours into minutes allowing engineers to configure multi-axis handling systems, size electric and pneumatic motion components, access CAD models, validate compatible accessories, generate quotations, and simplify ordering within a connected engineering environment.

These engineering tools also help reduce deployment risk by ensuring selected components and accessories are interoperable before systems are built or commissioned. Additionally, they preserve project configurations for future modification, expansion, or reorder, helping manufacturers to adapt their systems more easily to changing production requirements.

Festo’s Didactic learning system supports workforce development through hands-on mechatronics and industrial automation training tailored to the needs of manufacturers, OEMs, and technical education programs. The MPS 400 learning system is a modular Industry 4.0 learning factory that provides experiential training in automation technology, intelligent machine networking, IO-Link connectivity, and modern mechatronics workflows used throughout advanced manufacturing environments.

Festo has been continuously elevating the state of manufacturing with innovations and optimized motion control solutions for over 100 years. With more than 50 years of presence in the U.S., Festo prepares workers for current and future manufacturing technologies through its Didactic Learning Systems and partners.

The decommissioning of Tesla’s original Model S and Model X assembly line at its Fremont factory in Northern California has been completed in just 46 days. The teardown involved heavy machinery dismantling concrete pits, removing robotic arms and conveyors, and clearing the space for new production. This rapid process marks a significant step towards repurposing the valuable factory floor space for high-volume production of Tesla’s Optimus humanoid robot.

The decision to retire the Model S and Model X originated during Tesla’s Q4 2025 Earnings Call in late January 2026, where CEO Elon Musk announced that production of these vehicles would wind down by the end of Q2 2026. He described it as bringing the programs to an ‘honorable discharge.’ Custom orders ceased around early April 2026, with the final vehicles rolling off the line in early May.

A special signature delivery ceremony on May 20 marked the emotional close for these vehicles, which had defined Tesla’s early success and luxury EV segment since the Model S launch in 2012. The primary reason for tearing down the lines was to repurpose the valuable factory floor space for high-volume production of Optimus, a crucial step towards Tesla’s broader strategic shift from traditional vehicle manufacturing toward robotics and artificial intelligence.

Tesla is targeting rapid scaling with internal ambitions pointing toward tens or even hundreds of thousands of units annually by the end of 2026. Longer-term, the company is constructing a much larger second-generation Optimus facility at Giga Texas, with potential capacity reaching millions of units per year. The goal is to make Optimus a transformative product that could eventually surpass Tesla’s automotive business in scale and value.

Optimus, designed by Tesla, is a general-purpose humanoid robot aimed at performing repetitive or dangerous tasks in factories, warehouses, and eventually homes. Powered by Tesla’s AI and Neural Networks, it aims to be a versatile, affordable platform for various industries. Production of Optimus Gen 3 is already underway in limited form at Fremont, with full-scale output on the converted line expected to begin in late July or August.

Tesla’s aggressive pivot towards its next major initiative, Optimus, marks a significant shift in focus from traditional vehicle manufacturing. The company views this move as crucial for leveraging its expertise in autonomy, AI training, and high-volume production. As one era closes at Fremont, another is rapidly taking shape with the potential to revolutionize industries beyond automotive.

Tesla’s CEO Elon Musk has been vocal about his vision for a future where robots like Optimus play a significant role in various sectors. He believes that by making these technologies accessible and affordable, Tesla can drive widespread adoption and create new opportunities for businesses and individuals alike.

The decommissioning of the Model S and X lines is just one aspect of Tesla’s broader strategy to transform its operations and focus on emerging technologies like robotics and AI. As the company continues to push boundaries in innovation, it remains to be seen how this shift will impact the automotive industry as a whole.

A large crowd of protesters marched through San Francisco’s downtown area on Saturday, calling for a halt to the development of artificial intelligence. The protest was organized by Stop the AI Race, a group led by activist Michaël Trazzi, who has been advocating for a global agreement to pause AI development. According to estimates, around 200 people participated in the march, carrying signs with messages such as ‘stop slop,’ ‘it’s not too late to regulate,’ and ‘in a race off a cliff no one wins.’

Despite its ambitious goal to remove more carbon than it emits by 2030, Microsoft has seen a significant increase in greenhouse gas emissions. The company’s operational emissions rose by 25% in 2025, largely due to the construction of massive data centers to support its AI push. This surge in energy demand is putting pressure on the tech giant’s sustainability efforts.

The rapid expansion of data centers is driving up electricity consumption. As Microsoft continues to build more facilities, it needs increasing amounts of power to keep them running. The company has been using fossil fuels for some of this energy, which contributes significantly to its emissions.

One factor that has contributed to the increase in reported emissions is Microsoft’s decision to suspend purchases of renewable energy credits (RECs). RECs are certificates that allow companies to claim they have used clean power. By not buying these credits, Microsoft’s reported emissions rose accordingly.

Microsoft President Brad Smith and Chief Sustainability Officer Melanie Nakagawa acknowledged the tension between AI infrastructure growth and sustainability goals in a recent statement. They described it as ‘real’ and ‘productive,’ highlighting the need for more efficient solutions to meet growing energy demands.

The company is reevaluating some of its earlier commitments, including matching data center electricity use with renewable energy every hour. Smith and Nakagawa emphasized that this does not mean Microsoft is lowering its ambition but rather being more precise about what sustainability requires and willing to refine strategies as conditions change.

Microsoft’s situation reflects a broader trend in the tech industry. Many companies have scaled back their environmental promises following changes in U.S. government regulations and policies limiting sustainability initiatives. For instance, Chevron Corp. has signed an agreement with Microsoft to supply power from a natural gas-powered plant for its new data center facility in West Texas.

The tension between AI expansion and climate goals is intensifying as the industry’s energy demands continue to outpace efforts to reduce emissions. While Microsoft’s investments in carbon removal and renewable energy have increased, they have not kept pace with the massive energy needs of new data centers. The company had previously paused some REC purchases to focus on direct investments in clean energy infrastructure.

Microsoft has invested billions into carbon removal contracts and renewable energy projects but still faces significant challenges. The shift away from buying RECs reflects a broader push for more direct impact, though it has temporarily worsened the reported footprint.

With four years remaining until its 2030 carbon negative target, Microsoft will need to adapt quickly as conditions evolve.

Creating high-quality video content has become increasingly accessible with the rise of artificial intelligence (AI) tools. Gone are the days when producing a professional-looking video required extensive resources and expertise. Today, turning a written idea into a finished video can be achieved in a matter of minutes, not hours or even days. This shift is liberating for individuals who need to convey their message without breaking the bank: educators explaining complex concepts, marketers announcing new launches, or creators publishing daily content on social media platforms that never seem to stop.

A major problem in long-running conversations with Large Language Models (LLMs) is that the prompt payload grows out of control, loaded with redundant and irrelevant information. This can cause performance to slow down drastically, making it hard to understand what’s happening in the conversation.

To solve this issue, I created a deterministic pipeline that trims away unnecessary state before the model even sees the input.

A class-action lawsuit has been filed against Microsoft in the U.S. District Court for the Eastern District of Wisconsin, alleging that the company’s Fairwater data center is causing excessive noise pollution. The plaintiffs, three residents of Sturtevant, claim that the facility’s operation and maintenance have resulted in unreasonable and excessive noise on their properties, causing property damages through private nuisance and negligence.

The lawsuit comes as part of a growing trend of communities pushing back against the construction of data centers, which are often criticized for their environmental impact. Data centers require massive amounts of energy to power AI development and other computing needs, leading to concerns about air pollution, water usage, and increasing energy costs. Communities in several states have launched protests and pushed for legislation to regulate or halt the construction of these facilities.

The Fairwater data center is part of a larger campus that Microsoft plans to build on, with both the company and electronics manufacturer Foxconn planning to expand their operations there in the coming years. The facility has already generated complaints about noise pollution from diesel generators and heating, ventilation, and air conditioning (HVAC) systems.

According to the lawsuit, the plaintiffs have experienced significant disruptions due to the excessive noise produced by the data center’s HVAC systems, including chillers, cooling towers, air-handling units, and condenser fans. The suit also claims that Microsoft has failed to implement adequate acoustic barriers or shields to mitigate the escape of noise beyond its property.

The lawsuit seeks an unspecified amount in damages for the plaintiffs’ losses due to the data center’s operation. In response to the allegations, a spokesperson for Microsoft stated that the company is ‘aware of the lawsuit related to our facility in the Village of Mount Pleasant.’

Microsoft has made efforts to address noise complaints from residents in the past. In April, the company investigated humming noises coming from the data center’s cooling fans and implemented changes to mitigate the issue. The tech giant announced that it had fixed the problem by June.

However, despite these efforts, residents living near the data center continue to express concerns about its impact on their quality of life. Larry Neumiller, a resident who has lived in the area for nearly 40 years, stated that not only have dump trucks been a constant bother but dust from construction sites is constantly blowing around.

Dust and light pollution are just some of the issues faced by residents living near the data center. Resident Roger Johansen complained about the bright lights used during construction work, which he said has ruined his ability to see the stars at night. Another resident, Brian Schue, expressed concerns that the area had become a ‘dust bowl’ due to the constant dust from construction.

Microsoft’s project website notes that street sweepers will operate for approximately 10 hours per day to address the dust issue. However, despite these efforts, residents continue to express frustration with the data center’s impact on their community.

The village leadership in Mount Pleasant has expressed support for the data center, calling it a ‘historic milestone’ for the area. Village President David DeGroot stated that the data center is part of an effort to create one of the most advanced technology campuses in the world.

Microsoft is ending support for a significant number of products this year, affecting millions of devices worldwide. The list includes Windows 11 24H2 and Office 2021, among others, which will no longer receive security updates or bug fixes after their respective expiration dates. This marks the second consecutive year Microsoft has ended support for more products than in the previous year, with a total of 15 products set to expire in 2026.

After months of speculation, Tesla has finally made its new Model Y L variant available for purchase in the United States. This move comes as a surprise to many, given that CEO Elon Musk had previously expressed doubts about bringing this model to the American market.

The Model Y L is based on the latest generation of the Tesla Model Y Juniper and features an extended length and longer wheelbase compared to its regular counterpart. The result is a vehicle capable of accommodating six passengers comfortably.

One of the key differences between the Model Y L Launch Series and other trims is the inclusion of enhanced tech and improvements. For instance, this variant comes equipped with 18 speakers and one subwoofer, providing an upgraded audio experience compared to the 15 speakers and one subwoofer found in the Premium AWD trim.

Additionally, Tesla has re-tuned the suspension for adaptive damping and reduced road noise, ensuring a smoother ride. The third-row seats also feature child-safe airbags, further enhancing safety features. Furthermore, the six-seat setup boasts adjustable middle-row captain seats and powered armrests to facilitate easy entry into the 3rd row.

The Model Y L Launch Series is priced at $61,990 in the US, which is a significant increase from the price of the Model Y Performance, currently listed at $57,990. This means that customers will have to pay an additional $4,000 for the upgraded features and capabilities offered by the Model Y L.

Tesla has also announced that it will be offering a free 12-month subscription to Full Self-Driving (FSD) AI with the purchase of a new Launch Series Model Y L. After this period, customers can expect to pay $99 per month for continued access to FSD capabilities.

The estimated delivery timeline for the entire US is between October and November 2026, suggesting that production has already commenced at Giga Texas. Some sources have even spotted new Model Ys in the outbound lot of the factory, further indicating an active manufacturing process.

It’s worth noting that Tesla did not launch the Model Y L in Canada due to ongoing tariff conflicts between the two countries. However, Puerto Rico remains one of the few North American markets where this variant is currently available.

The initial production batch of the Model Y L Launch Series has been dubbed the ‘Launch Series’ by Tesla, a designation that typically comes with exclusive perks and benefits for early adopters. These include a badge that sets owners apart as pioneers in their community.

Yahoo Finance has emphasized the importance of user privacy, stating that it uses cookies to provide its services and applications. The company also notes that these technologies are used to verify users, implement security measures, remove unwanted emails, prevent misuse, and measure usage of their websites and apps.

In order to use Yahoo’s locations and applications, users must accept all cookies from the company and its 249 partners, who are members of the IAB Transparency & Consent Framework. This acceptance allows for information storage in devices and access to these data (essentially using cookies) as well as precise geolocation data and other personal details such as technical identifiers and browsing history.

Users can opt out of cookie usage and personal data collection by clicking on the ‘Reject all’ button, which will prevent Yahoo and its partners from storing information in devices or accessing it. This decision also blocks personalized advertising and content display, measurement of ads and content performance, target audience research, and service development.

If users wish to adjust their preferences, they can click on the ‘Manage settings for data protection’ option. At any time, individuals may revoke consent or change their choices by clicking on the link to manage cookie settings or view the privacy table on Yahoo’s websites and apps. For more information about how Yahoo uses personal data, refer to its Privacy Policy and Cookie Statement.

A comprehensive showcase of Taiwan’s technological prowess in manufacturing was on full display at the recent Automate 2026 event. The Taiwan Excellence Pavilion, featuring 23 award-winning companies, drew significant interest from attendees with its cutting-edge innovations in industrial automation and robotics.

The pavilion, organized by Taiwan’s International Trade Administration (TITA) and the Taiwan External Trade Development Council (TAITRA), highlighted the island nation’s growing role as a trusted technology partner for modern U.S. manufacturing. The event provided a platform for Taiwanese companies to demonstrate their capabilities in supporting next-generation smart manufacturing.

The level of engagement from manufacturers, system integrators, and automation leaders throughout the four-day event was impressive, reflecting the increasing demand for technologies that improve agility, simplify integration, and accelerate innovation. According to TAITRA spokespersons, Taiwan Excellence companies are uniquely positioned to meet this demand through their highly integrated ecosystem.

Throughout Automate 2026, attendees participated in educational presentations and technology showcases highlighting Taiwanese innovations. The event featured a series of daily events and product tours that allowed visitors to explore the technologies driving the next generation of smart manufacturing.

The Taiwan Excellence Showcase was one of the highlights of the event, featuring six companies – TECHMAN ROBOT, Syntec, TOYO, Axiomtek, SINTRONES, and DFI – presenting technologies addressing key trends discussed throughout Automate 2026. These included robotics, AI-enabled automation, industrial computing, and smart manufacturing.

Guided tours of the pavilion provided attendees with a deeper understanding of how Taiwan’s award-winning technologies support modern manufacturing. The tours allowed visitors to hear directly from exhibitors, explore real-world applications, and gain insights into the latest innovations in industrial automation and robotics.

The strength of Taiwan’s innovation ecosystem was acknowledged by Clarissa Schwendeman, director of marketing for the Association for Advancing Automation. She noted that collaboration between manufacturers, technology providers, integrators, engineers, and global partners is driving progress in automation and that Taiwan Excellence plays an important role in advancing global business and technology.

Many trends highlighted at Automate 2026 – including AI-enabled automation and smart factory integration – are increasing the need for technologies that improve flexibility, interoperability, and operational efficiency. Taiwan’s highly integrated ecosystem of technology providers, manufacturers, and engineering specialists is well-positioned to help manufacturers address these challenges while accelerating innovation.

The Taiwan Excellence Awards were established in 1993 by Taiwan’s Ministry of Economic Affairs (MOEA) to recognize exceptional achievements in product innovation. The awards assess products based on four important factors: research and development, design, quality, and marketing – with the key criterion being that the products are made in Taiwan.

Quantum computing design automation just got a huge boost thanks to QC Design’s Plaquette framework. This new tool lets researchers simulate real-world physical qubit imperfections with unprecedented accuracy.

For years, the field has been stuck on uniformly distributed Pauli noise assumptions - an oversimplification that can underestimate logical error rates by over 10 times too much. But not anymore: thanks to Plaquette’s ability to define device physics exactly once using Kraus operators or experimentally reconstructed quantum channels.

QC Design’s architecture is designed to automate hardware-aware simulation for circuit-based, measurement-based, and fusion-based architectures. This automation lets teams calculate authentic logical performance boundaries, allocate error budgets, and determine real physical-to-logical qubit overhead requirements all in one go - without needing a team of experts to rewrite the code.

The Plaquette framework consists of four distinct backend simulator classes: stabilizer samplers for rapid Pauli noise calculations; XPauli samplers for solving state leakage and environmental noise sectors; near-Clifford samplers tailored to capture coherent control over-rotations and miscalibrations; and full-state simulators providing exact, unapproximated reference calculations.

QC Design has validated the performance of its XPauli and near-Clifford engines against full-state simulations involving tens of thousands of physical qubits. And the results are impressive: Plaquette’s simulation results match physical hardware test data within statistical uncertainty - a game-changer for quantum hardware manufacturers.

Real-world physical qubits experience complex open-system physical noise that varies depending on their modality. For example, superconducting transmons suffer from leakage out of the primary computational subspace; neutral atoms experience intermediate-state scattering during Rydberg gate execution; and trapped ions induce motional heating as their underlying vibrational string modes absorb ambient phonons.

Traditional simulation workarounds obscure actual physical processes by using abstracted mathematical approximations rather than real device physics. This requires extensive custom software engineering for every minor adjustment to a hardware team’s fabrication recipe - not an ideal situation.

The Plaquette framework addresses these limitations by allowing teams to define their device physics exactly once and then automatically mapping that unified error description into numerical representations required by its backend simulator classes. This automation streamlines the simulation process and lets researchers accurately calculate logical performance boundaries, allocate error budgets, and determine real physical-to-logical qubit overhead requirements.

The Plaquette framework’s ability to automate hardware-aware fault-tolerant simulation is a significant advancement in quantum computing design automation. By providing an accurate and reliable method for simulating real-world physical qubit imperfections, QC Design has opened up new possibilities for the development of efficient quantum computers that can actually be built with confidence.

The limitations of idealized Pauli noise models are well-documented in the field of quantum computing. But now that Plaquette’s simulation results match physical hardware test data within statistical uncertainty, we know that real-world physical qubits require complex open-system physical noise descriptions - and these need to be taken into account when designing quantum computers.

Birmingham, AL - Researchers are working to validate a cutting-edge artificial intelligence (AI) model designed to predict long-term clinical outcomes in patients with early-stage hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2−) breast cancer. The study aims to integrate digital pathology images with limited clinical data to provide more accessible and cost-effective biomarker testing than traditional molecular assays.

According to the American Cancer Society, approximately one-third of all newly diagnosed cancers in women each year are breast cancer cases. With a median age at diagnosis of 62 years, this type of cancer is relatively rare among younger women. Despite some fluctuations over the past few years, incidence rates have been rising by about 1% annually.

The study’s focus on early-stage invasive breast cancer patients with HR+/HER2− subtype highlights the need for more effective prognostic and predictive tools in cancer care. Enhancing these tools has the potential to improve patient quality of life while reducing overall disease burden. Artera’s multimodal AI (MMAI) model is designed to support clinicians in making risk-based decisions for adult women with HR-positive early-stage breast cancer who have no clinically or pathologically defined distant metastases, within recommended clinical guidelines.

The study will use a retrospective chart-review approach, analyzing patients with a median follow-up of 10 years. Participants with noninvasive disease (pTis) or recurrent or metastatic disease (pM1) at baseline were excluded from the analysis. The primary end point is distant recurrence or metastasis, while exploratory end points include breast cancer–specific mortality, overall survival, and recurrence- or disease-free survival.

The study’s data analysis tools will leverage digital pathology images with limited clinical data to generate a prognostic algorithm score of interest. This unique approach addresses critical gaps in cancer care by providing more accessible, rapid, and cost-effective biomarker testing than traditional molecular assays. By validating Artera’s MMAI model, researchers hope to improve patient outcomes and streamline the decision-making process for clinicians.

Enrollment at the University of Alabama at Birmingham (UAB) Hospital is anticipated to be completed by April 2026.