DEV Community: Valentin Podkamennyi

Secure AI Agent Deployment with Microsoft Execution Containers

Valentin Podkamennyi — Fri, 04 Sep 2026 00:35:03 +0000

Microsoft Execution Containers provide a cross-platform framework for isolating AI agents within secure sandboxes to protect private data and system integrity. This technology allows developers to manage the lifecycle of autonomous code while ensuring that unpredictable agentic workflows do not access sensitive local files or unauthorized network resources.

The Evolution of Agent Security and Isolation

Trust remains a significant hurdle for developers building modern AI agents, particularly those operating on edge systems. When agents combine local processing with cloud-based intelligence, they often require access to sensitive information to be effective. However, granting this access creates a risk that the agent might call unintended APIs or compromise private user data.

Historical attempts to launch autonomous agents in the 1990s largely failed because of these security concerns. Delivering arbitrary code to local machines proved too risky for mainstream adoption. Today, hardware-assisted virtualization has changed the landscape. This technology serves as the foundation for modern security models, including isolated operating system components and cross-platform tools like the Windows Subsystem for Linux.

Microsoft now utilizes these virtualization advancements to build a more reliable framework for agent operations. By running agents in secure containers or microVMs, the system separates their activities from the primary operating system. This isolation ensures that even if an agent receives a poorly constructed prompt, it cannot delete critical system files or leak sensitive information.

Managing Developer Environments

Developers need a way to build code in flexible environments while still planning for restricted production deployments. Microsoft Execution Containers (MXC) address this by offering a policy-based restriction model. This framework allows for the creation of managed, isolated containers that follow specific security protocols.

Applying Policy-Based Controls

The MXC system works across Windows, macOS, and Linux, providing a unified abstraction layer. This means a developer can write a security policy once and apply it across different operating systems. These policies are essential for defining the boundaries of what an agent can and cannot do during its execution.

Architecture and Implementation of MXC

MXC is an open-source project developed using the Rust programming language. It relies on JSON for configuration and provides TypeScript SDKs to help developers manage the various stages of a sandbox. These stages include provisioning, starting, executing, stopping, and de-provisioning the environment where the agent resides.

The framework supports both one-shot executions for quick tasks and long-running workflows for complex business processes. To use the tool, developers need a Rust toolchain and a current version of Node.js. While pre-built binaries exist, building from the source code ensures access to the latest security updates and policy improvements.

Recent updates to the platform have enhanced networking security and refined the policy schema. The development team emphasizes that these policies represent user intent, with a default stance of denying all actions. Users must explicitly define what an agent is allowed to do, rather than trying to list every possible forbidden action.

Real-World Integration with GitHub Copilot

One prominent example of this technology in action is GitHub Copilot. In its experimental mode, users can initiate a sandboxed session via a command-line interface. This session restricts the agent’s access to specific directories like the current working path and temporary folders while limiting network connectivity to outbound requests only.

Understanding Policy Enforcement

The effectiveness of MXC depends on the underlying virtualization environment. It cannot provide more security than the host hypervisor allows. However, it simplifies the management of these environments by removing the need for developers to interact directly with kernel-level networking and hypervisor tools.

Advanced Sandbox Configurations and MicroVMs

Beyond standard containers, MXC supports specialized environments like the Windows Sandbox. This lightweight desktop feature can be configured to allow agents read-only access to specific host files while remaining isolated. This setup is ideal for “shadow” agents that process data in the background without interfering with the user’s active session.

For high-risk applications, developers can utilize microVMs through technologies like Hyperlight. MicroVMs host single-process applications in a microkernel-style environment, offering even deeper isolation than traditional containers. This allows a low-risk orchestrator to trigger more specialized, higher-risk agents without endangering the entire system.

This approach is highly efficient for resource management. Developers can spin up a dense network of secure agents, using the same amount of memory and processing power typically required for a single traditional virtual machine. This efficiency is particularly valuable in edge computing environments where hardware resources are often limited.

Enterprise and Cloud Integration

Microsoft is also incorporating MXC into its cloud-based offerings, such as Windows 365 for Agents. These Cloud PCs allow agents to run in virtual environments that mirror local systems. If a failure occurs or a sandbox is breached, the environment can be reset instantly without impacting the physical hardware or user data.

Future Outlook for Agent Tooling

As a relatively young project, MXC will continue to change as the code matures and new sandboxing technologies emerge. It currently serves as a vital tool for those looking to secure edge endpoints and internal workflows. By providing a structured way to enforce trust, it enables the next generation of autonomous AI tools to operate safely.

The project highlights a shift toward user-centric security, where the primary goal is ensuring that agents remain helpful without becoming liabilities. As more organizations adopt agentic workflows, standardized isolation frameworks like MXC will become a standard part of the development lifecycle. This ensures that the power of AI remains contained within the boundaries set by the people who use it.

IonQ Researchers Test Quantum Error Decoder on MacBook Pro

Valentin Podkamennyi — Wed, 02 Sep 2026 16:10:02 +0000

IonQ researchers recently demonstrated that a standard MacBook Pro can manage error correction for a large-scale quantum machine. The study involved decoding simulated workloads for a system performing over one million operations. This finding suggests that everyday high-performance processors can handle the intensive data demands of future fault-tolerant quantum computers.

Scaling Error Correction with Standard Hardware

The research team, consisting of Min Ye, Andrii Maksymov, and Nicolas Delfosse, focused on an end-to-end decoding system. This system is designed for trapped-ion quantum computers that utilize hundreds of logical qubits. During the experiment, the team processed workloads on a single Apple M4 Max processor. The simulation reached the scale of a MegaQuOp machine, which is defined by its ability to execute one million logical operations reliably.

A primary challenge in quantum computing is that these machines do not yet exist at this scale. To overcome this, the researchers used a compiled set of quantum applications. They simulated the stream of error data that a physical machine would produce. This allowed them to test how well the decoder could keep up with a real-time quantum computation.

The results showed that the decoder functioned with minimal impact on total computation time. Under a physical error rate of 0.01%, the decoding process added less than 0.3% to the total time. Even when the error rate increased to 0.05%, the added delay stayed under 12% for all tested workloads. These statistics indicate that classical processing power is currently capable of supporting advanced quantum error correction without creating significant bottlenecks.

Managing Noise and Data Backlogs

Quantum computers are highly sensitive to environmental noise, which can flip the state of a qubit. To combat this, error correction distributes information across many physical qubits to create a single logical qubit. Specialized decoders must then interpret measurement patterns to identify and fix errors. If the decoder is too slow, it creates a backlog that forces the quantum computer to wait.

In many fault-tolerant designs, the next step of a calculation depends on the result of a previous error check. A slow decoder could essentially cause the entire quantum process to stall. Previous research often relied on custom chips or specialized graphics cards to solve this problem. However, the IonQ study shows that a well-optimized system can run on a standard CPU.

The researchers achieved this efficiency by optimizing how the decoder stores and updates information. They reduced the memory requirements of the system by more than 90%. This allowed multiple decoding processes to run at once without overtaxing the computer hardware. By using a fixed mathematical graph and updating only the error probabilities, the team minimized the computational work required during each cycle.

Evaluating Realistic Quantum Workloads

The study utilized IonQ’s walking cat architecture, which relies on quantum low-density parity-check codes. This specific design is intended to protect more data using fewer physical qubits. The simulation included magic-state factories, which are essential for performing complex T gates. These gates allow quantum computers to move beyond simple tasks and perform general-purpose calculations.

The largest test configuration featured 408 logical qubits and over 11,000 physical qubits. The team tested three specific workloads, including models used to study quantum spins and entanglement patterns. One benchmark involved over 1.3 million logical measurements. These tests were designed to see if the decoder could handle both the width of the machine and the duration of long calculations.

Timing for the tests was based on syndrome-extraction cycles of 1 to 5 milliseconds. These intervals represent near-term and long-term goals for trapped-ion hardware. Because trapped-ion systems operate at a different speed than superconducting systems, the classical hardware has a slightly larger window to process error data. This timing is a critical factor in why a standard laptop processor was successful in this specific scenario.

Limitations and Future Developments

While the results are promising, the study has specific boundaries. The performance was measured using a circuit-level noise model rather than raw data from a physical large-scale processor. Real-world hardware often presents unpredictable issues like correlated noise or calibration shifts that simulations cannot always replicate. The findings are also tied specifically to the trapped-ion architecture and its unique operating model.

The researchers noted that different types of quantum hardware might require more intensive decoding. If a machine uses faster hardware cycles or different error-correcting codes, a standard laptop might no longer suffice. Additionally, the team observed rare instances where the decoder could fail to converge on a solution. While these events are infrequent, they would require a calculation to be restarted entirely.

Future efforts will likely focus on how these classical systems can scale as quantum hardware continues to evolve. The ability to use standard processors for decoding could lower the barrier to entry for building the first generation of reliable quantum computers. By avoiding the need for custom-built silicon, researchers can iterate on error correction software more quickly as they work toward functional, fault-tolerant machines.

QuEra Automates Quantum Laser Maintenance with Anthropic AI

Valentin Podkamennyi — Mon, 31 Aug 2026 16:36:12 +0000

QuEra Computing recently utilized an artificial intelligence agent to build software that automatically repairs critical laser systems in quantum computers. This development allows the hardware to recover from technical disturbances in seconds. It represents a major step toward deploying these advanced machines at customer locations without requiring constant on-site support.

Automated Recovery Systems for Quantum Hardware

The Boston company focused on a specific challenge involving the lasers used to control neutral atoms. These atoms act as the qubits within QuEra’s systems and require extreme precision to function. Even minor environmental shifts can cause laser frequencies to drift, which halts calculations until an operator restores the system.

Historically, keeping these lasers locked at the correct frequency was a manual task. While some routine disruptions were already automated, complex failures still required intervention from the original designers. The new approach uses an AI agent to handle these sophisticated recovery tasks autonomously.

To achieve this, the company used a tool called the Model Hardware Standard. This framework allows AI models to interact with physical lab equipment within strict safety parameters. The AI agent, Claude, analyzed hundreds of failure scenarios on a testbed to develop a permanent control program.

This project produced a traditional piece of software rather than keeping the AI in constant control. Engineers can now inspect and verify the code produced by the machine. This ensures that the recovery logic is transparent and follows established safety protocols during live operations.

The performance results of the new controller were significant during extensive testing. In a series of 700 trials involving seven different fault types, the software successfully restored the laser 695 times. The few unsuccessful attempts were linked to the test equipment itself rather than the logic within the controller.

Most of these recoveries occurred in fewer than six seconds, a massive improvement over traditional methods. A human specialist would typically need up to ten minutes to resolve the same issues. Furthermore, the software never incorrectly reported a successful fix, which is vital for system reliability.

The testing environment was not limited to a sterile lab. The testbed sat in a working facility with regular foot traffic and temperature changes. The controller managed every natural disturbance during the pilot period without any help from human engineers.

Beyond simple repairs, the AI agent improved the overall stability of the laser system. It identified settings that reduced background noise by 80 percent compared to previous methods. This level of optimization matched the work of highly experienced physicists and corrected subtle errors that humans often overlook.

Scaling Quantum Technology for the Enterprise

As quantum computers move from research centers to corporate data centers, maintenance becomes a logistical hurdle. Larger machines use more lasers, increasing the probability of a failure that requires tuning. If a machine resides at a customer site far from the manufacturer, repairs can lead to long periods of downtime.

Manual adjustments often take 30 minutes and might require an engineer to travel or work during off-hours. This bottleneck prevents quantum technology from being a reliable resource for enterprise clients. Automating the maintenance of these subsystems is essential for the industry to grow beyond its experimental roots.

The current pilot focused on one critical subsystem, but the company plans to apply this method more broadly. Modern quantum computers contain many components that need constant monitoring and calibration. Using AI to generate maintenance code could eventually lead to a machine that manages its own health entirely.

The Model Hardware Standard used in this research was a joint effort between Anthropic and the Janelia Research Campus. It is currently in a research phase to ensure that giving AI control over hardware remains safe. Organizations can currently apply for access to use the framework for similar scientific and industrial purposes.

The leadership at QuEra noted that the cost of maintaining peak performance is currently one of the biggest challenges in the field. Customers expect these computers to run without needing a specialist in the room at all times. By using frontier AI models to build these controllers, the company reduces the cost and complexity of operations.

The transition to automated maintenance supports QuEra’s long-term roadmap for cloud and on-premises delivery. The company is already coordinating with Amazon Web Services to provide cloud access to its upcoming Libra system by 2028. These future systems will need the high uptime that only automated recovery can provide.

Integrating with high-performance computing environments is another key goal for the company. Partnerships with Hewlett Packard Enterprise and NVIDIA highlight the need for quantum systems that behave like standard data center hardware. Reliable, self-healing subsystems are a prerequisite for these types of professional integrations.

The success of this AI-driven approach suggests that software development for hardware control is changing. Instead of teams of specialists spending weeks writing scripts for every possible error, they can now guide an AI to explore and solve those problems. This speeds up the commissioning of new hardware wavelengths from weeks to a single night.

Future Implications for Quantum System Reliability

The ability for a system to fix itself in seconds changes the value proposition for potential users. If a quantum computer can stay online 99 percent of the time without human help, it becomes a viable tool for daily research. This level of autonomy is particularly important for national laboratories and supercomputing hubs.

While the physics of quantum computing remains complex, the operational side is becoming more user-friendly. Reducing the need for “on-call” scientists allows those experts to focus on improving the core technology. It also allows the end-user to treat the quantum computer more like a standard server and less like a delicate experiment.

The researchers demonstrated that the AI agent could handle multiple laser wavelengths with minimal reconfiguration. After the initial success, the agent was tasked with setting up a second laser system. It completed the task during an unattended overnight run, which would have normally taken a human engineer weeks of hands-on effort.

Safety remains a top priority when allowing software to modify hardware settings. The Model Hardware Standard includes emergency-stop procedures and strict boundaries that the AI cannot cross. This allows the system to experiment with new settings without the risk of damaging expensive optical equipment.

The validation of the AI’s work by an independent measuring instrument proved that the machine-generated settings were superior. The manual tuning process used by experts previously missed certain noise patterns that the AI identified. This suggests that AI-generated controllers might actually be more precise than those written by humans.

The company’s goal is to build a quantum computer that requires zero manual intervention for its basic operations. This vision requires every subsystem to have its own automated recovery logic. The current results suggest that this is a realistic goal for the near future of the technology.

This development also highlights the evolving role of AI in scientific research and industrial manufacturing. By acting as a bridge between high-level logic and physical hardware, AI can solve engineering problems that are too tedious or complex for human teams. It allows for a more iterative and rapid development cycle in hardware engineering.

As QuEra prepares for its 2028 release of the Libra system, these automated tools will likely become standard. The focus is shifting from simply building a functioning qubit to building a reliable, scalable machine. This shift marks the transition of quantum computing from a scientific curiosity to a commercial product.

The collaboration between AI developers and quantum physicists is likely to continue. As AI models become more capable of reasoning about physical systems, their utility in the lab will only increase. This project serves as a template for how other hardware-intensive industries might use AI to automate maintenance and optimization.

Ultimately, the success of this pilot means that the next generation of quantum computers will be more resilient. By solving the problem of laser stability, the company has cleared one of the most common causes of system failure. This paves the way for a more stable and accessible quantum future for researchers and businesses alike.

OpenReel Video offers browser based editing features

Valentin Podkamennyi — Mon, 31 Aug 2026 16:35:59 +0000

OpenReel Video provides a flexible and cost-free solution for individuals who need to edit video content without installing complex professional software. This web-based platform allows users to manage timelines and apply advanced transitions directly from their internet browser. It simplifies the process of combining multiple files into a single project.

Professional features in a web environment

Modern video production often requires more than just simple trimming. While standard smartphone applications handle basic cuts, they frequently lack the depth needed for multi-layered projects. OpenReel Video bridges this gap by offering a timeline-based interface that mirrors high-end desktop applications. This layout allows for precise control over how different clips interact, enabling users to synchronize audio and visual elements with accuracy.

The interface is designed to be intuitive for anyone who has used traditional editing suites. You will find your media assets organized in a panel on the left side of the screen. A central preview window allows you to monitor your progress in real-time as you make changes. To the right, an inspector tool provides detailed options for adjusting specific attributes of your selected clips. The bottom of the screen houses the timeline, which serves as the primary workspace for arranging and trimming media.

Starting a new project is straightforward and fast. When you launch the application, you choose the aspect ratio that fits your final destination. Options include vertical formats for social media stories, horizontal layouts for traditional displays, and square formats for feed-based posts. Once the project parameters are set, you can import files from your local storage and begin dragging them onto the timeline. This workflow eliminates the learning curve often associated with migrating to new production software.

Speed is a significant advantage when using a browser-based tool. There is no need to wait for a large installer to download or for a system reboot. Users can transition from an idea to a finished export in just a few minutes. This efficiency makes it an excellent choice for quick social media updates or professional presentations that require a polished look without a massive time investment.

Privacy and local processing power

One of the most distinct characteristics of this platform is its commitment to data security. Unlike many cloud-based editors that require you to upload your footage to a remote server, this tool processes everything locally. This means your private videos never leave your computer or mobile device during the editing process. It utilizes the hardware capabilities of your own machine to handle the rendering and playback tasks.

Local processing offers a significant privacy advantage for sensitive projects. You do not have to worry about who might have access to your raw footage on a company server. Because the data remains on your internal storage, you maintain total ownership and control over your digital assets at all times. This architecture is particularly appealing to corporate users or individuals handling personal family memories.

There are some technical considerations to keep in mind regarding this local approach. Since the browser is doing the heavy lifting, the performance depends on the specifications of your hardware. Users with older computers or limited memory might notice slower response times when working with high-resolution 4K files. However, for most standard editing tasks, the performance remains fluid and reliable on modern hardware.

The open-source nature of the software adds another layer of transparency. Developers and tech-savvy users can inspect the underlying code to verify how the application handles data. This community-driven model ensures that the tool remains free from hidden tracking or intrusive advertisements. It stands as a rare example of a high-quality utility that prioritizes user rights over monetization.

Advanced capabilities and mobile accessibility

Despite being a free tool, the platform includes several modern features powered by artificial intelligence. These tools are designed to automate some of the more tedious aspects of video production. For instance, the automatic captioning feature can save hours of manual transcription work. This is vital for creators who want their content to be accessible and engaging on platforms where users often watch without sound.

Text-to-speech functionality provides another level of utility. If you do not have access to a professional microphone or prefer not to use your own voice, you can generate narration directly within the editor. Various filters are also available to give your footage a consistent aesthetic or to correct lighting issues. These additions move the software beyond a simple trimmer and into the territory of a comprehensive production suite.

Accessibility extends to mobile devices through dedicated applications and clever browser workarounds. There are native apps available for both major mobile operating systems. If you prefer not to download an app, you can still use the web version on a smartphone or tablet. By switching your mobile browser to desktop mode, you can access the full suite of tools while on the go. This flexibility ensures that you can start a project on a laptop and make final tweaks on a phone.

The export process is designed to maintain high visual standards. Once your project is complete, the software generates a high-quality file that you can save directly to your device. Testing indicates that the output quality is comparable to many paid alternatives. There are no watermarks or artificial limitations on the length of your videos, which is a common frustration with other free services.

Efficiency for diverse user needs

This tool serves a wide range of users, from hobbyists to professionals who need a secondary editor for quick tasks. It removes the barriers of entry associated with expensive subscriptions and steep hardware requirements. By operating within the browser, it creates a universal experience that works across different operating systems like Windows, macOS, and Linux without compatibility issues.

The lack of an account requirement is a major benefit for those who value their time and inbox space. You do not have to provide an email address or create a password to start working. This “no-strings-attached” approach is refreshing in an era where most software requires a constant connection to a subscription service. You simply visit the site, complete your work, and close the tab when you are finished.

Because it is free of advertisements and upsells, the user interface remains clean and focused. There are no distracting banners or pop-ups urging you to upgrade to a premium version. This focus on the user experience allows for better concentration and a more streamlined creative process. It functions as a pure utility designed to solve a specific problem efficiently.

Keeping this tool bookmarked is a wise move for anyone who occasionally works with digital media. You never know when you might need to quickly stitch together a few clips or add a music track to a presentation. Having a reliable, private, and powerful editor available in your browser ensures that you are always ready to produce professional content at a moment’s notice. It represents a significant step forward for web-based creative applications.

Marvell Technology Data Center Growth Drives Target Hike

Valentin Podkamennyi — Mon, 31 Aug 2026 16:35:34 +0000

Marvell Technology recently reported fiscal second-quarter financial results that exceeded analyst expectations, yet the stock experienced a significant price drop. This article examines the underlying strength of Marvell’s data center business and why major financial institutions remain optimistic about its long-term trajectory despite immediate market volatility.

Analyzing the Data Center Performance Shift

The core of the recent financial update centers on the rapid expansion of Marvell’s data center operations. Morgan Stanley analyst Joseph Moore updated his outlook on the semiconductor firm, increasing the price target from $224 to $246. This adjustment reflects a growing belief in the company’s ability to capture a larger share of the infrastructure market. The firm maintained an equal-weight rating, signaling a cautious but positive view of the current valuation.

A major driver for this revised target is the updated growth projection for the data center segment. Marvell now anticipates this specific business unit to grow by approximately 60 percent by calendar year 2027. This is a notable increase from the previous estimate of 50 percent. Because data center sales now represent nearly 79 percent of the company’s total revenue, even small improvements in this sector have a massive impact on the overall financial health of the organization.

The demand for these products is fueled by massive capital expenditures from hyperscale cloud providers. Organizations like Google and Amazon are investing billions into their infrastructure to support expanding digital services. Marvell provides the essential components that allow these massive server farms to function efficiently. This direct connection to the largest spenders in the technology world provides a level of insulation from broader economic shifts that might affect consumer electronics.

Financially, the company delivered a revenue total of $2.74 billion for the fiscal second quarter. This represented a 13 percent increase from the previous quarter and a 37 percent jump compared to the same period in the prior year. Earnings per share reached $0.94, which surpassed the consensus among Wall Street analysts. The data center division alone accounted for $2.17 billion of that revenue, marking an 18 percent sequential increase and a 46 percent year-over-year gain.

Understanding Market Reactions and Valuation Factors

Despite the positive numbers, Marvell’s stock price fell roughly 10 percent on August 28, closing at $216.62. This downward movement confused many observers given the “beat and raise” nature of the earnings report. The explanation lies in the high expectations baked into the stock price leading up to the announcement. Investors often price in future success well before it is officially confirmed, leading to a “sell the news” event when the actual data is released.

A specific deal with Google contributed to this market dynamic. On August 19, just before the earnings call, Marvell announced an expanded partnership for custom chips. This agreement includes a provision for Google to purchase up to 58.97 million shares of Marvell at a price of $206.58. While the $12.2 billion value of this deal is impressive, management clarified that the revenue from this partnership through fiscal 2028 was already included in their existing long-term forecasts.

Traders who bought the stock in anticipation of the Google deal adding new revenue were disappointed to find it was already accounted for. This led to a quick exit for short-term investors, causing the price to retreat. However, for those looking at the company’s fundamentals, the underlying business remains on a steady path. The company is successfully transitioning its business model to reduce its reliance on a few large contracts.

The shift in Marvell’s strategy involves moving beyond just large custom application-specific integrated circuits, or ASICs. While these chips are tailored for specific high-volume customers, they carry high development costs and significant risk if a project is canceled. By diversifying its portfolio, the company is creating a more predictable revenue stream. This includes expanding into optical digital signal processors, which manage high-speed data transmission, and advanced switching technology for network infrastructure.

Future Outlook and Strategic Diversification

Marvell is increasingly focusing on what the industry calls “attach” products. These include network cards and storage controllers that are sold alongside larger systems. The company’s interconnect business is currently projected to grow by more than 70 percent this fiscal year. This rate of expansion far exceeds previous targets and demonstrates that the company is winning market share in specialized networking niches where performance is critical.

To maintain its current momentum, Marvell must continue to prove its dominance in the networking space. The market for artificial intelligence infrastructure is a key battleground. While Nvidia dominates the processor market, Marvell provides the essential connectivity and storage management that allow those processors to work together in a cluster. This “plumbing” of the data center is becoming more valuable as AI workloads become more complex and require faster data movement.

There are still hurdles to clear before the stock reaches the new price targets set by analysts. The company’s valuation remains high relative to its historical averages, and stock-based compensation is a factor that some analysts believe weighs on the net earnings available to shareholders. For the stock to see another major leg up, the company will likely need to show that its custom silicon and attach revenues are growing even faster than current models suggest.

Investors are now looking toward the upcoming investor day in October as the next major catalyst. This event will provide a deeper look into the long-term roadmap and potential new partnerships. If the company can demonstrate continued leadership in optical networking and secure more high-profile custom chip wins, it could justify a higher valuation. For now, the focus remains on execution within the data center, which has become the undisputed engine of the company’s growth strategy.

The broader semiconductor market has seen massive gains in 2026, and Marvell was one of the top performers before the recent pullback. Even with the post-earnings drop, the stock remains significantly higher than its starting point for the year. The company’s ability to outpace the S&P 500 highlights the intense investor interest in companies that provide the backbone for modern cloud computing. As long as cloud spending remains at record levels, Marvell’s position as a key supplier appears secure.

Breaking Up with Google: My Journey Leaving the Google Ecosystem

Valentin Podkamennyi — Thu, 27 Aug 2026 14:00:00 +0000

People who have known me for a long time have always seen me advocating for Google products and services. I have used Gmail since its beta days and Google phones since the Nexus One. I am familiar with almost every service they offer and have relied on them daily. However, for the last two years, their customer service has become unacceptable.

You now have to fight through layers of unhelpful AI bots just to find a human. Even then, you often deal with unprofessional and incompetent representatives. This issue appears everywhere across Google Fi, the Google Store, Google One, YouTube Premium, Google Home Premium, and the Nest Protect. As an ex-Googler, I understand the importance of good support, and the current level fails that standard.

I am also exhausted by having to adjust new privacy-related toggles every two to three weeks. In May 2026, I finally started switching to alternatives, beginning with the most obvious choices first.

Phase 1: The Obvious Switches

I started with the services that had clear, direct replacements. Communication and productivity tools were the first to go. I moved my email, calendar, meetings, and cloud storage to Proton. These switches happened quickly because the alternatives matched my needs perfectly.

I replaced my browser and search engine to block data collection. Password management moved to a dedicated secure vault. Messaging went to an encrypted platform for private conversations. The table below lists these initial replacements:

Google	Alternative
Gmail, Calendar, Meet, Drive	Proton Mail, Calendar, Meet, Drive
Google Authenticator	Proton Authenticator
Google Chrome	Brave Browser
Google Password Manager	Proton Pass
Google (Pixel) VPN	Proton VPN
Google Keep	Standard Notes
Google Chat	Signal
Google Search	DuckDuckGo / Brave Search
Google News, Discover	Brave News
Google Finance	Fidelity App

Phase 2: Replacing Hardware and Media Services

The next group required more effort, research, and hardware changes. I replaced fitness trackers and payment systems with devices that offer better data control. My music and photo libraries moved to a self-hosted server instead of a paid subscription. Home automation now runs on open software rather than a closed ecosystem.

These updates gave me full ownership of my equipment and files. I no longer depend on a single ecosystem for my connectivity, entertainment, or smart home. The setup takes time, but the control is worth the extra work. This table shows the equipment and digital service replacements I made:

Google	Alternative
Google DNS	AdGuard DNS and Quad9
Google Nest WiFi	Omada router + Access Points
YouTube Music Premium	Jellyfin + UGREEN NAS + Finamp
Google Photos	UGREEN NAS + Proton Drive (Photos)
Google Home	Home Assistant
Google Pixel Watch, Fitbit	Garmin vívoactive 6
Google Pay, Wallet	Garmin Pay
Google Health, Fit	Garmin App

Phase 3: The Remaining Challenges

This section lists the items I have not finished moving. I have listed a few potential candidates, but I have not picked final alternatives yet. I will update the table below as I find solid alternatives:

Google	Alternative
Google Android	GrapheneOS
Google Play Store	F-Droid, Aurora
Gemini	Proton Lumo, Mistral.ai, Duck.ai
Google Contacts	Nextcloud + DAVx5
Google Maps	HERE WeGo
Google Nest Cams	Tapo MagCam
Google Smart Lock	Tapo Smart Door Lock
Google Nest Doorbell
Google Nest Hub Displays
Google Nest Thermostats
Google Smart Smoke & CO Alarm
Google Home Speaker
Google TV Streamers
Google Pixel Buds
Google Translate
YouTube Premium
Google Fi (Family plan)
Google Voice

Final Thoughts

Leaving a massive ecosystem demands effort, but the result is total control over your data. The deeper you go into the ecosystem, the harder it is to pull out. The switch proves that privacy and independence are possible without sacrificing functionality. This journey is not just about changing apps, it is about reclaiming your autonomy online. The list of remaining challenges is long, but the transition continues.

It is ironic that while writing this article in Proton Docs, the service went down, highlighting that even privacy-focused providers face outages.

This article contains affiliate links, meaning I may earn a commission if you make a purchase through them.

Princeton University Leads National Quantum Hardware Center

Valentin Podkamennyi — Thu, 27 Aug 2026 08:10:15 +0000

Princeton University will spearhead a major new initiative to revolutionize the manufacturing of quantum computing hardware. Funded by a $27.9 million grant from the National Science Foundation, the project focuses on overcoming technical hurdles in processor fabrication. This five-year program unites nine research institutions to modernize the core components of superconducting quantum systems.

Overcoming Longstanding Hardware Obstacles

The newly established center, known as MARQUIS, focuses on Manufacturable and Resilient superconducting Quantum Information Systems. For nearly twenty-five years, the quantum research community relied on a specific set of materials and fabrication techniques. While these methods supported early prototypes and small systems, they lack the reliability required for large-scale operations. MARQUIS intends to break this stagnation by introducing modern industrial processes into the laboratory environment.

Nathalie de Leon, a professor of electrical and computer engineering at Princeton, serves as the director of the new institute. She notes that the field has reached a point where fundamental elements require complete reinvention. Current systems work well for small experiments, but scaling up to scientifically useful levels demands a shift in how engineers build the most basic hardware pieces. The goal is to move past the materials technology that has remained stagnant for a generation.

The complexity of quantum manufacturing requires a diverse set of skills that rarely exist within a single laboratory. By forming this institute, the National Science Foundation brings together experts in materials science, quantum device physics, and advanced semiconductor processing. This collaboration is necessary because the barriers to progress are too high for any individual team to overcome alone. The institute provides the structure for these different disciplines to work in tandem.

Valla Fatemi, a physicist from Cornell University and deputy director of the project, emphasizes the necessity of this multi-disciplinary approach. He explains that the technical challenges are so deeply rooted that only a concentrated, collective effort can resolve them. The partnership includes prestigious names like MIT, Stanford, and the University of California at Santa Barbara. It also integrates industrial perspectives from an advisory board featuring leaders from NVIDIA, Google Quantum AI, and Applied Materials.

Innovation in Superconducting Qubits

The technical focus of MARQUIS centers on the Josephson junction, which is the heart of a superconducting qubit. This component consists of three distinct layers where a microscopic oxide film sits between two metal layers. These junctions allow electron pairs to tunnel through the barrier, creating the physical conditions needed to process quantum information. Almost every modern quantum processor uses an aluminum and aluminum oxide construction that dates back to the late 1990s.

De Leon and her colleagues have already demonstrated that significant improvements are possible. In previous research, her team successfully reimagined the materials used in these circuits to achieve a fifteen-fold increase in performance compared to standard industry chips. This breakthrough proved that moving away from traditional aluminum-based designs is the path forward. MARQUIS will expand on this foundation to find even more resilient and high-performing material combinations.

Standardizing these new fabrication methods is a primary goal for the research group. In the traditional semiconductor world, manufacturers guard their best techniques as trade secrets, making it difficult for academics to keep pace. MARQUIS will pull back this curtain by including experts who understand industrial waypoints and semiconductor standards. This bridge between academic discovery and industrial application ensures that new designs are actually manufacturable at scale.

Beyond the hardware itself, the institute will create specialized test beds for validation. These platforms allow researchers to compare different designs and test them within mid-scale processors. These mid-scale systems act as a bridge between tiny laboratory experiments and the massive processors envisioned for the future. By providing a common testing ground, the institute helps the entire research community verify which new materials and designs truly perform as expected.

Building the Future Quantum Workforce

The National Science Foundation views these institutes as essential for maintaining national leadership in emerging technologies. Brian Stone, an official at the NSF, points out that the agency has spent decades funding the basic science behind quantum mechanics. Now, the focus shifts to practical activities that leverage that knowledge for the benefit of the public. MARQUIS is one of eight centers receiving part of a larger $290 million investment in quantum infrastructure.

Education and workforce development represent a significant portion of the institute’s mission. As the hardware becomes more complex, the demand for specialized engineers grows. MARQUIS will develop specific training programs to prepare students and professionals for roles in the quantum industry. This includes creating curriculum that blends traditional semiconductor engineering with quantum physics, ensuring a steady pipeline of talent for American tech companies.

The challenge of making better superconducting qubits has often been described as a graveyard of failed ideas. Many researchers have tried and failed to improve upon the original designs from decades ago. However, the leadership at MARQUIS believes that a coordinated “dream team” approach provides the best chance for success. By pooling the resources of nine institutions, the project aims to turn risky scientific endeavors into reliable manufacturing standards.

As the five-year program progresses, the results will likely influence how quantum computers are built worldwide. The transition from handcrafted laboratory devices to standardized, manufacturable processors is a critical step in the evolution of the technology. Through MARQUIS, Princeton and its partners are setting the stage for a new era of quantum engineering. This effort ensures that the next generation of processors will be built on a foundation of modern materials and industrial-grade reliability.

Perplexity launches on-device AI for data privacy

Valentin Podkamennyi — Thu, 27 Aug 2026 08:10:00 +0000

Perplexity introduced a new tool on Tuesday designed to execute artificial intelligence tasks directly on a user’s local hardware. This offering promises to keep sensitive information secure by processing it on-site and only connecting to the cloud when specifically instructed to handle complex research assignments.

Technical Architecture and Operational Benefits

The new service is officially named Portable Computer. It functions as a localized version of the existing Perplexity platform. The system is engineered to run on Nvidia DGX Spark hardware. It currently utilizes Qwen 3.8 27B or a specialized version known as PPLX 27B. The company indicated that a 30B open model will be available in the near future.

The entire operational stack resides on the local machine. This includes the orchestrator, the planner, and the tool router. The system also runs its own scheduler, task queue, and local search index. Currently, the software requires a Linux operating system to function. However, the company noted that a version compatible with Windows is under development.

For many organizations, the primary draw of this technology is the reduction of operational expenses. On-device processing does not subtract from a user’s cloud token credits. Costs are only incurred when a user explicitly moves a task to the cloud for deeper reasoning. This local-first approach helps companies manage their budgets while still accessing high-end AI capabilities when necessary.

Data sovereignty serves as another major advantage for the enterprise sector. Companies can keep proprietary details, such as contract terms or internal strategy, strictly on their own servers. If a task requires external data, like current market trends, the local orchestrator manages the hand-off. The system can connect to over 15 different frontier models for these advanced needs.

Integration is a key feature of the Portable Computer ecosystem. It works with common workplace applications including Google Drive, Gmail, Slack, and GitHub. This allows the AI to interact with existing workflows without moving all the company data to an external server. Professional consultants have noted that keeping the agentic control plane local represents a significant step forward for the industry.

Managing Local and Cloud Boundaries

The ability to decide which tasks stay local and which go to the cloud is handled by the device itself. A post-trained model makes these determinations based on the complexity of the request. This ensures that the decision-making process is not reliant on a remote server. It represents a shift in how AI agents are managed within a corporate infrastructure.

While many AI tools have offered local model execution for some time, this package is more comprehensive. It bundles the decision-making tools and the execution environment into a single local unit. This structure aims to give IT managers more visibility into how data is being moved. By isolating the core logic, the system reduces the footprint of sensitive data in the cloud.

Challenges in Hardware and Security Implementation

Despite the potential benefits, industry analysts pointed out several hurdles for widespread adoption. The physical requirements to run these models are significant. Most standard office computers do not possess the necessary specifications. Specifically, the system requires a local GPU with at least 24GB of VRAM to operate effectively.

The initial financial outlay for such hardware can be high. While companies might save on monthly token costs, they must invest in specialized graphics cards and memory. This hardware barrier might limit the tool to specific departments or high-value roles. Analysts suggested that until the full pricing model is clear, the total value proposition remains difficult to calculate.

Security experts also raised questions about how the boundary between local and cloud environments is enforced. There is a risk that users might accidentally approve cloud uploads. Much like clicking through software terms, a user might click a pop-up without realizing they are sending data off-site. Adversarial prompts could also potentially trick the system into bypassing local restrictions.

The distinction between local-first and local-only is a major point of discussion. Experts warned that autonomous agents often operate across multiple files and apps. This complexity can make it hard for a human user to see exactly what is happening. Some suggested that IT departments might need the ability to disable cloud escalation entirely at the network level.

Concerns Regarding Data Exfiltration

Some security professionals argued that existing application-level controls are insufficient for regulated industries. They pointed out that combining local data access with a cloud path creates a potential for data leakage. This is a common concern with many modern AI assistants that use connectors to link different software services.

For a product to meet strict compliance standards, it might need more than just user permission prompts. Some experts called for mandatory proxies and deep packet inspection on all data leaving the device. They also recommended deterministic rules that block specific categories of information regardless of what the AI model thinks is appropriate.

Without centralized management, some see this more as a privacy-focused consumer tool than a corporate compliance solution. The market for regulated industries likely requires tamper-evident logs and administrative policies that a local user cannot override. The race is on to see which provider can first deliver a truly managed local AI experience that satisfies audit requirements.

Protective Measures and User Consent Protocols

Perplexity provided details on the specific safeguards built into the Portable Computer. The company stated that data stored in local documents cannot trigger a cloud upload on its own. The software is designed so that local content does not have the authority to override product controls or initiate external communication.

An explicit action from the user is required for any data migration. First, the user must go into the application settings and turn on a specific toggle to allow cloud escalation. If this setting remains off, the system is restricted to local processing only. This puts the primary control in the hands of the individual or the administrator before any work begins.

Even when the escalation feature is active, the system does not automatically send data. The user must review a specific request within the app for every single task that needs cloud resources. The interface provides a large pop-up window that clearly explains what is happening. This is intended to prevent accidental approvals during a busy workday.

Granular Control for Every Task

The company emphasized that permission is not a blanket authorization. Approving one task for cloud processing does not grant the AI permission to use the cloud for the rest of a session. Every new request requires a new approval. This granular approach ensures that the user remains aware of every instance where data leaves the local machine.

The product also limits what the AI agent can see and do. It enforces restrictions on the types of outbound actions available to the system. By strictly defining these boundaries, the developer aims to mitigate the risks associated with autonomous AI behavior. These layers of defense are intended to address the concerns raised by the security community regarding data exfiltration.

The localized orchestrator is the heart of this security model. It acts as a gatekeeper that manages the flow of information between the user’s files and the external world. By keeping the planner on the device, the system ensures that the logic of the task remains private. Only the specific segments of data needed for a query are ever packaged for the cloud.

As AI continues to integrate into professional environments, the focus on local execution is likely to grow. This approach balances the need for powerful computing with the necessity of data protection. While hardware costs and management features are still evolving, the move toward on-device intelligence marks a significant change in the technology landscape. Companies must now weigh the benefits of lower operational costs against the investment in local infrastructure.

TPM Requirements for Post-Quantum Cryptography Readiness

Valentin Podkamennyi — Tue, 25 Aug 2026 18:18:15 +0000

The Trusted Computing Group has established a new set of requirements to help organizations determine if Trusted Platform Modules are prepared for the era of post-quantum cryptography. This guidance provides a technical benchmark for evaluating whether hardware vendors can protect electronic devices against the future threat of quantum-enabled cyber attacks.

Establishing the Post-Quantum Baseline

The newly released guidance provides a framework for businesses to verify the security claims made by hardware manufacturers. By creating a standardized set of requirements, the organization ensures that companies can demand proof of protection. This prevents a situation where vendors might claim their products are compliant without offering the full suite of necessary security features.

A primary focus of this initiative is the PC Client Platform TPM Profile 1.07. This profile serves as the minimum technical requirement for any module to be considered ready for the next generation of cryptographic challenges. It builds upon the existing TPM 2.0 Library Specification Version 1.85 to include specific elements for quantum-safe protection.

Organizations must understand that security in the quantum age involves more than just swapping out one mathematical algorithm for another. True resilience requires a comprehensive approach to hardware-anchored trust. This includes maintaining the integrity of platform identities and attestation over very long periods.

Data and identities established today may need to remain secure for several decades. If the underlying hardware is not built to withstand quantum decryption methods, that long-term security is at risk. Current statistics indicate that a vast majority of businesses still lack a formal roadmap for this transition.

The Trusted Computing Group president, Joe Pennisi, emphasizes that businesses must look at the broader picture of security. Individual algorithm support is only one piece of the puzzle. Real security comes from a hardware-anchored root of trust that can handle the complex demands of quantum-safe attestation and platform integrity.

Defining Readiness and Upgradability

To simplify the transition for IT managers and developers, the organization has introduced two specific designations for hardware modules. These categories help clarify exactly what a piece of hardware is capable of at the time of purchase or deployment. This categorization is vital for lifecycle management and long-term procurement planning.

The first designation is the PQC-ready TPM. This label applies to any module that currently implements the full requirements of the PC Client Platform TPM Profile 1.07. These devices are prepared out of the box to handle the specific cryptographic demands of a post-quantum environment.

The second designation is the PQC-upgradable TPM. This category includes hardware that does not currently support the 1.07 profile but has the internal capability to receive firmware or software updates to meet those standards later. This distinction helps businesses protect their existing investments while planning for future security needs.

By using these clear definitions, the organization aims to bring a sense of order to the market. Vendors can no longer use vague marketing terms to describe their readiness. Instead, they must align with these specific technical designations to prove their hardware can withstand modern and future threats.

This structured approach also allows vendors to innovate beyond the minimum requirements. While the 1.07 profile defines the baseline, manufacturers are free to include additional optional algorithms. This competition can lead to even stronger security implementations over time as the industry moves away from vulnerable legacy systems.

The shift toward these new standards is a critical step for global digital infrastructure. As quantum computers become more powerful, the window for transitioning traditional encryption shrinks. Having a clear path for hardware deployment ensures that the foundation of digital trust remains solid.

Certification and Future Standards

The Trusted Computing Group is not merely stopping at defining these requirements. The organization has announced plans to expand its existing certification programs. This expansion will allow for the official certification of modules that successfully meet the PQC-ready criteria outlined in the latest profiles.

Once these certification programs are fully operational, they will provide a definitive seal of approval for hardware. This takes the guesswork out of the hands of IT professionals. Instead of manually auditing vendor specifications, they can look for the official certification to ensure their systems meet the necessary security thresholds.

The development of these certification requirements is an ongoing process. The organization intends to release more details regarding the specifics of the testing and validation procedures as they are finalized. This transparency ensures that all stakeholders in the tech industry can prepare for the new certification audits.

Furthermore, the integration of these standards into the broader security ecosystem is essential. Trusted Platform Modules are the silent workhorses of modern computing, handling everything from secure boot processes to encrypted storage. Updating these modules is a fundamental requirement for the survival of secure digital commerce and communication.

Transition planning is now a priority for any organization that handles sensitive data. The guidance provided by the organization helps bridge the gap between theoretical quantum threats and practical hardware solutions. It gives engineers a technical target to hit when designing new systems or upgrading old ones.

The move toward quantum-resilient hardware is a marathon, not a sprint. By establishing these benchmarks now, the industry has a fighting chance to stay ahead of the curve. The focus remains on providing a reliable root of trust that remains unshakeable, even in the face of revolutionary computing power.

Google Migration Center adds AI for cloud planning

Valentin Podkamennyi — Tue, 25 Aug 2026 18:17:40 +0000

Google Cloud is integrating Gemini-powered Quick Assessments into its Migration Center to help organizations accelerate the planning phase of cloud transitions. The update uses generative artificial intelligence to provide near-instant total cost of ownership modeling and automated service mapping for complex enterprise workloads and legacy data systems.

Accelerated planning with generative intelligence

The process of moving enterprise operations to the cloud typically involves months of manual data collection and financial forecasting. Organizations must catalog every server, database, and application before they can even begin to estimate the cost of a transition. Google aims to eliminate these early hurdles by using its Gemini large language model to handle the heavy lifting of data synthesis.

The new Quick Assessment feature changes how IT managers interact with migration data. Instead of traditional spreadsheets and manual entry, users can upload high-level infrastructure details or VMware exports. The AI then builds a Compute Engine cost model automatically. This allows teams to interact with their data through a chat interface to refine the results and ask specific questions about the migration path.

Shifting the financial landscape of cloud adoption

Industry experts note that this technological shift significantly alters the economics of the discovery phase. Many companies hesitate to start migrations because the initial assessment itself requires a substantial investment of time and money. By using automated tools, Chief Information Officers can obtain a preliminary view of savings and required resources without hiring expensive outside consulting firms for the initial business case.

This speed allows for better prioritization of IT resources. Organizations can quickly see which workloads provide the best return on investment for a cloud move. They can identify low-hanging fruit and high-impact applications early in the cycle. This rapid feedback loop enables leadership to make data-driven decisions about their digital transformation strategy without waiting for long-tail manual audits to conclude.

Limitations of automated assessments

While the speed of these tools is impressive, technical leaders must remain aware of their inherent limitations. An AI tool is only as accurate as the data it receives. It can calculate costs based on inventory lists and storage prices, but it often lacks visibility into deeper operational realities. Critical factors like application interdependencies, strict compliance requirements, and real-world performance spikes still require human oversight.

IT professionals should view these AI-generated outputs as directional indicators rather than final, investment-grade documents. Workloads that are sensitive to latency or subject to heavy government regulation require a more nuanced discovery process. Relying solely on a “months to minutes” marketing claim could lead to unforeseen technical debt if the automated model misses complex architectural requirements.

Competition among major cloud providers

Google is not alone in its pursuit of AI-driven automation for enterprise transitions. The entire hyperscale market is currently in an arms race to provide better modernization tools. Amazon Web Services continues to build out its Transform service, which uses intelligent agents to analyze legacy code and automate the refactoring process for older applications.

Microsoft is also competing in this space with Azure Copilot. Their approach involves an agentic mode where multiple AI agents work together to help users deploy infrastructure and optimize existing environments. This industry-wide trend signals a shift away from manual migration services toward a future where the cloud platform itself manages much of the technical and financial complexity of onboarding.

Streamlining the VMware transition

One of the most significant aspects of the new Google update is its focus on VMware environments. By allowing direct exports from VMware tools to feed the AI assessment, Google is making a play for the massive install base of on-premises virtualization users. This integration simplifies the path for legacy data centers to transition into hybrid or fully public cloud architectures with minimal friction.

The automated service mapping further assists by visualizing how different components of a data center interact. Understanding these relationships is vital to preventing outages during a move. When the AI maps these connections automatically, it reduces the risk of “breaking” an application by moving its components in the wrong order. This level of automation is becoming the standard expectation for modern cloud management suites.

Long-term impact on enterprise IT strategy

As these tools become more sophisticated, the role of the IT administrator is changing from data gatherer to strategic orchestrator. Instead of spending months building a business case, teams can focus on the actual execution and optimization of their cloud footprint. The reduction in planning time means organizations can respond more quickly to market changes and technological advancements.

Ultimately, the goal of integrating Gemini into the Migration Center is to lower the barrier to entry for cloud adoption. By providing clear, instant visibility into the financial and technical requirements of a move, Google is betting that more companies will commit to large-scale transitions. While human validation remains a necessary step, the era of the manual, year-long migration assessment is rapidly coming to an end.

Skala AI Integration Enhances CP2K Quantum Simulations

Valentin Podkamennyi — Tue, 25 Aug 2026 18:17:11 +0000

Microsoft Research AI for Science and the Center for Advanced Systems Understanding (CASUS) successfully integrated the Skala AI model into the CP2K software ecosystem. This collaboration enables scientists to conduct quantum mechanical simulations of complex molecular systems with unprecedented precision. The development represents a significant shift in computational chemistry.

Bridging AI and Quantum Mechanics

Density functional theory serves as a fundamental pillar for modern simulation applications. While this method earned a Nobel Prize in 1998, it faces significant hurdles when applied to massive systems. The primary difficulty lies in the exchange-correlation functional, which dictates how electrons interact within a system. Traditional mathematical formulas for these interactions often require massive computational resources. This limitation frequently restricts high-accuracy simulations to very small systems with few particles.

The Skala model introduces a different methodology to solve this long-standing problem. Rather than relying on traditional human-derived mathematical formulas, Microsoft utilized a neural network. This AI learned how electron densities in various regions influence each other through extensive data analysis. This shift positions Skala as one of the premier AI-based exchange-correlation functionals available to the global scientific community. It offers a balance between the high precision of complex calculations and the speed of simpler models.

Initial results from the collaboration between CASUS and Microsoft demonstrate that this AI-driven approach is effective. Early testing focused on specific molecular cases where researchers noted a substantial increase in accuracy. Lead author Franz Pschel confirmed that the model achieved a significant leap in simulation quality for their test scenarios. These improvements do not come at the cost of speed, which is a common trade-off in quantum physics calculations.

Expanding the Reach of Research Tools

The decision to integrate Skala into CP2K was driven by the existing popularity of the software. CP2K is an open-source platform widely used for modeling molecules, solids, and biological systems. By placing the AI model within this ecosystem, Microsoft ensures that researchers do not have to change their primary workflows. Dr. Sebastian Ehlert noted that CP2K is a cornerstone of the field, making it the logical choice for deploying new tools.

This integration process involved rigorous numerical verification to ensure results remained consistent. The teams developed a suite of tests to verify that the AI model produces correct physical values across different computing environments. CASUS provided specialized expertise in numerical verification to help solidify these standards. Such testing ensures that the transition from traditional math to neural networks does not introduce errors into sensitive scientific data.

Enhancing Computational Efficiency

CP2K excels at performing dynamic simulations involving thousands or even tens of thousands of atoms. Its architecture allows it to run tasks in parallel on specialized computer hardware. This efficiency makes it the preferred tool for studying battery materials, semiconductors, and catalysts. The addition of Skala enhances these capabilities by allowing the software to predict molecular forces and energies with greater reliability.

The software recently received updates specifically designed to support AI-based models. These updates allow CP2K to use training data to improve the scale of simulations over time. By combining classical molecular dynamics with quantum mechanical methods, the platform can handle complex biological structures like proteins. The synergy between Skala and CP2K’s parallel processing creates a powerful environment for large-scale materials science research.

Future Applications in Solids and Liquids

The current version of Skala available in CP2K focuses on molecular systems. However, the development team plans to expand these capabilities shortly. Future updates will likely include support for periodic solids, such as metals and semiconductors. This expansion is critical for engineers working on next-generation electronics and industrial materials. Liquids are also on the roadmap for future integration, which will benefit researchers in chemistry and fluid dynamics.

As the AI model is not a static tool, it will receive continuous updates through the CP2K platform. This ensures that the scientific community always has access to the most refined version of the technology. Prof. Thomas D. Khne expressed confidence that this initial success will lead to further achievements in the field. The goal is to make advanced quantum simulations accessible to a broader range of disciplines, including engineering and environmental science.

Impact on Global Scientific Collaboration

The announcement of this integration generated significant interest within the international research community. Since the initial preview in early 2026, scientists have waited for the official release to test the model on their own data. The availability of Skala within an open-source framework like CP2K encourages transparent scientific inquiry. It allows researchers to verify results and contribute to the improvement of the underlying AI.

The partnership between a private tech giant and a public research center like Helmholtz-Zentrum Dresden-Rossendorf highlights a growing trend. Collaborative efforts are becoming necessary to manage the increasing complexity of scientific software. By pooling resources, these organizations can tackle problems that would be difficult for a single entity to solve. This model of development helps bridge the gap between theoretical AI research and practical application in the lab.

Advancing Materials Science and Engineering

Modern engineering requires a deep understanding of how materials behave at the atomic level. Whether designing a better battery or a more efficient solar cell, scientists must predict how molecules interact over time. The high level of interest in Skala stems from its ability to provide these answers faster than previous methods. This speed allows for more rapid iteration in the design of new technologies and chemical processes.

The move toward AI-integrated simulations marks a new era for computational chemistry. Researchers no longer have to choose between extreme precision and practical timescales. As Skala continues to evolve, its impact will likely be felt across multiple industries. The integration with CP2K serves as a blueprint for how AI can be embedded into established scientific tools to push the boundaries of what is possible in quantum mechanical research.

Final verification steps confirmed that the integration is stable and ready for widespread use. Scientists can now download the latest version of CP2K to begin utilizing the Skala functional in their projects. This deployment is expected to trigger a wave of new studies as researchers apply the AI model to diverse challenges. From drug discovery to semiconductor manufacturing, the combination of AI and quantum simulation is poised to accelerate innovation across the board.

Quantum awareness strategy for business leaders

Valentin Podkamennyi — Mon, 24 Aug 2026 18:08:33 +0000

Lionel Martellini, a finance professor and director of the EDHEC Quantum Institute, recently discussed the intersection of physics and business. Martellini argues that future executives need a foundational understanding of quantum mechanics to navigate the coming wave of technological shifts and avoid falling for industry hype or misinformation.

Bridging the gap between physics and finance

Martellini followed a unique career path that eventually led him to lead the first quantum institute within a business school. Initially focused on finance, math, and statistics, he later pursued a PhD in physics during his 40s. He spent time working on gravitational waves and black holes before realizing that these two disparate worlds could merge.

By launching the EDHEC Quantum Institute, Martellini aims to ensure that quantum science is not restricted to a small circle of specialized experts. He believes that as second-generation technologies mature, they will impact every major industry. Preparing the next generation of decision-makers requires more than just high-level summaries; it requires a deep dive into how these tools change business models.

The current educational landscape often separates technology from leadership. Martellini argues that this divide is increasingly obsolete. Students at his institution are encouraged to become technology fluent, ensuring they can lead companies in an environment where AI and quantum computing are no longer niche topics. This hybridization of knowledge is a core pillar of his educational philosophy.

Developing a quantum narrative

The way society talks about quantum technology is often counterproductive. Experts frequently tell physics students to focus on the math rather than the meaning. Conversely, business professionals are often told that the subject is too mysterious for them to grasp. Martellini rejects both of these approaches.

He focuses on teaching fundamental principles like superposition and entanglement to non-scientists. By understanding the historical debates between figures like Einstein and Bohr, students gain a better perspective on the reality of the technology. This foundational knowledge helps them understand how speedups in computing are actually achieved.

Moving beyond the fear factor

Many discussions about quantum computing revolve around the risks to encryption and cybersecurity. While these are valid concerns, Martellini prefers to focus on the opportunities. He views the study of quantum mechanics as an invitation to embrace the complexity of the world rather than seeing things in black and white.

Even if a student never works directly with a quantum computer, the mental framework required to understand the field is valuable. It teaches people to think in terms of states and nuances. This intellectual growth is just as important as the technical applications themselves.

Identifying the dangers of quantum washing

A significant portion of Martellini’s work involves warning leaders about quantum washing. This term describes the tendency for companies to overpromise what the technology can currently do. Similar to greenwashing in the environmental sector, quantum washing can mislead investors and executives about the timeline for practical results.

One form of this trend involves overselling the benefits of current hardware, which is still in the noisy intermediate-scale quantum era. Many people are claiming advantages that do not yet exist in a stable, fault-tolerant way. This creates a disconnect between expectations and the actual state of the science.

Over-engineered use cases

Another subtle form of quantum washing involves creating problems specifically designed to be solved by quantum computers. Martellini points out that many papers on portfolio optimization assume conditions that do not exist in the real world. In actual financial practice, the process of selecting stocks and the process of optimizing their weights are separate steps.

Forcing these steps together just to demonstrate a quantum benefit is not useful for finance professionals. Leaders need to distinguish between theoretical academic exercises and practical tools that fit into existing workflows. Without this critical eye, companies risk wasting resources on solutions that do not address their core needs.

The importance of return on investment

Discussions about technical superiority often ignore the financial costs of implementation. Martellini emphasizes that a quantum advantage is only meaningful if it offers a positive return on investment. If the cost of running a quantum algorithm exceeds the profit it generates, it is not a viable business solution.

Business leaders must ask tough questions about the ROI of these new systems. They should not be swayed by the novelty of the hardware alone. A clear-headed approach to costs and benefits is the only way to integrate these technologies successfully into the corporate world.

Preparing for the future of financial services

The financial sector is often cited as the most likely early adopter of quantum technologies. While Martellini is cautious about short-term claims, he sees immense potential in the long term. Once fault-tolerant systems arrive, the impact on the industry will be transformational, affecting everything from risk assessment to complex pricing.

In the near term, special-purpose hardware like quantum annealers might offer some utility. However, Martellini notes that many current applications still feel like a solution searching for a problem. He encourages finance professionals to remain patient and focus on building the infrastructure needed for future breakthroughs.

Executive education and training

There is a growing demand for business professionals who are quantum-literate. Firms in the technology sector need CFOs, marketing directors, and HR managers who understand the stakes of the industry. These hires do not need to be engineers, but they must be able to speak the language of the scientists they work alongside.

Conversely, end users in aerospace and finance are starting to recognize the opportunity cost of waiting. While they may not feel an urgent need to retrain their entire staff today, they know the shift is inevitable. Getting ahead of the curve allows these organizations to be ready when the hardware finally reaches maturity.

Resources for the curious leader

Finding the right educational material remains a challenge for many executives. Most books are either too superficial or too dense with complex equations. Martellini is currently co-authoring a book to bridge this gap, specifically tailored for finance professionals who need depth without unnecessary jargon.

He believes that the best way to learn is through a structured approach that respects the reader’s intelligence. By providing the right tools, he hopes to empower leaders to make informed decisions about the future of their companies. The goal is to move from mystery to mastery.

Final reflections on the field

Martellini remains inspired by the giants of physics who started the first quantum revolution. He identifies Albert Einstein and Richard Feynman as key figures whose work continues to influence the modern world. Their ability to question fundamental reality serves as a model for how today’s leaders should approach technological change.

The path forward requires a mix of enthusiasm and skepticism. By fostering quantum awareness, Martellini is helping to ensure that the business community is a partner in the next scientific frontier rather than a bystander. Building this bridge is essential for the healthy development of both science and commerce.