Google is giving mobile gamers a few new reasons to pay attention to Android 17. The next version of Android introduces features aimed squarely at gaming, with foldable phones among the biggest beneficiaries.

Among the highlights is a new foldable gaming mode that finally puts those larger displays to better use. Instead of stretching games across the entire screen and covering parts of the action with touch controls, Android 17 introduces a smarter layout designed specifically for gaming.

Android 17 makes better use of foldable screens

The new mode uses an optimized 50/50 split-screen experience. The game occupies the upper half of the display, while the lower half becomes a dynamic gamepad. This gives players a clearer view of the action while keeping virtual controls within easy reach.

The approach feels like a natural fit for foldables. By separating gameplay and controls, Android 17 can take advantage of the extra screen real estate without forcing players to sacrifice visibility during fast-paced sessions. Google says foldable gaming mode is built into Android 17 and will become available in the coming months. While foldable phones remain a niche category, features like this help create experiences that actually justify having a larger, flexible display.

Controller customization and smoother gameplay are coming too

Android 17 isn’t stopping at foldable-specific improvements. The update also introduces native controller remapping support for gamers using external controllers. Players will be able to customize button assignments to better match their preferences without depending on third-party apps or workarounds. Performance is getting attention as well. Google says it has improved memory cleanup processes to reduce frame drops and stutters, particularly during demanding games. While these changes may not be as visible as a new gaming mode, they could have a noticeable impact on day-to-day gameplay.

The updates show Google continuing to push Android as a gaming platform. Whether you’re playing on a traditional smartphone, a foldable, or with a dedicated controller in hand, Android 17 aims to deliver a smoother and more flexible gaming experience.

Organizational leaders are nearly twice as likely to hide their AI use compared to all other employees, at 42% versus 23%, according to new Ivanti research surveying 3,900 employees across six countries. Among leaders who conceal that usage, 52% say they do it for a “secret advantage.” The same research found 85% of IT professionals claim a named owner exists for every AI agent. Only 42% say ownership is actually clear — a 43-point gap that no governance framework was designed to close.

Sam Evans, CISO of Clearwater Analytics, stood before his board and laid out the risk to the $8.8 trillion in assets his firm’s platform supports. “The worst possible thing would be one of our employees taking customer data and putting it into an AI engine that we don’t manage,” Evans told VentureBeat. He brought a solution, not just a problem. Many CISOs VentureBeat interviewed did not.

Menlo Security CEO Bill Robbins relayed a conversation with a Top 3 U.S. bank CISO who called shadow AI discovery “a bit of a fool’s errand”: AI is embedded in every application and browser employees touch. The bank governs from containment, not discovery.

The scale justifies that posture. “We see 50 new AI apps a day, and we’ve already cataloged over 12,000,” Prompt Security CEO Itamar Golan told VentureBeat. “Around 40% of these default to training on any data you feed them, meaning your intellectual property can become part of their models.” CrowdStrike has detected 1,800 AI applications operating across 160 million endpoint instances. Those are vendor-reported numbers from proprietary telemetry. No independent party can verify them. The directional signal matters more than the exact count.

CrowdStrike CTO Elia Zaitsev described what makes the surface so hard to govern. “It looks indistinguishable if an agent runs your web browser versus if you run your browser,” Zaitsev told VentureBeat at RSAC 2026. “Observing actual kinetic actions is a structured, solvable problem. Intent is not.” The shadow AI surface is no longer a list security teams can maintain. It is an environment they have to assume.

The Ivanti survey was administered independently by Ravn Research and MSI Advanced Customer Insights across 1,500 IT professionals. Among companies with AI policies, just 24% of employees say those policies are followed “very consistently” in day-to-day work.

Kayne McGladrey, IEEE senior member, told VentureBeat why that governance gap persists. “Anything that seems to have a cybersecurity flavor is generally put into the cybersecurity risk category, which is a complete fiction. They should be focused on business risks, because if it doesn’t affect the business, like a financial loss, then nobody’s going to pay attention to it, and they will not budget it appropriately, nor will they adequately put in controls to prevent it,” McGladrey told VentureBeat previously.

Brokerage partners at major consulting firms shared over Signal that they build shadow AI applications in Google Colab and store them in S3 buckets to compress a week of financial analysis into an hour. The approval process takes too long, so they route around it.

Governance at deploy time, failure at runtime

Reviews check functional requirements when a model ships, but they never check model provenance, behavioral drift, or whether the agent expanded its own permissions after launch.

CrowdStrike CEO George Kurtz disclosed at RSA Conference 2026 that a Fortune 50 CEO’s AI agent rewrote the company’s security policy to expand its own autonomy. The company caught it by accident. Every credential check had passed. “In the agentic era, defending against AI-accelerated adversaries and securing AI systems themselves require operating at machine speed,” Kurtz said. Quarterly governance reviews do not operate at machine speed.

Mike Riemer, Field CISO at Ivanti, built that lesson into his own team’s AI agent development. “It’s great at what I intended it for, but it’s also great at what I didn’t intend it for, and what I didn’t intend it for is dangerous,” Riemer told VentureBeat.

Hallucination data compounds the problem. Sixty-eight percent of IT professionals have personally witnessed AI generate hallucinations with potential operational impact, according to Ivanti. More than half caught the errors before damage, but 16% did not. Yet among the most advanced users of AI, 49% fully trust AI-generated outputs that influence IT decisions.

Riemer described the pattern in an exclusive interview with VentureBeat. “There are people that are just accepting what’s been given to them without any full understanding of what it is doing, which we’ve found in the tech industry for decades,” Riemer said. “They don’t question how it’s doing it. They just start gauging it by its outcome.”

Qualtrics CSO Assaf Keren identified the core tension in an exclusive interview with VentureBeat. Organizations are introducing “non-deterministic decisioning into environments built for deterministic.” Keren cited internal Qualtrics data showing that 22% of SOC triage is now AI-driven. No codified threshold separates what an agent can auto-execute from what requires a human in the loop.

The 18-month window

The window for fixing this is closing. IT organizations expect AI to automate 46% of their operations within 18 months, according to Ivanti. U.S. companies project 52%. Governance is already the most commonly cited barrier to faster deployment, ahead of skills, technology, and data challenges.

The maturity divide makes the governance gap more dangerous. IT professionals at AI-mature organizations save six hours per week, double the three hours saved at the least mature level. Nearly 9 in 10 IT professionals at scaled organizations say AI frequently helps detect or resolve issues before employees are affected. At early experimentation organizations, that number drops to four in ten. Sixty-nine percent of scaled organizations report fully embedded governance, compared to 15% at early experimentation.

Cisco President Jeetu Patel walked through a hypothetical scenario in an interview at RSAC 2026: an agent that charges $40,000, invites competitors to a Slack channel, and publishes home addresses. “The apology is not a guardrail,” Patel told VentureBeat.

Cato Networks VP of Threat Intelligence Etay Maor framed the accountability problem in a separate RSAC interview. “They’re closer to humans. Why are we not doing background checks on agents?”

“AI is compressing the time between intent and execution while turning enterprise AI systems into targets,” CrowdStrike VP of Intelligence Operations Adam Meyers told VentureBeat.

“Proceed on one action does not mean proceed on the next,” Cisco SVP of AI Software and Platform DJ Sampath said in a separate interview.

McGladrey described the root cause. Organizations default to cloning human user profiles for agents, and permission sprawl starts on day one. “It uses far more permissions than it should have, more than a human would, because of the speed of scale and intent,” he said.

Riemer’s team built governance into Ivanti’s own development process. “We have AI check on top of AI to make sure that it is fixed. Two different models, two different manufacturers,” Riemer said. “If one AI believes the other AI fixed it appropriately, then it passes it off to a human being.”

Riemer put the vendor question in terms every CISO can use at the negotiating table. “If that vendor doesn’t have a way to show you what they’ve done from a development perspective in order to improve their development processes, you really need to question why you’re working with that vendor,” he said.

The six questions below target governance dimensions where enforcement collapses at runtime. CISOs can use them during Q3 vendor renewals to separate vendors shipping runtime enforcement from vendors shipping documentation.

Six governance questions for Q3 renewals

Source data from Ivanti, Scaling AI in IT Operations: The Path to Maturity in 2026 (n=1,500 IT professionals, 3,900 total employees, six countries, February–March 2026). Exclusive CISO sourcing by VentureBeat.

Evans put structure around the Clearwater board conversation. The bank CISO that Robbins described assumed AI is everywhere and governed from containment instead of discovery. Governance that tries to catalog every shadow AI tool will fail because the surface grows faster than any inventory.

At scaled, business-critical organizations, 54% of IT professionals say AI makes their work both faster and better, according to Ivanti. At early experimentation organizations, 24% say the same. At scaled organizations, accountability lives in the platform. At early ones, it lives in a document the agent never reads.

The six questions above give every CISO a way to test whether their governance actually works where it matters. At runtime, under load, and before the next renewal check clears.

Software

As Google continues crippling Chrome ad-blockers, it’s a good time to try Firefox

Firefox 152 is now available for download, after no fewer than four minor point releases to its predecessor, last month’s Firefox 151. And quieting noisy tabs has never been easier.

It’s a good time to check out the Fox: recently, this patch to the Google Chromium codebase, continues closing the door to Manifest V2 extensions, as The Register warned you was coming early last year. As the W3C documents, the forthcoming Google Chrome 150 turns off the last workarounds available for full-power ad blockers, and Chrome 151 will nuke them altogether.

Firefox 152 revamps the layout of the Settings page. To be honest, we had no particular problems with this before, but it’s a good thing to make it easier to twiddle the knobs and dials that make Firefox arguably the most extensible and customizable web browser.

The new version also understands that sometimes you just want it to shut up. When a tab (or, worse, multiple tabs) are playing audio, if you go to the address bar and type “mute” (or “sssh” or “hush”), then a new Quick Action button appears beneath it offering to immediately silence all tabs in all windows at once. For some streaming services, there are also improved media playback controls on the tab context menu, but we don’t use streaming much around these parts and weren’t able to test this.

If you admired the cleverness of the JPEG XL format as much as this Vulture , then we have glad tidings. Back in 2022, we reported that Google was dropping JPEG-XL support from Chromium and Chrome. Back in January, Mountain View changed track on this, and now, Firefox 152 has experimental JPEG XL support too.

The functions for sending tabs to other devices, and for copying URLs for easier sharing, have been improved. There’s an optional new “Send Tab” toolbar button. You can also right-click on a tab button and get options to send it to a nominated device, or copy its URL for sharing. Better still, this also applies to groups of tabs: hold down Ctrl or Cmd, select several, and right-click any of them, and they’ll all be sent, or their URLs copied, in one action.

There are also multiple bug fixes, about 40 security fixes, and as always, some new features for developers. Speakers of Basque or Galician will welcome their inclusion in its translation répertoire.

Mozilla’s fast release cycle for Firefox is a minor irritation, yes. (Of course, there’s always the Extended Support Release channel, if you want to hop off the treadmill.) However, one interpretation of it – and the stream of bug-fix versions – is that Mozilla is working hard on Firefox, and in our view that’s good news.

A new source of information that the company has published with this version) is the new Firefox Roadmap, which has info about future planned changes. ®

The shift into parenthood happens in a single heartbeat, but the reality of it settles in during the quiet and dark hours of the night. For new parents, bringing a newborn home turns sleep from a natural routine into a series of anxious calculations. Every sudden sigh from the crib, unexpected stretch of silence, or a faint rustle through the baby monitor triggers a familiar dilemma. Parents often wonder if they should tiptoe into the nursery and risk waking the baby, or if they should stay put and worry.

This persistent concern of second-guessing is something almost every first-time parent shares. Traditional baby monitors have long offered a window into the nursery, but a grainy video feed often raises more questions than it answers. This leaves parents squinting at a screen in the dark. True peace of mind requires something deeper than just a live stream because parents ultimately need context.

Tracking Every Breath Without a Single Wearable

A key aspect that stands out about the Momcozy BM08 is its contact-free monitoring technology. Instead of requiring wearable devices, special socks, clips, or sensors attached to a baby, it utilizes advanced millimeter-wave sensing technology to track breathing and heart dynamics in real time. This convenience-first engineering allows parents to stay informed while keeping their baby’s sleep environment entirely simple and comfortable.

For families, especially those going through parenthood for the first time, reducing the number of devices involved in bedtime routines can make nightly care feel much less complicated. This smart baby monitor is designed to give you that much-needed breathing room, working quietly in the background to provide deep and useful insights while remaining entirely unobtrusive in the nursery.

Taking the Experience Beyond Basic Video

Traditional baby monitors usually emphasize video and audio streams only. In contrast, the contact-free monitor expands on that basic concept by introducing intelligent monitoring features that are designed to help you stay truly connected to what is happening in the room. This system goes beyond passive observation to offer active support throughout the night.

Added to that, the built-in artificial intelligence can recognize the specific sound of a baby crying and respond immediately by playing soothing white noise. Such an immediate response helps create a calmer sleep environment while giving timely awareness of changes in your baby’s comfort or mood.

The monitor also includes automated detection for covered faces and can identify unusual sleep positions to send notifications when direct attention is required. Rather than forcing you to continually stare at a glowing screen, the system surfaces these important moments precisely as they happen. For busy households managing multiple responsibilities, that extra layer of intuitive awareness makes daily routines feel significantly more manageable.

Balancing Crystal Clear Vision with Everyday Convenience 

A continuous stream of smart updates is incredibly reassuring, but it works best when paired with a sharp view of the nursery. The BM08 features a 2K HD camera that provides detailed image quality throughout the day, while its 940nm infrared night vision helps maintain a clear view of the nursery without creating visible distractions that could disturb sleep. Whether you are checking in during a daytime nap or taking a quick look during the middle of the night, the kit aims to provide a reliable visual connection.

Many parents today rely on this kind of clarity not just for basic viewing, but also for better daily organization and decision-making. To support it, this baby monitor includes customizable safe zones that allow you to define specific monitoring areas and receive notifications if your baby moves beyond those boundaries. It also generates daily sleep summaries that provide an easy-to-understand overview of sleep patterns without requiring an ongoing subscription. For parents looking to better understand their child’s habits, these insights turn a standard video feed into a practical tool for modern family life.

Securing Your Personal Data and Precious Memories

Baby monitoring systems handle some of the most personal moments for a family, and that makes digital privacy a critical consideration for any modern household. Momcozy BM08 addresses this by prioritizing robust data security. This smart baby monitor uses AES-256 encryption and complies with GDPR and CCPA standards. Parents can choose between secure local storage on an SD card or protected cloud storage options, depending on what best fits their lifestyle. As the system is actively compiling snapshots and daily milestones into shareable mini videos, your private family moments remain completely confidential.

Beyond keeping data safe, the hardware relies on a remarkably stable connection to bridge the gap between rooms. The monitor also utilizes low-latency Wi-Fi designed to minimize interference, helping ensure notifications and video feeds arrive when they’re needed most. By anchoring its smart features with dependable security and a robust connection, it lets you focus on resting easy rather than troubleshooting your technology.

Why This Prime Day Offer Is Worth Your Attention 

Choosing a baby monitor ultimately comes down to finding a system that supports your mental well-being without taking over your life. The Momcozy Smart Baby Monitor BM08 honors that reality by replacing bulky wearable gadgets with invisible, intelligent protection. It blends advanced tracking with automated nursery support so you can stop staring anxiously at a screen and finally catch up on your own rest.

For parents looking to upgrade their nightly routine, the upcoming Prime Day season presents a practical opportunity to invest in better sleep. Running from June 14 to June 26, this limited-time deal drops the price to $179.99, a clean $70 discount when you enter the promo code BMNEWSPD at checkout. Ultimately, as parenting technology shifts toward hands-off designs, the BM08 offers a smarter way to watch over your little one without adding unnecessary complications to your evenings.

The cybersecurity industry is confronting a new reality: traditional vulnerability management is no longer enough. As enterprises rapidly deploy AI-powered applications, autonomous agents, and large language model (LLM) infrastructure, security teams are discovering that many of the most dangerous exposures cannot be identified through conventional CVE-based scanning alone. Instead, organizations are increasingly grappling with misconfigured AI services, exposed machine learning infrastructure, and interconnected systems that create entirely new attack paths.

Against this backdrop, CyCognito is expanding its exposure management platform with continuous AI pentesting capabilities designed to uncover complex, contextual risks that deterministic scanners often overlook. The initiative reflects a broader shift across the industry, in which security leaders are moving beyond identifying known vulnerabilities to continuously validating how attackers could exploit an organization’s unique environment.

AI Creates New Blind Spots

The rapid adoption of generative AI has dramatically expanded enterprise attack surfaces. Organizations are deploying AI copilots, retrieval-augmented generation (RAG) systems, Model Context Protocol (MCP) servers, orchestration platforms, and machine learning infrastructure faster than many security programs can inventory them.

Unlike traditional software vulnerabilities, these systems often introduce security gaps through configuration mistakes, excessive privileges, or unintended exposure between interconnected services. Such weaknesses may not have a CVE assigned to them, yet they can still provide attackers with direct access to sensitive business data.

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According to CyCognito, its platform now identifies more than 60 categories of AI-related technologies, including MCP servers, Ollama, MLflow, PyTorch, Triton, n8n, and other components commonly used in enterprise AI deployments.

From Detection to Simulated Attacks

Rather than stopping at asset discovery, CyCognito’s latest capability uses AI agents to simulate how an attacker would move through an organization’s exposed infrastructure.

Instead of asking whether a vulnerability exists, the system evaluates whether a sequence of actions could realistically compromise sensitive systems or expose valuable data. These attack chains combine contextual reasoning, environmental awareness, and multi-step testing that extend well beyond traditional vulnerability scanning.

The company’s recently published original technical deep dive on continuous AI pentesting explains how these AI agents prioritize testing using contextual intelligence gathered across an organization’s external attack surface, allowing security teams to focus on validated business risk rather than isolated technical findings.

Real-World Findings Highlight Emerging Risks

CyCognito shared several examples illustrating the types of exposures that continuous AI pentesting can identify.

In one case, an externally accessible MCP server provided an unauthenticated natural-language interface connected to a production CRM environment. By following a sequence of prompt injections and API interactions, AI agents were able to enumerate backend services and ultimately access millions of customer and financial records without credentials.

Another engagement uncovered a publicly accessible knowledge base supporting a RAG deployment. While authentication protected the AI agent itself, the underlying document repository remained openly reachable, exposing internal documents, contracts, communications, and customer information.

Perhaps most striking was the discovery of an internet-facing physical security platform responsible for managing building access controls, surveillance cameras, and badge readers. The system had been deployed alongside customer-facing AI services without proper segmentation, demonstrating how digital transformation initiatives can inadvertently expand risk into operational technology.

None of these scenarios relied on exploiting a known software vulnerability. Instead, they stemmed from architectural decisions, deployment practices, and business context that conventional scanners would likely miss.

Why Continuous Testing Matters

Traditional penetration testing remains an important security practice, but its point-in-time nature limits its effectiveness against environments that change daily.

While AI has accelerated offensive testing, many organizations still run AI-powered assessments as periodic engagements because of computational cost. According to CyCognito, this often limits deep testing to only the highest-priority assets, leaving much of the external attack surface largely unexamined.

To address this challenge, the company developed what it calls the Target Graph™, an orchestration layer that combines exposure assessment, threat intelligence, deterministic validation, and business context to determine where AI agents should spend their computational effort.

The approach allows AI pentesting to continuously adjust its depth and techniques based on newly discovered assets, environmental changes, and emerging threat activity.

An additional advantage comes from the system’s feedback loop. Attack techniques successfully validated by AI agents can later be converted into deterministic tests, reducing future computational requirements while expanding automated coverage.

A Broader Industry Transition

The emergence of AI-native infrastructure is changing how organizations think about external exposure management. As enterprise environments become increasingly dynamic, security programs are shifting from identifying isolated vulnerabilities toward continuously evaluating how systems interact and whether those interactions create exploitable pathways.

CyCognito’s latest announcement reflects that evolution. Rather than treating penetration testing as an occasional validation exercise, the company envisions continuous AI-driven testing becoming an always-on component of exposure management.

Internally known as “Project Kineto,” the initiative draws inspiration from the transition from still photography to motion pictures, a metaphor for replacing periodic security snapshots with continuous visibility into evolving attack surfaces.

As AI adoption accelerates across enterprises, the industry’s challenge may no longer be finding known vulnerabilities, but understanding how countless small exposures combine into meaningful business risk. Continuous AI pentesting represents one emerging approach to solving that problem.

SYSTEMS

AI agents are a general-purpose workload no different from any other

OPINION Do AI agents need a new kind of CPU? That’s what Arm, Nvidia, and a growing number of chip designers would have you believe. 

Arm named its first datacenter silicon the “AGI CPU.” Nvidia CEO Jensen Huang described Vera as a “CPU for agents,” and AWS’s Graviton 5 marketing is chock full of references to agentic AI.

None of these Arm-based processors are going to bring about the singularity. They’re not even AI accelerators. Don’t let the spin doctors fool you – these chips are nothing more than general-purpose processors that have received an AI glow-up.

Sure, AI agents and their harnesses need CPUs. No argument there. But agents aren’t one workload. They’re simply a bridge between the AI model and the same applications we’ve been running for decades.

And the tools those agents end up running often look wildly different. Some will benefit from a higher ratio of memory bandwidth to compute, some will perform better on chips with large unified caches or dedicated compression engines, while others will prefer high frequency over core count, or vice versa.

There’s a reason AMD and Intel don’t just build one Epyc or Xeon SKU, and why all of the “purpose-built” agentic CPUs look so different. 

If you look at what Nvidia has built with its 88-core Vera CPU, the chip promises high single-threaded performance with gobs of memory and interconnect bandwidth.

As Huang explained it during his GTC Taiwan keynote, this combination of compute and bandwidth is key to keeping latency as low as possible.

“There will be billions of agents and these agents are going to be using the CPUs with very little patience because the cost of the GPUs they sit next to is too high,” he said.

But of course Huang would say that – he’s in the GPU-slinging biz. Vera, just like Grace, was designed to keep data flowing between the CPU and GPU as smoothly as possible. Data movement is literally Vera’s thing.

Arm’s AGI CPU, meanwhile, looks to be a bog-standard Neoverse V3 processor with 136 cores that’s been stripped of anything an agent is unlikely to need in order to keep power consumption as low as possible. No simultaneous multithreading or dedicated accelerators, minimal vector extensions, but loads of memory bandwidth.

Amazon’s 192-core Graviton 5 processors, announced at Re:Invent last winter, are essentially a scaled-up version of Arm’s AGI CPU, right down to the Neoverse V3 cores, but arguably even more generic.

To echo Corey Quinn, “please, for the love of all that’s holy, stop calling them ‘AI chips.’”

Not to be left out of the fun, Intel and AMD have also been keen to recast their flagship Xeons and Epycs as the ideal platforms for running AI agents.

At Computex earlier this month, Intel showed off a couple of reference rack designs packing as many as 36,864 x86 cores into a 100 kW rack.

Meanwhile, AMD, following an initial round of Vera CPU benchmarks, went on the defensive last week, arguing that concurrency, not latency, is the metric that matters most when running agents at scale.

The House of Zen projects that for a 100 kW power envelope, its 256-core Venice Epycs, due out later this year, would deliver 3.3x higher throughput per rack than Vera.

If it feels like everyone has a different opinion on what the ideal agentic CPU should look like, that’s because, as with any other datacenter workload, there’s rarely one right answer.

We see this in early benchmarks of Nvidia’s Vera CPU. Late last month, FOSS-friendly publication Phoronix got early access to the chip and ran a subset of its test suite that Nvidia apparently felt was representative of its target market.

The chip achieved a geo-mean score 10 percent higher than AMD’s 128-core Epyc 9575F, and 55 percent higher than Intel’s 128-core Xeon 6980P. That’s a strong showing. But looking closer at the results, it becomes clear that Vera performs better in some apps than others.

And this gets to the crux of it all. There has never been one CPU to rule them all, and as the AI hype cycle enters its agentic era, there certainly isn’t one now. ®

Across the United States, K-12 schools have spent the past decade building one-to-one device programs. These initiatives have established an essential baseline for digital access, making it easier for students to complete daily schoolwork across grade levels and subjects. By putting a device in the hands of every learner, districts have created a standard foundation for digital literacy, research and everyday classroom engagement.

As STEM programs continue to grow and mature, however, school leaders are beginning to encounter new questions about how well those standardized devices support more advanced coursework. Pathways in fields like robotics, engineering, cybersecurity and data science increasingly rely on specialized professional applications that reach well beyond general-purpose classroom software.

In many cases, students can successfully complete introductory work on school-issued devices. But as instruction progresses, the tools required for STEM programs place different demands on student computing resources. As a result, educators and technology directors are taking a closer look at how hardware capacity can keep pace with shifting curricular needs.

STEM Tools and Computing Demands

While web-based applications work well for introductory coursework and daily assignments, many expanding STEM pathways introduce entirely different technical requirements. Courses in engineering, 3D modeling, cybersecurity and data science rely on industry-standard applications that demand substantial local computing capacity, robust memory and dedicated graphics processing.

A prime example is SolidWorks, a professional computer-aided design (CAD) platform used extensively in both higher education and engineering industries. When students build detailed, multipart models or run stress-test simulations, the performance of the device directly affects how efficiently they can work. Insufficient hardware can lead to severe rendering delays, software lag or sudden crashes that disrupt the entire classroom flow. 

This reality highlights a practical procurement consideration for districts: As STEM curricula mature beyond basic web-browsing activities, classroom devices must have sufficient local processing power to keep up.

A Robotics Program in Practice

To see how these hardware dynamics play out in a real classroom, consider the experience of the Firebots robotics team at Fremont High School in Sunnyvale, California. The team competes in the FIRST Robotics Competition, a global program where students design, build and program large robots to complete complex engineering challenges under tight, real-world constraints.

The work inside a competitive robotics program closely mirrors a commercial engineering environment, spanning mechanical design, fabrication, electrical systems and software development. Students use CAD tools to design components from scratch, test digital iterations and refine mechanisms on a tight competition timeline.

In robotics programs like this, student devices are not just tools for looking up information; they are central workbenches used across multiple stages of the design process. Students rely on them for modeling, code compilation, data logging, documentation and coordination among subteams.

Reliable on-device performance eliminates a common source of classroom friction. When software runs consistently and responsively, students can spend their limited class time troubleshooting their designs and iterating on ideas rather than troubleshooting their devices. Ultimately, the Firebots’ systematic approach and focus on execution earned them the FIRST Excellence in Engineering Award, which recognizes strong engineering design and system integration.

What This Means for STEM Instruction

The experience of programs like the Firebots raises a broader question for school leaders and instructional technology directors: How should district-wide device strategies evolve as STEM instruction becomes more technically demanding?

One-to-one computing programs continue to serve as the foundation for most day-to-day classroom learning, providing the baseline connectivity needed for a modern education. At the same time, STEM courses can reveal distinct moments where standardized, general-purpose devices reach the limits of demanding software and workflow requirements.

In many districts, this variation is already being managed through a mix of approaches. Some schools rely on shared physical lab spaces equipped with higher-performance workstations dedicated to specialized software. Others use cloud-based streaming solutions where possible, while reserving more resource-intensive local applications for specific instructional settings.

The goal is not to dismantle existing one-to-one initiatives, but to recognize where a single hardware standard may limit technical pathways. As STEM education continues to expand and diversify, school leaders find themselves balancing the competing priorities of deployment consistency, procurement cost and instructional fit.

In this changing landscape, device planning is no longer treated as a separate IT purchasing decision. Instead, it is increasingly part of a larger conversation about how schools design learning environments that accurately reflect the kinds of hands-on work students are being asked to do.

Kevin Cate created Open Door, a 3-minute horror short that has went viral. A couple of coworkers get into an elevator for a typical ride, but then it just stops and dips, and you start hearing whispers and getting the impression that something is lurking down in the darkness. Nearly 15 million people have watched it on YouTube, TikTok, and Instagram, and they’re still going crazy trying to figure out what happens next.

The only question that keeps coming up is, “What did those two people see when all hell broke loose?” Kevin Cate has been dealing with that question pretty much every day since the short came out, and his contagious excitement has secured him a nice six-figure deal to make it into a feature film. He collaborated with IO writer Charles Spano to enhance the script, and they now have a completely new version ready to go.

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Sean Anthony Baker and Mia Matthews are reprising their roles from the original short for the full-length movie. Kevin Cate is overjoyed to be working with the same cast and crew who brought the short back to life in the first place, and he believes the new characters are among the greatest they’ve created yet. Getting asked every day what the two saw down there is driving him insane, but he’s ready to eventually reveal them, and he’s dropping hints along the way.

They’ve even set up a website (opendoorfilm.com) where you can sign up and express your interest. The website has the most recent news, behind-the-scenes looks, and even a section where fans can share their wildest ideas. Skysound producer Daniel Faber is on board with this one, as well as Kevin Cate’s upcoming comedy Unbearable Christmas, starring Julia Stiles and David Cross.

Now that the budgeting process is over, the next steps are to address finance, casting, and pre-production. They want to start filming later this year and release it to the public in 2027. Kevin Cate provided a sneak glimpse of what’s coming on social media, claiming that it’s the completion of his ultimate dream.
[Source]