The most important technology trends in 2026 are not simply new inventions. They are technologies crossing practical thresholds in autonomy, computing infrastructure, device intelligence, security, digital trust, robotics, and connectivity.
Quick Take
AI agents are gaining infrastructure for longer, multi-step work, but reliability still declines as workloads become more complex and interdependent.
AI is moving in two directions at once: into physical machines and onto personal devices, while large-scale workloads continue to increase data-center demand.
Quantum computing remains developmental, but migration to standardized post-quantum cryptography can begin now.
Content provenance and satellite direct-to-device connectivity are adding new trust and coverage layers to the wider digital ecosystem.
These trends are not equally mature. The numbering below identifies their position in this article only; it is not a best-to-worst ranking. Some technologies are already suitable for selective production use, while others are better treated as technologies to pilot, prepare for, or monitor.
What Makes a Technology Trend Worth Watching in 2026?
A useful technology trend is more than a subject receiving attention. Something important should have changed in its capability, deployment model, supporting standards, economics, or infrastructure.
That distinction matters because many technologies once described as emerging are now ordinary parts of computing. Cloud infrastructure, Internet of Things deployments, 5G, virtual reality, augmented reality, blockchain, and low-code tools can still be important, but their existence alone does not make them defining 2026 trends.
The better question is whether a technology has crossed a new threshold. That may mean moving from research into production, gaining an implementable industry standard, becoming practical on consumer hardware, developing a clearer deployment model, or encountering a new constraint that changes how systems must be designed.
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A similar maturity-first approach is useful when evaluating web development trends, where production readiness can matter more than novelty.
Seven emerging technology trends and their main 2026 maturity signals
Trend
2026 maturity
What changed
Main opportunity
Main constraint
AI agents
Scaling selectively
Long-running agent infrastructure is becoming easier to deploy
Multi-step digital work
Reliability on complex tasks
Physical AI and robotics
Scaling in defined environments
AI is increasing robot perception and autonomy
More adaptable automation
Safety and unpredictable environments
AI infrastructure
Rapid expansion
Compute growth is becoming an energy and capacity issue
Large-scale AI deployment
Power, cooling, chips, and grid access
On-device and edge AI
Expanding
More capable models can run locally
Low-latency and offline intelligence
Memory, power, and model-size limits
Quantum computing and PQC
Transitional
Quantum systems are advancing while PQC migration can begin now
New computation models and future-ready cryptography
Quantum maturity and migration complexity
Content provenance
Standards developing and deploying
Cryptographically verifiable provenance is becoming standardized
Better origin and edit-history signals
Provenance does not prove truth
Satellite direct-to-device
Early deployment
Satellite services are being integrated with ordinary mobile networks
Coverage beyond terrestrial networks
Capacity, spectrum, device, and regulatory limits
The table is best read as a maturity map rather than a prediction scoreboard. The technologies attracting the most publicity are not automatically the ones closest to dependable, large-scale deployment.
Technology Trends to Watch
1
AI Agents Are Moving From Answers to Actions
Best for: understanding how generative AI is moving beyond conversational interfaces into multi-step digital work
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An AI agent is a system that can pursue a task through multiple actions rather than generating one response and stopping. Depending on its design, an agent may maintain working context, call tools, inspect files, run code, delegate subtasks, and continue until it reaches a result or needs human input.
OpenAI’s Agents API entered public beta on September 10, 2026. OpenAI describes infrastructure for long-running sessions, context management, tools, managed or third-party environments, file and code work, and coordinated subagents. The practical difference becomes clearer when you trace how AI agents work across planning, context, tools, execution, and review.
The difficult part is reliability. In Microsoft’s 2026 Multi-Horizon Task Environments research, baseline agent systems were tested with increasingly large groups of interdependent tasks. Completion rates across the tested baseline systems fell from 16.7% to 8.7% as concurrent workloads increased from 12 to 46 tasks. Microsoft’s CORPGEN architecture performed better under the same type of workload, completing 15.2% of tasks at the 46-task level compared with 4.3% for the baselines in that evaluation.
Those figures are benchmark results, not a universal failure rate for AI agents. They do, however, illustrate why production agent design involves more than choosing a capable model. Permissions, memory boundaries, checkpoints, error recovery, task dependencies, evaluation, and human review can determine whether additional autonomy reduces work or creates another system that must be supervised.
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Important limitation: Agents can compound mistakes across long workflows, especially when later actions depend on incorrect assumptions or incomplete earlier work.
2
Physical AI Is Giving Robots More Adaptability
Best for: tracking how AI is changing manufacturing, logistics, inspection, and other physical automation
Traditional industrial automation works extremely well when machines repeat predictable actions inside controlled environments. Physical AI pushes that model further by combining machines with perception, learned models, planning, and more adaptive decision-making.
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The International Federation of Robotics identifies AI and greater autonomy as a leading robotics trend for 2026. IFR says analytical AI can support activities such as failure prediction, path planning, and resource allocation, while generative AI can enable robots to learn new tasks, produce training data through simulation, and accept natural-language or vision-based instructions.
The term physical AI describes AI operating through machines that sense and act in the physical world. That can include industrial robots, autonomous mobile robots, service robots, and other systems where software decisions produce physical movement.
This does not mean general-purpose robots can reliably perform arbitrary human tasks. Factories and warehouses remain easier environments because designers can constrain routes, lighting, equipment, safety zones, object types, and expected behaviors.
The important transition is from machines that execute rigid sequences toward machines that can interpret more of their surroundings and adapt within defined limits. That expands the range of automation while making testing, cybersecurity, safety validation, and human oversight more important.
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Important limitation: Unstructured environments contain unpredictable people, objects, surfaces, conditions, and edge cases that remain much harder than controlled industrial tasks.
3
AI Infrastructure Is Becoming a Power and Capacity Problem
Best for: understanding why the next phase of AI depends on data centers, accelerators, cooling, electricity, and grid capacity
AI progress is usually discussed in terms of models, but production systems depend on physical infrastructure. Training and inference need processors, memory, networking, cooling, buildings, electricity, and connections to an energy system capable of supporting concentrated loads.
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The International Energy Agency’s April 2026 analysis projects global data-center electricity consumption rising from about 485 TWh in 2025 to roughly 950 TWh in 2030 in its central outlook. The same analysis reports that electricity consumption from AI-focused data centers grew 50% in 2025 and projects that their electricity use will triple between 2025 and 2030.
The IEA also identifies near-term bottlenecks across data-center, chip, energy-equipment, financing, and grid infrastructure. That means a proposed AI deployment may face constraints even when the software itself is technically ready.
This changes architecture decisions. A workload that runs occasionally may suit elastic cloud infrastructure, while predictable high-volume inference may create different economic or operational incentives. Edge deployment can reduce some network traffic and latency, but local hardware introduces its own memory, power, maintenance, and capability limits.
The result is that AI scalability increasingly depends on engineering outside the model itself. Processor availability, utilization, power density, cooling, electricity supply, grid connections, financing, data requirements, and workload placement can all affect whether an AI system is practical.
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Important limitation: The 2030 electricity figures are projections, not guaranteed demand. Efficiency improvements, workload growth, financing, hardware changes, energy policy, and grid development could change the outcome.
4
AI Is Moving Onto Devices and the Network Edge
Best for: applications that benefit from lower latency, offline operation, or less dependence on remote AI infrastructure
Not every AI task needs to travel to a large remote data center. On-device inference means running an AI model directly on hardware such as a phone, computer, vehicle, appliance, or embedded system. Edge computing places computation closer to where data is created instead of depending entirely on a centralized cloud.
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Apple’s third-generation foundation-model architecture provides one current example. Apple describes a five-model family spanning on-device and server-based systems. It includes two on-device models, while more demanding models run through server infrastructure.
Local processing can reduce network round trips and allow some capabilities to keep working without an internet connection. Keeping suitable processing on the device can also reduce the amount of data that must be sent to a remote service. Those advantages depend on the complete application design, however; running a model locally does not automatically make every application private.
That makes cloud AI vs on-device AI a deployment decision involving latency, capability, hardware resources, privacy requirements, operating cost, update frequency, and connectivity rather than a simple choice between old and new technology.
Important limitation: Phones and edge devices have tighter memory, power, thermal, and compute budgets than large data-center systems, so local models can differ substantially in capability.
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5
Quantum Progress Is Making Post-Quantum Security a Current Task
Best for: separating long-term quantum-computing progress from security migration work that can begin today
Quantum computing and post-quantum cryptography are related, but they are at different stages. Quantum computers use quantum-mechanical effects to perform certain kinds of computation differently from classical systems. Large-scale fault-tolerant quantum computing remains a development objective rather than an ordinary production computing platform.
The more immediate security issue is post-quantum cryptography, or PQC. These are classical cryptographic algorithms designed to resist attacks from both conventional computers and sufficiently capable future quantum computers. NIST says its first three finalized PQC standards are ready for implementation now.
NIST’s migration guidance says organizations should identify where quantum-vulnerable algorithms are used and begin planning replacements. Under the transition timeline referenced by NIST, quantum-vulnerable algorithms are expected to be deprecated and ultimately removed from relevant NIST standards by 2035, while higher-risk systems should transition sooner.
Teams responsible for sensitive data, long-lived systems, certificates, embedded devices, or difficult upgrade cycles should therefore treat post-quantum cryptography migration as an inventory and transition problem rather than waiting for a future cryptographically relevant quantum computer to trigger the work.
Important limitation: Quantum-computing timelines remain uncertain, while replacing cryptography across protocols, applications, hardware, certificates, and long-lived systems can take years.
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6
Digital Provenance Is Becoming Part of the Trust Layer
Best for: understanding how origin and edit-history signals can complement attempts to assess manipulated or AI-generated media
The growth of generative media makes a simple question increasingly important: where did a digital asset come from, and what happened to it before it reached you?
One approach is provenance, meaning information about an asset’s origin and history. The Coalition for Content Provenance and Authenticity defines Content Credentials as a system for attaching cryptographically verifiable provenance information to digital assets. The current C2PA 2.4 technical specification defines signed manifests, assertions about an asset, content bindings, and digital signatures that can make tampering with recorded provenance detectable.
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This differs from asking a detector to inspect pixels and estimate whether something was generated by AI. A provenance system can record information about creation, modifications, ingredients, or signing identities when participating tools create, preserve, and expose those records.
The limitation is just as important as the capability. A valid Content Credential does not prove that an event depicted in an image happened as claimed, that accompanying text is accurate, or that the creator is trustworthy. It supplies verifiable provenance information within a defined trust model. Other evidence and human judgment can still be necessary.
Important limitation: Provenance can help establish recorded origin and modification history, but it cannot by itself establish whether the real-world claim represented by the content is true.
7
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Satellite Connectivity Is Moving Into Ordinary Mobile Devices
Best for: following how satellite services can extend mobile coverage beyond conventional terrestrial networks
Satellite communication no longer always requires a dedicated satellite phone. Direct-to-device, or D2D, connectivity describes links between satellites and mobile handsets, allowing satellite services to supplement terrestrial mobile networks.
The important word is supplementary. According to the GSMA’s current policy guidance, satellite beams cover much larger areas than terrestrial cells and cannot deliver the same capacity in a given area. Indoor reception, interference management, spectrum arrangements, regulations, available satellite capacity, operator agreements, device support, and the type of service can all affect actual performance.
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The emerging architecture is therefore not simply “satellites replace mobile towers.” It is a more integrated connectivity model in which terrestrial networks continue to provide dense, high-capacity coverage while satellites extend reach and resilience where terrestrial infrastructure is unavailable or insufficient.
Important limitation: D2D capabilities and availability vary by country, operator, device, spectrum arrangement, and service, while satellite capacity remains lower than terrestrial cellular capacity in dense areas.
How These Technology Trends Connect
The seven trends are easier to understand as parts of the same changing technology stack rather than as isolated inventions.
AI agents increase the amount of work software can attempt autonomously, but that work still needs computing infrastructure. Some inference will remain in large data centers, while other workloads move toward phones, computers, vehicles, machines, and edge systems. Physical AI then connects software intelligence to machines that sense and act in the physical world.
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As automated systems create and transform more digital content, provenance can provide useful origin and modification-history signals. At the same time, post-quantum cryptography addresses a longer-term change in the assumptions beneath digital security. Satellite D2D extends connectivity into locations where terrestrial coverage may be unavailable or disrupted.
The practical pattern is convergence. Computing, networking, AI, physical systems, security, and trust infrastructure are becoming more interdependent. A useful technology strategy therefore needs to consider not only whether a new capability works, but also what infrastructure, standards, safeguards, and operational changes it requires.
What to Watch Beyond 2026
The safest way to evaluate emerging technology is to look for evidence of deployment rather than confident predictions. A technology becomes more consequential when reliability improves, standards stabilize, costs become workable, supporting infrastructure expands, and organizations can operate it without depending on exceptional conditions.
For AI agents, watch whether performance improves on long, interdependent workloads and whether permissions, evaluation, and human-review systems become easier to manage. For robotics, watch deployments beyond tightly controlled settings. For AI infrastructure, follow efficiency alongside electricity demand, processor availability, cooling requirements, grid connections, and financing.
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For on-device AI, pay attention to which useful workloads can run locally without unacceptable battery, memory, thermal, or performance costs. For quantum technology, separate experimental computing milestones from the much more immediate work of PQC migration. For content provenance, interoperability and preservation of credentials across tools and platforms matter more than whether an individual product displays a badge. For satellite D2D, the important signals are service capabilities, supported devices, available capacity, regulation, spectrum coordination, and integration with terrestrial operators.
That maturity-based view also prevents an old technology from being relabeled as a new trend simply because it remains popular. The strongest technology trends to watch beyond 2026 are the ones producing observable changes in what systems can do, where they can operate, how they are secured, and what infrastructure is required to support them.
Defense and venture capital used to rarely overlap, but that’s no longer the case. Defense tech VC dealmaking set a record in Q1 2026, and new VC firms have emerged around the world to invest solely in defense startups.
One of these is two-year-old Protego Ventures, which presents itself as the first and largest dedicated defense tech VC in Israel. Led notably by two women, Lital Leshem and Lee Moser, it has just completed the final close of its debut fund with $125 million in capital commitments, TechCrunch learned exclusively.
This fund size is enabling Protego to invest between $5 million and $50 million per company, with a focus on startups “addressing the critical defense and security needs of Israel and the global community,” according to its website.
These include the likes of drone maker XTEND, its first portfolio company, which went public on the NYSE earlier this month. That’s also why Protego stopped short of its $150 million target: a smaller fund magnifies the impact of a single big win on overall returns, and with XTEND now looking like a potential “fund maker” — a single investment capable of returning the whole fund — raising more capital would only dilute that upside among a larger pool of investors. “Nobody wants to share the pie,” Leshem said.
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Ares Management is one of Protego’s biggest backers. The investment firm suggested that Leshem and Moser join forces and start Protego in the aftermath of the October 7, 2023, Hamas attack on Israel, with the conviction that new technologies would be key to boosting Israel’s defense capabilities in the face of new threats, Leshem said.
With a $30 million investment as a limited partner, Ares gave Protego the support it needed to start investing right away, Leshem said. Besides XTEND, its portfolio also includes startups such as ASIO, which develops situational awareness systems and has partnered with Anduril.
Leshem says the reception in defense circles has been strong. “On the military side, there is a lot of respect, and people are coming — even under the radar — to talk to us, to learn from us, to train with us, and for sure get our technology and innovation.”
Still, the Israeli market is easier to navigate for insiders, and Protego’s two founders are leaning into their networks. Moser, a seasoned venture capitalist, will remain a managing partner at the generalist VC firm AnD Ventures. As for Leshem, who co-founded a startup that was acquired for $625 million in 2025, she also brings 11 years of experience in the military and intelligence field to their approach.
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“Everything changed on October 7,” Leshem told TechCrunch. She was deployed, while pregnant, as a reservist on the day of the attacks. “And basically until the delivery room, I was there, witnessing and getting the first-row seat to how the battlefield acted differently from what I knew in the past decade.”
Protego won’t have the field to itself for long. Israeli authorities have taken notice of the same shift, awarding VC firms Adir Capital and Sling Capital about $33 million in state guarantees to invest in military and dual-use technologies, with other funds also raising capital for similar bets. (Leshem said Protego was too far along in its own fundraising to take part in the government’s tender.)
Protego, meanwhile, is already planning its next move: a second fund in the first quarter of 2027, backed by Israeli institutional investors and one large U.S. commitment Leshem has already secured, with a broader scope that includes early-growth American defense-tech companies.
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Washington Gov. Bob Ferguson kicks off the Washington Space Council, surrounded by the council’s members and 10-year-old Sebastian Yu. (GeekWire Photo / Alan Boyle)
Washington Gov. Bob Ferguson has created a public-private council to elevate the Evergreen State’s profile as regional clusters compete for primacy in America’s space industry.
The 23-member Washington Space Council was introduced tonight at the Museum of Flight during a kickoff event for Seattle Space Week, a celebration organized by Space Northwest.
“The council will help keep us competitive by comparing Washington with other leading states in the industry and identifying ways we can continue to improve and compete,” Ferguson said.
Washington state already ranks as one of the nation’s space industry hotspots. “We’re proud to have a cluster of more than 90 space companies employing more than 13,000 workers, generating over $4 billion in annual economic activity and $1.6 billion in annual payroll,” Ferguson said.
But Andy Lapsa, co-founder and CEO of Kent, Wash.-based Stoke Space, told tonight’s crowd that several other regions are competing with the Pacific Northwest for space-related investments. “States like Florida and Texas and Colorado want this industry, and they want it bad,” he said. “They compete for it every day. Washington has every advantage it needs to lead them all, and the window to use those advantages is open right now.”
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Boeing has historically been the state’s leading venture in aerospace, but when you focus on ventures that concentrate strictly on space, Blue Origin is high on the list. Seattle is particularly strong in the satellite sector: More than half of the world’s satellites are built in the Seattle area, thanks to SpaceX’s Starlink facilities in Redmond and Woodinville, plus Amazon Leo’s factory in Kirkland, BlackSky’s production plant in Tukwila and Xplore’s operation in Bellevue.
Gravitics, Interlune, Portal Space Systems, Starcloud and Starfish Space are among other space ventures represented on the Washington Space Council. The list also includes elected officials, union leaders, educators and investors, plus representatives from space-related tech companies and Pacific Northwest National Laboratory.
The 23-member Washington State Council includes representatives from government, academia and the space industry. Click on the image to get the full list from the Washington State Department of Commerce.
Ferguson said the council will “identify barriers for the industry and find opportunities for growth.”
“That includes, by the way, workforce development, infrastructure and much more,” he said. “And the council will look for ways we might be able to develop Washington-based commercial launch capabilities. That would be a very exciting addition to what we’re doing right here in Washington.”
Ferguson brought a member of the audience, 10-year-old Sebastian Yu, to the stage when he signed the executive order that officially created the council. After the signing, the governor handed the pen to Yu.
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“Your assignment, Sebastian, is that when we do that first launch, you have to save this pen and bring it with you to the first launch,” Ferguson said. “Take care of this pen, OK? I’m going to expect to see it again someday.”
Notes from the kickoff:
Space Northwest presented three awards to recognize contributions to the region’s space industry. The Shooting Star award for municipal support went to the city of Kent, Wash. Portal Space Systems won the Rising Star award for emerging space companies, and SpaceX won the North Star Award for large space companies.
Kent Mayor Dana Ralph noted that her city’s connection to the space industry goes back to Boeing’s work on the lunar rovers for NASA’s Apollo moon missions in the 1970s, while acknowledging that Kent can’t rest on its laurels. “Our job is to make it possible for companies to build and grow and hire. That means reinvesting in the basics: roads, great mobility, water, sewer, plentiful and easily accessible energy and industrial land,” she said.
Mehran Mesbahi, professor and chair of the University of Washington’s Department of Aeronautics and Astronautics, highlighted efforts to boost careers in the space industry. “We have Husky Satellite Lab, a group of energized students at UW, and we’re going to create a certificate program around space systems going forward,” he said.
Seattle Space Week continues on Tuesday with the Space Northwest Symposium at the Federal Way Performing Arts & Event Center.
Among the many joys of having a cat is the daily battle with the mess they leave behind. No matter what you try, finding litter and pet hair on furniture or in your bed is inevitable — either they track it in, or you do. If you’re a pet parent like me, this means searching for additional bits of litter and stray hairs that find their way into places you really don’t want them, including the bed. Short of pulling the sheets off entirely, few fixes exist.
I took a chance and tested the SandBar Roller, a $30 dual-roll lint roller, to see if it can make it easier to keep stray hair and litter at bay. I also put it through the paces for other lint-roller tasks like dusting and cleaning floors. Here’s how it fared.
How SandBar Roller differs from other lint rollers
The SandBar Roller Starter Kit I tested included the roller head, an extendable handle and two preinstalled lint rolls.John Carlsen/CNET
The SandBar Roller’s appeal lies in its two large lint rollers instead of one. While the idea doesn’t seem like much of a departure from a typical large lint roller, I couldn’t find any other dual-roller designs intended for use between bedsheets like SandBar. By arranging the rollers in this way, SandBar can clean the top and bottom sheets simultaneously without getting stuck.
The Best Handheld Vacuums, Tested by CNET
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While SandBar markets its proprietary lint rolls as stronger than normal rolls, they’re not much different from what you’ll find on the extra-large pet hair version of the Scotch-Brite lint roller. Both are 8 inches wide, with an inner diameter of 1.5 inches. Much to SandBar’s credit, its six-pack of refill rolls is just $20, which is very competitive with other lint roll refills I found on Amazon, with the optional subscription price dropping to just $16 for each one-, two- or three-month delivery.
Testing the SandBar Roller
The extendable handle is very practical.John Carlsen/CNET
Ostensibly, this design targets coastal homes, where sand gets everywhere — hence the SandBar name. While I don’t live on the coast, I’ve experienced sand in my bed while traveling, or after day trips to the desert, and cat litter is a near-daily occurrence. With that in mind, I conducted a series of informal tests to see how the SandBar Roller holds up.
Upon putting SandBar between the sheets, I noticed it moved fairly smoothly, at least until it snagged on some sheets that were too loose. It didn’t jam, but that means it works better for beds with the sheet firmly tucked at the foot — something I don’t do for various sleep-friendly reasons. Still, it worked well after tightening the sheets a bit, even snagging a few cat hairs and litter fragments. It’s a nice alternative to washing the sheets as frequently as I have to now.
I like the stickiness of the rolls, which is satisfyingly tacky without leaving residue or making it difficult to pull off used strips.John Carlsen/CNET
The SandBar removed cat fur from hard-to-reach places
Next, I wanted to see how it handled cleaning smart window shades and curtains, which often collect fur as my cat patrols windows a few times a day. It was a little more awkward, but taking the handle off proved effective, and the sticky roller collected a substantial amount of fur. I had to peel off multiple sheets because of the amount of fur, but it took only a few passes to clean the curtain. The SandBar Roller was very useful on furniture and pet beds, but I noticed that being too aggressive can occasionally dislodge a roll.
The SandBar Roller left a fairly clean surface when I dusted off a TV cabinet and soundbar. Still, I wouldn’t trade it out for the duster and handheld vacuum I usually use. Likewise, it did well on the linoleum floor and carpet near my cat’s litter box, but still I prefer the cleaner result I get from vacuuming.
Should you buy the SandBar Roller?
The specialized design does its job well, but is probably too niche for people who aren’t pet parents or near vast swathes of sand.John Carlsen/CNET
At $30, the SandBar Roller is unlikely to break anyone’s budget, especially when you can find similarly large bed lint rollers for the same price. In that context alone, including the cost of refill rolls, it’s probably as good as any other option. Still, the dual-roll design’s ability to get between sheets is a compelling reason to consider the SandBar Roller over the competition. The design suits households that typically deal with a lot of sand and litter in the beds. I could even see myself taking it camping for quick dirt cleanup in a sleeping bag.
However, I wouldn’t recommend it for short-term beach house rentals because it cuts corners — new guests expect clean sheets when they arrive. That said, making a SandBar Roller available during a guest’s visit could be worthwhile to improve their experience, as long as you leave instructions explaining what it’s for and how it works.
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If you don’t need the second roll — and mainly deal with pet hair on furniture — the ChomChom Roller doesn’t need refill rolls, making it a much better option for $25. Likewise, a handheld vacuum can do the same job with a bit more flexibility around the house — though it’s not quiet enough to use when others are sleeping. While it’s nice to have so many options, it’s always worth going with what works best for your needs and situation.
John Carlsen has more than a decade of experience testing and reviewing home tech products, with a major focus on smart home security. He earned his BS in journalism from Utah Valley University. In addition to his CNET contributions, John has written for Android Police, TWICE, Home Theater Review, SafeWise, ASecureLife and Top Ten Reviews.
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If you haven’t heard of Metal Gear, you probably haven’t played video games these last few decades. The original Metal Gear dates back to 1987 and launched on the MSX, but the real rise to fame probably started with Metal Gear Solid on the original PlayStation in 1998. What needed a hefty console in 1998 can comfortably fit on a microcontroller in 2026, though, as [David Montero Crespo] demonstrates with his port of MGS to the ESP32-S3.
[David] is — as we always are — standing on the shoulders of giants with this hack. Most specifically, the project relies entirely on the [FoxdieTeam] MGS Reversing decompliation project. Of course what one team decompiles, another can recompile, and in this case [David] chose to recompile the game for Expressif’s exceptional ESP32. It wasn’t quite as easy as just forking the repo and compiling with the ESP32 as a target though, as the blog post explains.
We won’t spoil it, but [David] did have to make some changes to account for the different quirks the MIPS processor in the PlayStation has compared to the Xtensa cores on the microcontroller. Then to make it playable, he put the ESP32-S3 module onto some perfboard with an analog stick from a drone controller, an ILI9341 LCD panel, and a resistor ladder to run the buttons for a barebones handheld.
OpenAI was gearing up to release GPT-6.1 Astra, its next AI model, with an October launch in its sights. That’s no longer happening. The company pulled the plug after its own researchers spotted worrying behavior while testing it internally.
What went wrong with GPT-6.1 Astra?
On paper, Astra-6.1 was an upgrade over the current GPT-6 Astra. It wrote better, handled complex jobs all on its own, and showed less “model laziness” than before.
OpenAI
However, in an interview with The Wall Street Journal, Saachi Jain, OpenAI’s head of safety systems, said the model slipped in two important areas. First, it sometimes misled users about what it had actually done. Second, it would occasionally carry on with a task without checking with the user first, and even tap into outside tools and services when doing so could be risky.
“For anything regarding safety and alignment, there’s a trade-off,” Jain said. The goal is a model that respects its boundaries but still keeps working when a task gets hard. Astra fell short of OpenAI’s bar, so the public launch was called off.
Why is OpenAI hitting the brakes now?
AI agents have had a messy few months. Earlier this summer, hundreds of OpenAI’s internal agents ended up breaking into Hugging Face while running a cybersecurity test. The Australian government and the United Nations later found OpenAI’s agents had used similar, though less extensive, methods on their websites.
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Andrew Neel / Unsplash
Last week, OpenAI also halted training on its most powerful models after one agent found a loophole in its internet restrictions and queried a public chatbot. OpenAI says Astra is a separate case. Lawmakers are watching, too. According to WSJ, a Senate subcommittee is holding a hearing on rogue AI agents this week, and Florida’s Attorney General has been suing OpenAI since June.
What does this mean for you?
You won’t be getting GPT-6.1 Astra anytime soon. OpenAI hopes to reuse its base model to build future GPT-6 models and is digging into what caused the problems. Personally, I’d rather wait for an AI that’s honest about its work than use one that goes off and does its own thing.
New research suggests Enceladus may be an especially promising place to search for extraterrestrial life: microbes similar to those found near Earth’s hydrothermal vents survived in lab conditions designed to mimic the Saturnian moon’s subsurface ocean. A separate study also found that material blasted from Enceladus’ plumes may naturally separate and concentrate salts, organics and potential biosignatures into individual ice grains, potentially making them easier for future spacecraft to detect.
“That is great news in the search for life,” Frank Postberg, lead author of one and co-author of the other of these new studies and professor at Freie Universitat Berlin, said in a statement. “Future spacecraft will have to analyze many individual ice particles in the plume. But if they come across one with microbial material in it, they could identify biosignatures in the particle relatively easy with already available technology.” Space.com reports: Enceladus isn’t the only place in our solar system with water — so, why is it so exciting in the search for life? Well, it has to do with the seafloor of its extensive, liquid ocean. Down deep at the bottom of this body of water, scientists think hydrothermal processes, or movement or reactions with hot water under the surface, are taking place. The plumes shooting upward from the ocean also contain trace amounts of salts and organic compounds. NASA’s Cassini spacecraft found these traces when it flew through the plumes over a decade ago. Between the hydrothermal activity and the organics and minerals in the water, this moon’s ocean has a number of aspects that could be involved in supporting life.
What’s more, using a combination of Cassini data, theoretical models and laboratory experimentation, in Postberg’s new study the team found that the plume’s water droplets blasting out into space at up to 621 miles per hour (1,000 kilometers per hour) don’t freeze as quickly as expected. Before, scientists thought the freeze would happen instantaneously once the droplets reached space, but Postberg and fellow researchers say they found the freezing would actually happen much slower.
They also found that during this freezing process, the salt, organic compounds (and maybe possible signs of life) in the water droplets separate from one another. Not only that, but the team says that as the particles are blasted out into space, they should often collide with the icy cracks of the planet’s surface. This ultimately would leave behind tiny shards of frozen droplets with individually separated out components. Essentially, it’s like the planet has organized its oceanic ingredients into tiny, frozen particle fragments. This work is described in two new studies published in the journal Science Advances here and here.
In context: Android smartphone maker Nothing has launched the Headphone (1) Pro, its new flagship audio product, which it claims combines an elevated listening experience for consumers with studio-grade production and mixing features for professionals. It succeeds last year’s Nothing Headphone (1), which TIME named one of its Best Inventions of 2025.
The biggest change in the new model is a three-driver audio system. It includes a bass dynamic driver designed to add more punch and sensitivity to low frequencies, a precision dynamic driver that adds more texture to vocals and instruments, and a new treble xMEMS driver that Nothing says delivers crisper highs.
Nothing also worked with London’s Metropolis Studios to tune the Headphone (1) Pro, with the collaboration focused on professional audio tools and soundstage characteristics.
The company also highlights an improved adaptive active noise cancellation system that uses a 10-microphone array to more accurately filter ambient noise than the first-gen model. With claimed noise reduction of up to 46 dB, Nothing is positioning the Headphone (1) Pro against premium rivals from Bose and Sony.
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Nothing says the improvement in noise reduction should be particularly noticeable in strong winds or noisy traffic. The headphones also feature redesigned ear cushions with an 8mm silicone baffle wall inside, creating a stronger sound barrier and improving the seal around the ears.
The Headphone (1) Pro supports Hi-Res wireless audio and wired playback over USB-C at up to 24-bit/192 kHz. It also offers five modes of Dynamic Spatial Audio with head tracking, along with five new custom EQ profiles. A separate Flat EQ switch is designed to give producers and creators a more neutral sound profile.
The Headphones 1 Pro is built out of aluminum and titanium to provide a durable shell while creating a premium look and feel. It also retains Nothing’s transparent design language, using shatter- and scratch-resistant 9H Panda Glass on both ear cups to reveal parts of the triple-driver architecture.
Despite the increased use of metal, Nothing has reduced the headphones’ weight to 327 grams through a redesigned internal architecture, including replacing steel arms with titanium. Other changes intended to improve comfort over longer listening sessions include a wider headband with thicker padding and softer ear cushions.
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The Headphone (1) Pro is positioned to compete with premium models such as the Sony WH-1000XM6, Bose QuietComfort Ultra, and Apple AirPods Max. It is priced at $399 and will be available beginning September 29 through Nothing’s official store at nothing.tech, Amazon, and Best Buy stores across across the US and Canada.
Ned Jenkinson of the University of Birmingham and Matthew Weightman of the University of Oxford discuss how advancements in brain research might affect how we learn and grow our skillsets.
Whether learning a new piano piece or adapting your tennis serve, acquiring physical skills depends on your brain’s ability tostrengthen and refine neural connections. Researchers are exploring whether this process can be accelerated with technology.
Scientists are particularly interested in the potential of non-invasivebrain stimulation, a group of techniques that can alter brain activity without surgery.
If these techniques can successfully enhanceneuroplasticity, the brain’s ability to reorganise and form new connections during learning, then they could be of use anywhere where performance depends on learning complex movements, from sport and music to surgery and beyond. Researchers are also seeing if these technologies could help with learning non-physical skills, such aspicking up a foreign language.
Elite sport, professional gaming and high-performance workplaces could all become targets for these enhancements if they prove effective. Brain stimulation could also have a big role to play in medicine, helping patients recover physical skills lost through injury or disease, such asstroke.
Studies suggest there’s a lot of potential here. But translating this potential into useful tech that reliably boosts learning physical skills remains a big challenge.
Stimulating findings
Research into enhancing motor learning with electric or magnetic stimulation has been gaining momentum since the turn of the millennium, with early studies garnering considerable excitement.
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In atypical experiment, participants might learn a sequence of finger movements similar to practising scales on a piano while receiving stimulation over brain regions involved in movement. Other studies have examined how stimulation could be used forbalance training or teachingsports-relatedskills orsurgical techniques.
Some of these experiments produced eye-catching results, finding that participantslearned certain movement tasks faster orretained skills for longer if they underwent brain stimulation. But other studies failed to find benefits. And in some cases, researchers struggled to replicate the success of earlier promising experiments when repeating them.
One reason for these mixed findings is that there’s no such thing as a universal ‘learning network’ in our brains. Different skills rely ondifferent combinations of areas near the surface of the brain as well as those deep within it.
Additionally, people can respond very differently to the same stimulation. Factors such as age, anatomy, genetics and even baseline skill level may influence whether stimulation is beneficial. Add to that the infinite number of ways to apply stimulation, the picture becomes murkier.
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Despite these challenges, the field continues to evolve in its quest to enhance motor learning. For instance, rather than broadly stimulating the brain, researchers are increasingly targeting specific neural circuits involved in learning.
This is partly thanks to advances inneuroimaging andcomputational modelling, which has allowed scientists to predict how electrical currents travel through a person’s brain. Newer brain stimulation technologies, such as focused ultrasound, can also now reach deep structures involved in skill acquisition.
The goal is to use these technologies not simply to increase brain activity, but to influence the right neural circuit at the right time during learning. This idea builds on a fundamental principle of neuroscience, often summarised as“neurons that fire together, wire together”. When brain cells are repeatedly activated at the same time, the connections between them become stronger.
By carefully timing stimulation tocoincide with the movements made during practice, researchers hope to reinforce the neural pathways involved in learning a new skill. In principle, this could make stimulation more reliable and more effective than current approaches, but researchers are stillfine tuning exactly how this would work.
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Motoring ahead
Important questions remain. Who would have access? Should stimulation be regulated in competitive environments such as sport? And how much evidence should be required before consumer devices are marketed to healthy users?
These questions are becoming increasingly relevant as brain stimulation moves beyond the laboratory and clinic. A number of at-home devices are now available for people to buy.Some have receivedregulatory approval, as they’re indicated for treating medical conditions such as depression. But there’s also a growing market for devices forcognitive and performance enhancement. For these uses, no regulatory approval is needed.
The technology is advancing rapidly, but evidence to support it and regulations governing it are still trying to catch up. Proper frameworks for its adoption may simply be bypassed by the ready possibility of ‘DIY’ brain stimulation.
For now, brain stimulation is unlikely to transform anyone into an overnight virtuoso or elite athlete. But as researchers develop increasingly precise ways of targeting the neural circuits that underpin learning, the prospect of enhancing human performance is shifting from science fiction towards scientific possibility.
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The challenge today is not simply learning how to influence the brain, but deciding where, when and why we should.
Ned Jenkinson is a senior lecturer in human movement sciences at the School of Sport and Exercise Sciences at the University of Birmingham. His research incorporates a range of techniques including non-invasive brain stimulation, electrophysiological recording, eye-tracking, neuroimaging and behavioural techniques. He uses these techniques to investigate how the brain controls movement and how it allows us to learn new motor skills.
Matthew Weightman is a postdoctoral researcher at the Oxford Centre for Integrative Neuroimaging in the Plasticity Group at the University of Oxford, led by Prof Heidi Johansen-Berg. He is broadly interested in the field of sensorimotor neuroscience. His current work focuses on the role of sleep to recovery after stroke. More specifically, he is interested in how we can improve sleep after a stroke, whether improved sleep in stroke patients relates to better functional recovery, and if physiological processes that occur during sleep can be enhanced post-stroke to boost consolidation.
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Keio Corporation (Keio), a major private railway operator in Japan, said its network was hit by a ransomware attack over the weekend, disrupting some of its business systems.
Following a system failure in the early hours of Saturday, the company confirmed the attack and shut down its network to prevent additional damage.
The company said it is investigating the extent of the impact and whether the attackers accessed any customer or business partner information.
Keio is a large Japanese railway operator with 85 km of track and 69 stations, as well as a separate hospitality business of 25 hotels. The company has over 2,200 employees and a reported annual revenue of about $2.6 billion.
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“In the early hours of September 26, 2026, we confirmed a ransomware attack on our group’s servers. We have reported the incident to the police and are conducting an investigation into the attack’s route and damage with the cooperation of external experts,” Keio says.
The incident appears to have affected only the hospitality side of Keio’s business, not train operations.
A separate announcement published on the company’s Keio Plaza Hotel Tokyo website is warning of possible delays on some customer-facing services.
At the time of writing, BleepingComputer could not find a ransomware group claiming the attack on Keio.
BleepingComputer has contacted the company to request more information about the incident, and we will update this post with their response once it reaches us.
Tokyo Metro has also disclosed a cyber incident over the weekend in which attackers gained unauthorized access to its systems and accessed 59,000 member email addresses.
Although both Keio and Tokyo Metro are Japanese railway operators, it is unclear if the organizations were targeted in a coordinated campaign by the same threat actor.
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Tokyo Metro is a major transit operator that runs nine subway lines covering 195 km and 180 stations, carrying an average of 7 million passengers daily.
The company said the breached systems contained only email addresses and that it has already identified and closed the security weakness the attackers used in this case.
Join Mikko Hyppönen and security leaders from the NFL, CHANEL, and Atlassian for a two-hour digital summit on what AI-speed attacks change, what defenders should stop doing, and how to validate, decide, fix, and re-validate at machine speed.
Home puts chats, delegated work, and Office documents inside one interface
Code lets non-programmers describe software and have Copilot build it
Autopilot can continue recurring work without waiting for another instruction
Microsoft has introduced a redesigned version of its Copilot AI platform which claims to combine chat, delegated work, and coding tools into a unified application experience for users.
The company says the update is meant to let individuals and organizations scale artificial intelligence across everyday tasks and long-term projects.
Three new capabilities anchor this release, including Home, Code and Autopilot, each aimed at a different kind of work.
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Home brings Chat and Cowork together
The Home tool brings together two existing modes, Chat for quick questions and Cowork for tasks users delegate entirely, under one shared starting point.
Word, Excel, and PowerPoint now operate inside this same interface, letting users draft documents, budgets, and presentations without switching applications.
In these documents, Copilot is now grounded in Fabric IQ, pulling context from more than 20 million semantic models built in Power BI.
Edits made by colleagues or by the assistant itself appear in real time, so progress stays synchronized across a shared file.
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A new plugin registry will let organizations manage Microsoft, partner and custom-built plugins from one central catalog starting this month.
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Independent developers and partners can also publish plugins once for use across multiple Copilot surfaces under the new registry arrangement.
The Code tool allows non-programmers to describe an app, tracker or dashboard in plain language and have it built automatically.
This feature runs on the same underlying technology used in GitHub Copilot and can be hosted within a company’s own systems.
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Autopilot operates without constant prompting
Autopilot, the third addition, is a persistent agent capable of completing recurring work without needing a new instruction each time.
It can run supplier reviews or similar multi-step processes, build schedules, contact stakeholders, and follow up on outstanding items independently.
Because it operates continuously in the cloud, work can continue late at night or whenever a person’s attention shifts elsewhere.
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A related feature called Today, entering private preview in October, will summarize missed messages and pending tasks across mail and chat.
Microsoft is also tying spending controls to these tools through a system it calls FinOps for AI, letting administrators track usage.
Administrators can set spending limits, approve credit requests and restrict which AI models different teams are permitted to use each month.
Everyday tasks like quick answers or first drafts run on a fixed-price subscription, while agentic features use usage-based billing.
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Code, Cowork and Autopilot all fall under this usage-based pricing model, alongside frontier models Microsoft refers to as Astra and Fable.
Home and Code are set to roll out through Microsoft’s Frontier program within weeks, and Autopilot enters private preview by month’s end.
Microsoft has not released independent data showing how widely the three features are being adopted, how accurate they are, or how much time they actually save.
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