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Why Did Some WWII Bombers Have Glass Nose Cones?

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During the Second World War, the United States built over 30,000 four-engine B-17s and B-24s. Another 3,970 B-29 Superfortresses were constructed as well, the type that dropped the atomic bombs on Hiroshima and Nagasaki. Although the B-17, the bomber that helped win WWII, was the smallest of the three, it was known as “the flying fortress” because it could take the most punishment. A single B-17 was built every six hours at the Douglas Aircraft Company in Long Beach, California. Meanwhile, the B-24 Liberator — with 1.5 million parts (an average Ford had a mere 15,000) — rolled off the line every 63 minutes.

The bomber’s key role was, well, to drop bombs. Bombardiers were tasked with manually — and by manually, we mean visually (more on that later) — aiming their ordnance using a top-secret device known as a Norden M-9 Bombsight. There weren’t precision-guided munitions that used onboard computers capable of calculating trajectories or smart bombs that could steer themselves with articulating fins to home in on a target. Aviators were dropping what amounted to “dumb” bombs using only their Mark I eyeballs — totally by sight. To do so, however, they needed to see the target they were trying to hit physically. This led to the prevalence of glass nose cones.

Yet traditional silica-based glass would shatter if used in these new modern bombers. DuPont invented Lucite, a tough acrylic plastic, in 1931, and German chemist Otto Röhm came up with Plexiglas in 1933. Both were widely used by Allied and Axis powers during WWII instead of glass for things like windows, plane canopies, ball turrets, and nose cones because they weighed less and were bullet-resistant.

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Bombs away!

Often called glazed glass noses, these see-through domes were part and parcel of most iconic WWII bombers, including the B-17, B-24, and B-29. The Germans used them on the Heinkel He 111, Junkers Ju 88, and the prototype Amerikabomber Messerschmitt Me 264. The British also equipped their Avro Lancaster and Handley Page Halifax with them. Even after the war, some U.S. planes like the B-47 Stratojet and Douglas A-3 Skywarrior continued to use them, as did Britain (i.e., the Blackburn Beverley and Armstrong Whitworth Argosky). The Russians kept utilizing them mainly because they lagged behind both technologically and philosophically.

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Keep in mind that WWII was greatly shaped by technological innovation and is considered the most industry-driven war in history, much of which is quaintly archaic by today’s standards. Bombers in World War I were nothing more than wood-and-fabric biplanes that either used very basic mechanical levers, primitive drift sights, or someone simply reached out and dropped bombs over the open sides — all of which amounted to nothing more than blind guesswork.

However, the Norden M-9 Bombsight was an exponential leap because it was something else entirely. Developed by Carl Norden for the U.S. Navy, it was first used by the Army Air Corps in 1932 and supplanted the inferior Sperry S-1 bombsight. A trained bombardier entered wind direction, airspeed, and altitude into the analog computer, which then took into account wind drift and spit out the optimal target location. Stability was provided to the telescopic sight (used in high-altitude runs) by a built-in gyroscope. It might sound like the ancient Greek Antikythera mechanism, but by WWI standards, it gave bombardiers extraordinary precision.

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iOS 27.2 lets you create separate profiles in the Apple TV app

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Handing your phone to a kid usually means their cartoons take over your entire watch history, but Apple just fixed that annoyance. With iOS 27.2, currently in beta, the Apple TV app finally supports separate user profiles on iPhone and iPad, a feature that’s existed on the Apple TV box for a while but never made it to Apple’s mobile devices until now (via 9to5mac).

What can you do with Apple TV profiles?

After updating, open the Apple TV app, and you’ll see a screen listing any profiles already tied to your account, along with an option to add a new one. Setting one up only requires a name, a content rating, and whether the profile belongs to a child. The setup also supports profiles for people who don’t have their own Apple Account. That makes it possible to create a dedicated profile for a younger child.

If you choose a kid’s profile, Apple defaults to a PG rating automatically, though you can dig into the settings for more specific control over which shows and movies fit their age. If profiles aren’t something you want cluttering your phone, just select the “Don’t Show This Again” option to skip the whole thing.

Why does this matter?

Apple TV profiles aren’t exactly new, and tvOS 26.2 added the guest mode option last November. The real benefit shows up the moment you hand your device to a kid. Their viewing stays contained to their own profile, meaning your “Continue Watching” row doesn’t suddenly fill up with content you never chose, and your own account stays exactly as you left it.

It also gives kids a safer space to browse on their own, since what they can see gets capped by the rating you’ve picked. For those who want this option right away, iOS 27.2 is currently rolling out as a developer beta, with a wider public release expected in the coming weeks.

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Porsche’s wireless charging pad is on sale in Europe, and it bolts to the floor

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Porsche has opened orders in Germany and other European markets for an 11 kW wireless charging pad for the Cayenne Electric, at EUR 6,961.49 for the floor pad and the car-side hardware together, with efficiency of up to 90%. The pad must be permanently installed in the ground, and Europe’s rules on installing home charging are weaker than the phrase right to plug suggests.

Porsche has opened orders for a wireless charging pad for the Cayenne Electric in Germany and other European markets, the first offered on a car in either Europe or North America, InsideEVs reported.

It delivers up to 11 kilowatts at an efficiency Porsche puts at up to 90%, against roughly 94% for a cable. That is enough to fill the 113 kWh battery overnight, and well ahead of the 70% to 80% a phone manages.

In Germany the three-phase floor pad costs EUR 5,116.99 and the car-side hardware EUR 1,844.50, which is EUR 6,961.49 together. Ordering only the pre-installation, for a later retrofit, costs EUR 226.10.

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The pad weighs about 50 kilograms, is waterproof and takes the weight of the car. Radar sensors cut the power if they find metal, an animal or a person.

Park assist lines the car up over the pad. The receiver is liquid-cooled, the transmitter air-cooled, and the system sends direct current straight to the battery, going around the onboard charger entirely.

It also has to be fixed to the ground.

That makes it a building alteration rather than a plug, and Europe’s right to plug is thinner than the phrase suggests.

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Article 14(8) of the recast buildings directive asks member states to streamline and accelerate installation for tenants and owners alike. Transposition fell due in May, and there is no binding requirement to pre-equip existing blocks.

Transport and Environment calls that too weak to count as a right, because a request can be refused on serious and legitimate grounds the directive never defines.

France is the strongest. Co-owners there get three months to object, and only on grounds of technical infeasibility or existing shared infrastructure.

Germany, Italy, Poland and Spain have partial rules, typically covering owners but not tenants. Britain has no specific provision at all.

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A Cayenne Electric starts at EUR 105,200, so the early buyers will mostly own the floor they are drilling into. The car itself is built at Volkswagen’s plant in Bratislava, where series production began in February.

Porsche will take the pad to its other markets in stages, with one exception. China, where inductive charging is already deployed and Xiaomi is about to sell a robotic arm that plugs the cable in for you, is not on the list.

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I feel like the Galaxy Z Fold 8 is the right kind of passport foldable

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Passport-style foldables are returning, but in a foldable format. Samsung completely changed direction with the Galaxy Z Fold 8, Xiaomi followed with the 18 Fold, and Apple has now entered the category with the iPhone Duo. Even though the original Google Pixel Fold explored the same short-and-wide idea years ago, this is looking like the new smartphone trend in 2026.

After spending years with book-style foldables, I am warming up to this decision. I prefer the idea of keeping the phone compact when closed and opening it only when I need more room.

Someone recently told me they thought the iPhone Duo looked beautiful. Their only complaint was that they wished it ran Android. Don’t get me wrong, I like what Apple has done with the Duo in plenty of places. The software transitions are gorgeous. Still, the iPhone Duo is incredibly squat when closed. The outer display does match the inner one in terms of aspect ratio, but I can’t help but feel like the design lags behind.

Passport foldables aren’t all the same

Put the two phones next to each other, and they initially look like variations on the same concept. The Galaxy Z Fold 8 has a 5.5-inch 10:16 cover display and opens into a 7.6-inch 4:3 canvas. Samsung designed the outer screen for quick scrolling and messaging, then lets the inner display handle tasks that need more immersion.

Apple does essentially the same thing with the Duo: a 5.4-inch outer display opens into a 7.6-inch inner screen. Apple even claims that the outer display is compact enough for one-handed use while the inner screen handles multitasking and entertainment.

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Features Galaxy Z Fold 8 iPhone Duo
Closed dimensions 123.9 x 81.9 x 9.7mm 117.8 x 84.1 x 11.3mm
Outer display 5.5 inches, 10:16 5.4 inches, 14:10 (roughly)
Inner display 7.6 inches, 4:3 7.6 inches, 14:10 (roughly)
Weight 201g 254g

Overall, the Duo is a little shorter and heftier, not to mention wider too. Those numbers explain quite a lot about how differently the two devices come across in the hand. Samsung’s Fold 8 is wide enough to feel distinct from a conventional slab without tipping too far into miniature-square territory. The Duo goes farther, and that extra width combined with the weight makes the whole thing look and feel more substantial when you’re simply trying to use the outer screen.

The UI debate

Apple knows the Duo’s proportions create a software challenge. Its own developer guidance explains that the Duo’s wider aspect ratio creates more horizontal space but reduces the amount of useful vertical room. Apple therefore moves controls that normally sit at the top and bottom of an iPhone onto the side of the interface, where they preserve vertical space and become easier to reach.

It also reinforces why I’m not entirely sold on the hardware shape. On a normal iPhone, developers have spent years assuming a tall display. We’re all familiar with how navigation bars go along the top, while tab bars sit at the bottom, and your thumb moves through an interface built around those proportions. Apple deserves credit for actually changing up this formula.

I already like how iOS 27 adapts across the Duo, and the software is one of the device’s biggest strengths. But Samsung’s Fold 8 has a simpler advantage with its 10:16 cover display that is still tall enough.

Samsung’s Galaxy Z Fold 8 is meant to be your main device

The ergonomics are another part of this conversation that gets lost when we obsess over diagonal screen sizes. The difference between 81.9mm on the Fold 8 and 84.1mm on the Duo looks tiny on a spec sheet. Once you combine it with the Duo’s shorter shape, 11.3mm folded thickness, and 254g weight, the device becomes a much chunkier object to move around in your palm.

Something that really impressed me was just how much Samsung’s phone weighs. At just 201 grams, it is surprisingly light for a book-style foldable. I’d argue that this is what the pinnacle of foldables should look like. All the specs and features aside, having a phone that feels handy is much better than a phone that is a chore to hold after a while.

Samsung can give me the unusual passport proportions without making every quick interaction remind me that I’m holding two halves of a folding phone. Not saying it’s as light as the truly compact phones like the Galaxy S26 and Pixel 11 Pro, but it gets realistic enough.

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I’d pick the Z Fold 8 any day

I’ve already argued that compact, wide foldables could solve one of the biggest problems with small phones. You get something manageable for calls, notifications, and quick interactions, then unfold it whenever you need more room for gaming, email, reading, or multitasking.

The question is how compact you make that outer experience. The Duo embraces a much squatter display and then uses thoughtful software to make that unfamiliar canvas work. Samsung’s Fold 8 feels a little closer to a conventional phone without losing the passport design. Its best part is still that it’s lighter and thinner, which makes the shape easier to drive daily.

I might change my mind once I try out the Duo… maybe. Even so, the Galaxy Z Fold 8 is much closer to what I want from a passport foldable.

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After accusations of selling ‘perv glasses,’ Meta prepares to sell a pair without a camera

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Meta’s camera-equipped smart glasses have proven more successful than other entrants into the market, but they have also deeply disturbed certain consumers who see them as invasive emblems of a dystopian surveillance society run amok. Now, as the company weathers complaints that it’s selling “pervert glasses,” it has reportedly decided to sell a pair that doesn’t come with integrated spy equipment.

The Information reports that Meta is developing a new smart glasses model, dubbed Luna, that, sans cameras, comes equipped with a convenient system to communicate with the company’s AI chatbot and Muse, its AI consumer agent. The glasses include six built-in microphones so users can communicate with the chatbot, as well as a button on the side of the glasses that, when pressed, activates the AI system.

Luna may be unveiled as soon as Meta Connect, Meta’s annual hardware/developer event happening in Menlo Park next week, the outlet notes.

TechCrunch reached out to Meta for more information.

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The smart glasses industry has grown over the past few years, with Meta positioned near the front of the pack of providers. However, concerns over usability, cost, and privacy have dogged the business, with many consumers still unsure of how, why, or if they should use the devices. Meta’s Reality Labs, which is responsible for developing its smart glasses line, is still losing a gargantuan amount of money, as its earnings report from April revealed.

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Emerging Technology Trends to Watch in 2026 and Beyond

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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.

ix-step AI agent workflow from Task and Plan through Tools, Sandbox, specialist subagents and human 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.

AI infrastructure chain linking Models, Accelerators, Server Racks, Cooling, Power, Grid and Generation.

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.

IBM’s March 2026 quantum roadmap, for example, describes 2026 work toward examples of quantum advantage and prototypes for real-time error correction. IBM explicitly labels its roadmap milestones as company goals that are subject to change, so they should not be treated as guaranteed industry timelines.

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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.

Five-step cryptography roadmap from Inventory and PQC through Transition and Quantum to Retirement.

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 GSMA describes satellite D2D as a supplementary coverage and resilience layer that can extend service into sparsely populated or inaccessible areas and provide another communications path during some terrestrial network outages.

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.

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.

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Physical AI Safety Under Attack From Silent Backdoors

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This article is brought to you by VicOne.

Robot safety has traditionally asked: Can a machine remain safe when something goes wrong? Physical AI raises a harder question: Can a machine remain safe when an attacker changes what it sees, decides, or does even when nothing appears to have failed?

As AI and robotics continue to advance at an unprecedented pace, modern robots perceive through multimodal sensors, interpret context using AI models, and translate those interpretations into physical action. As they move into dynamic environments, their safety increasingly depends on the integrity of the data guiding their decisions.

That dependence creates risks that conventional safety assessments may not fully capture. Recent research has demonstrated that manipulating what a robot sees, hears, or interprets can influence its behavior without requiring direct control.

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Such manipulation can occur anywhere across its complex sensing and decision-making system — a layered attack surface encompassing training pipelines, system infrastructure, and runtime perception.

Layer One: Corrupting intelligence at its source

In 2017, BadNets demonstrated that a model could behave normally under most conditions, yet fail in the presence of a specific hidden trigger. In one example, a subtle pattern caused a stop sign to be misclassified as a speed limit sign without affecting the model’s behavior on other inputs.

What began as a classification vulnerability has since evolved into action manipulation.

At NeurIPS 2025, researchers introduced BadVLA a backdoor attack targeting Vision-Language-Action (VLA) models that allow robots to see, interpret instructions, and produce coordinated physical movement. Rather than altering a single label, the attack caused conditional deviations in the robot’s action trajectory when a trigger was present. Without the trigger, the model largely preserved normal task performance, while the backdoor remained effective under task transfers and model fine-tuning.

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A related study in 2025, GoBA, showed that ordinary objects such as a coffee mug could serve as a reliable trigger. The researchers reported a 97 percent attack success rate without degrading performance on clean inputs.

A critical safety question today is whether Physical AI models remain within their task and safety boundaries under adversarial conditions.

These studies expose a blind spot in model validation: A model may pass testing yet produce corrupted behavior when a hidden trigger appears in operation.

So a critical safety question today is whether Physical AI models remain within their task and safety boundaries under adversarial conditions. Simulation tools such as NVIDIA Isaac Sim, when paired with VicOne Radeis, can test the effects of manipulated inputs before deployment.

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VicOne LAB R7 demonstrates Radeis, a Physical AI safety validator for NVIDIA Isaac Sim that tests how adversarial visual inputs affect robot behavior before deployment.VicOne

Layer Two: System vulnerabilities as gateways to AI control

Even a securely trained model can be subverted if the surrounding system stack is vulnerable.

In September 2025, researchers disclosed UniPwn, a Bluetooth exploit chain affecting quadruped and humanoid robots from a major manufacturer. Hardcoded cryptographic keys allowed traffic decryption, authentication checks were bypassed, and command injection enabled root-level execution. The exploit is also described as “wormable.” A compromised robot could scan nearby units and potentially affect an entire fleet.

VicOne Lab R7’s demo shows how chaining three wireless exploits can trigger uncontrolled robot behavior within 60 seconds, resulting in operational disruption.VicOne

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Middleware creates another exposure point. Vulnerabilities in ROS 2 and DDS-based systems can enable arbitrary code execution or abuse unauthenticated topics to deliver malicious commands. With sufficient access, an attacker could override motor commands or replace AI model weights without directly attacking the model architecture.

In this case, the components may still function as designed. What has changed is the trustworthiness of the commands flowing through the system. Vulnerability management can help teams identify known risks before deployment, while continuous monitoring can surface emerging threats.

Layer Three: Manipulating perception and reasoning at runtime

At runtime, manipulating inputs that shape perception or reasoning may require neither firmware modification nor a network breach.

In 2024, RoboPAIR demonstrated how carefully structured prompts could redirect LLM-controlled robots into unsafe trajectories. BadRobot exposed a deeper architectural weakness: in several cases, a robot verbally refused a dangerous command while its motion controller executed the action anyway.

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Vision-based manipulation is equally powerful. VLAttack showed that an adversarial patch within the camera’s view could reduce a VLA model’s task success rate to zero. FreezeVLA showed that a single adversarial image could freeze a robot’s decision-making loop, making it unresponsive to subsequent instructions.

Runtime assurance must therefore look beyond whether individual components remain available and assess whether cyber events are beginning to affect physical behavior.

In each case, the camera may still work, the model may still run, and the controller may still respond. Yet the resulting behavior can be unsafe because the robot is acting on manipulated perception or reasoning.

Runtime assurance must therefore look beyond whether individual components remain available and assess whether cyber events are beginning to affect physical behavior. Security event correlation, behavioral-impact assessment, and policy-bounded response supported by edge AI, can help contain the affected path without unnecessarily stopping the entire robot fleet.

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From point-in-time safety to lifecycle assurance

The risks across these three layers reveal the missing layer in robot safety assurance: cybersecurity. Functional safety addresses failures and unexpected operating conditions; cybersecurity extends that assurance to deliberate manipulation, including attacks that may leave the underlying system apparently functional.

This requires assurance across the robot’s lifecycle. During design, teams need to understand which cyber risks could invalidate assumptions behind intended behavior. Before deployment, they should test whether realistic attacks can cause a robot to deviate from its task or safety boundaries. In operation, monitoring should identify whether cyber events are beginning to affect behavior, contain the affected path, and preserve safe operation where possible.

Diagram of end\u2011to\u2011end AI robot security from development to operation monitoring VicOne’s lifecycle approach combines AI model and vulnerability scanning, simulation-based validation, and continuous monitoring to help secure robots from development through operation.VicOne

While cybersecurity does not replace functional safety, it helps ensure that Physical AI remains within acceptable boundaries even when what it sees, decides, or does is under attack.

For a deeper look at the cybersecurity risks and defense strategies shaping autonomous robotics, download our whitepaper “Securing the Rise of AI Robots: Cyber Risks, Real-World Threats, and Defense Strategies.”

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China’s Humanoid Robots Are Walking Off The Assembly Line On Their Own

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Tesla revealed its Cybercab concept back in 2024, only to have thousands of Waymo vehicles flooding cities across the United States before even one Cybercab rolled off the assembly line. Tesla then converted its Fremont, California factory — which also produces the Model 3 and Model Y — into a factory for its humanoid robot, Optimus, which is still nowhere to be seen — and Chinese automaker Xpeng has its own humanoid robot, Iron, walking off the assembly line. Literally. 

With this small step for robot kind, Xpeng has now completed production of the world’s first “advanced general-purpose humanoid robot,” according to a press release. The manufacturing system itself was also 80% autonomous, focused on consistent, fast-paced mass production, with the goal of scaling it up and expanding. The robots are currently expected to be mass-produced by the end of 2026, with launch and delivery following in 2027. During Tesla’s “We, Robot” event back in 2024, Optimus was revealed to be controlled by humans, but CEO Elon Musk still claims that the $20,000 to $30,000 robots will be delivered by 2027 as well. 

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What is Xpeng’s Iron humanoid robot?



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Xpeng’s Iron humanoid robot is designed to fit into the real world, with CEO He Xiaopeng stating that he expects Iron to become “part of everyday life” – improving people’s lives while becoming their companion. Iron is meant to do a variety of tasks that people may not want to do, including factory work or making a morning coffee. To complete these tasks, Iron has three AI chips for a plethora of computing power, along with human-like dexterity and mobility. Xpeng claims that Iron is already assisting in its own manufacturing process, which would mean it has come a long way since its tech demos back in 2025 (Iron also appears less “naked” now as well)

You won’t find many specific details on the official Iron page on Xpeng’s website, which may not be instilling investors with the utmost confidence in the automaker. Xpeng’s stock is down nearly 50% in the first half of 2026. It’s not only Xpeng and Tesla competing to launch a humanoid robot by 2027 — other companies are also working on their own creations (with varying degrees of creepiness), and China continues to lead the way in this mysterious new technology frontier.

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Arena App In 2026, Ranked Worst To Best

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Over the past few weeks I’ve been playing Magic: The Gathering: Arena (AKA MTG: Arena) on my Android phone and my laptop (with a touchscreen.) I want to sum up my background here quickly so you know where I’m coming from: the last time I played Magic was probably two decades ago, and I played a lot starting when I was a teenager. Now I’m married and have some kids and I don’t have a whole lot of free time — but I wanted to see if I could still play the game.

So I started playing just before The Hobbit set was being released, and I learned a few valuable lessons. The following is a brief guide for those of you who’ve played Magic — probably in your distant past — and you’re considering playing Magic again here in the present.

The following “reasons to play MTG: Arena” are ordered, to the best of my ability based on my experience with MTG: Arena so far, according to the experience you’ll probably have if you’re looking to have a good time jumping in on your phone, tablet, laptop, desktop, or otherwise.

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For Nostalgia And Classic MTG Simplicity

There are certain parts of the Magic experience today that I cannot imagine the eldest of the elder players reconciling with. This game still has all the basics — you draw seven cards, you take turns trying to knock out your opponent’s life total, you buy packs of cards and hope you get the cards you want for the deck you’re building — but it’s all become a significant amount more complicated.

If you’re hoping to make a green stompy deck with a bunch of trample minions, you can still do that, but you’ll probably face decks with multiple colors and card combinations that depend largely on tokens and +1/+1 counters. Its not out of the ordinary to face someone who’s created a double-digits-large hydra minion or several stacks of cat tokens by their 5th turn — the potential for madness is high. 

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If you’re looking for that most basic version of the game from the 1990s, it is not present in MTG: Arena. To be fair, it hasn’t existed for a long time before Arena existed, but still: Magic: The Gathering: Arena only includes Magic cards in sets from around 2017 on forward (except in special limited games and circumstances — but you can’t have a Cursed Scroll in your collection.)

You Miss Magic But You’re A Senior Citizen

You may be an elder player of Magic — you were probably buying booster packs for $2.50 at your local Sports Cards store. Times were great! You miss the fun parts of the game, collecting cards, making decks, seeing how well your deck does against your peers.

But you’re not in a place where you’re going to bike downtown, buy a few tacos, and join a tournament this Saturday afternoon. That’s not everyone’s situation — Magic is for everyone, of course — but maybe MTG: Arena will offer you another way back in?

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It could. I played the game after being away from the game for an extended period of time and I was able to pick up the cards and have an OK time learning the latest set of rules. And yet — no matter how much I enjoy my time playing on this little screen, it’s still a screen.

There are some events in MTG: Arena in which you can win actual physical cards — whole boxes — but the road to winning is extremely steep. But once you’re that good at the game, maybe it’ll be the perfect time to bust out your new skills on your local tournament scene anyway? They’ll never see you coming!

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You Want To Play Magic On Your Android Phone Or iPhone

If you’ve played Magic in the real world and you’ve played games like Hearthstone on your phone in an effort to rekindle the magic, you’ll probably enjoy MTG: Arena’s approach. I played Hearthstone and thought it was the ideal system for this Collectable Card Game system for tiny touchscreens. Now that I’ve played MTG: Arena for a few weeks, I’m having trouble playing Hearthstone because the game feels …too simple?

While I still think the physical cards are the best way to appreciate the game, I do enjoy how MTG: Arena adds some effects and decorations to certain cards and card types without going overboard. If I cast a Smaug, (or was it “Desolation Of Smaug?”), the game shows fire and a shadow of the dragon cast across the battlefield as he flies overhead. That’s pretty fun.

If you can’t (or do not want to) get out and play the physical game, MTG: Arena is a decent alternative.

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To See Which Cards Are Worth Adding To Your Collection

If you play the physical game, with real cards, packs, and so forth, playing MTG: Arena can potentially be a great way to playtest cards before you bring them to a real life tournament. I played every single different type of Hobbit-themed event tournament (most of which was draft-based) in the app, and I got a good sense of what cards I wanted to actually own in the real world before I went out and bought a bunch of packs (or, for the discerning player, individual singles.)

There is, of course, a bunch of ways to spend real money in the MTG: Arena app, but you can participate by racking up in-game coins with in-game achievements (which are largely simple “cast this many blue spells” sorts of things).

If you’re the sort of person that wants the thrill of opening real packs more than anything, by all means, go for it. But the MTG: Arena app has been so effective at giving me a sense of the game before I bought more cards in real life, I really wish I had access to it back in 1998. Lucky you, you’ve got it right now.

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A Deal Hunter’s Guide to Amazon Prime Big Deal Days (2026)

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Amazon Prime Day events are some of the most confusing shopping occasions in existence: It’s not even a single “day” anymore. Amazon promises “millions of deals”—but the displayed discounts are often misleading, and while it’s advertised as a members-only event, some deals are available to people who don’t subscribe to Prime. Add in the frenzy of limited-time Lightning Deals and you’ve got a perfect recipe for spending too much money on stuff you don’t even really want.

Fear not! I’ve been covering deals for over a decade and am here to help. WIRED’s Reviews team is familiar with common shopping pitfalls, and we closely track the best Prime Big Deal Days prices. What time do sales start and end? How do you tell whether a deal is actually a deal? We pooled our collective expertise to get you prepared for the latest Amazon Prime Day Event, which is technically dubbed “Amazon Prime Big Deal Days” and will be on October 6 and October 7 this year.

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When Is Amazon Prime Day?

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Amazon Prime Day has a main sale in the summer and a secondary sale—called Prime Big Deal Days—in the fall. This year, Prime Big Deal Days will take place on Tuesday and Wednesday, October 6 and October 7.

When Do Prime Day Deals Start?

The event kicks off at 3:01 am Eastern time (midnight Pacific) and runs for 48 hours. New deals will drop three times each day of the event, at 3 am, 11 am, and 4 pm ET, with Lightning Deals going live sporadically as well. WIRED will cover the best deals from both Amazon and retailers that have competing sales.

Are Prime Day Deals Only for Prime Members?

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That’s the official line: You need to be an Amazon Prime member to shop Amazon Prime Day deals. There is a free 30-day trial available for eligible new accounts. (Prime membership comes with a lot of perks, and we’ve rounded up all of them here.) The trial will let you get in on the sale—just remember to cancel your membership to avoid any subsequent $15 monthly renewal charges.

However, there are usually plenty of discounts available if you’re not a subscriber. Other major retailers like Best Buy, Target, and Walmart typically hold competing sales during Prime Days. The prices are often close to what Amazon is offering on the same products, and sometimes the competing sales match the Prime Day price exactly. This is a good way to save even if you object to shopping on Amazon.

Is Prime Day Worth It?

It depends. For some items, Prime-exclusive event prices tend to be among the lowest we see all year. That’s especially true for Amazon hardware, like Kindles, Fire tablets, Fire TV sticks, and Echo devices. The kicker is that prices on nearly everything fluctuate throughout the year, and some products are discounted on a frequent basis. Even if the price is good, a deal on a product that goes on sale all the time diminishes the overall quality of that offer.

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The sheer volume of deals promoted by Amazon during sales like Prime Day is a blessing and a curse. The truly standout discounts can be difficult to pinpoint—there’s so much stuff on sale that just browsing the site can feel overwhelming. But if you’re in the market for something specific, there’s a good chance the item you want will be discounted. We’ve seen some fantastic Prime-exclusive discounts in the past, ranging from dirt-cheap Kindles and video doorbells to elusive price drops on gaming consoles and office furniture. The tricky part is finding the diamonds in the rough.

WIRED covers legitimately good deals all year long, including during Prime Day. Our policy is to only cover deals on products someone from our team has personally used and would buy or has bought themselves, to avoid promoting cheap junk. Maybe it’ll be a product we’ve reviewed or an item we use in our day-to-day lives. We also only cover actual deals—if the price isn’t lower than it normally is, we don’t include it. We aim to provide pricing context as well. Our tips below will help you find those great discounts on your own—in the same ways that we would. No gatekeeping here.

How Do I Know Whether a Deal Is Good?

ABC: Always be checking (prices, that is). Researching an item’s price is the most important aspect of determining the quality of a discount. Don’t fall prey to deceptive marketing language and inflated MSRP prices. The easiest step is to take a second to Google the items you’re considering so you can see the price across multiple stores. Amazon product pages now feature a Price History link to see up to 365 days of pricing history, though it may not always be accurate.

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One tool we use is CamelCamelCamel, which tracks Amazon’s prices over time. Paste the Amazon link or ASIN (found in the Product Information section on the Amazon product page) into CamelCamelCamel’s search bar to see the item’s lowest recorded price and its average price, and how frequently the price fluctuates. Some deals, such as Lightning Deals, are excluded from the pricing history, but this works for most items. It’s useful to see what an item has sold for in the past. And while Amazon’s Alexa for Shopping chatbot has price history information, it isn’t always reliable and often misses context (as evidenced by our personal testing). We also like Keepa, which has an extension (available for multiple browsers) that shows the recent price history for products directly on the Amazon page so you don’t have to open a new tab. When in doubt, check multiple sources.

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I Trained a Fly’s Brain to Generate WIRED Story Ideas

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Meet PitchFly, WIRED’s latest editorial recruit.

He has 165,112 neurons, and they’re all trained to generate story ideas. A sampling of his early output: “The Hidden Weather Problem Inside Surveillance”; “The Engineers Who Think Elon Musk Needs Less Computer Security”; and my personal favorite, “Everyone Wants Cooking. Nobody Has Solved Donald Trump.”

PitchFly uses a detailed map of the brain of a male drosophila—the common fruit fly. Developed by researchers from Google and a number of academic institutions, the map, known as a connectome, captures the way that 166,000 neurons and 125 million connecting synapses fire in response to stimuli. In essence, you can use it to simulate how a fly would respond to lots of stuff—it’s a very simple version of artificial intelligence based on charting real biological intelligence.

Because the researchers open-sourced it, you can easily use AI to import the connectome into a project with a little prompting. For PitchFly, I vibe coded a project in which the tiny digital drosophila brain generates story ideas. (I’m not sure why the fly has a little hat on, but I like its style.) This involved scraping together hundreds of the most popular story headlines from the site from the past year and feeding them into the connectome. I had Codex do the hard work, and it decided that the most efficient approach was to turn the headlines into words and phrases, then transform them into a representation that a neural network could understand. The connectome was fed the best-performing stories and told to generate its own ideas based on that.

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In other words, this isn’t a fly-based language model—although someone apparently created one of those. The fly brain has no idea what any of the words mean, or if any of it makes sense. It’s just remixing the patterns it has seen in pleasing new ways. A cynic might suggest this is exactly how some journalists generate their own pitches, but I think that’s a bit unfair—and judging by its lunatic ideas, PitchFly won’t be replacing me anytime soon:

  • The Tiny Shift in Agentic AI Is Rewriting the Rules of Food and Drink
  • What Security News Is Quietly Doing to Donald Trump
  • The Race to Reinvent Privacy Before Artificial Intelligence Breaks
  • Is China About to Make Digital Syndication Obsolete?

In the future, perhaps I could have the program continue to learn by reading new WIRED headlines. For now, though, this seems like a decent proof of concept, not to mention evidence at last that the average WIRED writer is intellectually superior to a fruit fly.

After the connectome’s release in early September, dozens of other weird and wonderful projects powered by fruit-fly intelligence sprang up.

An X user called Lyra Bubbles, for instance, demo’d a project that involved training the virtual fly brain to play the VR game Beat Saber. Alex Wormuth, a software engineer at Coinbase, created StonkFly, which uses the fly’s tiny brain to decide how to trade stocks. (It’s losing money, but it’s doing surprisingly well, all things considered.)

“The fly brings lighthearted, humorous relief” at a time when everyone is worried about the existential risks of AI, Wormuth told me in a DM. He says he found playing with the fruit fly connectome philosophically fascinating, too. “It sparks questions about ethics of this and whether there is any consciousness [in the replication of fly’s brain],” he says.

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