Cops Type ‘LMAO’ and Keyboard Mash to Query Vast License Plate Camera Networks

A deputy in Lake County, Indiana, once scanned a license plate across more than 19,000 cameras spanning 1,558 cities and towns. His official reason for the query? “LMAO.”

That single entry captures a pattern now documented across dozens of police agencies. Officers have filled the mandatory “reason” field in Flock Safety’s surveillance system with jokes, insults, shrugs and random keystrokes. The revelations come from an analysis of audit logs obtained through public records requests.

The Electronic Frontier Foundation reviewed the data and found entries such as “LOL,” “Hehe,” “Haha,” “idk,” “blah,” “TBD” and the longer plea “robbery I don’t remember the case number leave me alone.” Some officers typed insults like “idiot,” “dickhead,” “shithead,” “fuck this new search engine” or “WEIRD KID.” Others simply mashed the keyboard: “asdfg,” “gyghkkghghjkghjk,” “jhjhjkhj,” “nmbvcbnm.”

These searches took place from 2023 through late 2025. Flock altered its system afterward, making such logs harder to obtain through open records. Yet the entries expose something deeper than sloppy typing. They reveal a casual attitude toward a tool that tracks vehicle movements on a massive scale.

Flock Safety has built one of the largest automated license plate reader networks in the United States. Its cameras capture images of passing cars, extract plate numbers and store location data. Police can query the system for specific plates or broader patterns. The company markets the technology as a crime-fighting asset. But the audit logs tell a different story. And they arrive at a moment when Flock faces growing scrutiny.

Last month, USA TODAY published an investigation that examined more than 75 million license plate searches conducted by officers from 6,300 agencies between January 2023 and July 2026. The analysis uncovered patterns of potential misuse. At least 19 police and sheriff’s office employees in five states were arrested, fired, placed on administrative leave or investigated in the following weeks for improper use of the system.

One case involved a Georgia officer who queried her estranged husband’s plate almost daily for months, sometimes dozens of times in a single day. Another Texas detective searched more than 80,000 images without a warrant while investigating a suspected abortion. These examples sit alongside the frivolous entries uncovered by the EFF.

But the problem runs larger than isolated jokes. In a separate dataset of 11.4 million nationwide Flock searches over six months, the ACLU determined that more than 14 percent listed only the word “investigation,” with no case number or further detail. Similar vagueness appears repeatedly. Officers write “test,” a single letter or nothing at all.

The scale matters. A single query can pull data from thousands of cameras across hundreds of jurisdictions. One Goshen, Indiana, officer searched 6,474 automated license plate reader networks — representing 82,413 cameras — and entered “idk” as the reason. Harris County, Texas, sheriff’s employees used “LOL” or “lol” as a case number on multiple occasions. Kankakee County, Illinois, officers chose “idk” or “idk lol.”

Eatonton, Georgia, police entered strings such as “HJKNUILH,” “uiokjk.kuj” and “GJLHBNMN.” An Atlanta officer used “asdfga.” These are not outliers. The EFF identified button-mashing as its own category of search behavior.

Such practices raise basic questions about accountability. The “reason” field exists to create a record of legitimate law enforcement need. When that field contains gibberish, the record becomes meaningless. Privacy advocates argue the entire system operates with insufficient oversight. Warrants are rarely required. Retention policies vary. Data sharing crosses jurisdictional lines with little transparency.

Flock has responded to criticism by pointing to its value in solving serious crimes. The company notes that its cameras have helped recover stolen vehicles, locate missing persons and support investigations into violent offenses. Executives have said the technology gives police a tool they would otherwise lack. Yet the same system that catches real offenders also enables casual browsing, personal vendettas and outright abuse.

Recent news shows the tension playing out in real time. On the same day the EFF findings appeared, Atlanta officials announced plans to introduce citywide surveillance rules. An internal police audit of 115,578 Flock inquiries flagged 79 searches for further review. Mayor Andre Dickens stated that “technology that gives government extraordinary capabilities must come with extraordinary accountability.” The proposed ordinance would make internal police safeguards legally binding across all city departments.

Public backlash has taken other forms. In College Station, Texas, a 19-year-old man cut power cables to two Flock cameras and covered them with American flags. He now faces a criminal mischief charge. Similar vandalism has occurred elsewhere as residents grow uneasy with the expanding camera presence. Flock itself reports that its network recently surpassed 100,000 cameras nationwide, even as more than 50 cities have canceled contracts over concerns about data sharing with federal agencies.

The company adjusted its systems in December 2025 after increased public attention. Those changes made certain audit logs less accessible through records requests. The timing aligns with the end of the period examined by the EFF. Critics see the move as an effort to reduce transparency rather than address underlying problems.

Earlier reporting added context. The Houston Chronicle documented how Houston police conducted thousands of searches with vague justifications such as “investigation,” “suspect” or simple gibberish like “asdf.” Usage grew rapidly. Weekly searches doubled in less than a year. Some officers admitted to running “random” checks while on patrol.

WIRED examined Flock’s newer AI-powered tools. The systems now allow officers to search not just by plate but by descriptive prompts. An officer can ask for vehicles or people matching certain characteristics within a drawn geographic area. The technology stops short of facial recognition but edges closer to broader behavioral tracking. Privacy researchers worry that weak controls on the “reason” field become even more consequential when queries grow more sophisticated.

The EFF’s report lands amid a wider debate. Some lawmakers push for stricter rules. Colorado recently required warrants before sharing automated license plate data with federal agencies. Other states consider similar measures. Police departments defend the technology as essential for public safety. They argue that focusing on misuse ignores the many legitimate successes.

Yet the logs keep surfacing. Insults directed at suspects or the system itself. Expressions of ignorance or indifference. Pure nonsense. Each entry represents a moment when an officer decided that documenting a real justification was unnecessary. Or perhaps too much trouble.

That indifference carries consequences. Every plate captured contributes to a detailed map of movement. Over time, those maps can reveal where people live, work, worship or seek medical care. When the system treats such data casually, trust erodes. Residents already express discomfort with cameras that never sleep. The joke entries only heighten that unease.

Flock continues to expand. New contracts outpace cancellations. The network grows larger and more interconnected. At the same time, journalists, activists and some officials dig deeper into how the technology is actually used. The gap between the company’s promises and the audit logs remains wide.

Short searches. Long retention. Easy access. Minimal friction. The combination creates conditions where “LMAO” can stand in for legitimate police work. Until the reasons improve, the system will continue to invite skepticism. And more headlines like this one.

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Ford’s Garage Shifts Into the Carolinas as Themed Dining Chain Plots Multi-State Push

Billy Downs sees momentum building across the Southeast. The president of Ford’s Garage spoke with conviction this week about the brand’s latest move. It plans to enter North and South Carolina through experienced franchise partners.

The Tampa-based chain, officially licensed by Ford Motor Company, draws on 1920s automotive heritage. Vintage cars sit outside its restaurants. Inside, gas-pump beer taps and suspended car parts create an immersive atmosphere. Burgers, craft beer and comfort food complete the package. Guests return for the experience as much as the menu.

With 35 locations now operating in eight states, the brand has posted strong numbers. Average unit volumes reached roughly $5.7 million in 2024, according to Franchise Times. Investment per site runs from $3.7 million to $6.6 million. That figure reflects the cost of custom theming and buildouts. Yet operators report solid returns. The concept sits in the upper tier of casual dining franchises.

Downs made the expansion plans clear. “There’s a lot of momentum across the Carolinas right now, and we believe Ford’s Garage can bring something distinctive to the region,” he told FSR Magazine in its Sept. 14, 2026 report. He singled out Charlotte and Raleigh in North Carolina. Columbia, Charleston and coastal South Carolina also rank high on the target list. The brand seeks long-term multi-unit developers. One group could cover Charlotte and Raleigh. Another might handle Columbia and Charleston markets.

This announcement builds on a busy 18 months. Ford’s Garage signed six multi-unit deals in 2025 alone. Those covered New Jersey, Northern Virginia, Houston, Nashville, Des Moines and Milwaukee. Tennessee operations began earlier this year with a four-unit agreement led by TN Legends, a group with deep local ties and prior restaurant experience. A new Chattanooga site at Hamilton Place Mall signals further Southern progress. The Carolinas push fits a deliberate pattern. Focus remains east of the Mississippi for now.

Franchise demand stays high. The chain made the 2026 Inc. 5000 list of fastest-growing private companies. Leadership credits consistent execution and menu refinements. Recent additions include “Fuel Efficient Fare” bowls that pair proteins, grains and vegetables. Brunch service, happy hour and dinner generate revenue across dayparts. That flexibility appeals to operators facing rising labor and food costs.

But challenges exist. The restaurant industry contends with softening traffic in some segments. Inflation pressures persist. Construction costs for these themed units run higher than average. Each new site hires 125 to 150 workers initially before settling near 110. Training emphasizes hospitality alongside the automotive storytelling. Get that wrong and the novelty fades fast.

Downs knows the stakes. The brand has modernized operations over the past year. Technology upgrades improve efficiency without diluting the nostalgic feel. Existing franchisees have embraced the changes. Some push for more. That internal buy-in matters as the company scales toward 10 to 12 openings annually after 2026.

North Carolina ranks among the top states for franchise development in 2026. South Carolina follows closely. Both benefit from population growth, business-friendly policies and tourism. Charleston draws visitors year-round. Charlotte’s suburban corridors expand steadily. Raleigh’s tech and education sectors bring younger diners who appreciate thematic concepts. Ford’s name carries weight here. The Blue Oval still evokes American innovation for many consumers.

The Post and Courier first reported interest in South Carolina markets last year. The paper noted plans for up to 12 units in North Carolina and four in South Carolina. It cited industry rankings that placed the states second and fifth for franchise growth potential. Southeast economic output was projected to outpace the national average. Those forecasts appear to be holding.

Ford’s Garage started in 2012 in Fort Myers, Florida, near Henry Ford’s winter estate. Franchising began in 2015. Growth accelerated in recent years. The concept now spans Florida, Virginia, Kentucky, New York, Indiana, Michigan, Ohio and Texas. A new Riverview, Florida restaurant under construction aims for a December 2026 opening. That will mark the 24th Florida site and the fifth in the Tampa Bay area.

Executives talk of entering 25 to 30 additional states over time. Yet they stress disciplined expansion. Only qualified multi-unit operators need apply. Financial backing must support the full investment range. Restaurant experience counts heavily, especially in fast-casual or full-service settings. The company wants partners who understand community engagement. A lone unit rarely succeeds in this format.

So the Carolinas represent more than new ZIP codes. They test whether the brand’s formula translates smoothly to fresh territory. Early results from Tennessee and Georgia deals will inform the approach. In Georgia, announced in June 2026, the chain targeted Atlanta, Savannah and other cities with similar language about economic momentum and tourism.

Industry watchers note the appeal. Themed dining has performed well post-pandemic. Consumers seek experiences that stand apart from standard chains. Ford’s Garage delivers that through its licensed connection to an American icon. The decor doesn’t feel corporate. It feels like a lovingly restored service station. That authenticity helps.

Still, competition looms. Other automotive-themed concepts exist. Casual burger chains continue to proliferate. Success will depend on operations. Food quality must remain high. Service must match the theatrical setting. Beer selection needs to stay fresh and local where possible.

Downs sounded optimistic. The brand, he said, looks forward to connecting with the right franchise partners. Those partners will shape the Carolinas story. If they execute, Ford’s Garage could become a regional staple. New communities may soon hear the sound of classic engines mixed with the clink of beer glasses. The garage doors are opening wider.

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Hybrid Physics-Informed CNN Framework Boosts Accuracy in Dynamic System Forecasting

The paper titled “Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge” by Emmanuel de B’zenac, Bryan Lucas, and Patrick Gallinari explores methods for blending neural networks with established equations from physics to create more accurate predictive models. Published on arXiv at https://arxiv.org/abs/1802.07228, this work addresses limitations that arise when purely data-driven approaches encounter complex dynamic systems governed by known physical laws.

Traditional machine learning models often treat prediction tasks as black-box problems, learning patterns directly from large datasets without regard for underlying principles. In fields such as fluid dynamics, meteorology, and climate science, however, researchers already possess differential equations that describe system behavior with high fidelity under certain conditions. The authors propose a framework that integrates these equations into the architecture and training process of neural networks, producing hybrid models that respect physical constraints while adapting to real-world observations that may deviate from idealized assumptions.

The core idea rests on representing physical processes through partial differential equations, or PDEs, which capture how quantities like velocity, temperature, or pressure evolve over time and space. Rather than discarding this knowledge, the approach embeds the differential operators directly into the network layers. This embedding ensures that the learned function adheres to the structure of the governing equations, reducing the search space during optimization and improving generalization beyond the training data.

One key contribution involves the design of a neural network layer that approximates differential operators using convolution-like operations. These layers mimic finite difference schemes commonly used in numerical solvers but remain fully differentiable, allowing end-to-end training with gradient-based methods. By parameterizing the coefficients within these operators, the model can learn corrections to classical equations when data reveals discrepancies caused by unmodeled effects such as turbulence or material heterogeneity.

The authors demonstrate the framework on several benchmark problems. In the case of advection-diffusion processes, the network successfully recovers both the transport velocity field and the diffusion coefficient from sparse, noisy observations. The hybrid model outperforms purely data-driven baselines and traditional numerical methods when measurement density is low. Similar gains appear in wave propagation tasks, where the learned solver maintains stability over long time horizons that cause standard integrators to diverge.

A central technical element is the incorporation of physics-based loss functions. Instead of penalizing only the mismatch between predicted and observed states, the training objective includes terms that enforce satisfaction of the PDE residual at collocation points throughout the domain. This residual minimization resembles the physics-informed neural network concept introduced in subsequent literature, yet the 2018 paper presents an earlier formulation focused on convolutional architectures suitable for grid-based simulations.

The method also supports assimilation of observational data into running simulations. By treating the neural network as a differentiable surrogate for the forward model, researchers can back-propagate through time to adjust initial conditions or parameter fields. This capability aligns with four-dimensional variational data assimilation used in operational weather forecasting, but replaces expensive numerical adjoints with automatic differentiation provided by modern deep learning frameworks.

Experiments on synthetic datasets generated from the Navier-Stokes equations reveal that the hybrid approach captures both laminar and mildly turbulent regimes with fewer parameters than a standard convolutional recurrent network. The physics-constrained model exhibits lower generalization error when tested on different Reynolds numbers or domain sizes. Visualizations of the learned velocity fields show coherent vortex structures persisting over hundreds of time steps, whereas unconstrained networks tend to dissipate energy unnaturally or develop instabilities.

Beyond accuracy, the paper emphasizes computational efficiency. Once trained, the neural solver can run orders of magnitude faster than high-resolution numerical methods on graphics processing units. This speed advantage opens possibilities for ensemble forecasting, uncertainty quantification, and real-time control applications that were previously limited by the cost of repeated PDE solves.

The authors discuss extensions to systems with unknown or partially known physics. When certain terms in the governing equations cannot be derived from first principles, the network learns additive correction fields expressed as additional convolutional layers. These learned corrections remain interpretable because they are added to the baseline physical operator, allowing domain experts to inspect which components of the model deviate from theory and potentially discover new phenomenological relations.

Challenges remain in scaling the method to three-dimensional problems with realistic boundary conditions. The paper acknowledges that memory requirements grow rapidly with spatial resolution, suggesting future work on multi-scale architectures or adaptive mesh strategies. Another limitation involves stiff equations where rapid transients demand very small time steps; purely explicit neural integrators may require implicit formulations or operator splitting to maintain stability.

Despite these open questions, the framework offers a principled way to combine centuries of accumulated scientific knowledge with the flexibility of modern machine learning. Rather than viewing physics and data-driven modeling as competing paradigms, the approach treats them as complementary sources of information that together produce more reliable predictions.

Subsequent research has built upon these ideas in numerous directions. Climate modeling groups have applied similar techniques to improve subgrid-scale parameterizations in global circulation models. Oceanographers have used physics-informed networks to reconstruct velocity fields from satellite altimetry data. Materials scientists have adapted the framework to predict stress distributions in complex microstructures where constitutive laws are only approximately known.

The original paper stands as an early milestone in a growing body of literature that seeks tighter integration between scientific computing and deep learning. Its emphasis on embedding differential operators within network layers continues to influence the design of specialized architectures for scientific machine learning. Libraries such as PyTorch and TensorFlow now include built-in support for automatic differentiation of spatial derivatives, making the implementation of these ideas more accessible to practitioners across disciplines.

Practical deployment of such models requires careful validation against both simulated and experimental data. The authors recommend cross-validation strategies that test not only pointwise accuracy but also global conservation properties such as mass, momentum, and energy balance. When these invariants are preserved, confidence increases that the model has internalized the correct physics rather than merely memorizing training trajectories.

Visualization tools play an important role in model interpretation. By plotting the magnitude of learned correction terms alongside classical solutions, researchers can identify regions where the baseline equations break down, such as near boundaries or in regions of high shear. These insights can guide further theoretical development or suggest targeted laboratory experiments to refine understanding of the underlying phenomena.

The work also touches on questions of uncertainty quantification. Because the neural network parameters are learned from data, Bayesian or ensemble methods can produce distributions over possible solutions that reflect both aleatoric noise in measurements and epistemic uncertainty about model form. Propagating these uncertainties through long rollouts remains computationally demanding but essential for risk-sensitive applications like early-warning systems for extreme weather.

In summary, the framework presented in the 2018 arXiv paper provides a concrete recipe for constructing neural networks that respect physical laws while remaining adaptable to new observations. Its combination of convolutional operators, physics-based loss terms, and differentiable time integration offers a practical path toward more trustworthy predictive models in science and engineering. As computational resources continue to expand and datasets from sensors and high-fidelity simulations grow richer, approaches that tightly couple domain knowledge with flexible function approximators will likely become standard tools in the scientific computing toolbox. The ideas introduced there continue to shape how researchers across multiple fields approach the challenge of learning from both data and equations simultaneously, fostering models that generalize better, run faster, and maintain physical consistency even when confronted with previously unseen conditions.

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Google’s Goto Gambit: Search Links Now Hide Destinations to Thwart Scrapers

Google has altered a fundamental piece of its search results page. The blue links that millions click every day no longer point directly to the destination site in the HTML. They route through google.com/goto first.

This shift, which began as a limited test in mid-2026, reached near-universal coverage by late August. Observers noticed it. Tools broke. And Google confirmed the move.

The company isn’t shy about its goal. A spokesperson told Search Engine Roundtable: “We have a long history of deploying technical measures against evolving forms of abuse, and we regularly take steps to protect our services and users.”

But the change does more than block bots. It disrupts workflows for SEO professionals, rank trackers, data providers, and even casual users who inspect links. And it raises fresh questions about transparency in an era when search results feed everything from AI summaries to competitive intelligence.

The old system was straightforward. Hover over a result or view the page source, and the href attribute contained the actual URL. Click it, and the browser went straight there, often with a brief stop at google.com/url for tracking. The destination sat in plain sight, encoded but readable.

Now many results show something like https://ift.tt/OMslj1g…. The parameter holds opaque data. It isn’t simple base64. It functions as an internal reference to Google’s index. The real address appears only in the HTTP Location header after a request to that goto endpoint.

Technical teams responded quickly.

Providers such as SerpApi, Autom, and others updated their pipelines. They issue a HEAD request to the goto URL, read the Location header without following the redirect, and return the final destination. Autom’s blog post details the process clearly. The company now resolves these links server-side so customers continue to receive clean destination URLs in API responses.

SerpApi announced a fix on September 6, 2026. Its update notes that the change affected the link field in results but not titles, snippets, or rankings. The company restored direct URLs across organic results, news, videos, and AI Overviews. (SerpApi blog)

Yet not every organization moved at the same speed. Ahrefs acknowledged temporary data inconsistencies in a LinkedIn post by Tim Soulo. The firm pointed to its own web index as a long-term buffer. Rank trackers that relied solely on parsing HTML faced heavier lifting. Some needed hundreds or thousands of additional requests to resolve links at scale.

Derek Perkins, founder of Nozzle, tracked the rollout with data. He reported near-100% usage of goto links across several residential IP providers by late August. His chart showed a sharp spike after months of gradual testing. Perkins noted on X that the opaque encoding forces providers to follow redirects. At volume, that creates friction Google can tighten further if it chooses.

The change appears most consistent for signed-out users and private browsing sessions. Logged-in experiences sometimes still show direct links, though that varies. Tests documented by OpenWeb Ninja on September 8, 2026, found every organic title link across web, images, news, videos, and AI Overview surfaces used the goto format in signed-out desktop and mobile sessions.

Browser extensions sprang up to restore old behavior. One tool, available for Chrome, Edge, Firefox, and Safari, rewrites copied links back to their clean destinations. Its site explains that the redirect makes links harder to share and results harder to analyze. (Google Goto URL Fix)

Attribution questions followed. Would the extra hop strip referrer information and turn organic visits into direct traffic in analytics platforms? Testing by Attributer across 10 device and browser combinations found the referrer survived every time. Visits continued to register as organic search. (Attributer blog)

But side effects linger. Hover previews in some browsers no longer reveal the target domain immediately. Chrome extensions that modify SERPs sometimes break. SEOs who rely on visual inspection of results pages lose a quick signal. Brodie Clark documented these frustrations in a September video on X, noting the impact on logged-out workflows that many professionals use daily.

Google has deployed similar redirects before. The classic google.com/url?q= format exposed the target in the query string. This new approach does not. The destination still appears elsewhere in the page source for rendering purposes — favicons, sitelinks, attributions — but the primary click handler now hides it.

That distinction matters. Scrapers can still extract some URL data from the page. Yet the shift raises the cost and complexity of large-scale extraction. AI companies building search indexes or training data on web content face new hurdles. Google sued SerpApi over scraping in the past. The company lost a recent motion, but the arms race continues.

Search Engine Land reported Google’s confirmation and framed the update as a direct response to third-party tools and AI engines. The extra server hop adds latency for bots while remaining invisible to ordinary users. Click behavior stays the same. The page loads the intended site after the brief redirect. (Search Engine Land)

Industry reaction splits along predictable lines. Data providers treat it as another obstacle to overcome. Many have already adapted. SEO consultants see it as reduced transparency. Some worry it erodes trust when users cannot easily verify where a link leads before clicking.

Barry Schwartz at Search Engine Roundtable first covered the test in July. His August 26 article captured the moment it moved from experiment to confirmed rollout. He highlighted screenshots showing the goto URL in the status bar where the clean domain once appeared.

Recent coverage shows the story isn’t finished. As of early September, some users report seeing direct links return in limited tests, suggesting Google may still tune the rollout. Others see goto everywhere. The company has offered no public timeline or guarantee of permanence.

For site owners the change brings little direct effect. Their pages, URLs, and analytics remain intact. The redirect does not alter how Google crawls or indexes content. Yet it does change how competitors, tools, and AI systems see the search landscape.

That may be the point. Google protects its index. It limits unauthorized bulk access. And it does so without changing the user experience in any obvious way. The click works. The page loads. Only the machinery behind the scenes shifts.

Still, the move fits a broader pattern. Search has grown more opaque over time. Featured snippets, AI Overviews, and zero-click results already reduce traffic to original sources. Now the very links that drive remaining clicks carry less visible information.

Providers will keep adapting. Some will use browser-based rendering. Others will rely on their own indexes, as Ahrefs highlighted. A few may pass increased costs to customers. The question is whether Google will escalate. If HEAD requests to goto URLs become rate-limited or obfuscated further, the challenge grows.

For now the industry has absorbed the blow. Autom, SerpApi, and others demonstrate that resolution is possible. But the episode serves as a reminder. Google controls the data pipe. When it changes the shape of that pipe, everyone downstream adjusts or risks falling behind.

The goto links are here. They likely aren’t leaving. And the tools, workflows, and assumptions built around the old, transparent SERP structure have another adaptation ahead of them.

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Chinese Satellite Images Tied to Fatal Iranian Missile Attack on U.S. Troops

Three American service members died in their sleep. Iranian ballistic missiles slammed into a housing area at Muwaffaq Salti Air Base in northeastern Jordan on July 17. The strike marked the first U.S. fatalities since a fragile April ceasefire in the wider conflict with Iran.

Now fresh evidence points to a previously hidden hand. U.S. officials have linked high-resolution satellite imagery of the base, supplied to Tehran by Chinese entities, directly to that deadly attack. The imagery was acquired both before the missiles flew and afterward, when Iranian forces apparently reviewed the damage. The Wall Street Journal first reported the connection on Sept. 11, citing officials familiar with the intelligence.

The finding carries weight. It represents the clearest indication yet that commercial Chinese technology has contributed to the loss of American lives in the Middle East war. Yet U.S. officials stopped short of accusing the Chinese government itself of direct involvement. They also declined to name the specific Chinese companies that provided the pictures.

But the pattern is familiar. Months earlier, in May, the Trump administration sanctioned three Chinese firms — MizarVision, Earth Eye and Chang Guang — for supplying, collecting or publishing satellite imagery that Iran could use against U.S. forces. One of those companies, Chang Guang, had already drawn earlier sanctions for aiding Houthi attacks in Yemen. The pattern suggests Iranian forces improved their targeting accuracy over the summer even after U.S. and Israeli strikes had degraded Tehran’s own intelligence apparatus.

Senior American officials raised the Jordan base imagery with Chinese counterparts. The response was blunt. Beijing demanded proof and dismissed the concerns. Chinese officials have repeatedly rejected U.S. warnings that their companies were feeding imagery to Iran. China’s embassy in Washington offered no direct comment on the latest allegations but insisted its cooperation with Tehran stays within international law.

The timing adds pressure. President Donald Trump is scheduled to meet Chinese leader Xi Jinping at the White House on Sept. 24. The satellite revelation threatens to complicate an already tense agenda that includes trade, technology restrictions and regional security. U.S. frustration with Beijing has built for months over exactly this issue.

The July 17 strike was no isolated event. Iranian missiles hit the same Jordanian base three times within 24 hours. They targeted both manned aircraft and unmanned systems in addition to the troop housing. Army Sgt. Angel S. Rampersad, 28, of Ozone Park, New York; First Lt. Tyler James Feehan, 25, of Hawaii; and Pfc. Isabella Gonzales, 19, of Texas, were killed. Four other U.S. personnel were wounded and evacuated for treatment. The New York Post identified the victims by name while covering the WSJ disclosures.

Iran has multiple ways to gather intelligence. It operates its own satellites. It draws on human sources inside the region. Russian assistance has also played a role. Yet the Chinese imagery appears to have filled a specific gap. High-resolution pictures can reveal recent construction, changes in defensive postures or the precise layout of sleeping quarters and aircraft shelters. Such detail helps planners refine aim points.

This isn’t the first time Chinese satellite capabilities have surfaced in the Iran conflict. In April, the Financial Times revealed that Iran’s Islamic Revolutionary Guard Corps secretly acquired a Chinese-built reconnaissance satellite known as TEE-01B. Launched in 2024 by Earth Eye Co., the satellite was tasked to image U.S. bases across the Middle East before and after Iranian strikes. Leaked Iranian military documents, time-stamped coordinates and independent orbital analysis supported the reporting.

That earlier episode involved direct control of a satellite. The latest case centers on commercial imagery purchases. The distinction matters. Beijing can more easily distance itself from private transactions. Yet the cumulative effect is the same. Iranian forces gain better visibility of U.S. positions. American commanders must now assume that many of their fixed installations are under constant commercial scrutiny.

Commercial satellite imagery has transformed modern conflict. Resolution has improved dramatically. Tasking has grown faster. Private operators, including Chinese ones, now offer capabilities once reserved for governments. The Pentagon itself has wrestled with the implications. A December 2025 assessment noted that some China-based commercial satellite firms have conducted business exchanges with the IRGC.

U.S. forces have tried to limit the flow. One American satellite operator agreed to withhold imagery of the conflict zone at government request. Lawmakers have pressed for tighter controls. Rep. John Moolenaar, chairman of the House Select Committee on China, warned earlier this year that commercial imagery exploited by Beijing poses an urgent threat to U.S. troops.

The Jordan incident highlights a broader vulnerability. Fixed bases are hard to hide. Housing areas, runways and maintenance facilities don’t move. High-resolution before-and-after shots let adversaries measure strike effectiveness and adjust tactics. In the July attack, imagery may have helped Iranian crews identify which parts of the base remained active or where troops were billeted.

American officials are also examining whether Chinese data is helping Iran track moving naval targets. Tehran has launched ballistic missiles at U.S. aircraft carriers and other warships in recent months. Its accuracy against mobile vessels appears to have improved. Satellite imagery alone cannot provide real-time targeting for ships at sea. But it can reveal operating patterns, home ports and replenishment routines.

China’s position remains consistent. It denies providing direct military assistance to Iran. It frames its satellite industry as commercial and open to global customers. Yet U.S. sanctions on multiple firms suggest Washington sees a pattern of deliberate support. The sanctioned companies include firms with ties to China’s broader space infrastructure.

The human cost brings the issue home. Three young soldiers lost their lives. Their families received the news that an adversary halfway around the world had used imagery from another rival power to improve its strike. That chain of events is difficult to dismiss as abstract geopolitics.

So far the Biden administration had pressed China quietly on these issues. The Trump team has taken a more public and punitive approach with sanctions. The latest intelligence could push Washington toward further measures. Additional entity listings or export controls on satellite technology are logical next steps.

Yet escalation carries risks. China is America’s largest trading partner. The two economies remain deeply intertwined even amid strategic rivalry. A summit between Trump and Xi was meant to manage tensions. Now it must absorb another flashpoint.

Iran, for its part, has shown no sign of slowing its attacks. The July strike on Jordan came after earlier exchanges that damaged U.S. defensive systems in the country. Satellite imagery of those adjustments would have obvious value to Iranian planners.

Defense analysts have long warned that commercial remote sensing would democratize intelligence in future conflicts. That future has arrived in the Middle East. Chinese firms are among the most aggressive players in this market. Their imagery is detailed, frequently updated and available for purchase.

The U.S. military has responded by hardening bases where possible. It has dispersed forces. It has improved deception measures. But permanent installations in allied countries cannot simply vanish. Jordan hosts U.S. aircraft critical for regional operations. The base at Muwaffaq Salti supports both manned and unmanned missions.

Recent commercial satellite images released after the July strike show clear damage to aircraft shelters, taxiways and housing structures. Independent analysts using European and American satellites have documented multiple impact points. Those same types of images, if acquired in advance by Iran, would explain the precision of the attack.

The episode underscores a new reality in great-power competition. Commercial technology now bridges the gap between state and non-state actors. A private Chinese company can sell imagery that ends up guiding missiles toward American troops. Governments can claim distance while reaping strategic benefits.

U.S. officials continue to investigate the full scope of Chinese imagery support. They are mapping which systems Iran received and how the data was integrated into targeting packages. The results could shape policy for years.

One thing is already clear. The war in the Middle East has moved into space. Satellites above the battlefield are shaping outcomes on the ground. And in at least one case, that connection proved fatal for U.S. service members.

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Dogecoin’s Rough Ride: ETF Closure and Macro Headwinds Trigger Sharp Sell-Off

Dogecoin took a beating on Thursday. The meme coin dropped more than 5% in afternoon trading, underperforming the broader market as Bitcoin held steady near $78,000. But the reasons run deeper than a bad day. They point to limited institutional appetite, crowded trades unwinding fast, and shifting economic signals that hit risk assets hardest.

Traders noticed the move early. By late afternoon Eastern time, DOGE had shed nearly 5%. Some reports pegged the decline closer to 7% at its worst, taking the price down toward $0.083. Short sentences capture it best. Quick. Brutal. And not entirely surprising given what was unfolding behind the scenes.

The most direct blow came from Bitwise Investment Advisors. The firm announced it would close and liquidate its spot Dogecoin ETF. Trading ends Oct. 13. Liquidation follows on Oct. 22. Shareholders receive cash through their brokers. The Motley Fool highlighted how this decision stood out on a day already filled with negative momentum. Fewer Dogecoin ETFs exist compared with those tracking Bitcoin or Ether. Their shutdown signals thinner demand from everyday investors.

But the ETF news didn’t arrive in isolation. Bond yields climbed. Oil prices pushed toward $102 a barrel amid tensions with Iran. The producer price index came in roughly as expected, yet it reinforced worries about sticky inflation. When safer fixed-income assets offer better returns, appetite for speculative bets like cryptocurrencies fades. CoinDesk reported Dogecoin led losses among major coins. BNB fell about 4%. XRP dropped 3%. Ether and Solana gave back 1% to 3% each.

Leverage made things worse. Data from CoinGlass showed $8.91 million in Dogecoin positions liquidated over 24 hours. Longs accounted for $8.59 million of that total. The imbalance reached 2,691%. Bulls who bet on a breakout got caught. U.Today detailed how the price failed to clear the 200-day moving average near $0.088 to $0.096. Four straight days of declines followed a weekend surge that topped out at $0.095.

Support levels now sit between $0.082 and $0.084. A break lower could test $0.08, a level where more than 30 billion DOGE tokens have changed hands historically. Technical rejection at key averages amplified the move. Yet many analysts describe this as a positioning reset rather than a fundamental breakdown.

Whale activity tells a mixed story. Large holders accumulated. One wallet associated with SANGRIX sold 3 million DOGE on Sept. 7 for roughly $267,665 to fund working capital and AI infrastructure. Still, overall whale holdings hit a record 108.5 billion DOGE. Futures open interest climbed to 16.38 billion tokens, worth about $1.5 billion. These figures suggest conviction among big players even as retail sentiment sours.

Positive developments exist too. Dogecoin went live on Solana this week through the Sunrise Protocol. Trading volume on the new bridge exceeded $19 million on day one. The DOGE-1 lunar mission, backed by Dogecoin, prepares for a SpaceX launch on Sept. 14. Such news would normally spark buying. This time it barely registered against the macro tide.

Bitcoin printed a golden cross. Its 50-day average moved above the 200-day average, a pattern that preceded a 90% rally in 2019. The largest cryptocurrency stayed relatively firm near $78,000, down only 1%. That resilience highlighted Dogecoin’s higher beta. When risk appetite shrinks, meme coins feel it first. And right now, the Federal Reserve’s next moves loom large. Markets price in a possible quarter-point rate hike after months on hold. Friday’s consumer price index report could sharpen those expectations.

Institutional interest in dedicated Dogecoin products remains modest. The 21Shares Dogecoin ETF held just $2.66 million in assets as of Sept. 9. Other vehicles show slightly larger figures but still pale next to Bitcoin or Ethereum funds. KuCoin News noted this lack of support from ETFs contributed to the vulnerability.

Yet Dogecoin refuses to fade entirely. Its community stays active. Cultural relevance persists years after its 2013 launch as a joke. Elon Musk’s occasional mentions still move markets. Some treasury firms disclosed plans to add DOGE to compliant portfolios. These undercurrents suggest the sell-off may prove temporary.

The Technical Picture and Support Tests

Price action reveals clear levels. Rejection from the 200-day EMA created the initial catalyst. Leveraged longs then unwound in a cascade. Bollinger Bands have tightened across timeframes, hinting at an imminent volatility expansion. Traders watch whether $0.08 holds. A bounce from there could retest $0.09 or higher. Failure opens the door to further weakness.

Broader market structure matters too. Bitcoin dominance rose to nearly 59% as altcoins bled. Total crypto market capitalization fell more than 4% in 24 hours. This rotation away from higher-risk names explains much of the disparity.

Looking Past the Immediate Pressure

The current decline doesn’t erase longer-term developments. Record whale balances. Growing futures interest. New distribution channels on Solana. Upcoming mission milestones. All point to sustained, if volatile, interest.

Macro conditions can shift. Cooler inflation readings or a softer Fed stance would ease pressure on risk assets. Until then, Dogecoin trades in a tight range with clear downside risks. Investors who entered with leverage learned the cost once again. The rest watch support levels and wait for the next catalyst. One thing remains clear. This asset never moves quietly.

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OpenAI’s GPT-Live-1 API Hands Developers a Voice Model That Never Stops Listening

OpenAI just opened the doors wider on voice AI. The company released GPT-Live-1 to its API on September 10. This model listens and speaks simultaneously. No more rigid turn-taking. Conversations flow with interruptions, acknowledgments and background reasoning all happening at once.

The new offering builds directly on technology first shown in ChatGPT earlier this year. Developers can now integrate it into their own applications. They pair the voice layer with backend models of their choice. One handles fluid dialogue. Another tackles complex tasks. The split keeps responses quick while delivering smarter answers.

From ChatGPT Experiment to Developer Platform

GPT-Live-1 first appeared inside ChatGPT in July. It replaced the older Advanced Voice Mode for many users. The model used a full-duplex architecture from the start. It processes incoming audio and generates output in a continuous stream. Decisions happen many times per second. Speak. Listen. Pause. Interrupt. Call a tool. All without breaking the audio loop.

That design solved persistent problems. Previous systems chained speech-to-text, a large language model and text-to-speech. Each handoff added latency. Interruptions often failed. Context got lost. GPT-Live-1 collapses those steps into one model for the voice layer. It sends harder questions to a separate reasoning engine in the background. The conversation continues uninterrupted.

OpenAI detailed the improvements in its announcement. On the Full Duplex Bench, GPT-Live-1 scores 30 percentage points higher than GPT-Realtime-2.1. Turn-taking latency falls to 0.8 seconds from 1.4 seconds. Tool-calling accuracy rises to 87 percent from 60 percent. In a banking voice support test, the pass rate jumps to 32 percent from 12.4 percent. These numbers come from OpenAI’s own evaluations. They paint a picture of measurable progress. (OpenAI)

Yelp already put the technology to work. Its Host service manages restaurant reservations over the phone. CTO Alex Levy reported better call handling. The system manages interruptions more gracefully. It keeps callers engaged even while looking up availability or confirming details. Real customer deployments like this one test the model under pressure. They reveal where the gains matter most.

The API version gives developers fresh controls. They decide how the voice agent speaks and acts. Twelve new voices arrived with the release. Accents, dialects and languages vary. Automatic speech recognition transcripts and response text come standard. Pricing sits at $0.05 per minute for the front-end voice layer. Not cheap. Yet the cost stays separate from the backend model. Teams can choose GPT-6 Astra for deep reasoning or cheaper options for simple exchanges. (The Decoder)

But the real story runs deeper than benchmarks. Voice agents have struggled with one fundamental flaw. They feel robotic because they wait. Users pause. The system stays silent. Or worse, it talks over them. GPT-Live-1 changes the rhythm. It offers verbal nods like “mhmm” when appropriate. It stays quiet during thoughtful pauses. It adjusts instantly to corrections mid-sentence. The result sounds closer to talking with a person. Not a machine waiting for its cue.

Industry watchers noted the shift months ago. Early leaks in June pointed to a bidirectional model then called GPT-Bidi-1. It promised exactly this capability. OpenAI refined the approach through summer testing in ChatGPT. The July launch of GPT-Live set the stage. Now the API release invites builders to experiment at scale. (The Register)

Competition looms. Google offers Gemini Live. Other providers push their own real-time voice tools. Yet OpenAI’s move carries weight. The company leads in developer mindshare. Its benchmarks position GPT-Live-1 at the top of the Tau3 voice-agent intelligence ranking when paired with GPT-6 Astra at medium effort. That combination handles end-to-end customer service tasks better than prior setups. Airlines, retailers and telecom operators stand to benefit first.

Developers face trade-offs. The $0.05 per minute adds up in high-volume call centers. Backend reasoning costs extra. Integration requires careful design. Still, the simplification appeals. One voice model. Configurable intelligence. Native support for interruptions and parallel tool calls. The architecture reduces brittle handoffs that plagued earlier voice agents.

OpenAI plans to expand voice options and language support in coming months. More accents. Broader dialects. The roadmap hints at longer, more agentic interactions. For now, the focus stays on fluid conversation. Make the AI sound attentive. Keep the exchange moving. Let complex work happen offstage.

Early reactions on X reflect cautious optimism. Engineers experiment with the new endpoint. Some note the price. Others highlight the jump in natural behavior. One developer called it a solid step for voice infrastructure. Another predicted faster adoption in reservation and support apps. The conversation around voice AI just got more interesting. And more practical.

This release marks another incremental gain in a series of voice updates. Each version closes the gap between scripted assistants and genuine dialogue. GPT-Live-1 doesn’t solve every challenge. Hallucinations can still occur. Context windows have limits. Costs require monitoring. Yet it gives developers a stronger foundation. One that listens while it talks. One that reasons without going silent. The kind of tool that could finally make voice the default interface for many tasks.

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