Security teams log 54% of successful attacks and alert on just 14%. The rest move through your environment unseen.
The Picus whitepaper shows how breach and attack simulation tests your SIEM and EDR rules so threats stop slipping by detection.
Buried deep inside everything announced at WWDC this year was something I, an Apple Shortcuts enthusiast, can’t wait to try: the ability to make Apple Shortcuts using generative artificial intelligence. In macOS 27, you’ll be able to just type what you want a shortcut to do, and the app will build it.
Anyone who builds shortcuts regularly knows the process of doing so can be tedious, even if the end results save you a lot of time. So I’m excited about the idea of describing what you want in plain language and ending up with a working shortcut. Even if it doesn’t work perfectly (let’s face it, AI-built things rarely do), it’s a starting point that you can tweak to meet your needs.
The only downside: This feature doesn’t launch until autumn, when version 27 of Apple’s operating systems come out.
What if you want to try it now? It turns out that Federico Viticci, who founded and runs the fantastic blog MacStories, also couldn’t wait—so much so that he went and built his own version. It’s called Shortcuts Playground, which runs in either Claude Code or OpenAI’s Codex. (OpenAI’s Codex is free for now; Claude Code requires at least a Pro plan, which starts at $20 per month.)
To get started you first need to install the Shortcuts Playground agent; there are instructions on GitHub. Basically you will need to copy and paste a command into the Terminal. (I am not going to include the command here in case it changes.)
I tested this in Claude Code, but the tool works the same way in Codex. Once you’ve installed Shortcuts Playground you can trigger it by typing / followed by “shortcuts.” You’ll see a list of options pop up:
If you’re starting from scratch, I recommend using the shortcuts-playground:build option, followed by a rough description of what you want the shortcut to do. (The other option, shortcuts-playground:remix, is for making changes to existing shortcuts.)
The agent will get to work building a shortcut for you. Sometimes it will stop to ask you for more information, or to explain what is and isn’t possible to build in Apple Shortcuts.
While exploring this tool, I asked for a shortcut that compiled today’s weather, my calendar appointments, and my to-do list for the day, then read the entire thing out loud. The agent happily went to work.
Hackers are actively exploiting the critical CVE-2026-50522 vulnerability in Microsoft SharePoint to steal machine keys and maintain access even after affected servers are patched.
An attacker obtaining them can create valid authentication tokens to impersonate users and access available resources such as SharePoint sites and documents with the privileges of the forged identity.
Microsoft describes the security issue as a deserialization-of-untrusted-data flaw that allows a remote attacker to execute code over a network without authentication.
The flaw was addressed in July’s security updates from Microsoft. It was not marked as actively exploited, but the advisory noted an increased likelihood of being leveraged.
Offensive security company watchTowr has observed that hackers started to leverage CVE-2026-50522 against on-premise vulnerable SharePoint deployments, immediately after a valid proof-of-concept (PoC) exploit became public.
“On July 20th, watchTowr identified proof-of-concept exploit code for this vulnerability,” watchTowr states. “Within hours, our global honeypot network, Attacker Eye, captured exploitation attempts using this PoC that successfully compromised target systems.”
The researchers note that the attackers are stealing machine keys that allow them to maintain long-term access on breached systems.
Early warning threat intelligence company Defused detected “an undocumented SharePoint deserialization vector” being used in attacks as early as July 17 but could not link the activity to a flaw.
Yesterday, the company said that the attacks were likely driven by exploiting the CVE-2026-50522 SharePoint vulnerability.
At least one PowerShell demonstrative exploit for CVE-2026-50522 is available on GitHub from security researcher Janggggg.
The PoC attempts to trigger remote code execution by delivering a malicious .NET ‘BinaryFormatter’ payload as the cookie of a forged ‘SecurityContextToken’ within a WS-Federation sign-in response posted to SharePoint’s ‘/_trust/default.aspx’ endpoint.
If the token is processed by a vulnerable deserialization path, the payload results in arbitrary code execution on the SharePoint server.
BleepingComputer did not test the PoC exploit, but it looks structurally and technically legitimate.
It should be noted that Janggggg’s published the PoC on the same day watchTowr started to detect attacks leveraging it. However, it is unclear if the observed incidents made use of the publicly available exploit.
While applying the latest SharePoint security updates removes the vulnerability, watchTowr advises defenders to also rotate credentials on any asset that may have been exposed.
Security teams log 54% of successful attacks and alert on just 14%. The rest move through your environment unseen.
The Picus whitepaper shows how breach and attack simulation tests your SIEM and EDR rules so threats stop slipping by detection.
Modern robotics is a field that continues to grow over time, fueled by a mix of smarter AI, manufacturing efficiencies and at times, better materials that change what is possible on the ground.
The robots currently in use in factories and warehouses however have a key limitation that has yet to be addressed properly: Lithium-ion batteries often can not keep up with the power demand that modern robots have.
This is particularly reflected in how often they require a battery swap or a recharge: most lithium-ion-powered robots typically operate for one to two hours on a charge, a far cry from the industry’s ambitions of machines that can work a full eight-hour shift.
Speaking at the 2nd Battery Foundry Forum in Seoul on July 15 2026, Ko Young-seok, the executive vice president and head of product planning at the Korean battery maker SK On, argued that solid-state cells can deliver meaningful value for industrial robots that need extended runtime.
He also explained that whether manufacturers actually adopt them will come down to total cost of ownership (TCO), weighed against two cheaper rival approaches: battery swapping and ultra-fast charging.
The TCO framing implies that solid-state batteries, the battery industry’s most hyped next-generation technology and inherently expensive to boot, might attract industrial buyers simply because the math works in their favor relative to conventional Li-ion setups.
This is because one must factor in the cost of keeping spare battery packs, charging and/or swap times, and potentially additional robots to cover the resulting downtime, which could leave solid-state with a lower TCO than the competition despite the higher sticker price.
The framing may also be unavoidable given how batteries sit in a robot’s bill of materials today. A Li-ion battery accounts for under 2% of the total cost of an industrial robot, according to SK On, essentially a rounding error in the grand scheme of things, but switching to a solid-state battery could push that share to around 8%, a significant jump in overall costs.
For context, a widely circulated teardown of Tesla’s Optimus Gen 2 puts the battery pack at about $300 in a roughly $55,000 hardware cost structure, around 0.5% of the bill of materials, comfortably under 2%, though units with larger packs or lower overall costs would land higher, and some independent estimates, including McKinsey’s, put battery modules at 5–10% of a humanoid’s bill of materials.
Solid-state cells, with their higher energy density, are one of the most promising routes to a robot that works a human shift without stopping, but given their comparatively steep cost versus the competition, one can understand why SK On is aiming this pitch at the robotics and industrial players that need the technology and can afford to pay for it.
For applications with short duty cycles, swapping a cheap lithium-ion pack or fast-charging between tasks may simply remain the better economic answer, but for customers willing and able to pay for more sustained power, solid-state seems to be the new play, even as it remains elusive for commercial EVs given its cost.
SK On has skin in this game on a specific timeline. The company completed its all-solid-state pilot plant at its Future Technology Institute in Daejeon last September, built in partnership with US solid-electrolyte firm Solid Power.
It is developing two chemistries: a polymer-oxide composite cell targeted for commercialization in 2028 and a sulfide-based cell in 2029, a timeline it has already accelerated by a year. But it has competition waiting in the wings: rival Samsung SDI, working with the same American partner, is aiming for 2027.
Whether this leads to widespread adoption of tech expected to appear only in the most expensive EVs on the market this decade remains to be seen, but the TCO argument Ko makes might stick more easily with industrial customers than with consumer EV buyers, for whom pricing and budgets are key factors.
Via The ELEC
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Saronic has selected Brownsville, Texas, for ‘Port Alpha‘, a planned shipyard designed to produce autonomous vessels at unprecedented industrial scale.
The project is expected to attract more than $3 billion in private investment and begin construction during 2026.
When operational in 2028, the facility will combine shipbuilding, advanced manufacturing, robotics and software development across an 835-acre site.
The site could eventually expand to nearly 4,400 acres, giving Saronic space to develop one of the largest shipbuilding facilities in the United States.
Its initial production facilities will support vessels up to 850 feet long, while later expansion could accommodate ships exceeding 1,200 feet.
The Brownsville location includes waterfront access, a deep-water channel and multimodal transport links needed for large-scale maritime manufacturing.
Saronic selected the site after a year-long search that assessed workforce availability, infrastructure, logistics and future expansion opportunities.
The company expects Port Alpha to create up to 10,000 direct jobs over the next decade, covering welding, machining, robotics, software engineering and naval architecture.
Governor Greg Abbott said the project could generate approximately $750 million in annual wages once the workforce reaches its projected scale.
“America’s maritime future depends on our ability to build again,” said Saronic co-founder and CEO Dino Mavrookas. “Port Alpha is our commitment to that mission.”
Saronic plans to work with technical colleges, universities and local institutions to develop apprenticeships and training programmes supporting the facility’s workforce.
The company says the project could generate more than $160 billion in regional economic impact for Cameron County and $264.5 billion across Texas.
Port Alpha will not operate as a conventional shipyard focused solely on constructing traditional crewed vessels for established naval fleets.
Saronic intends to combine software-defined manufacturing with autonomous maritime systems and large-scale production methods.
The company already operates a growing shipbuilding network, including a Franklin, Louisiana, facility acquired in early 2025.
Saronic is investing $300 million there to add 300,000 square feet of production capacity for its 180-foot Marauder autonomous vessel.
The company is known for autonomous vessels including Corsair and Mirage, while Marauder is competing in the medium unmanned surface vessel programme.
These systems form part of a broader effort to expand autonomous maritime operations across different mission types.
Saronic says Port Alpha will support production at a scale and speed not seen in American shipbuilding since World War II.
The company has not yet disclosed detailed production numbers, vessel schedules, or the exact mix of autonomous platforms planned for the facility.
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AI models will do just about anything to complete the task you ask, including cheating to get there, according to new cybersecurity evaluations from the UK government’s AI Security Institute (AISI). The group found that leading models often take shortcuts to achieve a particular result and then misrepresent how they obtained that result. And they won’t always admit it when asked. “Every model we have tested for this behaviour attempted to cheat,” AISI said in a blog post on Tuesday. “Models did not reliably report this behaviour when asked, and often did not reason about it in their chain-of-thought, suggesting that detecting cheating will likely require robust monitoring methods.” Infractions included searching the internet for the answer, bypassing sandbox network restrictions, probing the evaluation harness, attacking a system other than the target, and guessing an answer. Cheating in this manner – employing a workaround or gaming a reward function to score better on a benchmark test, for example – has been widely documented by machine learning researchers. It doesn’t necessarily imply malicious intent, AISI said, but it’s nonetheless troublesome because it can produce misleading assessments of model capabilities. When AISI conducted evaluated five leading models, it found that all of them cheated. The results were as follows: GPT-5.4 cheated 67 times in 475 test runs (14.1 percent). GPT-5.5 cheated 54 times in 475 test runs (11.4 percent). GPT-5.6-Sol cheated 60 times in 475 test runs (12.6 percent). Claude 4.7 Opus cheated 43 times in 475 test runs (9.1 percent). Claude Mythos Preview cheated 37 times in 475 test runs (7.8 percent). Asking models whether they cheated or did anything wrong proved an unreliable auditing mechanism because the models didn’t always admit wrongdoing. “In our experiments, models did not consistently acknowledge attempted cheating when asked, and described it as wrong less than 50 percent of the time,” said AISI. Existing vetting methods, such as self-reporting and chain-of-thought logs, proved similarly dicey because models don’t always report their chain-of-thought. And there were instances where a model would consider whether a proposed action amounted to cheating and then decided to take the action anyway. Given the absence of reliable model cheating detection methods, AISI warns that its current approach – manual review coupled with LLM monitoring – may not be sufficient to catch deception, particularly as models become more sophisticated. “A more fundamental fix would be to train the models not to cheat in the first place – but given this kind of behaviour was reported in frontier models more than a year ago, robustly aligning it away may not be easy,” AISI concludes. ®

Seattle’s Copper has landed a high-profile new backer as it looks to accelerate growth of its consumer rewards platform, announcing Tuesday that Grammy-winning artist, DJ and entrepreneur Diplo has invested in the company.
Financial terms of the investment were not disclosed.
“I’m always looking for things that actually make sense for people,” Diplo said in a statement. “Copper’s one of those — you’re already on your phone, you’re already spending money, and this gives something back. That’s real.”
Copper says more than 4 million members use its platform to earn money through mobile games, cash-back offers and purchases.
Copper CEO Eddie Behringer, who previously co-founded Snap! Raise, said the company is building an alternative to consumer apps that monetize users’ attention.
“Most consumer apps are designed to take more from the user — more time, more money, more attention,” Behringer said in a LinkedIn post. “At Copper, we’re building the opposite.”
Founded in 2019, Copper originally launched as a banking app for teenagers. GeekWire covered the startup in 2022 after it raised $29 million in funding to expand into investing products, at a time when the company had nearly 1 million users.
The startup has since evolved into a broader consumer rewards platform. Copper has raised $42 million to date and recently ranked No. 2 among the Pacific Northwest’s fastest-growing companies in Deloitte’s Technology Fast 500 rankings, based on three-year revenue growth.
Diplo, whose real name is Thomas Wesley Pentz, has built a business portfolio that extends beyond music, investing in technology and consumer startups while launching ventures such as Diplo’s Run Club, a series of 5K races paired with music festivals.
He’s a three-time Grammy winner, and has collaborated with artists like Labrinth and Sia as part of the musical group LSD and worked with musician Mark Ronson on Silk City. He’s also the founder of record label Mad Decent.
In 2024, Copper discontinued its banking services following the collapse of fintech infrastructure provider Synapse, forcing the startup to pivot away from its original business. “Despite our prior planning, this event has forced us to close banking accounts much sooner than anticipated,” Behringer wrote at the time.
The company has since rebuilt around its rewards platform, which it says now serves millions of users.
Behringer said that the company’s mission was always about helping families improve their financial lives.
“As household costs rose, we saw an even bigger opportunity to help the person making everyday spending decisions earn more from the things they were already doing—from buying groceries to shopping in-store and spending time on their phone,” Behringer tells GeekWire via email. “Diplo’s investment is meaningful validation of how far that evolution has come.”
The company has partnered with Pangram for the new tools.
Substack has launched a new AI detection tool in partnership with Pangram. This will allow readers to scan Substack content for an assessment of how much of the material was written by AI. The tool can be used on text longer than 100 words that was published beginning today. Substack is also adding a new statement space for creators to explicitly share if and how they used AI for their content. The AI detection capabilities are available starting today on web and iOS, with Android support to come.
The blog post announcing the feature is surprisingly spicy. There’s a dig at LinkedIn about the presence of AI-generated content on that service and it dubs attempting to create feigned human connection with AI slop “Claudefishing.” Throwing shade is a risky maneuver here, because even the best tools for identifying gen-AI can’t guarantee a correct assessment. The Atlantic dug deeper into just how accurate AI detection tools, Pangram in particular, can be. Spoiler: they’re far from perfect.
Substack did acknowledge in the post that there are limits to what Pangram can detect and hinted at some other features it is considering around AI content and preferences. It emphasized that these new measures are aimed at setting expectations for readers, summing up its stance as “people should know what they’re getting.”
Google released three new AI models on Tuesday, all built on Gemini 3.5 Flash. The new models are more token-efficient, faster and more reliable across the board. The tech giant also provided an update on the much-anticipated Gemini 3.5 Pro and what’s to come after.
A new AI model from the likes of Google, Anthropic or OpenAI is released seemingly every week, with the latest, ChatGPT-5.6, released earlier this month. Google’s latest releases aren’t flagship models compared to what’s on the horizon, but each has its place, including a new model solely focused on cybersecurity.

Here’s what’s new in the latest Gemini models from today’s announcement.
Google called 3.6 Flash its “workhorse” model that’s now better at coding, knowledge work and multimodal performance. It also promises reduced token usage by up to 17%, and at a lower cost per token versus its predecessor, 3.5.
Google says it built the model based on both developer and customer feedback. A series of benchmarks shows 3.6 Flash’s gains in performance and average tokens per task compared to its predecessor.
Google’s fastest and most cost-effective model can deliver 350 output tokens per second and “significantly” outperforms previous generations when it comes to agentic workflows, according to the blog post.
Like 3.6 Flash, this model now supports computer use as a built-in tool to take on more agentic tasks.
3.5 Flash Cyber is a specialty model that prioritizes cybersecurity workflows in order to find and fix vulnerabilities. It works alongside an infrastructure agent called CodeMender to help cybersecurity teams quickly identify and patch issues.
According to a separate article from Google DeepMind, the new model is already finding and fixing bugs in Google’s internal codebases in Android, Chrome and YouTube. This model will initially be limited to governments and trusted partners, but access will expand in the future.
While the three latest models are the primary focus for Tuesday’s announcements, Google gave a brief update to its upcoming flagship AI model, Gemini 3.5 Pro. The model is said to currently be in testing with partners, and it plans to make it available as soon as it’s ready. How long that will take is anyone’s guess, but Google’s also already looking ahead to the next generation of AI, too.
Google says it has already begun pretraining for Gemini 4, which will be released at an undetermined date.
Both Gemini 3.6 Flash and 3.5 Flash-Lite are available starting today for developers in the Gemini API via Google AI Studio and Android Studio and the Gemini app. 3.6 Flash is also available in Google Antigravity, and 3.5 Flash-Lite is rolling out to Google Search.
Disclaimer: Unless otherwise stated, any opinions expressed below belong solely to the author.
Given the number of various support schemes provided by the government in Singapore, one might question whether there aren’t too many of them and if it wouldn’t be simpler to simply disburse one cash payment to every eligible person.
After all, it’s not like the government isn’t doing that already, depositing funds directly for GST Vouchers, Assurance Package payments and cost-of-living support, regularly appearing in recent years.
So why bother with CDC Vouchers, which require an entire digital infrastructure to allow their issuance and redemption? Wouldn’t a simple bank transfer be better?
At least not for everybody, and certainly not for the government, which is using its power to direct the money to specific parts of the economy.
Cash is liquid, and you can do whatever you want with it, including going for a nice day trip to JB to spend it there instead of Singapore. You may also use it for online shopping on one of the many ecommerce platforms, with most of the funds being sent to sellers in China or other countries.
This sees Singapore dollars exiting the domestic economy, benefiting others instead.
More prudent Singaporeans could opt to save it instead, which isn’t terrible in itself, but does keep the funds out of circulation.
Finally, the more reckless consumers could simply waste it on more “sinful” pleasures, still ending up short of money for daily necessities.
The voucher format allows the government to set strict rules on their use: with 50% allocated to shopping in supermarkets and another half to hawkers and smaller, heartland merchants.
This ensures that this pool of money is spent in the most beneficial way and provides the authorities with data on how the money is spent and where.
There is, however, one other purpose they have served very well since their launch six years ago, which is not spoken of.
CDC Vouchers originated as a COVID-19-era relief scheme, originally issued on paper and directed to the poorest households.
With the pandemic dragging on throughout 2021, the scheme was ultimately expanded to all citizen households by the end of the year and went digital with the launch of the RedeemSG app. Merchants could use it to accept the vouchers by scanning digital QR codes on customers’ phones, instead of dealing with paper.
In parallel, the government launched the Hawkers Go Digital scheme in Jun 2020, with generous subsidies and transaction fee waivers, which were meant to help hawkers adopt digital payments and reduce the risk of spreading the virus.
It was also a good opportunity to prod them to adopt mobile payments, which have become a staple in many countries around the world (most notably China, through its giant superapp WeChat).
The final waiver of the 0.5% fees ended just recently on Jun 30, 2026, after several past extensions. This means that hawkers are now going to have to bear the cost themselves, which might mean that some of them may prefer to return to cash-only payments.
But this is where the CDC scheme comes in.
Throughout the push for digital payments, the critics lamented that many elderly sellers might be struggling with the transition, not being very tech-savvy. What’s more, a skill once developed needs to be kept in use before it falls out of favour. Old habits die hard, after all.
Well, while we might see some return to cash, there is no returning to paper for CDC vouchers.
And because of the scale of the program, currently exceeding S$1 billion annually, hawkers have a strong incentive to keep their RedeemSG app to accept voucher QR codes.
In other words: there’s no exit from the QR era.
Of course, using the CDC app doesn’t force merchants to accept all digital payments, but since they still have to deal with QR codes to accept the vouchers, it provides very useful stickiness, which is going to keep most of them on the digital train.
Featured Image Credit: CDC/ depositphotos
Nvidia is hyping up its new Vera Rubin chip system this week, revealing new performance benchmarks for the GPU and CPU combo ahead of rival AMD’s annual product event in San Francisco on Thursday.
During a lengthy technical workshop last week at the company’s headquarters in Santa Clara, California, Nvidia executives boasted to a small group of journalists about the chip system’s increased power and efficiency capabilities. The biggest takeaway: Nvidia, which has long specialized in making GPUs, is increasingly trying to position itself as a supplier of CPUs that can power AI agents.
While GPUs are still the main hardware that companies use to train and run their AI models, the industry’s shift toward more complex, agentic systems has increased demand for CPUs, which can orchestrate data flows, networking, and other software tasks. That’s likely one reason Nvidia has been eager to promote itself as a supplier of complete AI systems rather than just AI chips.
Vera Rubin is Nvidia’s successor to its hybrid superchip system Grace Blackwell and represents the linchpin of its near-term future powering the AI industry. It’s designed to offer one CPU for every two GPUs. In a single Vera Rubin NVL 72 super chip system, there are 36 Vera CPUs for every 72 Rubin GPUs. Nvidia is also selling the Vera CPU as a stand-alone product, and it has reportedly told Chinese customers these could be ready as soon as August.
Nvidia executives emphasized that its new Vera Rubin NVL72 racks—a stack of chips packed into a single liquid-cooled platform—are much more “plug-and-play” than some of its earlier products. During a brief tour of a Nvidia data center lab in Silicon Valley, Nvidia executives shared that OpenAI already has one Vera Rubin rack in use.
Nvidia CEO Jensen Huang didn’t make an appearance at the workshop in Santa Clara last week; he was in Japan announcing the chipmaker’s new partnerships with a number of Japanese firms to develop AI for robotics. The briefings were instead led by Ian Buck, Nvidia’s longtime vice president of accelerated computing and the architect behind the company’s CUDA software.
“We’re on a road map to crank out new architectures, not just GPUs but CPUs,” Buck told reporters. “We’re going to keep innovating, because it’s do this or die in Silicon Valley.”
The meetings were held in Huang’s executive briefing center, where multiple desks nearby were piled with bags of Taiwanese snacks that the CEO brought back from his recent trip to Computex, a massive annual semiconductor trade show in Taipei, an Nvidia spokesperson told WIRED.
If you’re a fan of the Ford F-Series pickup trucks, then you’re likely familiar with their technology and design. For example, you may know about the automaker’s legendary Twin I-Beam front suspension. But you might not know that Ford chose to move away from this design on the F-150, not because it was flawed, but because trucks evolved to more modern solutions.
The Twin I-Beam initially gave Ford a way to improve ride comfort while also maintaining the rugged performance that truck buyers were accustomed to. But as pickups began to shift from being used primarily as utility vehicles to everyday drivers, customer expectations began to change. Ford redesigned the F-150, focusing more on precise handling, improved steering, and better control over the truck’s front end. Newer designs like dual A-arm suspension addressed those concerns, leading Ford to move the redesigned 1997 F-150 to a different front suspension design.
Even as the F-150 transitioned from the Twin I-Beam, the design remained one of Ford’s most well-known truck innovations and continues on the F-250 and F-350 trucks to this day. In fact, the Twin I-Beam setup was exclusive to Ford when it was introduced in 1965 and became closely associated with their most popular pickups. Vehicle axles are more complicated than you might think and as other companies used different suspensions for their trucks, Ford’s approach was more forward-thinking. It gave drivers a vehicle with independent front-wheel movement, while also maintaining durable performance.
As Ford was developing its innovative Twin I-Beam concept, the automaker introduced the Twin Traction Beam in 1980. This suspension was designed for four-wheel-drive trucks like the F-150 and Bronco, using the independent movement of Twin I-Beam while adding components for a driven front axle. Twin Traction helped reduce weight, improve ride quality, and lower the truck’s overall height. This design helped Ford modernize its 4WD lineup at the time of its production.
In the years since the development of the Twin I-Beam, Ford’s approach to truck suspension has continued to evolve. Instead of using a single design for every truck in the automaker’s lineup, the F-150 now features different suspension setups depending on its intended purpose. For example, the standard F-150 has a Hotchkiss-style suspension, with a solid rear axle and leaf springs for both towing and payload. In contrast, the F-150 Raptor features a five-link rear suspension with coil springs, and the all-electric F-150 Lightning uses a fully independent rear suspension.
This design evolution extends beyond the F-Series as well, with the 2025 Expedition getting a redesigned suspension influenced by the F-150. The Expedition may be one of Ford’s dinosaurs, but its new setup does include modified shocks, springs, and other components. These improvements function to match the Expedition’s combination of passenger comfort, towing capability, and off-road performance. This gives drivers the handling and towing ability they expect from a Ford vehicle.
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