Outside gatherings seldom feature an ideal climate. A quiet morning may rapidly shift to heavy wind, abrupt gusts, or an unanticipated downpour. If you’re displaying goods at a commercial exhibition, organizing a public function, visiting a produce vendor site, or executing an advertising drive, erratic atmospheric conditions might ruin a whole arrangement unless the canopy was designed specifically for such elements.

A tent offers more than just shade protection. It shields individuals, gear, merchandise, and brand identity while allowing events to proceed securely against shifting weather patterns. Yet, no single canopy functions identically once winds begin to rise significantly. Your custom event tents are built with wind performance in mind, helping you select structures that avoid injuries, limit property destruction, and provide attendees with increased assurance during the whole exhibition period.

Knowing what traits boost stability aids firms in buying gear that works well every single year.

Attributes Causing Tents to Remain Steadier During Blustery Conditions

Certain roof shapes let wind pass over the frame rather than hitting big flat areas hard. Open vents cut down on pressure too since they give air a way out via special holes, which lowers the upward push made by fast winds. The same aerodynamic thinking applies to your custom inflatable arches, which are engineered to channel airflow smoothly rather than resist it, making them a reliable choice for outdoor events where wind is a concern.

Smart design improves overall stability.

5. Match Tent Size to the Event

Larger tents catch more wind.

Selecting a cover fitting true space needs cuts unneeded wind drag. Small gatherings might need just small shelters, yet big shows ought to employ properly strengthened frames plus extra anchoring support.

The right size improves both safety and efficiency.

6. Inspect Your Equipment Regularly

Even durable tents require routine maintenance.

Prior to each event, check the frame, connectors, fabric, anchors, and fasteners for indications of wear or harm. Secure loose hardware and swap out broken parts prior to them turning into bigger issues under windy circumstances.

Regular inspections help prevent unexpected failures.

7. Use Sidewalls Wisely

In terms of wind resistance, fully enclosed sidewalls could create wind traps, which may increase the load onto the frame. If you’re expecting heavy winds, consider partially opening your sidewall (or using some vented panels), as this will allow for good airflow while keeping you protected against weather elements. Balanced airflow improves the stability of your tent.

8. Monitor Weather Conditions Throughout the Event

Check weather forecasts again right before you start setting up for an event. You might be able to avoid problems by watching wind conditions all day long.

If you see that the winds have exceeded those recommended by the manufacturers of your tent, consider lowering or dismantling the tent to reduce the risk of damage or danger to yourself and others. Staying alert protects more than just your interests.

Final Thoughts

Windy conditions ought not to stop a successful outdoor gathering from happening. Firms putting money into tough frames, dependable anchor setups, nice cloth materials, airflow shapes, correct sizes, and routine care get better steadiness and less worry during all events.

Top tents are not merely made for looks; they are designed for function. When weather turns uncertain, reliable gear shields your crew, clients, goods, and reputation so events proceed with assurance. 

AI has made it stupidly easy to fake being a real person online, so Facebook is rolling out a new badge to prove you’re not one of them. It’s called Facebook Verified, and the best part is that it won’t cost you a thing.

How do you get verified on Facebook?

I love that Facebook has made it easy to get verified. You record a short video selfie, and Facebook checks it against your existing profile photos to confirm it’s really you. The whole thing takes just a few minutes, and unlike some other social media platforms, Facebook isn’t charging you a subscription fee for this checkmark.

That said, not everyone gets to join the club. You need to be 18 or older, and your account has to be in good standing with Facebook’s rules around fraud, scams, and deceptive behavior. Show any signs of inauthentic activity, and you can forget about getting that badge. Also, Pages and ProMode accounts are not eligible right now.

Facebook has said that the feature is rolling out in phases, starting with select markets before it goes global, so don’t panic if you don’t see it on your account just yet.

Where will you see this badge?

Once you’re verified, the badge follows you to the spots where trust actually matters, like Marketplace, Facebook Dating, Groups, and your profile. Facebook says Feed posts will get the badge too, eventually.

You only have to verify once, and that badge travels with you everywhere. So next time you’re negotiating over a secondhand couch or matching with someone on Dating, you’ll know there’s an actual human on the other side.

Facebook also says that verification isn’t an endorsement. The company is upfront that the badge doesn’t mean it’s vouching for anyone’s trustworthiness. Verified users still have to play by the same Community Standards and Commerce Policies as everyone else.

As much as I abhor some of the Meta policies, this is a step in the right direction. As AI-generated profiles get harder to spot, a free badge that says “yes, I’m a real person” feels like a pretty useful thing to have around.

Three attacks, three names, and one identical flaw: AI coding agents treat a hallucinated identifier as a verified command.

By Shane Warden, Principal Architect, ActiveState

Ask an AI coding agent to fetch a tool. Occasionally, it returns a name that sounds right, but does not exist. Developers used to ignore this mistake, assuming a compiler or test would stop it. Unfortunately, that assumption is dangerous and wrong.

Effectively now we’re giving root access to language models that sometimes guess words, and attackers know how to use that against us.

The attack goes like this: an attacker can calculate URLs, software library names, and other output an LLM will produce and subsequently access somehow. The attacker grabs the name, then sets a trap and waits.They do not need to steal passwords. They do not need to send phishing emails.

They do not need a human to click a link. They need someone, somewhere to give an automated process permission to fetch something malicious.

Researchers at Tel Aviv University, Technion, and Intuit published a paper on July 8, 2026. The team, led by Aya Spira in Ben Nassi’s group, proved that these fake names are predictable.

They tested multiple prompts across Cursor, Windsurf, GitHub Copilot, Cline, Gemini CLI, and OpenClaw. Models hallucinated identical names up to 85% of the time for repository requests. They hallucinated identical names 100% of the time for skill installs.

“Slopsquatting exploited a fake package name. Phantom squatting exploited a fake domain. HalluSquatting exploits a fake repository or skill,” Warden says. “In every case, the agent trusts a name nobody verified.”

This Time, the Payoff Is a Botnet

That comparison undersells what is new here. Slopsquatting and phantom squatting get an attacker’s code onto one machine at a time. HalluSquatting scales further for attackers, because the agent does the delivery work a botnet operator used to need real machine compromise for.

There are no stolen passwords, no worms crawling from device to device, and no single operating system to target. Any machine running an exposed agent can be a target.

The researchers built this attack specifically to demonstrate that it repeats at scale, which is why they treat their own numbers as a floor rather than a ceiling: “Attacks always get better,” they write. “They never get worse.”

The Third Time Is a Pattern, Not a Coincidence

Security teams have seen this failure three times in six months.

January 2026 (Slopsquatting): Charlie Eriksen at Aikido Security caught AI agents trying to install a fake npm package named react-codeshift across 237 projects. He registered the name to stop attackers from taking it.

June 2026 (Phantom Squatting): Unit 42 at Palo Alto Networks found 250,000 domains that language models hallucinate. Anyone could register those domains.

July 2026 (HalluSquatting): Spira’s research team showed how attackers can take over AI agents by claiming predictable names in advance.

Every attack relies on the same core defect. The system trusts a name that a model generated without checking if anyone verified and validated it.

Design Principle Failure: Late Binding

This problem stems from two connected issues.

First, language models generate their outputs based on probabilities. Given the right inputs, they’ll produce predictable outputs (not deterministic, of course, but the paper shows that they’re predictable enough to produce attack vectors). This reminds me of rainbow tables, where you can pre-generate the output of password hashes.

You don’t have to know someone’s password if you can get the (insecure) hash and you know an input which creates that hash.

Second, developers build and run code and pipelines that execute commands based on the generated output of these language models. Unless you’re very careful, your agents will run code or fetch data before they take the opportunity to vet and verify the source of that code and data.

In software architecture, this kind of late binding offers flexibility. In security architecture, trusting an unverified external data source creates a massive vulnerability. The danger lives in the gap between text generation and code execution. What was safe and true may have changed since you last checked yesterday (or an hour ago).

Developers are rushing to ship products faster. They treat build infrastructure like disposable tooling, even while granting these tools expansive permissions to download, change, and deploy things. They have automated typosquatting and dependency confusion.

When agents now have permission to fetch and execute code without sufficient human review, your risks of attacks grow.

The Risk Extends Down the Dependency Tree

The risk extends beyond the top-level packages. An agent might select a real package with a real name. Modern security tools may check that top-level package. Do you know they rarely inspect the transitive dependencies three or four layers down the tree?

Developers cannot inspect those dependencies simply by reading the top-level source code or saying “That looks correct” and hitting Enter. Worse, if a dependency is compromised but the previous versions were fine, the rules you had in place yesterday may not protect you today.

If an attacker can compromise a deep dependency or anything it relies on, an automated pipeline can bring that compromise into your systems.

Traditional Security Backstops Fall Flat

The researchers note that their findings represent a minimum risk level. These attacks will become faster and more accurate in their targeting. Existing security tools fail against these attack patterns.

In June 2026, Trail of Bits bypassed agent skill store scanners in less than an hour. Scanners examine stated claims rather than hidden payloads.

SSL certificates and DNSSEC fail to stop this threat. An attacker who registers a fake domain can easily get a free Let’s Encrypt certificate. The certificate proves who owns the domain, but it cannot prove that the user intended to connect to it or that the domain is safe.

DNSSEC prevents other people from taking over a domain, but what if the domain were registered yesterday because an attacker predicted the latest model would send people there?

Fixing the Intake Pipeline

To secure your systems, you must ensure that none of your pipelines ever execute unvetted code or data. That vetting and verification has to happen automatically, at the speed of AI. Human review cannot keep up with automated AI tools.

Engineering teams must address this problem directly. They can spend significant time building internal verification pipelines, or they can adopt an existing governance solution.

Teams that patch individual tools will spend years chasing new variations of this exploit. Teams that fix the underlying design flaw will stop the attack before the model ever runs an untrustworthy command.

Organizations must resolve open source dependencies through something like ActiveState’s Curated Catalog, a private, policy-governed repository of vetted components. The catalog verifies the package before the agent downloads it. A package which fails this vetting is completely invisible to the agent, causing a failure before any bad code can enter your systems. 

This is a different defense than scanning. Trail of Bits broke scanners because scanners inspect an unknown upload at the moment of fetch, exactly when an attacker has optimized the payload to slip past.

The Curated Catalog removes that moment entirely: it only ever serves components that were vetted and verified before any agent asked for them, so there is no unknown upload left to bypass. This single step turns a statistical guess into a trusted resource or a deliberate failure which needs human investigation.

If you want to see how many of your team’s current fetches would pass unchecked today, request an OSS Risk Assessment from ActiveState and find out before an attacker does.

 

FAQ

What is HalluSquatting?

HalluSquatting is an attack where researchers pre-compute the fake repository, package, or skill names that AI coding agents predictably invent, register those names first, and load them with malicious instructions before a real user’s agent goes looking for them.

How is this different from slopsquatting or phantom squatting?

All three exploit the same flaw, an agent trusting a name nobody verified, but target different resources. Slopsquatting targets npm package names, phantom squatting targets web domains, and HalluSquatting targets repositories and agent skills, then executes the payload directly through the agent’s own tool-use permissions.

Can existing security scanners catch this?

Not reliably. Trail of Bits bypassed every public skill-store scanner they tested in under an hour, because scanners inspect an unknown upload’s stated content rather than its hidden payload. A static, post-hoc scan is racing an attacker who built the payload specifically to defeat that scan.

What should engineering teams do this week?

Turn on pre-fetch verification wherever it exists; most agent frameworks ship with it off by default. Route open source dependency resolution through a governed, pre-vetted catalog instead of letting agents fetch directly from public registries.

Does this affect every AI coding agent equally?

The researchers found hallucinated names were consistent across tools built on different underlying models, including Cursor, Windsurf, GitHub Copilot, Cline, Gemini CLI, and OpenClaw. This is a pattern in how agents are built and permissioned, not a flaw isolated to one vendor.

Sponsored and written by ActiveState.

SCIENCE

Replacement Raptors head for liftoff without a static fire test

SpaceX is gearing up for another attempt to launch Starship following a last-second abort on July 16 and a scrub due to weather yesterday.

The 90-minute launch window for Starship’s 13th flight test will open at 1745 CT on Friday, July 24. The launch was postponed by 24 hours due to weather, according to SpaceX.

Elon Musk’s rocketeers explained: “A key objective for the flight test is to get clear imagery from the ground of Starship’s heatshield as it flies at a higher dynamic pressure during ascent, which won’t be possible with today’s weather conditions. Visibility is forecast to be ideal for a Friday attempt.”

The first attempt last week was aborted in the final seconds after some of the booster’s Raptor engines failed to start. At the time, Musk said “two Raptors will be removed and replaced.”

Interestingly, the company did not perform a static fire test of the booster with the new engines on July 22, opting for what appeared to be little more than a leak check. The Register asked SpaceX what testing it performed, but it has yet to respond. In a post on X, the company only stated “additional preflight testing on Super Heavy complete.” There is speculation that last-second shutdowns, whether during a static fire or launch attempt, may not be kind to Raptor engines.

The Starship project is under greater scrutiny than ever. NASA requires it to have orbital capability for Artemis III in 2027, and a launch cadence high enough to support a lunar landing mission in 2028. SpaceX is also a publicly traded company and, while its current stock price has already fallen below its IPO, it could do without further rattling investor nerves with a failure.

Should all go well, the entire mission should last just over an hour. After launching Starship, the Super Heavy booster is expected to make a controlled splashdown in the Gulf of Mexico. Starship will use its Raptor engines to enter a suborbital trajectory before coming down in the Indian Ocean.

Before re-entry, SpaceX plans to deploy 20 Starlink V3 satellites, which will follow the vehicle on its suborbital trajectory, and attempt a relight of a Raptor engine. The latter is a critical step on the path to Starship going orbital. ®

Gravis Robotics, an ETH Zurich spinout, fits autonomy kits to excavators. A tie-up would extend Masayoshi Son’s robotics buying spree from the factory floor to the building site.

SoftBank is weighing a deal for Gravis Robotics, a Swiss startup that fits excavators and diggers with the sensors and software to run themselves, according to Bloomberg.

The report did not spell out the shape of any transaction, and the size, structure, and valuation were not disclosed. The discussions appear to be at an early stage, and there is no guarantee they lead anywhere.

What they do signal is where SoftBank is looking next. Masayoshi Son’s group has spent the past year assembling a robotics portfolio at speed, from an $800mn round it is reportedly circling for Agile Robots to smaller bets on the kind of autonomy kits that bolt onto existing machinery.

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Gravis Robotics spun out of ETH Zurich in 2022, from the Swiss university’s Robotic Systems Lab. Rather than build a robot from scratch, it retrofits standard heavy equipment, attaching a kit that fuses LiDAR, cameras, GNSS positioning, and hydraulic sensors so an ordinary excavator can trench, grade soil, and manage stockpiles on its own.

The company says the approach can lift site output by roughly 30%.

The kit has a name, or two. The hardware, Gravis calls Rack; the operator controls it through a tablet interface named Slate that switches between autonomous and manual modes.

The pitch is that a machine can be taught to feel the soil through its hydraulics rather than follow a fixed programme.

It is run by chief executive Ryan Luke Johns, an architect turned roboticist, alongside chief technology officer Dominic Jud and co-founder Marco Hutter, an ETH robotics professor.

Johns and Jud hold a Guinness World Record together for the largest robot-built dry-stone wall, which tells you something about the company’s temperament.

In November 2025 the startup raised $23mn in a round co-led by IQ Capital and Zacua Ventures, with Pear VC, Sunna Ventures, and cement group Holcim among the backers.

By its own account, Gravis now has machines working on active sites across seven countries. Its customer list, per the company’s own announcements, includes contractor Taylor Woodrow, which used the technology at Manchester Airport, plant-hire firm Flannery, and Holcim, which runs it in quarries. Those claims come from Gravis and have not been independently audited.

For SoftBank, construction is a logical extension of a much larger thesis. The group agreed last October to buy ABB’s robotics division for $5.4bn, and a four-bank syndicate only finished putting together the roughly $1.75bn loan behind that purchase this week.

It is also selling its remaining stake in Boston Dynamics to Hyundai, tidying up an older robotics bet even as it places newer ones.

The Gravis talks, if real, would join a run of similar moves. SoftBank led Agile Robots’ 2021 financing, the round that made the Munich firm Germany’s first robotics unicorn, and is now said to be anchoring a fresh $800mn raise there.

Earlier in 2025 it put $500mn into Skild AI, a startup building a general-purpose model to control robots. The common thread is machines that do physical work, and software good enough to run them.

Son’s pitch is that artificial intelligence has matured in software and the next frontier is giving it a body. SoftBank is reportedly preparing a US-based AI and robotics vehicle, provisionally called Roze, that it hopes to float at a $100bn valuation.

The wider market has moved with him, with global robotics investment more than doubling to $27.6bn in 2025, the year of record rounds for firms such as NEURA Robotics.

A digger that drives itself is a less photogenic proposition than a walking humanoid, but it may be a more immediately useful one. Construction faces a persistent shortage of skilled machine operators, and earthmoving is repetitive, dangerous, and expensive to staff.

Whether SoftBank ends up buying into Gravis, or simply kicking the tyres, the direction of travel is clear enough. The company that once bet on a chatty humanoid named Pepper now wants the machines that move actual earth.