One of the most crucial aspects of FDM 3D printing is ensuring sufficient material is extruded. Determining the right flow rate can be done manually, but some printers these days automatically perform this adjustment, which is very convenient. [Stefan] of CNC Kitchen investigates how to add similar functionality using existing bed-leveling sensors.
A major complication with extrusion in FDM printers is that the flow rate has to fit the printing speed. However, you can’t just immediately speed up or reduce the flow rate, as the melting filament is flexible and thus acts like a spring, especially as the extruder is exerting significant force on the filament, which adds compression.
The moment you reduce or increase the speed of the nozzle, you can get over- or under-extrusion, but the delayed response by the extruded filament means that you have to adjust for this change in advance. Ergo, the name ‘pressure advance’, also known as the K-value. Obviously, this is a parameter that differs with each material, printer, and other factors, so a direct measurement is always the best.
In the Bambu Lab X1 FDM printer, a Lidar scanner was used to scan various test patterns to automatically determine the optimal setting. This was later moved to the purge section of the extruder in newer Bambu Lab printers. On other FDM printers, the only available sensor in that area is typically the pressure sensor for bed leveling. Could this sensor make a similar measurement?
This wasn’t just an idle thought, but was inspired by the Snapmaker U1, which runs open-source Klipper, with tantalizing glimpses of how it does pressure-advance sensing in its extruder. This extruder also only contains a load cell, as do some Prusa printers. These much more open printers thus provided a test bed for some experimentation.
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With load cell data available, [Stefan] measured how various extrusion rates affect the load cell, which can then theoretically be correlated with the appropriate K-values for specific transitions. He created a calibration tool for a range of Prusa printers that works with stock firmware, though this is definitely still a work in progress. There are also a couple of similar open-source projects, such as this Auto PA Calibration project by [Mark].
Overall, K-value presets tend to work pretty well, but adding a pressure-advance calibration feature to existing FDM printers is definitely an interesting idea. There’s also the prospect of lateral sensing using this same bed-leveling sensor, which could allow the printer to sense much more than just the bed.
If you’re tired of buying ink cartridges every few months, this is a good time to switch to a cartridge-free tank printer. Right now, the HP Smart Tank 5101 is $170 (was $260) at Amazon, and comes with 2 years of ink already included in the box.
For home, dorm room, home office, and small businesses printing a mix of monochrome and color documents and photos, it’s an ideal pick. Particularly as HP‘s ink tank printers tend to have larger tank reservoirs compared to rivals like Epson‘s EcoTank range.
Today’s top HP printer deal
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Should you buy it?
✅ Buy it if…
You want to save on running costs because bottled ink is much cheaper than inkjet cartridges and you get four bottles free in the box. We recommend ink tank printers if you’re chiefly printing documents and photos.
❌ Skip it if…
You’re mostly printing text documents – a high-volume laser printer is a much better option for sharper, bolder on-page text.
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Why we recommend it
Having tested HP’s Smart Tank line alongside the Epson EcoTank, Canon MegaTank, and Brother INKvestment range, we strongly recommend ink tank printers over traditional inkjet models that use cartridges.
The pitch for tank printers like the HP Smart Tank 5101 is straightforward: instead of buying ink cartridges that run out every few hundred pages, you refill four visible ink tanks from bottles, and each bottle lasts dramatically longer. HP estimates the included set of ink bottles is good for roughly 6000 pages.
Setup leans on HP’s Smart app, which walks through Wi-Fi connection and initial configuration with guided, step-by-step prompts rather than dropping you into a printer’s typically clunky onboard menu system. Once connected, illuminated smart buttons on the printer itself are meant to guide you through common tasks like printing, scanning, and copying.
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Print speed is modest rather than fast — HP rates this around 12 pages per minute for black-and-white and 5 pages per minute for color in normal mode, with the higher-quality “best” setting slower still. That’s typical for tank printers in this price range, and fine for everyday home printing, but not the machine to reach for if you regularly print large batches of documents quickly.
Price Context & Historical Value
This isn’t the cheapest the 5101 has ever been – back in 2024, it dropped to an all-time low of $140 direct from Amazon. However, it’s cheaper now than any third-party seller has sold it before brand-new ($180 was the price back in April 2025). Typically, we see it selling at around the $190 to $250 mark when not on sale. The highest price it’s been sold for is $280. Right now, it’s also available for $170 at Best Buy, too.
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The Catch: What to know before you buy
A few honest caveats worth flagging: some reviewers have reported software quirks and occasional paper jams with this model, and if you print only occasionally rather than regularly, the ink in an inkjet’s print head can dry out and clog between uses. This is best suited to households or small offices that print often enough to make the low cost-per-page actually pay off.
Motorola’s next device appears determined to bring audio hardware for the rest of the room to watch along. It recently unveiled a powerful new Edge phone, and now, the Moto Pad 70 Groove will launch on July 31 with a nine-unit JBL speaker system capable of delivering up to 48 watts of output. The company has confirmed that the audio-focused tablet will pair those speakers with a 12.1-inch display and a sizable battery designed for long streaming sessions. But we still have no official word on its pricing and global availability.
Nine speakers give this tablet a unique purpose
Motorola
The unusual sound system contains four tweeters, three woofers, and two passive radiators. JBL handled the audio tuning, while Dolby Atmos and Hi-Res Audio complete the package. It also supports 7.2-channel surround sound, which makes it a solid entertainment machine.
A rotating ring on the rear doubles as a stand, allowing the tablet to sit in portrait or landscape orientation. Motorola has also placed dedicated volume controls on the back, and the device can operate as a Bluetooth speaker for music playing from a phone. Those additions could make the Pad 70 Groove particularly useful around the house. It could sit on a kitchen counter for recipes and podcasts, handle films without immediately demanding headphones, or provide music without requiring a separate portable speaker.
The rest of it isn’t bad either
Motorola
Motorola has confirmed a 12.1-inch “2.5K” panel with a 120Hz refresh rate. It reaches up to 800 nits in High Brightness Mode and supports HDR10 and Dolby Vision, making the device sound purpose-built for video rather than another generic Android slate with louder branding.
A 10,200mAh battery sits inside, with Motorola claiming up to 15 hours of video playback. The company will include a 68W charger. Going by its name, it is also a part of the Pad 70 series, and we did cover the Pad 70 Pro in the past. Performance and price will ultimately decide whether this is a good tablet. But the bigger doubt is if it will be released outside of India.
A new rumor claims that Apple is considering using what it will call a 7-inch screen for one of the 20th anniversary iPhones, although that isn’t as great an increase as it sounds.
One recent rumor claimed that Apple had begun production evaluation for a 2027 iPhone with a display that is curved on all four sides. Then another claimed that the iPhone 20 range would feature a significant redesign.
For the first time, though, a leaker is claiming that Apple is testing what would be its largest iPhone screen. According to Digital Chat Station on Chinese social media site Weibo, if it goes ahead with this screen, Apple will market it as being a 7-inch one.
The claim says, though, that it would actually be 6.96 inches. That’s close enough for Apple’s marketing, but it seems less significant since the current iPhone 17 Pro Max screen is 6.86 inches.
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Still, screen sizes are measured diagonally so such a difference could amount to the iPhone 20 Pro Max screen being up to 2% larger than the current model. That’s enough to be noticeable in the hand, although there’s no indication yet whether the pixel density will remain the same.
The last time Apple increased the screen size of its iPhones was in 2024. Then the iPhone 16 Pro Max went up from 6.7 inches to 6.9 inches, while the iPhone 16 Pro screen was 6.3 inches where its predecessor had a 6.1 inch display.
Digital Chat Station says that the new, larger screen size has not been decided on. This leaker has a reasonable track record with Apple leaks, and has recently reported rumors about the screen size of the iPhone Fold 2.
The organizations losing confidence in AI are the ones most likely to get it right.
Six months ago, 40% of IT leaders described their organizations as mature in AI deployment. Today that number is 23%. Before you read that as a setback, consider what it actually reflects.
We recently surveyed 800 IT leaders across the U.S. and U.K. for our Q3 2026 trends report, and the data tells a consistent story: the organizations revising their self-assessment downward are overwhelmingly the ones that have moved AI agents from pilots into production. They’re not losing faith in AI. They’re running into the problems that only show up when agents are doing real work in real systems, and they’re being honest about what they found.
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That kind of honesty is harder to come by than it sounds, and it matters more than the confidence number itself.
Deployment was the easy part
84% of organizations plan to expand AI use in IT operations over the next 6 to 24 months, so the drop in confidence isn’t a retreat. What it reflects is a more accurate picture of what production actually requires.
In a pilot, an AI agent does one thing in a controlled setting. In production, it accesses real systems, makes decisions that affect real workflows, and operates continuously, often without a human in the loop. The governance infrastructure that entails is materially different from what it took to get the pilot working. Most organizations built enough to ship. Fewer built enough to scale.
The IT leaders revising their self-assessment are confronting questions they didn’t have to ask at the pilot stage: Can we see every agent running in our environment? Do we know what each one can access? If an agent behaved unexpectedly last week, how long would it take to find out? For most organizations, at least one of those answers is uncomfortable.
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The gap between perception and reality is where risk accumulates
The graphic above captures the structural problem. Across confidence, governance, and autonomy, the same pattern holds: deployment is moving faster than the controls built around it.
The organizations that have closed this gap share specific characteristics. They’ve consolidated their IT environments rather than adding tools to solve each new problem, because every additional platform creates another place where agent identity, access, and accountability can go unmanaged. They treat AI agents as governed identities rather than tolerated shadow processes. And they measure what AI actually produces, not just what it deploys.
The payoff is tangible. Organizations in the top tier of our maturity model are five times more likely to report no barriers to expanding their AI agents than the average organization. They are not more cautious about AI. They are more confident in it, because they built the foundation that makes confidence earned rather than assumed.
The governance gap has a specific shape
The hardest problem in enterprise AI right now is not capability. It is accountability, and the data makes the specific failure point clear: non-human identity governance is the least adopted AI security practice we measured, in place at just 21% of organizations.
Non-human identities now outnumber human users in 83% of organizations, and that population is growing fast. Yet most of those identities exist without the governance structures that every human employee has as a matter of course: no formal record, no named owner, no defined scope of access, no offboarding process when their purpose expires. They keep running. They keep accessing systems. They keep accumulating permissions. We call these Zombie Agents, and they are the service account problem of the AI era, operating at machine speed and in every department.
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The accountability gap is where real risk lives. When a human employee takes an action, there is an implicit accountability chain. When an autonomous agent takes an action, that chain breaks unless it has been deliberately engineered. Most organizations have not yet engineered it, and the gap between the autonomy agents are being granted and the oversight structures in place to manage them is widening every month.
What the confidence drop is actually telling us
When AI maturity confidence was uniformly high across the market, that was worth worrying about. It meant most organizations hadn’t yet run into the hard parts. A selective drop, concentrated among organizations actively running agents in production, means the market is developing a more accurate picture of what AI operations genuinely require.
The organizations recalibrating are doing the work that makes long-term AI adoption possible: building identity infrastructure that covers agents alongside humans and devices, unifying the environments where governance needs to apply, and measuring outcomes rather than just counting deployments. They haven’t lowered their ambitions for AI. They have raised their standards for what it means to run it responsibly.
84% of organizations plan to expand AI use over the next two years. The ones that will do it well are honest enough, right now, to admit what they haven’t yet built.
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JumpCloud’s Q3 2026 AI Readiness Research report (n=800 IT leaders, U.S. + U.K.) is available here. The report covers AI agent deployment stages, identity governance gaps, IT unification benchmarks, and budget realism across mid-market and enterprise organizations.
Rajat Bhargava is CEO and Co-founder at JumpCloud.
Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.
Rudra Mitra will lead Amazon security services in his new role. (LinkedIn Photo)
Rudra “Rudy” Mitra, who spent more than 27 years at Microsoft and most recently led its Purview data-security business, is joining Amazon Web Services as vice president of security services.
Mitra will oversee an AWS portfolio that includes tools such as GuardDuty and Security Hub, which companies use to track security risks across their cloud accounts. AWS recently added AI-specific threat detection to GuardDuty and, perhaps notably given today’s news, extended Security Hub to monitor AI workloads and security inside Microsoft Azure.
He will report to Chet Kapoor, the former DataStax CEO whom AWS hired last year as vice president of search, security and observability, a role that reports to AWS CEO Matt Garman.
“Rudy brings decades of security experience, a passion for building, and a deep understanding of what customers need as the security landscape continues to evolve,” Kapoor wrote on LinkedIn.
Mitra joined Microsoft in 1999 straight out of college, working on early efforts to deliver Office as an online service before launching Purview, the company’s data-security and governance product, in 2014. He announced his exit from Microsoft last week, addressing what was next at the time by saying only that there was “more on that soon.”
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His departure comes amid a broader reshuffling of Microsoft’s security leadership this year under Hayete Gallot, who returned from Google in February to run the group and has been reshaping its executive ranks in recent weeks and months.
Gallot replaced Charlie Bell, who had joined from AWS in 2021 and continues at Microsoft as an individual contributor focused on engineering quality. She’s been overhauling the group’s product lineup, according to The Information, which reported last week that at least nine corporate vice presidents who reported to Bell have left the company this year.
On the inbound side at Microsoft, Naseem Tuffaha returned in June to fill the corporate VP role Kumar had left, after nearly two decades at the company and a stint away.
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When Gallot arrived, Microsoft named Ales Holecek, a longtime engineering leader, as the security group’s chief architect, reporting to her. David Weston, another veteran Microsoft executive, also reportedly shifted into the security unit earlier this year.
Longtime Slashdot reader schwit1 quotes an X post by Josh Walkos, author of the Substack We the Free: If you thought Flockwasbad check out Falconet. Falconet from Israeli company Cognyte serves as a cell tower simulator that intercepts cell phone data from all devices within range. Police mount these systems in Tahoes so the vehicles can collect information while driving through areas without any direct interaction with targets. This mobile approach generates ongoing records of phone locations and communications for everyone nearby rather than only suspects, which creates comprehensive movement profiles and bypasses traditional warrant requirements under the Fourth Amendment.
Cognyte sells the technology directly to U.S. agencies, as shown by the Texas Department of Public Safety purchase of four Tahoes where over three point eight million dollars went to the interception equipment. Adoption spreads through routine vehicle procurement with little external review of how the collected data is stored or shared. Once active the systems permit warrantless collection of private cell phone data across entire communities during normal patrols, which enables potential misuse and leaves individuals with no effective way to discover or contest the surveillance.
Chris Fall has resigned as director of the U.S. Center for AI Standards and Innovation just three months after being appointed to lead the Commerce Department’s federal AI testing institute. Arvind Raman, who oversees the Commerce office responsible for the institute, will serve temporarily in the role. “The Commerce Department did not provide a reason for Fall’s departure,” reports Reuters. From the report: Fall’s exit marks the latest change in direction for Trump’s approach to AI. The president upon returning to office in 2025 said the federal government should take a hands-off approach to the tech sector. He has since taken a more active role in monitoring the technology, though his public statements and policies appear to change week by week.
The institute is responsible for working with leading AI labs such as Anthropic, Google’s DeepMind and OpenAI to test their unreleased models for vulnerabilities. The group is staffed by scientists and engineers, who are focused on calculating the “demonstrable risks” posed by advanced AI models, according to the institute’s website. They want to limit opportunities for U.S. adversaries to use AI to develop chemical or biological weapons, or corrupt the data used to train American AI models.
Playing as Naoe puts the focus on stealth, using noise, light and shadow to slip past enemy patrols, while a new grappling hook opens up parkour routes across castle rooftops that were not available in earlier games in the series.
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Naoe’s kit also includes a hidden blade for instant assassinations along with shuriken and smoke bombs to create useful distractions, giving stealth focused players several ways to clear a room without ever triggering an alarm.
Switching over to Yasuke flips that approach entirely, trading stealth for silent bow takedowns and heavy melee combos with a katana or naginata, so a single stronghold can be cleared through patience or brute force depending on your mood.
Switching between the two protagonists mid mission is encouraged rather than locked to separate story chapters, letting you scout a stronghold as Naoe before switching to Yasuke for a more direct assault once guards are alerted.
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Both characters explore the same dynamic version of feudal Japan, where castle towns, ports and shrines shift with the weather and the seasons, giving the world a sense of change that keeps returning to the same location interesting.
And now with a glowing discount, you have the chance to explore feudal Japan in all its glory.
There is no shortage of examples of Major League Baseball attempting to wield overly broad trademarks its obtained to bully others, nor examples of MLB attempting to stretch its trademark rights much further than they go. MLB opposed a trademark for a Brooklyn burger joint on behalf of the Dodgers, a team that hadn’t played in Brooklyn for over five decades at that point. The league, at one point, tried to bully a local Little League for using the names of MLB teams, but not their logos, which is something that roughly every Little League team everywhere does. It attempted to trademark the names of three cities in which MLB teams play. And, my personal favorite and most appropriate for this post, the league opposed a finance company’s trademark application because it claimed two of its separate teams both owned the rights to the letter “W”.
The real lesson in all of this is that the League can’t be trusted with anything other than very narrow trademarks. Anything more broad than that causes them to act the fool. And perhaps this is a lesson the USPTO has actually learned, given that it recently denied MLB’s attempt to trademark the phrase “Play Ball”.
The United States Patent and Trademark Office denied MLB’s application to trademark “Play Ball” for clothing, the USPTO wrote in a final action filing on Friday.
“In this case, the applied-for mark is a commonplace term, message, or expression widely used by a variety of sources that merely conveys an ordinary, familiar, well-recognized concept or sentiment,” the USPTO wrote in its denial.
The USPTO also wrote phrases “that merely convey an informational message are not registerable.”
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Those are things that MLB’s well-dressed lawyers absolutely know, of course. But they attempted to bank on a complacent trademark office to try to sneak one past the goalie anyway, to mix metaphors. And if the league had gotten the mark, you can be one hundred percent certain it would have gone on yet another bullying campaign targeting apparel makers, other sports leagues, and who knows who else.
In fact, the most surprising part of all of this is that it appears to have taken 4 years for the USPTO to reach this decision. Josh Gerben breaks it all down like this.
Gerben said the rejection and public domain nature of phrases could depend on the class. Other companies have trademarked “Play Ball,” including a food company for bubble gum, a minerals company for surfacing playgrounds and “The Play Ball” for the gala fundraiser for the Strong National Museum of Play in Rochester, New York.
“In this case they are saying that the phrase has become so ubiquitous and it has this underlying meaning,” Gerben said. “For a clothing brand, the government doesn’t think it’s unique enough to be registered.”
Somehow, some way, we have to get past this practice of looking at trademarks as some kind of retroactive profit center, where a business gobbles them up and then corners a market that was already in existence. That’s all that this sort of attempt to lock up language is. The term “play ball” can be associated with Major League Baseball, certainly. It can also be associated with other sporting activities, or business negotiations, or any other number of things. That’s because it has become a generic phrase, no longer an identifier of the source of a good or service.
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Again, MLB’s lawyers knew all of this before applying for the mark. They just didn’t care.
A palm-sized drone flies through thick fog, artificial snow, and near-total darkness, dodging poles, transparent plastic sheets, and tree trunks without a single camera or laser. Its only guide is sound. Researchers at Worcester Polytechnic Institute built the system, called Saranga, by copying the way bats find their way in caves. The result is a lightweight, low-power approach that keeps working when vision-based sensors simply stop.
Cameras and LiDAR begin to fail as light becomes dispersed or just disappears. Radar, on the other hand, drains the batteries right when the machines need them. By contrast, ultrasound travels through smoke, dust, and snow in the same way as it does in clean air. Bats have been doing it forever, simply emitting short, high-frequency chirps and listening for faint return echoes that bounce off obstacles. The WPI team, led by Nitin Sanket of the Perception and Autonomous Robotics division, decided to give a flying robot the same superpower.
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They began with a quadcopter that they custom manufactured, measuring 16 cm across and weighing 460 kilos. It has two very tiny TDK InvenSense ICU30201 ultrasound sensors at the front, each with a broad sonic horn. Another one points downward to help with altitude. All of this ultrasound sensing requires only 1.2 milliwatts, and it all operates on a Google Coral Mini computer with no additional beacons or GPS, so there is no extra power expenditure.
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Propeller noise was the first major issue, as the spinning blades are basically spewing out some serious ultrasound noise that drowns out the weak echoes coming back from distant objects (we’re talking minus 4.9 decibels here, which is weak signal territory for the team), so they fixed it by physically taping a simple foam and plastic shield between the propellers and the sensors. This barrier shuts out the majority of the prop noise while allowing outward sound and returning echoes to pass through. With this piece of hardware fixed, the usable range increased from one meter to two meters.
Even after they sorted the prop noise with their shield, the returning echoes were still getting lost in the random noise, so they attempted utilizing classical filters to sort it all out, but it wouldn’t comply. They required something more sophisticated, so they trained a tiny neural network to sort through all the filth. They termed it Saranga (also a neural network), and it basically looks at a brief string of echo readings as if it were a little picture. It uses this to learn the forms of true reflection patterns, after which it can suppress random prop noise. Training employed a lot of synthetic data mixed in with some real propeller noise, so once they had it functioning, the model flowed over to the real world very easily, with no additional fine tuning required. Saranga is then “compiled” to function on the Edge TPU, and it only takes up approximately 0.5 gigabytes of memory and does an inference in around 15 milliseconds while using only a few millijoules of energy.
The cleaned-up echoes are then sent to a basic localization stage, and because the left and right sensors are at slightly different angles, they can determine the horizontal angle to an obstacle in the same way that bats do. The down-pointing sensor then provides the height. It’s all really easy; simply a quick list of surrounding obstacles, and then it’s up to the flight controller to say, “Hey, steer clear of all this while still traveling in that direction.” [Source]
In the Bambu Lab X1 FDM printer, a Lidar scanner was used to scan various test patterns to automatically determine the optimal setting. This was later moved to the purge section of the extruder in newer Bambu Lab printers. On other FDM printers, the only available sensor in that area is typically the pressure sensor for bed leveling. Could this sensor make a similar measurement?
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