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The U.S. Cybersecurity and Infrastructure Security Agency (CISA) has ordered federal agencies to secure their Windows systems against a vulnerability exploited in zero-day attacks. [...]

Sometimes, the hardest part about getting stuff done is simply remembering what you have to do — and when.

And ironically, lots of the tools that exist to help us juggle our endless array of incoming tasks only seem to make it even more overwhelming. Truly, it doesn’t take much for the very act of managing your tasks — or maybe even just figuring out the best way to do it — to become a chore in and of itself.

Like many perpetually perplexed plebeians, I’ve exerted far too much energy on the impossible-seeming task of finding a system for tracking tasks that (a) actually works — and (b) doesn’t feel like a burden of its own. I’ve gone through more tasks and reminders systems than any sane person should ever encounter in a lifetime.

And lemme tell ya: At long last, I’ve encountered one that’s the perfect blend of simplicity and power.

It’s a brand new, off-the-beaten-path Android app you probably haven’t heard of but that absolutely should be on your radar. It’s both easier and more effective to use than most of the big-name tasks apps out there right now — and it almost, dare I say, even makes managing your to-dos enjoyable instead of exhausting.

Lemme show ya how it works.

[Keep the knowledge coming with my free Android Intelligence newsletter — three new things to try every Friday and my Android Notification Power-Pack as a special welcome bonus!] 

A new gold standard for Android reminders

My fellow memory-challenged marsupial, allow me to introduce you to the amusingly named Ruff Reminders.

Ruff Reminders is an Android-first creation that’s only been in the Play Store for a matter of hours now —  though I’ve had the opportunity to use it during its development for the past couple of months, as it’s progressed from a, well, rough framework into a polished and well-rounded place for storing all of your tasks both personal and professional and ensuring you never forget anything.

If the Ruff name sounds familiar, by the way, you might be thinking of the similarly themed Ruff Writing app — which puts a simple scrolling scratchpad right on your home screen for on-the-fly thought storing. I’ve featured it as one of my must-have Android widgets for some time now.

Ruff Reminders comes from the same source — an indie Android app developer named Bardi Golriz — and it exists as a perfect companion to its sibling’s scratchpad concept.

So let’s get into it: When you first open up Ruff Reminders, you’re greeted with a simple screen showing you the current day and a prompt to add any new reminders you need into the mix. The idea is that your focus belongs on the here and now — and starting with what you need to do today is the best way to actually get your tasks accomplished.

Ruff Reminders always starts you with a view of your tasks for the current day.

JR Raphael, Foundry

Creating a new task is as easy as tapping the “quick entry” prompt toward the top of the screen and typing. You can also use the plus icon in the lower-right corner of the screen for a more elaborate and detail-oriented task creation mechanism — and that’s where some of Ruff Reminders’ most impressive powers come into play.

To wit: For any task you create, you can schedule yourself a reminder for…

• A specific date and time
• A dynamic date or time — as in every Monday, every weekend, the first day of each month, and so on
• And (drumroll, please…) a specific location — if, say, you want to be reminded about something when you get to the office, when you get home, or maybe even when you walk into a particular store or business

You can set all sorts of different reminders, including ones based on your physical location.

JR Raphael, Foundry

That last one in particular is a true treat to see. Like many Android-appreciating animals, I’ve been irked by Google’s ongoing retirement of location-based reminders all across the platform — first within the old Google Assistant system and then more recently within Google Keep as well. Ruff Reminders handily fills that void while offering a whole lot of other enticing extras that Assistant and Keep never provided.

For instance: For any location-based reminder, Ruff Reminders gives you the option to have a task pop up when you reach whatever location you specify either within a certain specific timeframe or anytime — and to have that reminder exist only once or as a recurring thing, every time you come or go from the location in question.

On that latter point, you can also set the reminder to trigger when you arrive at your chosen location or when you leave it — and you can choose exactly how wide of a radius the app uses to identify the spot — both of which add a whole other layer of flexibility and potential usefulness into the feature.

Ruff Reminders’ location reminders are especially versatile and powerful.

JR Raphael, Foundry

And all of that is still just the start.

Remembering — and beyond

Once you have tasks created, Ruff Reminders really does work to make sure you remember ’em. In addition to setting all of your own preferred reminder patterns for each new task you create, you can tell the app to always nudge you about still-pending tasks for the present day at specific times as well as to keep “chasing” you with more prominent alarms — even multiple alarms, if you want — for items you haven’t finished.

All of those options exist within the dog-shaped Ruff icon in the lower-left corner of the screen:

width="1024" height="919" sizes="auto, (max-width: 1024px) 100vw, 1024px">Ruff’s “nudges” and “chases” add in even more flexibility and reliability with making sure you never forget anything important.

JR Raphael, Foundry

Once you’ve started a task, one tap on its line tells Ruff Reminders that it’s in progress and marks it accordingly. Another tap starts a full-screen timer (for any length you choose) to help you actually focus on the task. And pressing and holding the task marks it as finished.

You can also double-tap to reset an item’s status, if such a need ever arises.

Marking a task as in progress (left) exposes the option to begin a full-screen focus timer (right), if you want.

JR Raphael, Foundry

If something does still manage to slip by without getting completed, it’ll move down to the app’s command bar, at the bottom of the screen — where it shows up inside a red box with the number of unfinished past tasks front and center.

The Ruff Reminders command bar shows you how many missed tasks are still active and pending.

JR Raphael, Foundry

You can always tap that box to revisit and reschedule any missed tasks — or you can find any past task via the app’s swipe-up-from-the-bottom search system. But even more helpful are the ongoing reminders the app will keep bringing front and center whenever you tell it to keep chasing you about any particular item.

Tapping the double up arrows on a missed task moves it right back into your current “today” view.

JR Raphael, Foundry

What else? Let’s see — for any items you set as “ongoing,” Ruff will create a persistent notification so you can easily see what’s lingering on your list. And as you’d expect for any serious Android productivity app, Ruff Reminders has a widget that lets you look at all your tasks for the current day and add new tasks right then and there, on your home screen, without ever having to open anything up.

Between Ruff Reminders’ persistent notification of ongoing tasks and its home screen widget showing today’s tasks, you’ve got no shortage of ways to keep important stuff front and center.

JR Raphael, Foundry

For the true productivity-obsessed power-user nerds among us, Ruff Reminders also has a whole host of step-saving gestures built into its interface. Like all of the app’s more advanced options, you absolutely don’t have to mess with ’em if you don’t want to — but if you’re the type of person who likes learning shortcuts and flying around your phone with taps and swipes, you’ll be delighted by all the possibilities this unlocks. 

The more you use it, the more thoughtful and useful little touches you keep discovering — again, if and only if you want to explore those types of options.

Gestures galore await for the shortcut adorers among us.

JR Raphael, Foundry

What’s most interesting to me about Ruff Reminders is the space it fills between the everything-style, intensive-need to-do apps out there — things like Todoist or even all-purpose productivity tools like Notion, which are great for the right type of purpose and person but can be overwhelming overkill for more casual task tracking — and the super-simple, at times too-limited apps like Google Keep, which are fine for basic info-dumping but lacking in more powerful task management and reminder magic.

Ruff Reminders manages to be both simple and effective — an often overlooked middle-ground for those of us who want to track tasks and remember stuff in a way that goes beyond the most barebones basic approach but that doesn’t require an entire intricate platform to do it.

Oh, and as far as privacy goes, Ruff Reminder’s policy on that front is also refreshingly simple: It doesn’t collect or process any personally identifiable information. Period.

The app doesn’t have ads, either. Instead, it allows you to use its most fundamental setup for free and offers a paid subscription for its full set of features — three bucks a month or $20 per year, at the moment, with the latter price set to bump up to $30 after a while. (That pricing does also vary by country, so the rates will be slightly lower in certain parts of the world.)

For now, all you’ve gotta do is try it out and see if it works as well for you as it has been for me.

And if you need a helping hand to remind you, I know just the app to get the job done.

Increase your Android intelligence quotient with my free Android Intelligence newsletter — three new things to try every Friday and my free Android Notification Power-Pack today.

cPanel has released security updates to address a security issue impacting various authentication paths that could allow an attacker to obtain access to the control panel software. The problem affects all currently supported versions, according to an alert released by cPanel on Tuesday. The issue has been addressed in the following versions - 11.110.0.97 11.118.0.63 11.126.0.54 11.132.0.29 Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

The U.S. Cybersecurity and Infrastructure Security Agency (CISA) on Tuesday added two security flaws impacting ConnectWise ScreenConnect and Microsoft Windows to its Known Exploited Vulnerabilities (KEV) catalog, based on evidence of active exploitation. The vulnerabilities are listed below - CVE-2024-1708 (CVSS score: 8.4) - A path traversal vulnerability in  ConnectWise ScreenConnect Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

Microsoft is working to resolve a known issue that prevents some Microsoft Teams Free users from chatting and calling others. [...]

Tim Cook’s impending retirement as Apple’s CEO marks the end of an era — the years  when the Apple-versus-Microsoft fight dominated the tech world.

Of course, it’s been a long time since those two companies ruled by themselves. These days, Google, Meta, OpenAI, Anthropic and Amazon are just as influential. Still, Cook’s decision to step down as Apple CEO on Sept. 1 to become chairman of Apple’s board is a good time to revisit the debate: who’s the better leader, Cook or Microsoft CEO Satya Nadella?

Both men faced the unenviable task of replacing larger-than-life company founders and visionaries. Cook took over as CEO at Apple in August 2011, two months before Steve Jobs died. Nadella ascended to the top job at Microsoft in February 2014 after Steve Ballmer stepped down. (Ballmer had replaced founder Bill Gates as CEO in 2000.)

To determine who’s been a better leader — and positioned their companies better for future success — we need to look at the challenges each faced as CEO.

Cook’s and Nadella’s biggest challenges

In the 10 years before Cook took over at Apple, the company saw an unprecedented run of innovation under Jobs, creating products that redefined technology – not only what it could do but also shaping the way people live and work.

The iPod in 2001 launched the digital music revolution. That was followed two years later by iTunes, which completed Apple’s dominance in digital music. The iPhone in 2007 single-handedly created the mobile revolution, along with the follow-on App Store in 2008. That same year, Apple launched the iPad. Then, in May 2011, Jobs unveiled iCloud.

Cook’s two greatest challenges when he took over: make as much money as possible from Jobs’ innovations and continue to create groundbreaking products.

Nadella took over at Microsoft during what has been called the company’s “lost decade.” Quite simply the company had stagnated, launching no new significant products while milking its cash cow, Windows, as much as it could.

In that decade Microsoft couldn’t even handle Windows properly. Windows Vista, launched in 2007, is generally regarded as the worst version of Windows ever. And in  2012, Microsoft released Windows 8, also considered a big miss.

It’s said that, to a man with a hammer, the entire world looks like a nail. The same could be said about Ballmer and Windows: he saw it as the solution to every tech problem that existed. (Gates, by then board chairman, agreed.)

That myopic view led to several disasters, most notably missing out on the mobile phone revolution — even though Microsoft had in 2003 launched Windows Mobile, a smartphone operating system, well before Apple.

Nadella’s greatest challenges were legion: create innovative products, pivot away from Windows, and fix a toxic company culture that had executives spending their energy on turf battles.

How Cook has fared at Apple

By any measure, Cook has been a superb technocratic leader at Apple. His big achievements were in the behind-the-scenes nuts-and-bolts of manufacturing that don’t make headlines, but that helped Apple cash in on Jobs’ many innovations and become a multi-trillion-dollar company.

Most important, he transformed Apple’s supply chain. It was a complicated mess when Cook took over as CEO. He streamlined things by reducing the number of suppliers and manufacturers Apple dealt with. He also put into effect a “just-in-time” delivery of components that the company needed, reducing costs and making the manufacturing process more efficient and more nimble in responding to a fast-changing market.

He also recognized the value of services, transforming iCloud, Apple Music, the App Store, and Apple Pay into financial juggernauts, and rolling out Apple TV+ to critical acclaim. But Cook never succeeded in developing breakthroughs like the iPhone. The Apple Watch has been a hit, but it didn’t change the tech world in the way many of Jobs’ products did. (The jury is still out on the Vision Pro.)

Cook also completely missed out on AI. Apple is so far behind in that area at the moment you can’t even count the company as an also-ran. And that might spell trouble for the future. Making money from aging technologies might not be enough for Apple to remain dominant in the coming years when AI will be king.

Nadella’s tenure at Microsoft

Everyone knew Microsoft was getting a Cook-style technocrat when Nadella became CEO. What surprised everyone — including me — was that it got one of tech’s great visionaries as well.

Nadella took over a moribund company and accomplished one of tech’s greatest turnarounds. He put an end to the company’s divisive culture, de-emphasized the over reliance on Windows, increased its cloud presence, and killed Windows Phone, which had cost the company billions and remained a drag on its success.

Eventually, he reorganized the company to make it cloud-centric rather than Windows-centric, getting rid of a number of problematic executives, including Terry Myerson, who had been executive vice president of its Windows and devices business. Microsoft became a cloud company more than an operating system company.

He also forged a better relationship with customers. Rather than try to ram Windows down their throats at every opportunity, as Gates and Ballmer did, he embraced making Microsoft technology work with competing products. For example, he allowed Linux to run  SQL Server. He also bought the developer platform GitHub, and let it remain open and independent rather than become Microsoft-focused.

Then along came AI. Nadella recognized AI was the future, and by investing in OpenAI, forging relationships with other AI companies including Anthropic, and building an internal powerhouse of AI development, Microsoft became one of the world’s foremost AI companies.

The winner

So who’s been the better CEO? 

Cook took over a company with a wealth of ground-breaking products that had created entirely new markets, and did a great job of milking them for all they were worth. He’s certainly been great at making money for Apple. But he’s leaving behind a company that despite its profitability, faces a problematic future in an AI-first world.

Nadella took over a company whose innovative days were far behind it, rebuilt it from the ground up, killed off money-losing tech like Windows Phone, de-emphasized Windows, and then organized the company around the cloud.

In the last few years, he recognized the potential of AI and has been organizing the company around that. Microsoft is better positioned for the AI future than Apple. 

For those reasons, Nadella comes out on top. 

In yet another instance of threat actors quickly jumping on the exploitation bandwagon, a newly disclosed critical security flaw in BerriAI's LiteLLM Python package has come under active exploitation in the wild within 36 hours of the bug becoming public knowledge. The vulnerability, tracked as CVE-2026-42208 (CVSS score: 9.3), is an SQL injection that could be exploited to modify the underlyingRavie Lakshmananhttp://www.blogger.com/profile/[email protected]

AWS on Tuesday announced an expansion of its partnership with OpenAI and launched a major new agentic AI push with the introduction of a new desktop app for Amazon Quick, a personal AI assistant, and an expansion of Amazon Connect from a single product into four distinct offerings.

News of the enhanced partnership comes 24 hours after OpenAI and Microsoft stated they were changing their contract terms and revising certain exclusivity and revenue sharing conditions.

OpenAI’s agreement with AWS will see the latest OpenAI models, as well as its Codex coding agent, available on Amazon Bedrock, and the addition of new Amazon Bedrock Managed Agents, an AI agent builder for cloud environments powered by OpenAI.

Amazon Connect is being expanded, and will now include Amazon Connect Decisions, Amazon Connect Talent, Amazon Connect Customer, and Amazon Connect Health.

Colleen Aubrey, SVP of applied AI solutions at AWS, wrote in a blog post that the four Connect components “draw on our expertise incorporating agents throughout Amazon’s operations.”

30 years of operational expertise

Igor Ikonnikov, advisory fellow at Info-Tech Research Group, said, “the switching from generic AI-powered solutions to purpose-built and trained AI agents is a common trend, and a good one.”

Having specialized AI solutions, he said, “would increase their reliability and facilitate their adoption. If an AI agent has a well-defined role and feature set, it is easier to onboard it as a new teammate. The quality of the build and the expertise used in model training would be the differentiating factors.”

From this perspective, Ikonnikov said, “Amazon Connect Decisions seems to be an attractive supply chain management solution, as it is built on 30 years of Amazon operational expertise in handling hundreds of millions of SKUs coming from millions of suppliers from all over the world. Another important promise [from AWS]: AI agents comprising Connect Decisions ‘provide complete visibility and transparency into AI recommendations and decision-making,’ something highly commendable (too good to be true, though; I’d like to see it in action).”

The same, pointed out Ikonnikov, “would apply to Amazon Connect Customer AI. It has already been used for years by State Farm, Air Canada, and US Bank, with an impressive scale, complexity, and diversity of customer-oriented solutions.”

Amazon Connect Talent, he added, “is also based on impressive scale and expertise; Amazon claims to have hired 250,000 seasonal employees in 2025 alone. The problem with this specialized solution lies outside of Amazon’s control, but in the nature of the domain, where job applications could be tweaked to fit job requirements and now could also be auto-generated by other AI-powered software.”

Thus, it’s not just scale and expertise in dealing with humans that matter, but the new expertise of dealing with outside AI submitting job applications, said Ikonnikov.

Amazon Connect Health, he said, “is also backed by Amazon’s existing offerings, One Medical and Amazon Pharmacy. The concern about this AI agent stems from the sensitive nature of the health care data and security enforcement around its use, as well as the Connect Health Agent behaviour. It would require a mature control plane, something stricter and more police-like than Bedrock Agent Runtime.”

Turning questions into answers

In addition, AWS predicted in in a release that the Amazon Quick desktop app will “enable a rapid evolution of AI in the workplace. When AI knows you, your team, and your company, it can become an intelligent assistant that turns questions into answers, answers into actions, and actions into outcomes.”

Shashi Bellamkonda, principal research director at Info-Tech Research Group, said, “Amazon is filling a gap that it had with consumer-facing AI. Although they had several Nova models that an AWS user can access, it was on similar lines with Gemini and Claude, but very techie if you wanted to connect to Bedrock and create more functionality.”

He said, “[I am] happy to see that users won’t need an AWS account with Quick Assistant. It can potentially act as a substitute for Claude on the desktop, and if implemented well (and is, as Amazon claims, ‘proactive, has context, does work for you, and learns you over time,’), it can be powerful for both consumers and enterprises offering this to their teams.”

Bellamkonda pointed out, “we all need an agent, and even if Quick Assistant does not displace Claude, Amazon as an investor in Anthropic still benefits. They could potentially displace Microsoft Copilot and ChatGPT and be blessed by IT and security teams. Amazon will have to think user-friendly versus tech-expert audience.”

This is, he said, “a big move by Amazon, from being thought of as an AI platform infrastructure to an AI assistant product that can be used by a larger audience. This could help increase the number of customers who will move to Bedrock as they get familiar with AWS AI products.”

CIOs and CSOs, added Bellamkonda, “will create a sandbox to test this desktop, which listens to and ingests all interactions before introducing it across the enterprise. Amazon showed a large enterprise already using it. For individual users, it will take time for them to adopt this. I have no doubt about the power of this agent. I just wish Amazon made it easier in the last mile for non-tech users.”

Enterprises are quickly moving from AI experimentation to deployment, however, when agentic AI begins making more decisions, invoking more tools, and operating across fragmented data environments, there can be an erosion of visibility, governance, and trust.

SAS laid out its answer to that problem at its annual conference, SAS Innovate, introducing a new family of copilots, agent frameworks, Model Context Protocol (MCP) plugins, and management tools to help enterprises operationalize AI without losing control of it.

“What we’re seeing here is really a shift from AI that forms to AI that acts,” Marinela Profi, the company’s global AI and generative AI market strategy lead, said at the event. “This is a significant leap, because it introduces new requirements around trust, around governance, around accountability.”

Interacting with agents more intuitively

To begin with, SAS today announced SAS Viya Copilot, a human-governed, conversational AI assistant embedded into its Viya platform. It integrates Microsoft Foundry, operating within analytics workflows to help developers, data scientists, and other users instructing it in natural language to analyze data, build models, and make decisions across workflows.

“You have an expert assistant that allows you to take actions, ask questions, and help you navigate across the full analytical lifecycle,” Profi explained.

Its capabilities include: General Q&A across core Viya applications; production of documented and explainable AI-generated code; model pipeline guidance including recommendations and next steps; conversational dashboarding; and visual investigation with AI-assisted search and alert narratives. Copilot capabilities will eventually extend to data management, model management, and AI infrastructure, according to SAS. 

The company is initially launching two Copilots: Asset and Liability Management (ALM), for developing scenarios, executing and interpreting financial risk workflows, and translating natural language inputs into analytic models; and Health Clinical Data Discovery, for analyzing data, creating cohorts, and investigating research papers and other medical documents.

SAS plans to expand Viya Copilot into additional industries, including banking and manufacturing, later this year.

Going beyond embedded AI assistants, SAS is providing tools and infrastructure to connect and govern internal and external agents. The new SAS Viya MCP server standardizes connections so external agents can safely access SAS tools, data, and models, using the large language model (LLM) or interface of their choice (Claude, GPT, Gemini), without having to create custom integrations, duplicate logic, or bypass controls.

“The Copilot is not only answering questions for you, it can invoke capabilities across Viya in a more structured way,” Profi said.

In addition, a new Agentic AI Accelerator provides a collection of code, interfaces, components, and best practices that allow teams across skill levels (developers, low-code or no-code users) to design, build, deploy, and manage agents within SAS Viya, she explained.

Current Viya users can access both the MCP server and AI Accelerator via GitHub.

Maintaining human judgment

SAS continues to emphasize the importance of oversight, trustworthy AI, and human-in-the-loop control.

Furthering this mission, the company is introducing SAS AI Navigator. The Software-as-a-Service (SaaS) tool helps enterprises inventory, govern, and apply policies to underlying AI models.

Available in Q3 2026 on Microsoft Azure Marketplace, the platform will offer an end-to-end view of all AI models and tools in use in an enterprise, whether built in-house or provided by third parties. Using it, enterprises will be able to apply internal policies and external regulations and frameworks to AI use cases.

“It’s giving visibility into your AI inventory,” Reggie Townsend, VP of SAS’ data governance and ethics practice, said at today’s event. “But it also answers the really basic question: How are we doing?”

Enterprises want “enough data at a glance” to consider tension points when they’re juggling factors like reputation, efficiency, and cost, he pointed out. They’re also viewing trust as a new business differentiator, even as a currency. 

Navigator started with a really simple idea, he noted: “What happens if we can make being responsible irresistible?” AI governance is one way to preserve human judgment amidst what he called “tech asymmetry.”

Technology unevenness has been a long-standing problem; While there’s strong technical capability, enterprises struggle to adapt to the pace of change at scale. “What folks need to do is try to translate some of these capabilities into a sustainable business advantage,” said Townsend.

As AI capabilities (and offerings) continue to expand, he urged users to gain “sufficient literacy,” approach AI with curiosity, and think critically about how evolving tools can apply to both business and personal life.

“In an emerging landscape like this, we’ve got to suspend certainty,” he said. “Certainty breeds rigidity, and rigidity suspends this idea of nuanced judgment, which we need right now.”

The next chapter of AI is about scaling that judgment, governing at speed, and turning trust into that competitive advantage, he emphasized.

Getting to the right enterprise data

Enterprise data can be fragmented across many different ecosystems (on-prem, in legacy infrastructure, or in private or public clouds), noted SAS industry market lead Alyssa Farrell. Beyond that, she said, “[enterprises] have low trust in the data itself, which is leading to low trust in decisions.” Further, performance constraints can hamper AI progress.

To address these issues, SAS today announced a targeted refresh of SAS Data Management, its cloud-native portfolio built on the Viya platform, adding or expanding its AI-ready data management, governance by design, agentic AI and copilots, and cloud-native analytic acceleration. It provides lineage, transparency, and control capabilities within workflows where data is accessed, prepared, and activated, Farrell explained.

“Agents and AI crave data more than ever before,” she said. “It’s really important that organizations get this right from the beginning, especially if they’re adding automation to that decision process.”

The re-architected platform grounds AI in trusted data, making raw data assets usable for AI. Notably, it brings analytics and AI to the data itself through SpeedyStore, the company’s cloud-native analytical data platform, negating the need to move volumes of data for processing, Farrell explained. Enterprises still retain digital sovereignty and can control workflows across their various data stores.

“We’re making sure our customers have everything they need to meet this moment [and] tools that access the data, manage the data and gain value from it,” Farrell noted. “They can really proceed at scale to operationalize AI with confidence.”

This article originally appeared on CIO.com.

Researchers are warning that the VECT 2.0 ransomware has a problem in the way it handles encryption nonces that leads to permanently destroying larger files rather than encrypt them. [...]

Hackers are targeting sensitive information stored in the LiteLLM open-source large-language model (LLM) gateway by exploiting a critical vulnerability  tracked as CVE-2026-42208. [...]

Vimeo has disclosed that data belonging to some of its customers and users has been accessed without authorization following the recent breach at the Anodot data anomaly detection company. [...]

When Apple rolled out hardware chief John Ternus as the CEO to replace Tim Cook, the reaction was kind but muted. That’s because Ternus has said nothing yet to indicate he has a specific plan to position Apple for the future. (To be fair, he’s said next to nothing about anything — no easily found social media posts, no big speeches about anything beyond hardware, no major interviews showcasing his vision. 

I have long been a fan of Apple, but the “i” people have a lot of problems. Their failure to make Apple an AI leader — not the leader, just a leader — has dominated headlines for two years now. But the truth is that Apple has spent years without the passion and drive that marked the second coming of Steve Jobs as CEO.

The clearest example involves the iPhone and the Apple Watch. I used to routinely upgrade my devices once a year, or at least every two years. I am sitting here now with an iPhone 13 Pro Max and an Apple Watch Series 7–the same devices I’ve had for almost five years. 

Each year, I’d get excited about Apple’s new devices and look for just one clean reason to upgrade. I didn’t find it. The promise of AI was intriguing, but Apple didn’t deliver. The iPhone camera kept getting better, but my photos look just fine already. 

Apple did deliver one feature that would have made me upgrade: allowing an iPhone to record and quickly transcribe calls. But the company then rolled it out to all devices, meaning it offered little to push new iPhone sales. (Of course, Apple never bothered to tell users the transcription feature has a roughly 30-minute limit. For a guy who often does hour-long interviews, that’s a problem; I’m forced to stop a recording at the 25-minute mark and reactivate it. *Sigh*)

As for AI, I would love for the iPhone to actually be intelligent about all of the data swimming within its case. For example, as a reporter, I have apps for a large number of news organizations. On one election night, I got 16 alerts that a Senate race had been called. I don’t need 16; I just need one. If Apple Intelligence were really intelligent, it would understand that. It should also understand that when I’m driving to an appointment, I don’t need a calendar alert 15 minutes before my meeting when the phone should know — based on my destination and routing in Apple Maps — that I’m on the way.

All those little missteps add up. One of the critical talents a CEO at a company as large as Apple needs is either vision or a passion that can pass for vision. 

This brings us to the inevitable comparison between Jobs and Cook. Jobs was passionate, persuasive, inspirational and he truly had a plan for future products based on his gut feeling of what users would want or need. But Jobs was also undisciplined, harsh, and abrupt and someone who wasn’t always worried about the truth.

He was, therefore, a great business leader, but he had help. (Keep reading for more.) 

Cook was nearly the opposite of Jobs. He was precise, methodical, detail-oriented and he for the most part treated people well and with respect. But his speeches were lackluster and I have yet to meet anyone who dubbed him electric or inspirational. He was privately passionate about his work, but that passion rarely surfaced in public. 

Here’s my point about Jobs’ success: He did so well because he had Cook as a senior deputy. Having the ultimate technocrat in place allowed Jobs to focus on the bigger-picture future. 

There’s been chatter on LinkedIn suggesting that Cook was a weaker CEO than Jobs. There’s a valid argument for that, but many do not give credit to Cook for helping Jobs perform as well as he did. 

Earlier in Cook’s tenure, he did have one executive with a healthy chunk of the Jobs passion: Jony Ive. But Ive got tired of the technocratic nature of his boss and left in 2019 to work elsewhere. Turns out the best leadership duo is a visionary CEO with a technocrat deputy. It doesn’t seem to work the other way around. 

Customers and employees also want to see passion and vision from a CEO directly. And that brings us to the upcoming change.Can Apple under CEO Ternus get its AI act together? That is the big mystery. 

Apple certainly has the money and the clout to make AI work from either side of the buy/build path. But does it have a vision of what customers want — or more precisely, what they need?. Jobs had the knack for correctly guessing what customers would want once they got it, even if they didn’t yet know they needed it. 

Justin Greis, CEO of consulting firm Acceligence and former head of the North American cybersecurity practice at McKinsey, sees Ternus as an executive “who has also [along with Cook] been heads down on execution mode his entire career and he’s an insider. He knows how to keep (Apple) in its lane.”

Greis goes with the crowd in pinning most of his Apple hopes on AI. “If you look at the big AI companies, Apple is not on the map. Everybody is outpacing them. Siri simply doesn’t have the power that is needed to be valuable for their end-users.”

The AI magic is really not about simply using AI on-device. It’s about the value that can be delivered by a sophisticated integration of literally every piece of information coursing through a phone, your watch, a Mac or an iPad. 

A few years ago, people saw Apple as a gatekeeper controlling access to Siri. Back then, the assumption was that access to Siri would be worth tons of money. No longer. Plenty of people now use their iPhone to access generative AI offerings from a variety of Apple’s AI rivals. 

Apple can still win the AI mindshare battle, but only if it can truly deliver intelligent integration of everything that interacts with the phone. That package could be offered solely through Siri, allowing Apple to again control the almighty gateway. Sure, an iPhone user can access Claude or Perplexity — but if only Apple’s knighted partner can analyze your calendar, your contacts, your call history, your travel plans, your bank account, your photos, etc.— companies will again be willing to pay for access. 

That’s where Apple gold lies. The question is whether Ternus can mine it.

The ongoing shift from generative AI (genAI) to agentic AI provides an opportunity for enterprises to move to more nimble and less expensive forms of computing, according to analysts.

Early AI models were largely built on expensive GPUs from Nvidia and AMD that offered raw processing power. But newer agentic AI tools, rooted in business process and workflow management, can run on more efficient, cost-effective hardware.

As a result, IT decision-makers who still think they require GPUs for anything AI-related need to reconsider their hardware options in terms of both cost and capabilities, analysts said.

“A better way of thinking about this is the cost of AI compute and now agentic AI platform services or systems,” said Leonard Lee, principal analyst at Next Curve. “’AI computing’ or ‘accelerated computing’ has clearly transcended the GPU as an inference accelerator.”

The new hardware options include CPUs and specialized AI chips, also known as ASICs in semiconductor parlance. Although these chips have been around for years, they are now showing real utility as agentic AI goes mainstream.

For one, the CPU — the main chip in any computer — is seeing something of a revival. “The CPU is reinserting itself as the indispensable foundation of the AI era. The CPU now serves as the orchestration layer and critical control plane for the entire AI stack,” Lee said.

CPUs are both power efficient and well-suited for AI on the edge, although specialized low-power chips are more capable depending on the task, said Jim McGregor, principal analyst at Tirias Research. “It will still be more efficient to use an ASIC instead of a CPU, and in most cases it will be less expensive over the life of a platform,” he said.

The growth of inference provides an opening for optimized AI accelerators, which can handle those jobs more efficiently than GPUs, said Mike Feibus, principal analyst at FeibusTech. “…The relative importance of [the] CPU is rising.”

Nvidia — sensing that it needed a low-power chip beyond its power-hungry GPUs — has already introduced an ASIC for inferencing in its hardware stack. And it recently licensed AI chip technology from Groq for $20 billion.

Because Agentic AI involves a different computing model than genAI training on GPUs, enterprises need to consider the hardware options and pricing models available through cloud providers. “It’s more about model management than about model building — and the CPU is critical in providing workflow management,” said Jack Gold, principal analyst at J. Gold Associates.

Pricing variations continue to be an issue. Straight CPU compute is not billed the same as heavy GPU use, making it difficult to nail down costs, Gold said. “GPUs in training use more electricity generically due to near 100% utilization in a training workload, whereas in general-purpose compute, servers and CPUs run more like 40% to 60% utilization,” he said. “But it’s highly variable depending on what the agent is doing.”

Gold predicts that 80% to 85% of AI workloads will move to inference in the next two to three years, especially as tools become more agentic. “CPUs take on a major significance in making everything work. It’s why all the hyperscalers are now loading up on CPUs, not just GPUs,” Gold said.

Major cloud providers Google, Amazon and Microsoft , for instance, have their own CPUs and low-power ASICs for inferencing.

What looks at the moment like a resurgence in CPU demand is actually pointing to a larger issue: the growing complexity of AI infrastructure, said Gaurav Shah, vice president of business development and strategic partnerships at NeuReality.

The overhead around data movement, orchestration and networking is exploding, Shah said. “That’s what’s driving demand — not CPUs doing more AI, but systems struggling to keep up with AI,” Shah said.

Beyond enterprises, genAI companies, AI-native companies and neoclouds all will need to rethink their architecture. “The winners will be the architectures that deliver the most inference per watt, not the most cores per server,” Shah said.

Cybersecurity researchers have disclosed details of a critical security vulnerability impacting GitHub.com and GitHub Enterprise Server that could allow an authenticated user to obtain remote code execution with a single "git push" command. The flaw, tracked as CVE-2026-3854 (CVSS score: 8.7), is a case of command injection that could allow an attacker with push access to a repository to achieveRavie Lakshmananhttp://www.blogger.com/profile/[email protected]

A cybercrime group of Brazilian origin has resurfaced after more than three years to orchestrate a campaign that targets Minecraft players with a new stealer called LofyStealer (aka GrabBot). "The malware disguises itself as a Minecraft hack called 'Slinky,'" Brazil-based cybersecurity company ZenoX said in a technical report. "It uses the official game icon to induce voluntary execution, Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

Fleet, the independent, open-source, multi-platform MDM service, recently announced its new partner program for VARs and MSPs serving enterprise customers and recruited MobileIron co-founder Suresh Batchu to serve on the company’s board. With those moves in mind, I caught up with company CEO Mike McNeil to find out more about the Fleet’s plans.

Given the company’s roots in open source, working with partners is a good way to enable it to support a variety of enterprise needs, with resellers and MSPs playing an active role in customizing the core solution for those requirements.

Fleet and the Mac

Fleet is just as happy managing Macs as it is Linux systems and integrates well with existing tools — as long as they support open standards and APIs. This gives it a unique insight into Apple device adoption in the enterprise.

McNeil confirmed that both Apple and Linux systems are seeing rapid increases in deployment. “The new MacBook Neo is now cheaper than comparable PCs, so Apple adoption is increasing, but so are other OS options like desktop Linux,” he said. (Desktop Linux reached 3.16% market share in March, says StatCounter, while OS X hit 9.52% and Windows fell to 60.8%.)

That’s not to say migration to any platform is always easy. “I spoke to an IT director yesterday from a casino company whose team had bought a couple of Neos and tried enrolling them in Microsoft Intune, but gave up,” McNeil told me. This was because they hit an unrelated bug with their traditional MDM, didn’t have great diagnostics to work with, and the IT director then “assumed” that it must be because the Neo wouldn’t work for enterprise use. As it turns out, the issue was with the MDM, McNeil said.

“At Fleet, we’ve enrolled MacBook Neos ourselves with no problems, and seen customers do the same,” he said. “Enterprises are usually mixed OS environments, and [MDM] solutions limited to a single ecosystem, like Jamf that’s Apple only, are pretty restrictive.

Why partnerships matter

“Enterprises are very particular, and they often operate in vastly different ways,” said McNeil. “For example, there are many, many ways to automatically make sure employees can get on to a Wi-Fi network or a VPN on their first day at work.” 

Fleet, he said, works to balance needs between different parts of a company – infosec and IT, for example. “We optimize for baby steps, small iterations,” McNeil said, pointing out that new features are documented and explained as they are introduced.

“The first generation of device management was built for control and compliance,” said Batchu. “The next generation needs to be built for speed, automation, and how modern teams actually operate. Fleet is taking a fundamentally different approach with infrastructure as code and AI-driven workflows, and I’m excited to help shape that direction.

“In 2026, every company needs to do more with less.  Budgets are shifting towards AI and innovation, forcing leaders to extract more value from existing infrastructure. Some IT estates have been around for 20, 30, 35 years, and organizational structures, technical debt, and even entire jobs exist just to keep the lights on. But when you suddenly go from patching monthly to patching in hours, something has got to give.”

He argued that the adoption of a partnership model should help companies move through digital transformation with Fleet while maintaining tight budgets. Partners can help train employees and better understand the context of company need.

It’s also about making sure things are usable. Citing the “Concur” effect, which he describes as a product designed to satisfy high-level stakeholder requirements rather than the needs of those actually using the software, McNeil says he has a “personal vendetta” against complexity in software design.

What will enterprises need?

It’s a move to make every platform easy to manage using powerful tools optimized for the unique needs of customers. “By 2030, IT will need reliable infrastructure that works with the productivity and security tools they’re already using throughout their business.” IT and security teams won’t want separate platforms for each OS or function, and they’ll want to use chat to get projects started. 

AI is a constant. At least one current Fleet customer now has tens of thousands of computers running AI agents and recently gave each of its employees a headless “claw” — a powerful AI agent based on OpenClaw, the free, open-source AI agent software that is accessed via remote computers.

Fleet helps IT recognize the use of shadow AI tools across the business, as well as tracking other app installs, licenses, and use. “So whether you want to find out who’s using the Claude app, who’s using shadow AI tools they shouldn’t be using, or just how many extra, expensive Bloomberg terminal licenses you’re paying for that aren’t actually getting used, you can do that in Fleet, right from your MDM.” 

As McNeil sees it, the emerging AI services environment favors Linux for AI, with other platforms the province of human workers. “I don’t think we’ll see a world where most human users are running desktop Linux in five years, but I wouldn’t be surprised if Microsoft and Apple are neck and neck in the enterprise” by then,” he said.

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