Danny Bolella (Reddit):

If you read the comments on my articles or browse the iOS subreddits, there is a vocal contingent of developers betting that Apple is going to roll back Liquid Glass. […] I shared this exact sentiment with the Apple team.

Their reaction? Genuine shock. They were actually concerned that developers were holding onto this position. They made it emphatically clear that Liquid Glass is absolutely moving forward, evolving, and expanding across the ecosystem.

Their exact warning to me was that those who don’t adopt it now “are gonna find themselves in a tough position later.”

[…]

We had them confirm the hard truth: Xcode 27 will absolutely not have the deferral flag, and it will not respect it if you leave it there, anyway. When Q1 2027 rolls around and Xcode 27 becomes the mandatory minimum for compiling to the App Store, glass will be enabled globally, period.

Jeff Johnson:

What’s truly astonishing about the macOS Tahoe UI is that it’s now been SIX MONTHS since Tahoe was released to the public, yet it’s still full of glaring bugs. […] So many little things are off, out of alignment. It’s like Apple rushed out an alpha version.

Bolella:

The Apple engineers explained that a massive part of the initial Liquid Glass rollout was simply ensuring the foundation was solid. It had to be functional, it had to meet incredibly strict styling guidelines across every single Apple platform, and most importantly, it just had to work.

[…]

The team was visibly enthusiastic about what is in store for WWDC26 and Xcode 27. While they wouldn’t drop any specific spoilers, they gave the very strong impression that this upcoming cycle is where Liquid Glass takes its first massive step into maturity.

Jeff Johnson:

This is the Safari search field on Tahoe. Notice the position of the clear button.

John Gruber:

Perfect MacOS 26 Tahoe screenshot from the Journal app. Apple shipped this.

Simon B. Støvring:

Liquid Glass is a catastrophe.

Dave Mark:

I have SO many examples of this. Text fields that are cut off, text color choices that render text completely unreadable. In this regard, Apple design has lost the thread.

Leon:

the thing about modern Apple UI is they go for some deeply flawed vision that seems developed in a vacuum away from third parties, accessibility experts and engineers, and then when that fails they water it all the way down until people say “huh okay this isn’t that bad any more”

it just lurches from catastrophe to milquetoast and back again, with most of the time firmly in milquetoast territory

what i’d love, love to see is them - or anyone - come up with is a system vision that bakes in accessibility and pro / studio app design first.

Previously:

• Whither Liquid Glass?
• 2025 Six Colors Apple Report Card
• Building Zavala 4.0
• NetNewsWire 7
• Sebastiaan de With Rejoining Apple
• Liquid Glass Disbelief
• Alan Dye Leaving Apple for Meta
• Shipping Liquid Glass

Update (2026-03-26): CrabQueenInc:

Disheartening but not surprising. I feel so fortunate that I’m not in need of a new computer, but at least my options will be wide open when I get to vote with my wallet next time, as it were.

Jesper:

You need only live with Liquid Glass for a short while to see places that even the most glowing critic, who accepts at face value the intent behind the changes, would agree it completely drops the ball. Odd margins, nonsensical visual weight, hard to read text, constantly shifting dark-to-white-to-dark-again backgrounds, blurry messes. There is no part of Liquid Glass that “just works”, and Apple’s insistence on its excellence is what is so deeply concerning about the situation.

[…]

I guess hope springs eternal that the reason they don’t see the problem with it is that the second phase brings back some of the things that are so dearly missed, and that the people in charge of it has always seen it as part of the proposition.

[…]

The initial rollout would have to have been very rushed for this to not have been part of the first version, but a rushed rollout is exactly the kindest way to explain its current state.

CM Harrington:

It’s like they never used a mac before.

Nick Heer:

Regardless of whatever one thinks the visual qualities of Liquid Glass, the software quality problem is notable there, too. We are now on the OS 26.4 set of releases and I am still running into plenty of instances with bizarre and distracting compositing problems. On my iPhone, the gradients that are supposed to help with legibility in the status bar and toolbar appear, disappear, and change colour with seemingly little relevance to what is underneath them. Notification Centre remains illegible until it is fully pulled down.

See also: Hacker News.

Quinn:

There may be other outstanding issues, but you can now:

• On a macOS 15 or later host, install macOS 15 or later in a VM.
• On the guest, log in using an Apple Account in System Settings.
• And install Xcode.
• And add your Apple Account to Xcode.
• And then build and run a Mac app.
• Even if it uses a restricted entitlement.

Via Craig Hockenberry:

It’s a momentous day for Mac developers: you can now provision a device running in a VM. Whether you use VirtualBuddy, UTM, or another app, Xcode can now build, run, and debug apps on multiple versions of macOS without having to reboot.

This includes apps that have entitlements for iCloud and other Apple services.

[…]

Essentially, we now have a setup like the iOS simulator where you can work on two versions of the OS simultaneously. That’s a huge boost for developer productivity!

[…]

One thing you cannot do in a VM: use your Apple ID to login to the Mac App Store. So no testing your shipping app version.

This also means you can’t run TestFlight builds, because you can’t download the app from the store.

Previously:

• Xcode 26
• Unable to Update Virtualized macOS
• M4 Macs Can’t Virtualize Older macOS
• Securing Virtual Machines on Apple Silicon
• Sequoia Beta 3 VMs Don’t Support Mac App Store
• Using Apple Accounts With macOS Virtual Machines
• Limitations on macOS Virtual Machines

Few terms are as closely associated with AI hype as artificial general intelligence, or AGI. But Google DeepMind researchers have now proposed a framework that could more concretely measure how close models are to this tech industry holy grail.

Artificial general intelligence refers to a mythical AI system that can match the general and highly adaptable form of intelligence found in humans. As the number of tasks that large language models can tackle has rocketed in recent years, there’s been a growing chorus of voices suggesting the technology is creeping ever closer to this threshold.

But so far, there’s been no clear way to assess progress toward AGI, leaving plenty of room for speculation and exaggeration. To address this gap, a team from Google DeepMind has introduced a new cognitively inspired framework that deconstructs general intelligence into 10 key faculties. More importantly, they propose a way to evaluate AI systems across these key capabilities and compare their performance to humans.

“Despite widespread discussion of AGI, there is no clear framework for measuring progress toward it. This ambiguity fuels subjective claims, makes it difficult to track progress, and risks hindering responsible governance,” the researchers write in a paper outlining their new approach. “We hope this framework will provide a practical roadmap and an initial step toward more rigorous, empirical evaluation of AGI.”

This isn’t DeepMind’s first attempt to clarify the term. In 2023, the company proposed separating AI systems into different levels of capability, in much the same way self-driving systems are categorized.

But the approach didn’t really propose a way to measure what level AI systems have reached. The new framework goes further by building a firmer conceptual footing for the key aspects underpinning model performance and a practical way to evaluate and compare systems.

Digging through decades of research in psychology, neuroscience, and cognitive science, the researchers identify eight basic cognitive building blocks that they say make up general intelligence.

These include the perception of sensory inputs and generation of outputs like text, speech, or actions. Add to those learning, memory, reasoning, and the ability to focus attention on specific information or tasks. Rounding out the list are metacognition—or the ability to reason about and control your own mental processes—and so-called executive functions, like planning and the inhibition of impulses.

The researchers also outline two “composite faculties” that require several building blocks to be applied together. These are problem solving and social cognition, which refers to the ability to understand and react appropriately to the social context.

To judge how well AI systems perform on each measure, the researchers suggest subjecting them to a broad suite of cognitive evaluations that target each specific ability. They also propose collecting human baselines for each task. This would involve asking a demographically representative sample of adults with at least a high school education to complete them under identical conditions.

The results of these tests can then be combined to create “cognitive profiles” that give a sense of a model’s strengths and weaknesses. And by comparing the results against the human baselines, it should be possible to determine when a system matches or surpasses the general intelligence of an average person.

Crucially, the framework focuses on what a system can do rather than how it does it, which means the evaluation is agnostic about the underlying technology. However, the researchers concede that there is currently no good way to measure many of the core cognitive capabilities identified.

While there are already well-established benchmarks for faculties like problem solving and perception, there are no reliable tests for things like metacognition, attention, learning, and social cognition. In addition, many of the best benchmarks are public, which means the testing criteria are easily accessible and may have already been included in model training data. So the authors say they’re working with academics to build more robust, non-public evaluations to fill the gaps.

How useful the new framework will be depends on several factors. First, it remains to be seen whether the criteria identified by the DeepMind team truly capture the essence of human general intelligence. Second, they need to prove that acing this test actually leads to better performance on practical problems compared to narrower, specialist AI systems.

But considering the hand-waving nature of the debate around AGI so far, any framework grounded in well-established cognitive theory and rigorous evaluation represents a significant step forward.

The post Google DeepMind Plans to Track AGI Progress With These 10 Traits of General Intelligence appeared first on SingularityHub.

Eclipse Memory Analyzer (MAT) can be a bit tricky to use when you’re trying to identify which part of your code produced an .hprof file or caused a memory leak. The steps below help you quickly locate the objects responsible for excessive memory usage.

1. Start with review the Leak Suspects Report for likely culprits

Open the Leak Suspects Report to see automatically detected memory‑leak patterns. MAT highlights the largest retained objects and shows the reference chains that keep them alive. This helps you identify the classes, collections, or static fields in your code that are most likely causing the leak.1

It might help you to find the code responsible for the leaks with the stacktrace information.

2. It it does not help try with the Histogram

Open the Histogram view to identify the largest objects in memory. This view helps you spot suspicious classes or instances that may indicate a leak.

3. Then click “Path to GC Roots” and enable soft/weak references

This shows you how the selected objects are being kept alive in memory, even through soft or weak references.

4. CHoose class name with high retained heap

Then right‑click it and select Java Basics → Thread Stack. This helped me identify the issue and stacktrace responsible for the memory leak.

5. Based on a threadname find a corresponding class name and his retained heap

If you suspect a specific thread from your logs (e.g., http-nio-8080-exec-1) is involved in a memory leak, you can use MAT to inspect what that thread is retaining.

1. Open the Dominator Tree This view shows which objects retain memory and how much.
2. Filter by the thread name Use the filter box and type the thread name exactly as it appears in your logs:Codehttp-nio-8080-exec-1 MAT will show the corresponding TaskThread instance:Codeorg.apache.tomcat.util.threads.TaskThread @ <address>
3. Select the thread object This object represents the actual Java thread.
4. Check its Retained Heap The Retained Heap tells you how much memory would be freed if this thread were garbage‑collected.
   • A small retained heap (tens of KB) is normal.
   • A large retained heap (MBs) suggests the thread is holding references to large structures (e.g., request buffers, thread locals, classloaders, caches).

If you’re considering Microsoft 365 Copilot Business, or planning your next renewal, now is the time to act.

Starting July 1, 2026, Microsoft will update pricing for Microsoft 365 + Copilot Business bundles. Purchasing now lets you lock in today’s lower prices and take advantage of limited time promotional discounts available through June 30, 2026.

Built for SMBs: Microsoft 365 + Copilot Business

When we introduced Copilot Business last December, we designed it specifically for small and medium-sized businesses. Alongside it, we launched simplified Microsoft 365 + Copilot Business bundles, making it easier and more affordable for SMBs to get a complete, secure AI solution.

Copilot Business brings the power of AI directly into the Microsoft 365 apps your team already uses: Word, Excel, PowerPoint, Outlook, and more.

• Draft and summarize content fast
• Analyze data and uncover insights
• Automate everyday tasks
• Work confidently with built-in security, privacy, and admin controls

Pricing Update Effective July 1, 2026

Beginning July 1, Microsoft 365 pricing will increase for some products, while Copilot Business remains $21 per user, per month.