macOS Stuck on Checking for Updates? 4 Solutions

macOS Stuck on Checking for Updates? 4 Solutions

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macOS Stuck on Checking for Updates? 4 Solutions

Users often experience failure when updating macOS to the latest version. Usually, you never find difficulties updating the operating system on your Mac, but sometimes the process takes longer than usual. When your macOS stuck on checking for updates, you will see a spinning wheel or a freezing screen indicating that the update process is not responding. 

If you are stuck on the same screen, there is a problem, and you need to fix it to proceed further. Updating your computer is important as it assures that the Mac is running smoothly. The updates come with security patches that keep the computer secure from online threats. The following solutions will help you get rid of the problem efficiently. 

First, try restarting your Mac. This will sometimes clear up any stuck processes and allow the update to continue. If that doesn’t work, you can try resetting the Mac’s NVRAM. This will help to clear any corrupt data that may be causing the update to get stuck.

Finally, if neither of those solutions works, follow the steps below:

Let’s first discuss the reasons that might be causing the “Checking for updates…” error. 

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Reasons for “Checking for updates” Error: 

A couple of reasons can be responsible for the freezing screen resulting from a stuck macOS update. Some of these reasons include the following:

  • Unstable or slow internet connection
  • Third-party software conflict
  • Unresponsive Apple server
  • Lack of storage space on Mac
  • Any internal issue interfering with the update

Let’s now discuss the ways to resolve the macOS update-related issues.  

Ways to Fix “Checking for Updates” on macOS

macOS Stuck on Checking for Updates? 4 Solutions
macOS Stuck on Checking for Updates? 4 Solutions

Before you initiate any of the given solutions to fix the persisting problem, make sure you have a backup of data stored on the computer. Sometimes, the problem occurs because you might not be following the update process correctly. 


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New users must learn about updating the Mac operating system before initiating the process. For a detailed guide on downloading and installing macOS, visit https://setapp.com/how-to/download-and-install-old-mac-os-versions

Some of the resolution procedures to fix macOS stuck on checking for updates errors are easy, while others demand sound technical knowledge. 

Check Your Internet Connection

As mentioned above, poor internet connection can be the most common reason for freezing updates windows on macOS. Check if your internet is running fast and if you are getting the right bandwidth. Else, your computer will fail to reach the update server, and you will not be able to download the update-related files. 

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Check your router to ensure you are getting internet signals appropriately. Reset your router by turning it off and then on after waiting for 20 seconds. You can also try to reset network settings for more advanced resolution. Before you reset your network settings, disable the antivirus software first. 

Check Storage Space

macOS update files can be big, so make sure you have sufficient space available to accommodate these updates. To check the storage on your Mac computer, navigate to the Apple menu, click About This Mac, and tap on the Storage tab. You can identify the files that are no longer needed and remove them from the storage media. 

Some of the apps stored on your computer consume significant system resources while doing nothing. So, make sure to delete these apps to make space on the Mac storage media. While deleting apps manually is cost-effective, professional app uninstallers can help delete app-associated files completely from the computer.  

Check Apple Server 

Implementing this solution is possible only if the internet is running smoothly. On facing the “Checking for updates” error, you need to check that no outage exists on Apple’s side. If the Apple server is experiencing downtime, you can check this by visiting the Apple System Status page and clicking the macOS Software Update option. 

If a green icon is displayed next to the macOS Software Update, it indicates that there is no outage. If there is a problem with Apple Server, you will see a red dot displayed with all the options on the System Status page. You can also check your internet speed before checking the server status to ensure the smooth running of the process. 

Boot Mac in Safe Mode

macOS Stuck on Checking for Updates? 4 Solutions
macOS Stuck on Checking for Updates? 4 Solutions

Restarting your Mac computer can fix major issues, so it is recommended that you must do it once before initiating any other workaround. If a restart doesn’t solve the problem, boot your computer in Safe Mode. Running your Mac in Safe Mode, also known as diagnostics mode, will clear all the cache files and check the system. 

If the Mac updates install successfully in Safe Mode, restart the computer and continue using it normally. On the other hand, if you still see macOS checking for updates errors, try to update your Mac again. It is worth mentioning the procedure to enter Safe Mode differs in Intel and Silicon-based Macs, so keep that in mind. 

Conclusion 

Updating the Mac is easy unless your computer is in good condition, the internet is working properly and there’s enough storage space available for macOS update files. Once you face any issue, the process becomes cumbersome. 

Apart from the above mentioned fixes, you can try deleting macOS install data and resetting the Software Update preferences to check if these procedures can solve the problem.

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Will No-Code and AI ever make us all software developers?

Will No-Code and AI ever make us all software developers?

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Will No-Code and AI ever make us all software developers?

 In recent years, there has been a growing trend of “no-code” platforms and tools that allow users to create complex applications without a single line of code. At the same time, artificial intelligence is becoming more and more advanced, capable of completing tasks that were once considered impossible for machines to do. So, the question is: will no-code platforms and AI ever make us all software developers? Let’s take a look.

The Pros of No-Code Platforms
No-code platforms have a number of advantages. First and foremost, they lower the barrier to entry for people who want to create software but don’t have the skills or knowledge to do so. Second, they allow for rapid prototyping and iteration; with no need to write code from scratch, you can quickly put together a working prototype of your idea and make changes on the fly. Finally, no-code platforms can be a great way to learn about coding; by using them, you can get a feel for how coding works and what it’s like to work with code, without actually having to write any code yourself.

The Cons of No-Code Platforms
Of course, no-code platforms also have their disadvantages. One major downside is that they can be inflexible; if you want to add a new feature or make a change that’s outside the scope of what the platform allows, you’re out of luck. Additionally, no-code platforms can be expensive; while there are some free options available, many of the best no-code platforms come with a hefty price tag. Finally, because no-code platforms remove the need to write code, they can foster a false sense of security among users who think they know more about coding than they actually do. This can lead to problems down the road if those users ever need to hire a developer or collaborate with one on a project.

The Pros of AI
When it comes to artificial intelligence, there are also some clear advantages. First and foremost, AI is incredibly fast; it can process large amounts of data much faster than any human could hope to. Second, AI is unbiased; because it relies on data instead of human opinion, it can make decisions that are free from personal biases or prejudices. Finally, AI is scalable; as more data is fed into an AI system, it only gets smarter and more accurate over time.

The Cons of AI
AI also has its drawbacks. One major downside is that AI systems require a lot of data to function properly; without enough data points, they simply won’t work. Additionally, AI systems can be opaque; because they rely on complex algorithms that are often inscrutable even to their creators, it can be difficult (if not impossible) to understand how or why an AI system came to a particular decision. Finally, AI systems can be brittle; if something changes in the real world (e.g., a new law is passed), an AI system might not be able to adapt quickly enough and could become obsolete overnight.

The No-Code Movement
No-code platforms like Bubble, Webflow, and Carrd have been gaining in popularity in recent years. And it’s not hard to see why; they allow users to create complex websites and apps without a single line of code required. All you need is a basic understanding of how the platform works, and you can build just about anything you can imagine.

Of course, there are some limitations to what you can do with no-code platforms. They’re not quite as flexible as traditional development environments, so if you want to do something truly unique or complex, you’ll likely need to hire a developer to do it for you. But for most people, no-code platforms are more than sufficient for their needs.

Will No-Code and AI ever make us all software developers?
The Rise of “No Code”. I built my first website with… | by Ryan Hoover | Medium

AI-Powered Development Tools
In addition to no-code platforms, there are also a number of AI-powered development tools that allow non-developers to build software without any coding required. These tools range from simple website builders to full-fledged app development suites. And while they’re not quite as easy to use as no-code platforms, they’re still relatively user-friendly and require no coding knowledge whatsoever.

The Future of Software Development?
So will we all be software developers in the future? It’s hard to say for sure. No-code platforms and AI-powered development tools are certainly making it easier for non-developers to create complex websites and apps. But whether or not that will lead to everyone becoming a developer is impossible to predict. Only time will tell.

This is an interesting take from Harry Dewulf on Quora:

Have you ever heard of Microsoft Query?

Microsoft Query (in several forms) still exists and is still in use today. It’s the living embodiment of the infamous definition of madness oft (wrongly) attributed to Einstein that it’s doing the same thing over and over, and expecting different results.

Will No-Code and AI ever make us all software developers?
Will No-Code and AI ever make us all software developers?

There’s a reason people keep doing the same thing, even though it doesn’t give the intended results. Some strategies just look so much like they will solve the problem that it’s almost impossible to believe that they don’t. So people prefer to disbelieve the results than believe the solution can’t work. This isn’t insanity. It’s a perfectly sensible feature of inductive reasoning which is that the more times you try, the more likely you are to get the result you want. You’d be insane to argue with that. So you have to do quite a difficult (for many people, it seems) piece of reasoning: separate strategies and techniques susceptible to improvement by practice from those that are not.

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Of course, in the case of Microsoft Query, there’s a powerful economic motivator.

People want to believe that you can carry out analytical operations on data without having to use computer code.

The idea of this is just sufficiently removed from simple repetition of the same failed strategy that many people will never realise that they are repeating the same action expecting different results.


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The idea is that it’s easier to define complex relationships between data structures visually than verbally.

The theory being that drawing lines between visual representations of tables is easier than writing the words of an SQL statement.

People who don’t routinely work with data genuinely think that the choice is between the visual and the verbal, and they imagine that data analysts visualize data, so it must surely be easier to represent those visualizations directly, right?

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The problem is that although we often call it a visualization, when you are imagining a data structure…

… okay. This is going to get weird but come with me on it.

The set of all integers is a one-dimensional space.

Add a dimension and you get a graph with coordinates that we normally represent as two numbers. Those two numbers give a location on a flat plane. So you can plot points on a graph, and maybe join them up with your choice of line of best fit.

Add a dimension and you get 3D. You can still just about represent that as an image, if you have a good understanding of the mammalian visual system. You know about perspective, right?

So what if you need a fourth dimension?

Coordinates in 1 dimensional space are expressed like this: 5

Coordinates in 2 dimensional space like this: 2,5

Coordinates in 3 dimensional space like this: 1,2,5
… and if you’re with me so far, you’ll know that the first two numbers refer to locations horizontally, and the third vertically. 3D printers, CAD programs, Blender, 3 point geometry, vectors in three dimensional space, etc.

All this can be represented by images that look meaningful to the human visual system, which is handy, because that’s what they are for.

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But typical data structures can have dozens of dimensions.

Even the simplest ones usually have more than 4. Consider the database that underlies every Enterprise Management System. It has tables for products, clients and orders. You’ve already used up 3 dimensions right there. Supposing you need to create a proposal that demonstrates your ability to provide a subset of your product range to multiple client locations, taking account of vendor and resource availability and seasonal variations? That sort of thing is child’s play compared with evaluating the data from a clinical trial to determine if a new medical device is safe and effective, yet you’re already working with a minimum of five dimensions.

So sure, when thinking about data structures, we frequently “visualize” them, but not as cute 2d images that seem like 3d images “projected inside your mind.” Visualizing data structures so you can design queries for complex datasets is sometimes so difficult it has to be done iteratively. Processing time becomes a factor of query design. You can finish up with a sequence of queries, each of which is multi-dimensional.

Often, the only way to describe them is through the SQL statements that represent them. There will NEVER be a way of representing that as images, because the best that images can ever do is be 2 dimensions that fool your visual system into thinking there are 3 dimensions of space and one of time.

Microsoft Query persists because there will always be people who wishfully think that they can learn to analyse data without learning the “texty part;” without learning to process data (these days, mostly with Python it seems) and without learning to query data with SQL.

No-Code and low-code are wishful thinking except when they are teaching aids (as such, I will admit, they can be a good early stepping stone).

So no, no-code will manacle you, and you’ll love your manacles right up until you need to reach for the door handle.

Conclusion:
So what’s the verdict? Will no-code platforms and AI eventually make us all software developers? It’s hard to say for sure. However, one thing is certain: both no-code platforms and AI have their pros and cons. As such, it’s important to weigh those pros and cons carefully before deciding whether or not either one is right for you.

No one can say for sure whether or not the rise of no-code platforms and AI-powered development tools will ultimately lead to everyone becoming a software developer. But one thing is certain; these trends are making it easier than ever for non-developers to create complex websites and apps without any coding required. So whatever the future may hold, one thing is clear; the landscape of software development is changing, and changing fast.

With the rise of no-code platforms and AI, some people are wondering if we’ll ever see a future where everyone is a software developer. While it’s true that these technologies have made it easier than ever to create digital products, there are still some limitations that prevent them from becoming ubiquitous. In this blog post, we have explored the reasons why no-code and AI probably won’t make us all software developers—at least not anytime soon.

1. No-code platforms still require some technical knowledge.
2. AI is still in its early stages and has a long way to go before it can replace human developers.
3. The demand for software developers is still high, and there aren’t enough no-code/AI solutions to meet that demand.

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What are the top 10 Wonders of computing and software engineering?

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What are the top 10 Wonders of computing and software engineering?

Computer science and software engineering are fascinating fields that continue to evolve and surprise us. Computer science and software engineering are disciplines that are essential for the modern world. They have led to the development of many innovative products and services that have made our lives easier and more efficient. In this blog post, we’ll explore the top 10 wonders of computer science and software engineering.

The things Alan Keys  found to be astonishing and amazing (and shocking) are:

  1. Turing’s notion of machines that can simulate machines completely by interpreting their descriptions (exhibiting the programmable computer as “a language machine” and a “meta-language machine” — along with this is the simplicity of what is required to do so (a great book is Marvin Minsky’s “Computation: Finite and Infinite Machines”). Turing’s approach is much more of a “real CS” approach compared to Goedel’s earlier methods, and soon led to a large number of important next steps.
  2. How simple (a) it is to design a whole computer from just one kind of logical element (e.g. “NOT-BOTH”), especially when compared (b) to how Russell and Whitehead struggled to “bootstrap mathematics, etc., from logic at the turn of the last century. (This is one of those “Point of View is Worth 80 IQ Points” …)
  3. Lisp, and McCarthy’s general approach to “mathematical theories of computation” and having languages that can act as their own metalanguage. One of the great cornucopias of our field.
  4. Sketchpad by Ivan Sutherland for so many reasons, including: the approach to interactive computer graphics and the simulations of the graphic relationships, the “object-oriented” approach to definition and deriving new kinds of things (including “masters” and making instances from masters), enormous virtual worlds that are windowed on the display, the use of goal-directed programming with the system solving the simultaneous goals in real-time, etc. And more, including the demonstration that a simulated computer on a computer need look nothing like the underlying hardware or any “normal” idea of “computer”.
  5. The big Shannon et al. ideas about how to have imperfect things be organized in systems that are much more perfectly behaved even if the organizational mechanisms are themselves noisy. Includes all forms of “noise”, “representations”, “communications”, “machines”, etc. and poking deeply into Biology and how living things work. Nice implications for “stochastic computing” of many kinds which are needed more and more as things scale.
  6. The deep implications of “symbolic computation (now a very un-funded area) for being able to move from the trivialities of “data” (no matter how voluminous”) to the profundities and powers of “Meaning”. This used to be called “AI” and now has to be called “real AI” or “strong AI” (it would be much better under a less loaded term: how about “Flexible Competence”?)
  7. The Internet. Certainly the best thing done by my research community, and the first real essay into the kinds of scaling and stabilities that all computer science should be trying to understand and improve. This was a great invention and development process in all ways, and — by looking at Biology, which inspired but we really couldn’t use — it had a reasonable chance to work. That it was able to scale stably over more than 10 (maybe 11) orders of magnitude, as indeed planned, is still kind of amazing to me (even though it should have). Judging from most software systems today not being organized like the Internet, one is forced into the opinion that most computerists don’t understand it, why it is great (and maybe don’t even think of it as the fruits of “real computer science” because it just works so much better and more reliably than most other attempted artifacts in the field).
  8. Application: #1: Self-Driving Cars
    Self-driving cars are one of the most hyped technologies of the past few years. And for good reason! These autonomous vehicles have the potential to drastically reduce accidents and improve traffic flow. While there are still some kinks to be ironed out, it’s only a matter of time until self-driving cars become the norm.
  9. Application: #2: Artificial Intelligence
    Artificial intelligence is another technology that is rapidly evolving. AI is being used in a variety of ways, from personal assistants like Siri to chatbots that can carry on a conversation. As AI gets more sophisticated, its capabilities will only continue to grow.
  10. Application: #3: Virtual Reality
    Virtual reality is another exciting technology with a lot of potential. VR has already been used in a number of different industries, from gaming to medicine. And as VR technology gets more advanced, we can only imagine the new and innovative ways it will be used in the future.
  11. Application: #4: Blockchain
    You’ve probably heard of Bitcoin, the digital currency that uses blockchain technology. But what exactly is blockchain? In short, it’s a decentralized database that can be used to store data securely. Blockchain is already being used in a number of different industries, and its applications are only growing.
  12. Application: #5: Internet of Things
    The internet of things refers to the growing trend of interconnected devices. From your phone to your fridge, more and more devices are being connected to the internet. This allows them to share data and makes them easier to control. The internet of things is changing the way we live and work, and there’s no doubt that its impact will only continue to grow in the years to come.
  13. Application: #6: Data Science
    Data science is a relatively new field that combines statistics, computer science, and domain expertise to extract knowledge from data. Data science is being used in a variety of industries, from healthcare to retail. And as data becomes increasingly abundant, data scientists will become even more important in helping organizations make sense of it all.
  14. Application: #7: Machine Learning
    Machine learning is a subset of artificial intelligence that allows computers to learn from data without being explicitly programmed. Machine learning is being used in a number of different ways, from fraud detection to object recognition. As machine learning algorithms get more sophisticated, they will continue to revolutionize the way we live and work.
  15. Application: #8 Cybersecurity : Cybersecurity is a critical concern for businesses and individuals alike. With so much of our lives taking place online, it’s important to have measures in place to protect our information from hackers and cyber criminals.

These are just some of the many wonders of computer science and software engineering! Each one has the potential to change our world in amazing ways. We can’t wait to see what else these fields have in store for us!

What are the top 10 Wonders of computing and software engineering?
What are the top 10 Wonders of computing and software engineering?

Other notable wonders of computing:

#16 Mobile phones are handheld devices that allow us to make calls, send texts, and access the internet while on the go. They have become an indispensable part of our lives and have transformed the way we stay connected with others.

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#17 Social Media
Social media platforms like Facebook, Twitter, and Instagram have changed the way we interact with each other. They provide us with a space to share our thoughts, feelings, and experiences with friends and family members who might be located anywhere in the world.

#18 Cloud Computing
Cloud computing is a model of computing that allows users to access data and applications over the internet. It has made it possible for businesses to operate more efficiently by reducing their reliance on physical infrastructure.

#19 Big Data
Big data refers to large data sets that can be analyzed to reveal patterns and trends. It is being used by businesses in a variety of industries to make better decisions about everything from product development to marketing strategies.


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#20 Augmented Reality Augmented reality is a type of technology that overlays digital information on real-world objects. It has many potential applications, including education, gaming, and navigation.

#21 3D Printing 3D printing is a process of creating three-dimensional objects from a digital file. It has revolutionized manufacturing by making it possible to create customized products quickly and easily. These are just some of the things that computer science and software engineering have made possible! As you can see, these disciplines have had a major impact on our world and will continue to shape the future as we move into the digital age.

Conclusion: So there you have it! These are the top 10 wonders of computer science and software engineering according to me. Do you agree with my list? What would you add or remove? Let me know in the comments below!

Addendum:

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In the spirit of choosing artifacts over ideas, I would replace “symbolic computation “ with Unix.

I’ve mentioned elsewhere that one can praise in a sentence or two, but criticism ethically demands more careful substantiation.

All I’ll say here is that when Unix was being invented Xerox Parc was already successfully making systems with dynamic objects that required no separate OS layering or system builds. That said, Doug McIlroy did find the start of what could have been really fruitful ideas when he implemented “pipes” programming. If they had seen what could have been done if they had reduced the process overhead to zero bytes, and gone to a dynamic language, then something great could have resulted. By Alan Kay

What do you mean by organizing software systems like the internet?

Just to get you started: consider that the Internet’s (a) processes do not have to be stopped to fix, change, add to etc. (b) messages are not commands (c) units of computation are perfectly encapsulated (only the interior code code of a computer can decide to do anything or nothing) (d) units of transmission can be very badly damaged and messages will still get through, (e) scaling is more than 10 orders of magnitude (f) and on and on and on.

What SW systems to you know of that are remotely like this (that don’t depend intrinsically on what is wonderful about the Internet)?

This doesn’t mean the Internet is a perfect design at all. For example, the add-on of DNS was not nearly as good and lasting a scheme as the base semantics of TCP/IP. (It’s crazy that there are not unique IDs for every kind of entity manifested within the Internet system. Bob Kahn has been advocating this for several decades now — Joe Armstrong among others has pointed out that the modern hashing schemes (SHA256, etc.) are good enough to provide excellent unique IDs for Internet entities, etc.)

But the Internet did completely raise many bars qualitatively and astonishingly higher. It should be used as a starting point in thinking about how most SW systems can and should be organized.

Just a note about this big shift in thinking within the ARPA/Parc community — it is hard to pin down just when. But Wes Clark used to say that no computer is any good if it can’t work perfectly with 10% of its wires disconnected! Packet Switching (American version at RAND ARPA project in early 60s by Paul Baran) meant that you could do store and forward with a number of routes. If you made the protocol full-duplex, you could guarantee *eventually perfect* delivery of packets. At Parc the huge shift from “theoretical” to “punch in the face reality” came as we realized just how wonderfully well the extremely simple Ethernet was actually performing. This led to the articulated idea that no computation should ever require having to be stopped in order to improve/change/etc it.

In other words, make the systems design such that things will work “eventually as wished”, so you can spend your brain cells on (a) better designs, and (b) better optimizations. The Internet grew out of the whole community’s strong and emotional realizations around these ideas. By Alan Kay

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