I have been using Apple products almost exclusively since the mid-90s. Now and then, I engage in debates about the pros and cons of Apple products compared to their competitors, especially regarding the price difference. And of course, the question arises whether minimalism and using Apple products even go together. It creates an ambivalence between design culture and the contradiction of consumption.
Continue reading “How do minimalism and Apple products go together, when Apple is so expensive?”Working More Productively with the Apple Stage Manager
Apple’s new macOS version, Ventura, as well as the new iPadOS version 16, which will be released in the second half of 2022, bring many new features. One of the most hyped tools is the new multitasking feature, Stage Manager. Let’s take a closer look at it here.
What exactly does Stage Manager do?
Here’s what the press release says:
Stage Manager provides a completely new multitasking experience, where apps and windows are automatically organized, allowing users to quickly and easily switch between tasks. For the first time, users can create overlapping windows of different sizes in a single view on the iPad, drag and drop windows from the side, or open apps from the Dock to create groups of apps—enabling faster, more flexible multitasking. The window of the app the user is working in is displayed in the center, while other open apps and windows are arranged on the left side in order of their recency.
Apart from the marketing fluff, there are three key pieces of information here:
- On the left, apps and windows are arranged in order of their recency.
- You can group apps and windows.
- On the iPad, you can now use overlapping windows of different sizes (we’ll cover the limitations below).
Let’s first take a look at the macOS version. In the following screenshot, we can see 5 apps/windows on the left side. If you look closely, you’ll notice even more, as two apps/windows are already grouped (at the very bottom).
When you click on these windows, you’ll see them stacked on top of each other, here with a different example:
On my rather small 14″ screen, this doesn’t make much sense. While I can still switch between windows using Command-Tab, I can’t see the windows related to my task in the way I need to. With such a small screen, it’s probably better to place each window needed for a task separately in Stage Manager.
The organization of windows still doesn’t work all that well. For example, RStudio opens a new window when I commit code. This is not assigned to the main RStudio window but instead opens as a completely new window. This is also visible in the screenshot above with a Mail window. It doesn’t seem fully thought through to me.
However, what’s kind of nice: If you’re watching a YouTube video in a browser window, it will continue playing in the left sidebar. Not that you’d be able to see much, but in YouTube’s Theater mode, you can still follow the video a bit. How this benefits concentration is another matter.
What are the advantages?
At first, I was a bit disappointed with Stage Manager. What’s supposed to be better about switching between different apps for a task? For me, the advantage lies in something completely different, which Apple probably didn’t intend.
When you switch from one app to another today, you lose sight of the previous app. This can lead to forgetting what you actually wanted to do (“Quickly check what exactly was written in the email… oh, there’s a new email, I need to read that first”). However, because the previous apps are still visible, you’re quickly reminded of what you were actually supposed to do. This has worked quite well for me in the few days I’ve been using Stage Manager.
How does Stage Manager work on the iPad?
Stage Manager is also available on the iPad, but only for iPads with an M1 processor. My less-than-a-year-old iPad Air cannot use Stage Manager. Nevertheless, I was able to test Stage Manager on another iPad.
First of all, I wondered how much sense Stage Manager makes on a small iPad screen. Of course, iPads can also be connected to an external display, and it likely works well in that case. Otherwise, I see the same advantages and disadvantages as with the macOS version. Here’s the screen with grouped apps on the left:
The stacked windows on the iPad make even less sense to me here, though I only have an 11″ model.
Do you really need Stage Manager?
I’m a bit concerned that Stage Manager will meet the same fate as Mission Control: hardly anyone knows about the feature, and most users probably only stumble upon it by accident. Additionally, Stage Manager needs to be activated first. My guess is that most users install the new OS versions simply because they are installed automatically, not because they really want them (unlike in the past, when people eagerly awaited a new macOS version, like macOS 8 in 1997, for which you also had to pay nearly 200 euros). On the other hand, sometimes you only realize how good a feature is once you have it.
The other new features in the latest OS versions are cosmetic. The system preferences on macOS now look exactly like those on iOS and iPadOS. I’m really curious about Freeform, but unfortunately, it’s not included in the beta version yet.
Apple MacBook Pro M1 Max – Is it worth it for Machine Learning?
Another new MacBook? Didn’t I just buy the Air? Yes, it still has warranty, so it makes even more sense to sell it. I’m a big fan of the Air form factor, and I’ve never quite warmed up to the Pro models. However, the limitation of 16GB of RAM in the MacBook Air was hard to accept at the time, but there were no other alternatives. So, on the evening when the new MacBook Pros with M1 Pro and M1 Max were announced, I immediately ordered one – a 14″ MacBook Pro M1 Max with 10 cores, 24 GPU cores, a 16-core Neural Engine, 64 GB of RAM (!!!), and a 2TB drive. My MacBook Air has 16 GB of RAM and the first M1 chip with 8 cores.
Why 64 GB of RAM?
I regularly work with large datasets, ranging from 10 to 50 GB. But even a 2 GB file can cause issues, depending on what kind of data transformations and computations you perform. Over time, using a computer with little RAM becomes frustrating. While a local installation of Apache Spark helps me utilize multiple cores simultaneously, the lack of RAM is always a limiting factor. For the less technically inclined among my readers: Data is loaded from the hard drive into the RAM, and the speed of the hard drive determines how fast this happens because even an SSD is slower than RAM.
However, if there isn’t enough RAM, for example, if I try to load a 20 GB file into 16 GB of RAM, the operating system starts swapping objects from the RAM to the hard drive. This means data is moved back and forth between the RAM and the hard drive, but the hard drive now serves as slower “RAM.” Writing and reading data from the hard drive simultaneously doesn’t speed up the process either. Plus, there’s the overhead, because the program that needs the RAM doesn’t move objects itself—the operating system does. And the operating system also needs RAM. So, if the operating system is constantly moving objects around, it also consumes CPU time. In short, too little RAM means everything slows down.
At one point, I considered building a cluster myself. There are some good guides online about how to do this with inexpensive Raspberry Pis. It can look cool, too. But I have little time. I might still do this at some point, if only to try it out. Just for the math: 8 Raspberry Pis with 8 GB of RAM plus accessories would probably cost me close to €1,000. Plus, I’d have to learn a lot of new things. So, putting it off isn’t the same as giving up.
How did I test it?
To clarify, I primarily program in R, a statistical programming language. Here, I have two scenarios:
- An R script running on a single core (not parallelized).
- An R script that’s parallelized and can thus run on a cluster.
For the cluster, I use Apache Spark, which works excellently locally. For those less familiar with the tech: With Spark, I can create a cluster where computational tasks are divided and sent to individual Nodes for processing. This allows for parallel processing. I can either build a cluster with multiple computers (which requires sending the data over the network), or I can install the cluster locally and use the cores of my CPU as the nodes. A local installation has the huge advantage of no network latency.
For those who want to learn more about R and Spark, here is the link to my book on R and Data Science!
For the first test, a script without parallelization, I use a famous dataset from the history of search engines, the AOL data. It contains 36,389,575 rows, just under 2 GB. Many generations of my students have worked with this dataset. In this script, the search queries are broken down, the number of terms per query is calculated, and correlations are computed. Of course, this could all be parallelized, but here, we’re just using one core.
For the second test, I use a nearly 20 GB dataset from Common Crawl (150 million rows and 4 columns) and compare it with data from Wikipedia, just under 2 GB. Here, I use the previously mentioned Apache Spark. My M1 Max has 10 cores, and even though I could use all of them, I’ll leave one core for the operating system, so we’ll only use 9 cores. To compare with the M1 in my MacBook Air, we’ll also run a test where the M1 Max uses the same number of cores as the Air.
How do I measure? There are several ways to measure, but I choose the simplest one: I look at what time my script starts and when it ends, then calculate the difference. It’s not precise, but we’ll see later that the measurement errors don’t really matter.
Results: Is it worth it?
It depends. The first test is somewhat disappointing. The larger RAM doesn’t seem to make much of a difference here, even though mutations of the AOL dataset are created and loaded into memory. The old M1 completes the script in 57.8 minutes, while the M1 Max takes 42.5 minutes. The data are probably loaded into RAM a bit faster thanks to the faster SSDs, but the difference is only a few seconds. The rest seems to come from the CPU. But for this price, the M1 Max doesn’t justify itself (it’s twice as expensive as the MacBook Air).
Things get more interesting when I use the same number of cores on both sides for a cluster and then use Spark. The differences are drastic: 52 minutes for the old M1 with 16 GB of RAM, 5.4 minutes for the new M1 Max with 64 GB of RAM. The “old” M1, with its limited RAM, takes many minutes just to load the large dataset, while the new M1 Max with 64 GB handles it in under 1 minute. By the way, I’m not loading a simple CSV file here but rather a folder full of small partitions, so the nodes can read the data independently. It’s not the case that the nodes are getting in each other’s way when loading the large file.
Apple Notes – The True Memex for Knowledge Management and Productivity
In 1945 publiceerde Vannevar Bush zijn artikel “As We May Think“, waarin hij schreef over een systeem genaamd Memex. Memex voorzag systemen en benaderingen zoals HyperText en stelde voor om verschillende soorten materiaal te koppelen en doorzoekbaar te maken op basis van trefwoorden, “als een vergrote intieme aanvulling op zijn geheugen”. Gezien de technische mogelijkheden van die tijd zouden de gegevens nog op microfilm opgeslagen moeten worden, maar verder was het een vrij coole constructie.
Bush’s gedachten hadden een grote invloed op de ontwikkeling van het World Wide Web, en zeker heeft Wikipedia vandaag de dag veel van het idee achter Memex. Maar hoe zit het met ons eigen kennismanagement? Hoe slaat u uw gedachten, materialen, ideeën en notities op? Een probleem is dat niet alleen kenniswerkers worden blootgesteld aan enorme hoeveelheden informatie, die gefilterd, gesorteerd en doorgenomen moet worden.
In de jaren ’80 had Apple HyperCard, een propriëtair multimedia-hypertextsysteem dat ook populair was in het onderwijs. Tegenwoordig komt waarschijnlijk notion.so het dichtst in de buurt van zo’n systeem, en ik weet dat sommige van mijn studenten deze app gebruiken. Vroeger was het Evernote, vandaag Notion, morgen weer iets anders. En elke paar jaar zet je je gegevens over naar een ander systeem, of niet, omdat het veel te veel moeite is. Wie heeft notities uit oude Moleskines (die in de jaren 2000 populair waren) omgezet en gebruikt die nog steeds? Ik ben geen fan van constant nieuwe apps installeren, maar probeer ook hier niet meer te installeren dan nodig is, want gelukkig wordt de bijgeleverde software van Apple steeds krachtiger.
Met de nieuwe versie van macOS X, Monterey, introduceert Apple eindelijk tags in de notities op de Mac, na iOS en iPadOS. In plaats van de weinig flexibele mappen, die je natuurlijk nog steeds kunt behouden, is het nu mogelijk om een notitie meerdere tags toe te voegen en daarna naar meerdere tags tegelijk te zoeken. Dit komt veel dichter bij mijn manier van werken, want niet alles hoort altijd maar in één categorie. Gedachten die ik heb over een R-script, kan ik later ook in mijn blog gebruiken, enzovoorts.
But this is not the only new feature that Apple Monterey brings. The Quick Notes, already known from portable devices, now offer the ability to save text from websites and then refer to it. When you visit a website again later, the extracted text is highlighted. I haven’t tested what happens if the text on the website has been changed. Overall, this feature greatly helps in organizing a collection of saved URLs, bookmarks, reading lists, quotes, etc. (The following screenshot is from a Monterey beta:)
The fact that you can now extract text from images and screenshots on the Mac without any additional software—and actually even better than with third-party tools—makes my workflow much easier. If I find a passage in a physical book that I find interesting but don’t have a pen to mark it, I often take a photo of it with my phone. I’m not sure how many of these photos I have on my hard drive, always with the guilty feeling that I really should clean this up soon. In the new macOS version, the cursor in Photos automatically turns into a text cursor when you move it over text, and you can select and extract it right away. However, you have to be careful to hold the book in a way that this works, unlike in the photo below:
Unfortunately, this feature still doesn’t work in Notes, but I’m sure it will come eventually. You also can’t yet extract text from highlighted passages in Preview for Instant Notes with a reference; you can only copy the text, which, of course, is still very useful.
Despite all the criticism, Apple’s Notes, with its many links to other software on macOS, is getting very close to the concept of Memex. The question is increasingly whether users understand how to leverage all these features for their own use, and how to create a symbiosis between their workflows and such software. But Apple is definitely making notion.so a competitor, especially because of the team functions now available as well. Much like with Apple Reminders, there are fewer and fewer reasons to pay for a subscription to other software, which, in the worst case, is also less well-integrated with other Apple services.
GarageBand has been stopped – The case is to blame
Normally, I don’t use GarageBand, but since the youngest loves making music, I gave him the iPad to try it out. Unfortunately, the app kept crashing with the error message:
GarageBand has been stopped. GarageBand was interrupted by another app and cannot be used. Once the interruption is over, playback or recording can be resumed.
The solution was simple: the iPad case. I had bought a cheap case, simply because every device you buy usually comes with additional costs, and I thought I could avoid that. Apparently not. In the forums, the solution is blamed on a software issue that occurs when using a non-Apple case. I suspect there won’t be a fix for this anytime soon. But it’s pretty crazy that an iPad case costs at least 200 euros for a good one. The top model, the Apple Magic Keyboard, costs around 300 euros.
First experiences with the Apple Silicon Macs with the M1.
I have already experienced a processor change at Apple. My Apple career began in 1996 with a PowerBook 5300, which I absolutely loved—despite its 640×480 pixel grayscale display. On the one hand, a Mac laptop at that time was still something very special and rare (admittedly, at an exorbitant price, but it had been provided to me by my then employer), and it had a keyboard that felt incredibly good and, above all, sounded wonderfully. On the other hand, compared to the Windows PCs I had used before, it was also extremely reliable. With 8 MB of RAM and a 500 MB hard drive, it was also quite well-equipped. This PowerBook was the first to feature a Motorola PowerPC processor, so there had already been a kind of transition shortly before.
In 2006, Apple switched to Intel processors, a move that was extraordinary at the time, especially since in the 90s, Apple had aired commercials where a Motorola processor roasted an Intel processor. For the transition, Apple offered a program called Rosetta, which allowed PowerPC applications to run on Intel-based Macs. Typically, these programs ran slower. The commercial was actually referenced again when the first Intel Macs were introduced, around minute 1:05 of the presentation.
Now another transition. In 2019, I bought the 16″ MacBook Pro after many years with a MacBook Air. I hadn’t kept any other Apple computer longer than the Air, but over time, it had become too slow for what I was doing with it (R, a lot of work in the terminal with sed, awk, Lightroom, etc.). I hadn’t upgraded earlier because I absolutely didn’t want the awful butterfly keyboard. The return to scissor-switch keyboards began with the 16″ MacBook, but I still couldn’t get used to the huge device. Not to mention, it became incredibly hot and loud, and the battery life was far from Apple’s claims. For example, when I trained a machine learning model, the MacBook got so hot that I didn’t need to heat my office anymore. And during any Zoom or Webex call, the battery drained faster than an ice cube melting in the summer heat.
I spend quite a bit of time waiting for the results of a calculation, even if it’s only 20 or 30 seconds sometimes, but it adds up over the day, and sometimes it’s several minutes or even hours. I usually know in advance how long it will take, but I don’t start another task for just half a minute because it disrupts my train of thought. Data analysis is also a meditative act for me. So, the speed of a computer is extremely important. Not just for data analysis, but for all other tasks on the computer as well. It just has to feel smooth.
The speed of a calculation in R depends on many factors:
- Memory (yes, R loads everything into memory)
- Processor speed
- Parallelization
For memory, the first Apple Silicon models aren’t particularly well-equipped—16 GB is the maximum. It doesn’t help that the path from the processor to memory is especially short. The operating system uses part of the memory, the running programs also use some, so there’s not much left. Especially when working with large files, as I often do, which can sometimes reach 50GB or more, swapping is almost “predetermined.” Parallelization is not possible yet, as the necessary packages are not available—Homebrew, for example, is still not available.
Additionally, R is currently not available for the new Macs. It lacks (still) a Fortran compiler, and this is not only a problem for R but also for many machine learning software extensions for Python. Who would have thought that this old programming language could still have such a big influence today? Of course, R also runs via Rosetta, but then I could have just not bought a new Mac and let myself be used as a beta tester for Apple 🙂 But, small spoiler: Even with Rosetta, the Intel version of R runs faster on the M1, and it seems that’s not just the case for me.
I initially purchased a Mac mini with 8GB of RAM and a 512GB SSD to test how good the performance actually is and whether I could make the transition. I was able to pick up the Mac mini the same day from the Apple Store, and from the start, I was amazed at how smooth everything felt on this computer. R worked flawlessly, though RStudio showed error messages frequently. No big deal. But it soon became clear that the memory limitation was an issue. When trying to process a 200GB file (using sort, awk, sed in the shell), at some point, the hard drive filled up with swapping, and the process failed. Okay, maybe the mini is just a bit too weak for that task. What surprised me, though, was that not once did the fan kick in—this would not have been the case with the 16″ MacBook Pro. So, all in all, everything seemed great…
…except for the Bluetooth. My Mac mini also had the well-known Bluetooth problems. Specifically, the mouse loses its connection multiple times a day, which is extremely inconvenient when you’re showing a demo during a video conference. Not good, very frustrating. I tried all sorts of tips, including using a wired connection to the network instead of Wi-Fi. No improvement. It’s unclear whether this is a hardware or software issue. A chat with Apple Support dropped multiple times, and eventually, I got tired of it because, you know, I have a job too. An update to the Big Sur beta helped a little, and as of yesterday, the computer is running on 11.1, so I’m hoping it will be better now, and that it’s not a hardware issue.
Another not-so-pleasant experience was the sound. I have never experienced an Apple computer with such poor sound quality—my old PowerBook 5300 probably sounded better. They could have definitely done much more with the sound.
Despite the Bluetooth issues, after 2 days, I decided to also buy a portable Apple Silicon Mac. In full configuration (16GB RAM, 2TB SSD), it costs about the same as I could sell my 16″ MacBook Pro for on the used market, and at the same time, I get double the storage space. There used to be a rule that you should calculate how much storage you might need and then multiply that size by 4. Unfortunately, there are no 8TB SSDs for these computers yet.
The computer arrived after almost 3 weeks, one week earlier than expected. Here, I noticed a small speed boost, likely due to the doubled RAM. The 200GB file also went through smoothly now, thanks to enough space on the SSD. And, just like with the Mac mini, the computer hardly seemed to break a sweat. Only once did the computer get a little warm, but not hot, and certainly not as hot as the 16″ MacBook Pro. This is also reflected in the battery life. I have yet to drain the battery in a single day. No kidding. I plug the computer in at night, and I usually still have a few hours of battery life left. It’s a completely new feeling.
The Bluetooth issue also exists with the MacBook Air. This is unpleasant, and I wonder how it could have gone unnoticed in the tests Apple conducts. That a transition doesn’t go completely smoothly is understandable, and you’re always somewhat of a guinea pig when buying the first model after a major shift. For me, it’s a trade-off: How much time do I gain by having a fast computer versus how much time do I lose when something occasionally doesn’t work. The mouse connection is of course a hygiene factor; it should just work. But with the MacBook Air, I’m not as reliant on it. So far, I’m happy with my decision, though I would have preferred 32GB or even 64GB of RAM. But those options aren’t available yet.
The sound of the MacBook Air is much better than my old Air’s, but it doesn’t compare to the 16″ MacBook Pro. No surprise, the speakers are much smaller. Still, it’s better than the mini’s sound.
The instant wake feature actually works, and sometimes I wonder if the computer was even “asleep.” The keyboard sounds almost as nice as that of the PowerBook 5300, and if anyone wonders why a keyboard should sound good, well, aesthetics don’t stop at just the visual 🙂