Switching to Linux

I recently switched from Windows to Linux. It was time for a new laptop anyway, and I just couldn’t handle the Windows crap any longer. It started when my favorite AI libraries stopped being supported on Windows, and I found myself using the Linux Subsystem (WSL) more and more.

In this article, I’ll motivate the switch to Linux, and give you some pointers from my recent experience. You can make the switch, too. It’s not as hard as you may think.

What 365 means is: you pay Microsoft every day of the year.

Most of you will recognize what I mean by Windows crap. It conveniently forgets that I own paid licenses, and tries to jam me into the “365” program. Apps I’ve already paid for suddenly stop working, and then I lose time fixing them.  What 365 means is: you pay Microsoft every day of the year.

Then, it’s always trying to badger me into using Copilot which, by the way, is spyware. After updating my registry to turn off Copilot for good, I was not looking forward to the next release, with integrated “kernel level” spyware. If you want a privacy horror story, check out Microsoft Recall.

The Linux ThinkPad

I’ve used ThinkPads forever, like, since they were IBM. I was tempted to buy a Mac but, for this exercise, I wanted to have familiar hardware and change only the operating system. Lenovo sold me the accustomed T16 preloaded with Ubuntu Linux, which probably saved about $100.

One fun thing I learned is that hardware manufacturers feel the pain from Windows, not only because the license takes a big slice of their gross, but because they end up fielding the support calls! Here is Dell opting out, and here is HP.

You can set up Linux to look and feel exactly like Windows, if you wish, using distros like Zorin and Mint. It’s a testament to Ubuntu’s flexibility that people are running around creating distros that emulate other operating systems – MacOS, too.

My Ubuntu Stack

What I wanted, though, was the most vanilla, mainstream Linux experience I could get – and still be compatible with my Microsoft-oriented day job. That’s Ubuntu Linux with Gnome. Here are the apps:

  • VS Code – Obviously. I miss Notepad++ but, if you live in VS Code anyway, you can use it as a general-purpose file editor.
  • Kate – This is my one concession, so far, to the KDE ecosystem. I installed it mainly to handle markdown files. I also use the default Gnome text editor.
  • Only Office – This is for compatibility with MS-Office files. Libre Office is also popular. I can also run the MS 365 web versions (on my employer’s tenant) in Chrome.
  • Chrome – Again, for Microsoft compatibility. I run Outlook and Teams in Chrome – as Progressive Web Apps (PWA) to be exact, so they launch from my dock just like they do on Windows.
  • Firefox – This is my default browser, not Chrome – for privacy purposes. Did I mention privacy? I’m only running Chrome for the work stuff.
  • Thunderbird – I remember running Thunderbird email twenty years ago, and it still looks the same. Battle tested. This for my Virag Consulting mail. My work mail stays in Chrome.
  • Nemo – I replaced the default Gnome file manager with Nemo because I prefer its tree view. Much like File Explorer on Windows.
  • Network Share – Linux can access files on my Windows computer using the SMB protocol. This is the mount command in bash, but it’s much easier just to connect with Nemo.
  • Syncthing – I like to keep an offline copy of my network share on the laptop. It’s rare these days to be without an internet connection, but you never know.
  • Clipboard Indicator – This is Win-V on Windows, one of those little things you don’t notice until you don’t have it.
  • Flameshot – You’ll need a replacement for Snagit. There are a bunch. I chose Flameshot.
  • Slack – No surprise, Slack runs on Linux. Download the DEB version. Ubuntu is Debian-based, and you’ll be installing with APT.

That’s pretty much everything I need to work, travel, and code both personal and job-related. The Interactive Brokers trading app is a little rough, but they’re trying. At least they have a Linux app, and you can always trade on a web site – or your phone.

Claude, help me remap the Copilot key

I run four “workspaces,” which roughly match the four monitors I have at home. Teams and Outlook sit at the end, and I just Super-4 over there when I want to check my work messages. This is an old Linux feature that Windows recently caught up with.

Not only is TensorFlow happier on Linux, but so is Claude Code. It was always weird running Claude in a WSL window, with program files and test data in Windows. And, you don’t need to be coding! You can just start Claude in a terminal window and ask, “Claude, help me remap the Copilot key.”

Online Training

I have used Linux off and on over the years, but I needed a refresher, so I took Coursera’s online class, The Software Developer’s Guide to Linux. This lived up to the name. It was spot-on what I needed – Linux topics tailored for a developer, with enough of the sysadmin stuff to keep me out of trouble. I love being able to get just-in-time training for whatever my current project is.

I find that my usage style has changed with Linux. With Windows, I would faithfully shut down the laptop between sessions, and I’d be careful always to have power. Linux doesn’t seem to use much power, so I mostly just close the lid and let it suspend. I do a restart about once a week, for good measure.

The whole user experience, once you get used to it, just seems less brittle. If you’ve been fatigued with the Windows crap, as I was, hopefully this article encourages you to give Linux a try.

Saaspocalypse Now

For my sins, I have joined the “AI not kill SaaS” debate. I am motivating this with the Salesforce stock chart, which went off 30% in the recent “Saaspocalypse.” Charts for Thomson Reuters, Service Now, and Atlassian look about the same.

By 2030, more than 60 percent of software economics could flow through agentic systems rather than legacy SaaS seats.

So, why are people debating an accomplished fact? Because of a faulty thesis. This thesis (which I have actually read, not naming names) is that someone can vibe code a new Salesforce. This is a strawman. That’s not the thesis that wiped out $300 billion of market cap.

Someone probably could vibe code a new Salesforce app, but – that’s obviously not the same as killing Salesforce, the company, nor SaaS in general.

The thesis, according to Satya Nadella, is that business logic will come to reside in AI agents, leaving SaaS systems as mere databases. According to Goldman Sachs, by 2030, more than 60 percent of software usage could flow through agentic systems rather than legacy SaaS seats.

The fact that a single, well-prompted AI agent can now do the job of five or ten “seats” does not bode well for the old framework.

The more recent stock tankage in February – that 16% gap down in Thomson Reuters – is attributable to Claude Cowork, coupled with that day’s release of a prompt that does legal contract review. Yes, one single prompt. Again, it’s not feature coding – it’s the pricing model.

Consider Salesforce, for example. Each literal headset-wearing agent needs a “seat license.” With Claude Cowork, no human agent would ever interact directly with Salesforce. Robots talk to Salesforce, with 10X efficiency, and only escalate to humans when they have to.

As Phil Rosen puts it, “the fact that a single, well-prompted AI agent can now do the job of five or ten seats does not bode well for the old framework.”

None of this says that SaaS is dead, exactly. What it says is that SaaS vendors need to reinvent themselves – something legacy “growth to value” companies have historically failed to do.

Q-Day Is Sooner Than You Think

Information security people are worried about Q-Day, and maybe not worried enough. That’s the date when quantum computing will render today’s encryption methods obsolete. Information security depends on cryptography – secret code keys that are uncrackable because of large numbers and hard math problems.

The good news from quantum computing is that we’ll have a new generation of more-powerful computers, with the usual benefits – discovering new medicines, powering AI, and generating cat videos. The bad news is that we will have to come up with more-robust cryptography, in time for Q-Day.

Breaking Crypto

Quantum computers are not literally faster than today’s binary ones, but they support a new class of algorithms made possible by the weirdness of quantum theory. Oddly, the algorithms getting all the attention are not the ones for medicine or astrophysics, but those that defeat public-key cryptography.

Suppose someone wanted to find your four-digit PIN. They would have to try 10,000 different combinations (or half that, on average). This algorithm is “order n,” meaning that it varies linearly with the number of digits. See Know Your Time Series for more on “order n.” Grover’s algorithm for quantum search is order √n, which means only 100 tries.

Shor’s algorithm for prime factorization is, in fairness, kind of the first thing you would do with a new computer anyway, cryptography or no. It was my first homework assignment in Fortran (Euclid’s, not Shor’s). Cracking a four-digit code is no big deal. The backbone of information security today, RSA, uses a 2,048-bit key, which is more than 600 decimal digits.

How Many Qubits

Early microprocessors, like the Intel 4004, had about 2,250 transistors. Each transistor is like a switch that can be on or off, representing a binary digit, or “bit.” Google is proud of their latest quantum computer, Willow, with 105 quantum bits, or qubits. Shown here is its refrigeration unit. IBM advertises 1,000 qubits, but counting them is tricky.

Computers today sacrifice about 12% of their capacity, to error correction. Every eight bits in memory require a spare bit for error checking. Error checking overhead varies depending on the application. For quantum computing, this overhead is massive. It can take thousands of physical qubits to make one good “logical” one.

That’s why Google bangs on about error correction. Their 105 qubits may be stronger than IBM’s 1,000, depending on error correction. The latest paper on breaking encryption makes specific assumptions about how reliable the qubits are. It’s called How to factor 2048-bit RSA integers with less than a million noisy qubits, or 1,400 logical qubits.

When is Q-Day

Progress toward breaking RSA 2048 is happening on several fronts: better hardware, better error correction, and better algorithms (that tolerate errors). Gidney’s previous work, just four years ago, required 20 million physical qubits.

IBM plans to deliver a real, commercial-grade computer, “the first fault-tolerant quantum computer,” with 200 logical qubits, in 2029, with 2,000 in prospect around 2032. Startup IonQ is targeting 1,600 in 2028. They’re growing by acquisition, and targeting this audacious goal by stacking a bunch of new technologies.

Google is also in the hunt, but their roadmap is more complicated. As you know from the link above, Google doesn’t use the popular logical/physical shorthand. They talk about computing benchmarks that explicitly include error correction – kind of like Gidney’s “one million noisy.”

Depending on how you assess the roadmaps, Q-Day probably happens around 2030. But then, there’s “harvest now, decrypt later.” Hackers can start collecting your encrypted information today, and saving it to use later, when RSA 2048 falls.

So, the real question is: do you have confidential data that will still be important five years from now? In that case, Q-Day is today.

Chez Vicky

As a young consultant at Coopers, I had the privilege of being included as the technology person on a number of engagements with other specialties. One such was the Victoria’s Secret engagement, where I was able to work with the firm’s top retail experts. I am going to make a point here, about knowing your customer, but not without telling the story.

Our customers in the Detroit office were mostly from the manufacturing practice, and the guys teased me about shipping out to the Victoria’s Secret facility. “Wear a hardhat,” one wag said, “in case a box of panties falls on you.” We did, in fact, keep hardhats in the office.

I did not know a corset from a camisole, so I resolved to study the catalog until I knew the names of all the items.

The retail people were different. My tech counterpart arrived from Chicago with just a rollaboard, same as me. He was chafed because he had had to wait for Charles, the retail expert, with his train of checked baggage. Bemberg lining, doctor’s sleeves, Aston-Martin cufflinks. They were a different species.

My side of the engagement was to evaluate the client’s competence in software management, capacity utilization, contingency planning, staffing, budgeting, and so forth – routine work for me.

I also ran the day-to-day activities of collecting data and conducting interviews. Victoria’s middle managers were, unbelievably, all attractive women. I would have to tell my guys to stop hyperventilating. “Yes, she’s hot. She’s also a VP.  We’re interviewing her tomorrow.”

The men who worked there seemed inured to Victoria’s charms. The head of store ops banged through the statistics from memory. He knew which item, color, and style sold best in each market.

“The black satin tap,” he said, on this topic, “that one.” He pointed, without looking up, at a promotional poster. I confronted a life-size photo of a dark-haired woman modelling this item, to good effect. I did not know, initially, a corset from a camisole, and so I resolved to study the catalog – no, not the illustrated one – until I knew the names of all the items.

The firm’s seniormost retail expert, Marge Meek, took me under her wing. She was a retail god. Like, personal friends with Marhsall Field, or something. Marge took me to visit some stores, which turns out to be pretty important in retail.

“Okay Mark, who is the Victoria’s Secret customer?” Well, to start with, she is young, fit, well-educated, and upwardly mobile. I rattled off what I had read in the annual report.

“Now look around. Is that who you see here?” I am a tech guy. It would never have occurred to me to visit a store and study the customers.  Marge offered her own characterization, which was a little less flattering, but undeniably accurate.

Back at the job site, we reprised our field trip for the team. Our engagement partner had his own opinion. “Women that date Mexicans,” was Dean’s pronouncement. He was not well-liked by the retail people.