Many, many years ago, before Docker was released, I knew a guy whose team worked a lot with virtual machines.
All day long, he told me, they would configure and test and spin up and down virtual machines. I can’t remember what they used the machines for, but he told me that an actual, real problem his team faced was managing their attention. You change something in the Vagrant configuration, rebuild the machine, wait for five minutes, and then, once the machine is ready, you no longer know what you were trying to test because you switch to a different window and get stuck on Hacker News
So what they did to “fix” this problem, he told me in a tone that said “don’t make fun of me for this, this isn’t funny”, was to watch movies and TV shows on a second monitor. That’s right. His teammates would hit return after typing vagrant up, and instead of switching windows, they’d look over to their second monitor to watch a bit of Scrubs. In their peripheral vision they could see when the build was done and go right back to it. A little bit of light TV that’s constantly on is less distracting than switching windows.
Over the years, I’ve thought of this guy and his team many, many times. Every time I have to wait for a build, to be exact.
And now I think of him whenever I kick off agents to go run and do something for me. In the future — and this is one of the few things I’m sure about — a lot of code will be written while nobody is watching. There will be more agents, running longer, running everywhere, kicked off from anywhere. Where will our attention go? And how will we bring it back when we need to? Watching Scrubs is probably not the solution.
Zen of AI Coding. I wish I had written that. I nodded to nearly everything there, but to quote just two things, one: “The economics of software have changed.
When coding is cheap, implementation stops being the constraint. You can build ten things in parallel. You cannot decide, validate, and ship ten things in parallel, at least not without changing the rest of the pipeline. Cost of delay shifts. It is no longer about developer days. It is about time stuck in other bottlenecks: product decisions, unclear requirements, security review, user testing, release processes, and operational risk. Agents can flood these queues. Inventory grows. Lead time grows. Delay becomes more expensive, not less.” And two: “It is tricker then ever to resist the temptation to add features. Resist it. Build what is used. Kill what is not.”
Yaron Minsky: “I wonder if we’re starting to hit a deflationary era in software engineering. For the first time, we’re starting to talk about this in a planning context; it can make sense to put off some projects because we expect they’ll be easier to achieve in the future than today. […] But the difference is the sense that we can start to count on things getting faster. So if we have to get something done by a fixed deadline, we're starting to think that we can put off some of that work for longer than we would have in the past.”
Well worth the reminder: Good software knows when to stop. More isn’t more. In fact, it’s less today than it was yesterday. And it will be less than that tomorrow.
Naval recorded a new podcast episode: A Motorcycle for the Mind. I’m usually skeptical of his confidence, but he does have a fascinating clarity of thought and eloquence and I enjoyed listening to this one. Noteworthy what he thinks about the role of software engineers in the future: “Does this mean that traditional software engineering is dead? Absolutely not. Software engineers—even the ones who are not necessarily tuning or training AI models—these are now among the most leveraged people on earth. [...] But software engineers still have two massive advantages on you. First, they think in code, so they actually know what’s going on underneath. And all abstractions are leaky. [...] So if you want to build a well-architected application, if you want to be able to even specify a well-architected application, if you want to be able to make it run at high performance, if you want it to do its best, if you want to catch the bugs early, then you’re going to want to have a software engineering background.” Or this, about the flood of software that’s coming: “And remember: there is no demand for average. The average app—nobody wants it, at least as long as it’s not filling some niche that is filled by a superior app. The app that is better will win essentially a hundred percent of the market. [...] But generally speaking, people only want the best of anything. So the bad news is there’s no point in being number two or number three—like in the famous Glengarry Glen Ross scene where Alec Baldwin says, ‘First place gets a Cadillac Eldorado, second place gets a set of steak knives, and third place you’re fired.’ That’s absolutely true in these winner-take-all markets. That’s the bad news: You have to be the best at something if you want to win.” But is that true? Look around at some of the most widely used pieces of software: Microsoft 365, Android, WhatsApp, Chrome, Outlook, Jira — is it “the best”? Jira is the best at something, yes. For example: getting people to say “you just haven’t configured it correctly.” But is it the best software in its category, or is it instead the best at “being sold to large enterprises”?
Or take the most popular CI system in the world: GitHub Actions Is Slowly Killing Your Engineering Team.
Marc Andreessen agrees with Naval: “If the goal is to be a mediocre coder, then just let the AI do it. It’s fine. The AI is going to be perfectly good in generating infinite amounts of mediocre code. No problem. It’s all good. If the goal is, ‘I want to be one of the best software people in the world, and I want to build new software products and technologies that really matter,’ then yeah, you, 100%, want to still... You want to go all the way down. You want your skillset to go all the way down to the assembly, to assembly and machine code. You want to understand every layer of the stack. You want to deeply understand what’s happening at the level of the chip, and the network, and so forth. By the way, you also really deeply want to understand how the AI itself works, because you want to... If people understand how the AI works, they’re clearly able to get more value out of it than somebody who doesn’t understand how it works. You’re always more productive if you know how the machine works when you use the machine.
And so the super-empowered individual on the other end of this that wants to do great things with the new technology, yes, you 100% want to understand this thing all the way down the stack because you want to be able to understand what it’s giving you.”
And this take agrees with Andreessen: “The jobs apocalypse is the Population Bomb of our time.”
This is very, very, very, very good: The Structure of Engineering Revolutions. What a useful lens to look through at this moment.
Since we’re talking about Thomas Kuhn: should I feel bad that I’m linking to nearly every Adam Mastroianni post? Nah, they’re all really good and this one isn’t an exception: The one science reform we can all agree on, but we’re too cowardly to do.
And what a moment this is, isn’t it: Cursor Goes To War For AI Coding Dominance. “But if the AI doesn’t need a human collaborator, why bother with the editor? If writing and editing code line by line was no longer central to a programmer’s workflow, Cursor’s central product thesis was suddenly in question. […] Until recently, Cursor seemed nearly unstoppable. The company began 2025 with roughly $100 million in annualized revenue. By November, that figure had surpassed $1 billion. […] For now, Cursor’s continued growth comes with a big dose of anxiety. Inside the startup, revenue tracking became so distracting that the company stopped reporting daily figures in its #numbers Slack channel, according to people familiar with the decision.” Imagine working at the hottest and fastest growing startup of all time and then three or six months later it’s war time.
New Paul Graham essay that I thought was worth reading: The Brand Age. When I started reading this, I thought that surely he’s going to say that what he’s recounting here is happening to software: “Now the whole game they’d been trying to win at became irrelevant. Something that had been expensive — knowing the exact time — was now a commodity. Between the early 1970s and the early 1980s, unit sales of Swiss watches fell by almost two thirds. Most Swiss watchmakers became insolvent or close to it and were sold. But not all of them. A handful survived as independent companies. And the way they did it was by transforming themselves from precision instrument makers into luxury brands.” But he never did! I still think it’s about software though.
You might have heard of this guy: Don Knuth, Stanford Computer Science Department. He writes: “Shock! Shock! I learned yesterday that an open problem I’d been working on for several weeks had just been solved by Claude Opus 4.6— Anthropic’s hybrid reasoning model that had been released three weeks earlier! It seems that I’ll have to revise my opinions about ‘generative AI’ one of these days. What a joy it is to learn not only that my conjecture has a nice solution but also to celebrate this dramatic advance inautomatic deduction and creative problem solving. I’ll try to tell the story briefly in this note.” What a joy!
Ah, now this, this is the good stuff: Rust zero-cost abstractions vs. SIMD on the turbopuffer blog. I think there have been some comments on this not being an inherent limitation of the compiler, but I found it interesting to think about what it can and can’t see when trying to optimize a loop: “Herein lies the hidden opportunity cost of Rust’s zero-cost abstraction in our merge iterator. The iterator itself compiles down to the code you’d write by hand for a single call. In that sense, it is zero-cost.”
More hardcore engineering, from the COO at Epic Games: “The task: schedule operations for a custom VLIW SIMD architecture running a tree traversal with hashing. 256 items, 16 rounds, 5 execution engines with different slot limits. Starting point: 147,734 cycles (naive). Where Claude Code landed: 1,105 cycles — a 134x speedup.”
Look, we just bought a new MacBook Air with an M4 and it’s fantastic, so I’m not regretting anything, but those new MacBook Neos look amazing.
Raycast Glaze looks really interesting. I guess I should’ve put “looks” in italics because I’m still on the waitlist.
At last, reasons to be cheerful about European tech. That’s not my title. I want to be optimistic, but I’m skeptical. This paragraph resonated: “Mehran Gul, of the World Economic Forum, notes that Skype, a European startup, created just 11 millionaires in the early 2000s. PayPal, an American one, gave many more stock options to its employees, creating over 100. They, in turn, invested in newer Silicon Valley startups.” In Europe, startup options feel like and are perceived as and, I guess, truly are lottery tickets. Go to the Bay Area (which is, yes, an outlier) and suddenly everyone knows at least two or three people who are rich because of startups.
Eoghan McCabe, CEO of Intercom, offering “Intercom, the company I run, as a case study to help me explain how SaaS companies can be saved, and share the things we did, starting three years ago, to find relevance in this new world.” What a graph! Mind-boggling.
“Singaporeans to receive free premium AI subscriptions from second half of 2026”
Tim Ferriss on The Self-Help Trap: “Self-help is dangerous precisely because it easily becomes self-fixation. A focus on improving the self usually first requires finding problems with the self. This is quite the pickle. In a society that rewards problem-solving, you can end up hallucinating or exaggerating unease in order to fix it. This leaves you always in the red, always one step behind. Imagine a dog chasing its tail that has committed to being unhappy until it catches the tail… but it’s always just a few inches short. Still, it whirls around and around, ‘doing the work.’ Perfection always recedes by one more book, one more seminar, one more habit tracker. Put in more colorful terms, misdirected self-help turns you into a self-obsessed masturbatory ouroboros (SOMO).” I dare you to click through to the shop where he got the snake sticker — the sticker he put on the bottom of the MacBook. Anyway: great post.
Google released gws, the “CLI for all of Google Workspace — built for humans and AI agents.”
Hannah Ritchie, data scientist at Our World In Data but a lot more than that: Does that use a lot of energy? Electric lawnmower vs. air conditioning is good.
An Interactive Intro to CRDTs. Lovely. It’s from 2023 and that made me think that today, in 2026, no one would write a blog post like this, because why would you if anyone can press a button to have a custom version of this post generated for them? And that in turn made me wonder: but people will write in the future too and once we’ve crossed through the transitional period we’re in, what will those posts look like?
Since announcing his project Agentic Engineering Patterns a few weeks ago, Simon Willison has been steadily adding new chapters to it. For example: Hoard things you know how to do. “The key idea here is that coding agents mean we only ever need to figure out a useful trick once. If that trick is then documented somewhere with a working code example our agents can consult that example and use it to solve any similar shaped project in the future.” Wish I was a hoarder.
Is this the first universally beloved AI-generated video? I’m of the school that believes creativity is less about creating new things in a vacuum but more about making connections between things that already exist, but weren’t connected before. Creativity, I think, is remixing. Putting lego pieces together in a way no one’s ever put them together. That definition is, of course, recursive, because the lego pieces also have to be put together. But my point is: this video is creative. It’s not slop. And the fact that I’m dying to know what the prompt was — doesn’t that show everything will be different but that all will be well?
In the past few months I’ve been thinking a lot about different software companies and whether they’ll make it or whether they get eaten by AI instead. “If you own physical assets, if your value is in operations or in regulation or in contracts, then you’re probably safe,” is one thesis I keep coming back to. And, funnily enough, Spotify was one of the companies I marked “safe” in my mind: sure, the software can be replicated more easily now, but they have contracts with publishers and artists — they’re safe. But then here’s Jimmy Iovine saying that the music itself has no value anymore when packaged by streamers and, well, if that’s true, what’s left: Why Streaming is Minutes Away From Being Obsolete.
Daniel Gross published his /agitrades in January 2024 to wonder: “Suppose the progress doesn’t stop, just like GPT-4 was better than 3, GPT-5 is capable of basic agentic behavior -- i.e. able to accept a task, work on it for a while, and return results. Some modest fraction of Upwork tasks can now be done with a handful of electrons. Suppose everyone has an agent like this they can hire. Suppose everyone has 1,000 agents like this they can hire... What does one do in a world like this?” I hadn’t read the document when it was released, but, wow, it’s good. Impressive first-principles and long-range thinking. And now, more than two years later (two years!), John Coogan of TBPN revisited the questions to see whether they can be answered already. Equally fascinating.
To quote one of the top comments: “Dammit guess I’m drinkin garage beers now”
