My daughter's watching this episode right now, and it makes me wonder... could something like this happen if we all collectively decided not to use social media or AI tools?

Found myself nodding along during this entire article.

The vast majority of people in the software industry today were not in the industry in 2000. They did not experience ordering a floppy disk of software from a classified ad in a computer magazine. Or license codes on CD boxes. Or running a SparcStation server under the receptionists desk because that’s the only machine compatible with the business-critical software she used.

In short, most developers were professionally born into the era of SaaS and have never considered an alternative model. They have not even conceived that software could, or should, be sold in another way.

I'm excited to see what new business models pop up from this approach. Frankly, I am close to no longer needing to pay for a Claude Max plan with the way that open source models are performing on my M3 Max.

That era of building a viable SaaS business in a few months is gone. I mean, it technically still exists today but only in the arbitrage sense that the rest of the world hasn’t yet caught on to how quickly and easily software can be built. It’ll be gone soon, I promise.

If you could previously develop a new app in a few months, I can now build that by the end of the week—if not the end of the day. That’s especially because I don’t need to build any of the trappings of a multi-tenant app destined for the mass market. I can choose HTTP basic auth if it suits me. Or none at all. I might not worry about backups. I can host it alongside other internal apps with barely a glancing-thought towards scalability. I don’t need branding. Or marketing. Or billing. I can reuse internal design systems or let the AI run with whatever comes to its mind first.

The sophistication of the software I’ll produce this way is much lower than what an indie dev might have written 2 years ago. It’s not the same product—mine isn’t even a product—but it’ll solve my problem equally well. I don’t have to build the same amount of software to solve my problem that you do to deliver a solution to everyone’s problems.

You can see most of the software I've vibe coded for myself. I'm sure all of it will be useless garbage for you, but it's been hugely invaluable to me.

And most important of all: vibe coding from my phone has been the most fun I've had building software in decades.

Amid all this talk about the inevitability of “AI”, I think it’s okay for us to ask what kind of future we want, and then move toward it together. And it’s already happening across the industry. ProPublica’s guild conducted a strike earlier this year, in part to win contract language that would prohibit layoffs resulting from “AI” adoption. UK workers at DeepMind, Google’s AI Research Lab, voted to unionize, in part to block usage of their employer’s models in military contracts. Thousands of tech workers in the University of California system voted to unionize, and gained the right to bargain over the use of “AI” tools in the workplace. DAIR has released a hub filled with resources for people looking to push back against “AI” and automation at work.

I think we can figure out our future together, right now. And as Mandy reminds us, it all starts with conversation. We have to talk with our friends, colleagues, and coworkers. We have to talk about our concerns, and what we wish were different. We have to map out how we’ll collectively instrument change in our workplaces, and in our industries.

I want to fix this industry. I want you to have a place in it. I want us to have a place in it. Maybe you do, too.

So, really: what do you want to happen next? I’ve got some ideas, but I’d love to hear yours.

Google Chrome’s move to Manifest V3 for extensions is closing its final loophole and, with it, bringing the end of many ad blocker tools.

The move to Manifest V3 has been in the works for years at this point, with one of the main points of criticism from users being that the change would break most ad blockers due to the new permissions structure and Google’s focus on privacy. The impacts of that were felt broadly in 2024 and, now, Google is closing the book on Manifest V2 and, in turn, popular ad blockers such as uBlock Origin.

Your periodic reminder that you don't need to use Chrome to use the web.

I prefer Firefox at the moment as my daily driver. I'll switch to Brave if Firefox can't render a page properly for whatever reason.

I'm also carefully following the progress of Ladybird. I wish I was more interested in browser development so I could contribute via open source but (a) the complexity of code required to power a browser feels outside of my abilities at the moment, and (b) they recently closed off PRs from non-maintainers.

How many birds and butterflies did I miss looking at dumb phones? How many good notes or interesting pieces of trash or who knows what?

Maybe I do have a phone problem.

Never Not Nervous is a solid comic. This one spoke to me as someone who has attempted to use a Light Phone in the past.

Related: I spent the weekend in Sheboygan with my family, as is our annual summer kickoff tradition. We spent an hour or two on the beach, and I found a rock. It was a pretty dope rock; I spent 20 minutes slowly examining it, noticing that my up close vision is not quite what it used to be.

I took the rock home and it's sitting on my desk now.

I can't wait to get home and look at it some more.

Before running all of these tests, I actually did this the old-fashioned way. With pencil and paper and thinking. This entire project was inspired by putting Blonde on an 8-track and from experience, I can tell you this is a hard problem. The trouble is, I can’t tell you how I did it. There’s some human heuristic I used, definitely not an algorithm, and I can’t write it down. This seems to be what humans in 1977 who gave a damn did too. This is not what the dude making the Sublime 8-track did.

So I can’t tell you how to do this without testing all possible permutations, but human intuition can get pretty close. This sort of thing has shown up in other fields, like Foldit, an online ‘let humans perfect protein folding’ game. Classical computer algorithms can only get so close, and humans watching these classical algorithms got frustrated when they saw a solution the computer didn’t. Humans can see stuff that classical algorithms can’t. And now there’s a dozen Nature publications to prove it.

But now we have LLMs. They’re also a black box, and if you throw enough tokens and context at them, they’ll out-perform humans. They won’t be able to tell you how they did it, either.

This isn’t a victory for humans over algorithms or LLMs over humans, or anything like that. It’s just a fact that a dead and derided music format left behind a benchmark where human intuition beat classical methods that wouldn’t be in a textbook for a decade after the work was done. And half a lifetime later, LLMs would outperform humans for reasons we can’t really inspect.

So that’s something.

This was such a cool experiment and a fascinating head-to-head comparison of LLMs vs. human in the esoteric domain of "8-track music production".

I don’t want to go back to floppy disks. I like fast updates. I like security patches. I like sync. I like crash reports when they help fix real issues.

What I want is for “phone home” to be treated like a privileged capability, not an assumed right. In other domains, we treat privileged capabilities with care. We put them behind intentional choices. We build guardrails. And we treat abuse as a bug, not a growth opportunity.

What we don’t yet know is how architectural coherence evolves over years in a fully agent-generated system. We’re still learning where human judgment adds the most leverage and how to encode that judgment so it compounds. We also don’t know how this system will evolve as models continue to become more capable over time.

What’s become clear: building software still demands discipline, but the discipline shows up more in the scaffolding rather than the code. The tooling, abstractions, and feedback loops that keep the codebase coherent are increasingly important.

Our most difficult challenges now center on designing environments, feedback loops, and control systems that help agents accomplish our goal: build and maintain complex, reliable software at scale.

There's this very vocal camp of engineers on the internet who like to say things like "it was never about how fast I can type code" and share visceral takedowns of how sloppy and terrible vibecoding and agentic engineering codebases become over time.

I agree with their observations: over time, every vibecoded piece of software I've built becomes shelfware, artifacts of code which served a purpose but is no longer needed.

But I've been programming computers long enough to know that concerns about architecture and sane codebases end up bugging people so much that they invent new techniques to address them.

I am approaching agentic engineering just like I approached using a chainsaw for the second time in my life a couple weeks ago: by consuming a lot of videos and blog posts on how other people are doing it, and then running controlled experiments to see what works for me.

Information allows us to act more skillfully. Imagine you come to a fork on a road. Without a sign, you’d need a compass or a great sense of direction to choose correctly. But with a clear sign, you’d quickly know which road to take. The sign reduces ambiguity.

The Moylan arrow, too, disambiguates a choice. Pulling in on the wrong side of the pump is an annoying inconvenience. By making the driver smarter, the arrow improves the car’s UX. Critically, it does so without much cost to the manufacturer. That’s why it’s become pervasive.

The Moylan arrow works because it’s:

• Clear: legible and understandable
• Findable: located where you’re already looking
• Relevant: provides the exact answer you need
• Contextual: available when needed, but “quiet” otherwise
• Obvious: doesn’t need further instructions
• Cheap: of negligible cost to manufacturers

Jorge goes on to compare this list to the latest crop of chatbots and finds it comes up lacking.

I found this set of heuristics helpful:

Rather than ask, “how might we add AI to this system?,” consider the following questions:

• What is the person trying to do?
• Do they understand the system?
• What’s keeping them from choosing skillfully?
• What questions do they have? Which come up repeatedly?
• Which structural distinctions are ambiguous?

Sometimes, people in technology believe that we can solve problems by getting people to pay attention. This comes up in security, anti-virus efforts, anti-phish efforts, monitoring and alert handling, warning messages emitted by programs, warning messages emitted by compilers and interpreters, and many other specific contexts.

We are basically always wrong.