2026-08-17 03:00:00
Gordon Brander writes:
The thing about computers is that they can do anything you wish, so long as you specify your wish in exacting detail. However, LLMs relieve this constraint. An LLM can extrapolate what you mean (more or less) from just a few words. So computers can do vibes now.
It used to be that computers could only ever do exactly what you told them to do, nothing more.
If they did something unexpected (a.k.a. a bug), it was because you failed to 1) anticipate it could happen, or 2) specify with greater detail that it shouldn’t happen.
But in this new world, computers can extrapolate your intent.
LLMS can act like a fill-in-the-gap machine, which alleviates you from the pedantic demands of coding from years past.
There’s going to be a lot more “Oops, I didn’t mean to do that” in the future.
This should be what separates professional practitioners from hobbyists.
A good practitioner recognizes that leveraging some foresight is part and parcel of the job in order to mitigate unintended consequences.
Meanwhile, a hobbyist or amateur doesn’t even have the capability or experience to understand the assumed intent of an LLM until it’s too late and users have been affected.
Software we use everyday, software that many aspects of our lives increasingly depend on, needs less unintended consequences. Less “Oops, should’ve thought of that.”
2026-08-15 03:00:00
My whole life I’ve felt like I have great eyesight (and I still do).
But lately I’ve been noticing what feels like…some slippage.
Nothing big. Just this feeling in the morning like, as my brain and body wake up, my vision needs to as well.
The strange novelty of this phenomenon was operating in some small part of my subconscious the other day when I was scrolling the internet and come across this post from Chris Coyier:
you ever get to your mid fourties and have your eyesight drop off a friggin cliff
And I’m all like:
And then even more people weighed in on Chris’ thread, like “Hey me too!”
This is why I love the web. I continually get that feeling of, “Oh hey, it’s not just me!” On both a personal and a professional level.
And for that I’m grateful.
I love being able to connect with people in a way that makes me not feel so alone (or weird) in my experience of life.
In a polarized digital world, it’s good to remember that the web can still draw you to folks rather than push them away.
We’ve got a lot in common.
2026-08-12 03:00:00
Terry Godier has a post titled “Mea culpa” (which, given his framing, might’ve been better titled “Claude’s Culpa”). I’m not sure how much to even trust anything in his post given the backstory, but this line stood out:
I was careless in relying on AI [...] without doing the work to understand
Let’s face it: carelessness is the grain of AI. It’s what the tool encourages and makes easy by default.
Without constant vigilance and deliberate, active participation to cut against this grain — to maintain an understanding — careless outcomes are the default fruit of AI.
And given how good humans are at being constantly vigilant and deliberate, it seems reasonable to expect more stories like this one.
To analogize, AI is pitched like having your very own fruit tree. “Plant a seed, and soon enough, boom! Yummy, juicy fruit for you! So fast and easy!”
But, like most things, it’s not that easy.
Getting good fruit requires all kinds of effort: planting, fertilizing, watering, pruning, thinning, cutting out disease, etc.
Good fruit requires cultivation and care.
Otherwise you’ll just end up with something that looks like a peach, but when you (or someone else) bites into it, you realize it’s disgusting and barely edible.
2026-08-10 03:00:00
In the past, search engines and the open web granted access to other people’s knowledge but the task fell to you to synthesize an understanding from it and build a capability to act.
Now with LLMs, it’s possible to build things you don’t fully understand which then require a perpetual license in order to have the capacity to maintain and modify.
LLMs don’t just do things for you, they do things instead of you — and the doing is a huge part of the point. The struggle to understand, to synthesize, to explain, it all has a byproduct: you! Your own capacity to act in the world.
But now you can rent that capacity. Every time you reach for the LLM instead of struggling through it yourself, you’re borrowing against someone else’s capacity that you’re not developing in yourself.
It’s as if I used to be a car mechanic, fixing my own vehicles or looking to other mechanics for advice or a demonstration on approaching my problems. That process produced a working car — the output I wanted — but it also produced a capability within myself to fix the next one that came along.
But now, for expedience and speed, I drop all my cars off the LLM dealer for fixes and maintenance. Fast and efficient — but also more dependent than ever.
(See also: Eric Bailey on evolved antennas.)
2026-07-30 03:00:00
Every zeitgeist comes with new design idioms unique to its challenges. Many of them disappear as fads change, but others bake themselves into deeper parts of existing software interaction paradigms.
For example, there’s the hamburger menu (≡) which saw a proliferation during the rise of mobile due to the constraints around screen size. It has since spread to many other parts of software interaction design and will likely remain prevalent for a long time as a terse way of indicating “more menu-type content here”.
As another example, before AI what were the connotations of the sparkle emoji ✨? Personally, I don’t know, but now it means AI. (AI = sparkles and rainbow colors — it’s funny when you think about it. They should’ve just thrown unicorns in there for the trifecta. AI = sparkles, rainbows, and unicorns ✨🌈🦄. Apt.)
Some patterns are very specific to the interactions inherent to the nature of AI as a technology. For example: streaming text. This is a pattern made for and refined by chat interfaces, so it may not have tons of utility for reuse across other software interaction paradigms.
Then there are other patterns that’ve been refined by AI interfaces and are starting to spread to other places in software. For example, the “shimmering text” which in AI land implies a kind of “thinking” but is being repurposed to indicate any kind of asynchronous task (thinking, fetching, computing, etc.).
Then there are other influences my subconscious is picking up on. For example, a lot of AI apps use tiny icons. These are most obvious (to me) in desktop Electron apps because they clash with the system-level grain of applications. Take a look at this screenshot, where you have desktop AI apps on the left (Claude, Codex, Cursor) and macOS apps from Apple on the right (Finder, Photos, Mail). You can see how the AI apps all have much smaller, thinner icons than their native counterparts.
Are tiny icons our collective future in interfacing with computers? (Personally, I hope not.)
There are other aesthetics my brain associates with AI, like beige/cream colors, orange accents, and serif typefaces as well as whack-a-mole UI controls (you know, the ones where you click the toggle and the entire UI repaints and you have to move your mouse somewhere else in the UI to click the toggle again? The non-determinism of AI’s grain has seeped into its UI/X).
It all makes me wonder what other aesthetics are being born out of this AI moment and how many will spread, take seed, and become part of common software interaction paradigms for years or decades to come?
2026-07-28 03:00:00
Jason Grigsby has a great article where he surfaces an opinion from the Safari team about how AI agents shouldn’t get special treatment:
An agent acting on a user’s behalf is, in effect, assistive technology: it should operate a site as the user would, and the site should not single it out for different treatment.
Jason synthesizes different discussions happening at standards levels to argue, in essence, that agents should be required to use existing technologies and solutions (APIs, semantics, etc.) rather than get their own bespoke ones. And where there are gaps in the platform, solutions should be centered around closing those gaps generally for everyone (vs. specifically for agents).
Imagine that! Take the billions being invested in AI and funnel it towards improving and enhancing the existing technology agents already use and profit from. No bespoke solutions just for AI, but generalized solutions everyone can benefit from.
In other words: allow the rising tide of AI investment to lift all boats in the platform because the web is for everyone. As Jason says:
If we’re solving this problem for AI, perhaps we can find a solution that works for end users too.
His suggestion being that maybe we should frame AI needs in the web platform the same way we do other needs in the web’s priority of constituents: user needs come before developer needs, implementor needs, spec writer needs — or even agent needs. (UX over DX over AX.)
Now for the funny part. Here’s Jason:
let’s set aside for the moment the irony that AI is supposed to replace all of our jobs and become a super intelligence and at the same time we also need to add special AI training wheels for it to use the web.
It’s like that person you know who prides themselves on their independence, that they don’t bend to society and culture, and that they don’t need anyone or anything — oh, and by the way, could you spot them twenty bucks?
