Jim Nielsen’s Blog

807 blog posts across 14 years.

That’s how much I’ve published on my blog at the time of this writing.

And here’s the question I’ve been turning over in my mind: “How do I convey that kind of volume across time in a more interesting way than a mere reverse-chronological list?”

I’m not hating on reverse-chronological lists. I love my list view. I use it all the time to find stuff I’ve written.

But it’s only one way of navigating and digesting all my posts.

“What would be another way?”

Surely there are many answers to that question. And I’ll probably be exploring them more and more over time.

But I had an idea for a new view that I built out and shipped: my calendar view.

It’s also just a reverse-chronological view, but it’s meant to convey a sense of posting patterns through time more than it is meant to be a good browsing experience of content.

The view is simple: a calendar view of days in each year, and if I posted on a day, it gets a circle (bonus: if a post hit hacker news, it gets an orange square instead).

You can click on the dots to see the names of the posts from that day (and follow the link to them, but really this view is basically to scratch an itch of mine.

Because you can just make stuff for yourself, and that’s what I’ve done here. And now I’m writing about it because that means I get another little circle for today! Good job, Jimbo.

Check it out

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Dave Rupert wrote about shipping microlighter: a tool for handling syntax highlighting using the CSS Custom Highlights API. I saw his post the day he released it, and I had an implementation PR up for my blog by end of day.

Then, like I do with so many things, I let it sit there.

This is the period where my subconscious takes over. It does the work of, “How do I actually feel about that? Do I want to merge it? Do I have any regrets about what I did?” If I still want to merge it after a few days, that’s usually a good sign that I’ll be happy with the work. (Sometimes after a few days I say, “What the hell was I thinking?” and then it’s easy to simply close the PR with zero regrets.)

Well it’s a few days later and I still feel good about it, so time to ship!

My PR for this is pretty straightforward:

• Remove highlight.js dependency (and related plumbing)
• On paths that 1) match my post pages (i.e. /YYYY/:slug), and 2) have code on them, pull microlighter deps from a CDN and run it.
• Done.

Granted, there are trade-offs to this approach. I get it. Dave’s explainer for this tool on The ShopTalk Show vibed with me because I’ve been in his shoes many times: “Whoops, somehow syntax highlighting on my blog is broken again. Guess I need to fix it. Ugh. I’ve done prism, I’ve done highlight.js, I’ve done shiki. What should I do this time? Could I do this in a way that’s just less?” He clarifies:

I’m not coming at this like, “Everyone is doing it wrong!” I was just kind of like, “Could I do this in a way that suited me?”

Well, this approach suites me.

There’s a kind of conceptual elegance to it where syntax highlighting lives in the realm of a styling operation rather than a content transformation plus styling. In short: syntax highlighting, i.e. styling text, is a styling concern so solve it with CSS — no DOM manipulation required!

Plus, I mean, how cool is it that the code on the website is the same as the code in the DOM?!?

I guess this is how I know I still like working on the web, because seeing browsers do stuff like this that they couldn’t do before still feels really cool!

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On my icon gallery sites, I have metadata I’ve manually added over the years to tag certain icons as being predominantly “blue” or “orange” or some other color.

Then I use this metadata to present icons of (roughly) the same color. It’s kinda neat to be able to browse a wall of icons that are all the same color.

The thing is: I know there are a lot of icons I’ve missed tagging over the years. But I have no idea how many, and figuring that out seems like a really arduous task. How do I go through 2,000+ icons and find all the ones that look predominantly “orange” but haven’t been tagged as such yet?

Seems like a good task to throw at an LLM. But I don’t want to just say, “Go tag everything that’s missing” and blindly trust the output. I need to be able to make a decisions as to whether I think a particular color is “orange” or not.

What I need is a tool for the job. I’m a very visual thinker, so to continue curating these color categorizations, I need some way for the computer to do its thing really, really fast, and then pull me into the loop to visually make decisions.

Here’s how I am thinking about about accomplishing this task:

• Create a page with a list of colors on the left (red, green, orange, blue, etc.)
• When a color is clicked, show two columns. 1) All icons I currently have tagged for that color, and 2) all icons that might be that color but aren’t tagged as such (you, computer, process all my icons and do the work to figure this out and make recommendations).
• Allow me to select one or more icon(s) in the “not yet tagged as this color” column. Once I have all the ones I perceive as missing, give me a button to say “Copy” which copies the IDs of those icons.
• I’ll paste the IDs back here in the chat and you go add the corresponding metadata.

That seems like it would be a good tool to put me in control of visual decision making around color categorization. So I tell the LLM to run with it.

We chat back and forth. I think, “You probably need to run all the icons through some model to make the correlation?” But it’s like, “Nah bro, just make a ‘hue histogram’.” It tells me how. For a color like “orange”, I can:

[process] each PNG, skip transparent pixels, skip low-saturation gray, convert the rest to HSV, and score how much of the remaining mass sits in the orange hue band (roughly 15–45°). Rank icons that don’t already have colorId: orange.

Ok, sure. That sounds reasonable.

[This] scores each icon PNG by share of opaque pixels per color bucket, then writes a standalone HTML page: tagged vs maybe-missing, per color.

Let’s just make it, and then I’ll decide whether it’s good enough.

After a few iterations, the computer going “brr…”, and me saying “explain that like I’m dumb”, I have a really effective little tool!

The little threshold slider is a nice touch. It lets me fiddle around with the fidelity of the matches. In some cases, sliding it down reveals more icons I would’ve otherwise missed. In other cases, I’m like “What are you thinking? I don’t see that as ‘yellow’ at all!”

Supper effective little tool. I go through each color, select the ones I think are missing, paste the IDs back into the LLM, and then have it update each icon's metadata.

Boom, done! That all would’ve taken so long before. I would’ve never done it.

Takeaways:

• The LLM is good at making throw-away code. This doesn’t need to be “production-grade” code I depend on. Just something that’s good enough for me to get a job done, then toss. The resulting metadata is the goal, not the tool I use to get to the goal.
• The LLM is good at making one-off HTML pages for a specific task. In my case, all these images were hosted on a CDN, easy enough to just point at and have a standalone .html file that I can locate on my hard drive and open directly as a file:// URL. No bundling. No transpilation. None of that. I don’t even need a web server! Keeping things very basic on this project is paying off: I had really elemental building blocks that didn’t require additional third-party tooling. Just HTML, CSS, a little in-page JS, and images on a server!
• It’s fun to say, “Don’t do the work for me. Instead, help me make a custom-fit tool that facilitates me doing the work in the most empowering, correct way possible.”

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In his talk “Why AI Is Breaking Software Security As We Know It” (my notes here), Feross Aboukhadijeh talks about the Axios npm incident and how the maintainer got phished by succumbing to (amongst other things) a faux Microsoft Teams interface:

this is the kind of thing that AI makes easy to do, because it can vibe code that whole fake Microsoft Teams interface pretty trivially

You’ve probably seen these: interfaces designed to look like some other product in order to provide a facade of authenticity and exploit someone.

What struck me in listening to Feross was this idea of how the quality of your interfaces can be a protection mechanism against attackers.

I don’t know if I’ve ever heard someone say that out loud — interface and interaction design as a security control — but I’m saying it.

Now, of course, not everyone will consciously notice the level of polish that world-class professionals imbue in digital interfaces. But some will.

Personally, I’ve always used the quality and care of digital experiences as a heuristic for judging authenticity — and competency to be honest, e.g. “If this UI is so bad, what else will surely be bad?”

Granted, it was a much more dependable heuristic before AI came along. But even now, I can still suss out slop and carelessness which is a skill that continues to be a reliable, protective form of digital literacy (for me).

That’s all to say: a sloppy, careless approach to interface design not only hurts your brand in terms of customer perception, but it can be an attack vector. The easier it is to sloppily reproduce what you sloppily ship, the easier it will be for your product or brand to be leveraged as a vehicle for exploiting your customers.

If everything you make was produced from a single prompt, then everyone else is one prompt away from imitating you. The easier something is to make, the more likely it’ll be in the genre of “easy to exploit”.

One way to protect yourself (it’s not the only one way, security is never a binary “you are / are not secure”) is to do that extra work to make your experiences go above and beyond what you can easily get out of an LLM.

The protection here is having an interface and experience that is hard to replicate with the same level of fidelity that discerning users will notice — things like micro-interactions, loading behavior, UI copy and voice, handling of edge-cases, etc. That’s the stuff that’s hard (and expensive) to fake because it’s hard (and expensive) to notice you need to fake it.

tl;dr — Fidelity to craft is not only valuable from a product standpoint, but it’s also valuable from security standpoint. If attackers are going after low-hanging fruit, your fruit will be harder to reach if it’s up high.

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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.”

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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.

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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.

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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.)

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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?

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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?

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