This is such a brilliant idea! Why not allow an img element inside video element in order to provide a responsive, accessible poster image?
You know my thoughts on generative tools based on large language models, but this example of personal empowerment is undeniably liberating.
The issue isn’t with the code itself, but with the understanding of the code.
That’s the difference between technical debt and cognitive debt.
There’s a new meta tag on the block. This time it’s for allowing system-level text sizing to apply to your website.
Laura’s classic book is now a web book that you can read for free online.
Machine learning is amazing if … the value of a correct answer is much higher than the cost of an incorrect answer.
Related to Laissez-faire Cognitive Debt:
And that’s where I start to get really annoyed by a lot of the LLM hype. It’s pushing machine-learning approaches into places where there are significant harms for sometimes giving the wrong answer. And it’s doing so while trying to outsource the liability to the customers who are using these machines in ways in which they are advertised as working. It’s great for translation! Unless a mistranslated word could kill a business deal or start a war. It’s great for summarisation! Unless missing a key point could cost you a load of money. It’s great for writing code! Unless a security vulnerability would cost you lost revenue or a copyright infringement lawsuit from having accidentally put something from the training set directly in your codebase in contravention of its license would kill your business. And so on. Lots of risks that are outsourced and liabilities that are passed directly to the user.
I think of Cognitive Debt as ‘where we have the answers, but not the thinking that went into producing those answers’.
Lately, I have started noticing examples of not just where the debt is being accrued, but who then has the responsibility to pick it up and repay it.
Too often, an LLM doesn’t replace the need for thinking in a group setting, but simply creates more work for others.
LLMs are useful when you need a compromise between fast and good. You will never get a good outcome fast.
I’m afraid we are settling into a status of good enough when using “AI,” which is especially hurtful for accessibility.
I like the idea of adding this to personal websites:
Mastodon shows an “Alt” button in the bottom right of images that have associated alt text. This button, when clicked, shows the alt text the author has written for the image.
I don’t normally link to articles on Medium—I respect you too much—and I do wish this were written on Mike Hall’s own site, but this is just too good not to share.
And don’t dismiss this as a nostalgiac case study from the past:
At no point did the constraints make the product feel compromised. Users on modern devices got a smooth experience and instant feedback, while those on older devices got fast, reliable functionality. Users on feature phones got the same core experience without the bells and whistles.
The constraints forced us to solve problems in ways we wouldn’t have considered otherwise. Without those constraints, we could have just thrown bytes at the problem, but with them every feature had to justify itself. Core functionality had to work everywhere, and without JavaScript crutches proper markup became essential.
This experience changed how I approach design problems. Constraints aren’t a straitjacket, keeping us from doing our best work; they are the foundation that makes innovation possible. When you have to work within severe limitations, you find elegant solutions that scale beyond those limitations.
I heard you like divs…
As it currently stands, both the rapid growth of AI-generated content overwhelming online spaces and aggressive web-crawling practices by AI firms threaten the sustainability of essential online resources. The current approach taken by some large AI companies—extracting vast amounts of data from open-source projects without clear consent or compensation—risks severely damaging the very digital ecosystem on which these AI models depend.
AI companies with billions to burn are hard at work destroying the websites of libraries, archives, non-profit organizations, and scholarly publishers, anyone who is working to make quality information universally available on the internet.
Anyone at an AI company who stops to think for half a second should be able to recognize they have a vampiric relationship with the commons. While they rely on these repositories for their sustenance, their adversarial and disrespectful relationships with creators reduce the incentives for anyone to make their work publicly available going forward (freely licensed or otherwise). They drain resources from maintainers of those common repositories often without any compensation.
Even if AI companies don’t care about the benefit to the common good, it shouldn’t be hard for them to understand that by bleeding these projects dry, they are destroying their own food supply.
And yet many AI companies seem to give very little thought to this, seemingly looking only at the months in front of them rather than operating on years-long timescales. (Though perhaps anyone who has observed AI companies’ activities more generally will be unsurprised to see that they do not act as though they believe their businesses will be sustainable on the order of years.)
It would be very wise for these companies to immediately begin prioritizing the ongoing health of the commons, so that they do not wind up strangling their golden goose. It would also be very wise for the rest of us to not rely on AI companies to suddenly, miraculously come to their senses or develop a conscience en masse.
Instead, we must ensure that mechanisms are in place to force AI companies to engage with these repositories on their creators’ terms.
A fascinating look at the modern equivalent of the Longitude problem.
Many of us got excited about technology because of the web, and are discovering, latterly, that it was always the web itself — rather than technology as a whole — that we were excited about. The web is a movement: more than a set of protocols, languages, and software, it was always about bringing about a social and cultural shift that removed traditional gatekeepers to publishing and being heard.
You do not have to use generative AI.
AI itself cannot be held to account.
If you use AI, you are the one who is accountable for whatever you produce with it.
There are contexts in which it is immoral to use generative AI.
Correcting or fact checking generative AI may take longer than just doing a task yourself, or with conventional AI tools.
You do not have to use generative AI.
This magnificent piece by Maxwell Neely-Cohen—with some tasteful art-direction—is right up my alley!
This piece looks at a single question. If you, right now, had the goal of digitally storing something for 100 years, how should you even begin to think about making that happen? How should the bits in your stewardship be stored with such a target in mind? How do our methods and platforms look when considered under the harsh unknowns of a century? There are plenty of worthy related subjects and discourses that this piece does not touch at all. This is not a piece about the sheer volume of data we are creating each day, and how we might store all of it. Nor is it a piece about the extremely tough curatorial process of deciding what is and isn’t worth preserving and storing. It is about longevity, about the potential methods of preserving what we make for future generations, about how we make bits endure. If you had to store something for 100 years, how would you do it? That’s it.
A ban on tracking-based personalised advertising will provide an incentive to reinforce sustainable alternative models and, in fact, will be a condition for making them viable. The advertising industry already has sustainable, proven concepts for effective online advertising that do not require targeted tracking and personalisation (e.g. contextual advertising).