Tags: ai

Poisoning Well: HeydonWorks

Heydon is employing a different tactic to what I’m doing to sabotage large language model crawlers. These bots don’t respect the nofollow rel value …so now they pay the price.

Raising my own middle finger to LLM manufacturers will achieve little on its own. If doing this even works at all. But if lots of writers put something similar in place, I wonder what the effect would be. Maybe we would start seeing more—and more obvious—gibberish emerging in generative AI output. Perhaps LLM owners would start to think twice about disrespecting the nofollow protocol.

Bored of it · Paul Robert Lloyd

Same.

Open source devs say AI crawlers dominate traffic, forcing blocks on entire countries - Ars Technica

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.

Go To Hellman: AI bots are destroying Open Access

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.

FOSS infrastructure is under attack by AI companies

More on how large language bots are DDOSing the web:

LLM scrapers are taking down FOSS projects’ infrastructure, and it’s getting worse.

Please stop externalizing your costs directly into my face

Over the past few months, instead of working on our priorities at SourceHut, I have spent anywhere from 20-100% of my time in any given week mitigating hyper-aggressive LLM crawlers at scale.

This matches my experience with The Session. In fact, while I had this article open in a tab, I had to go deal with a tsunami of large language model bots. It’s really fucking depressing.

Please stop legitimizing LLMs or AI image generators or GitHub Copilot or any of this garbage. I am begging you to stop using them, stop talking about them, stop making new ones, just stop. If blasting CO2 into the air and ruining all of our freshwater and traumatizing cheap laborers and making every sysadmin you know miserable and ripping off code and books and art at scale and ruining our fucking democracy isn’t enough for you to leave this shit alone, what is?

Make stuff, on your own, first | Sean Voisen

AI can be incredibly useful when deployed skillfully in creative endeavors—as an ideation partner, as a scaffolding tool, by eliminating tedious tasks, etc.—but anyone making anything truly good with it is probably somebody who could already make something good first without it.

Dan wrote an interesting post with a somewhat clickbaity title; This Competition Exposed How AI is Reshaping Design:

I watched two designers go head-to-head in a high-speed battle to create the best landing page in 45 minutes. One was a seasoned pro. The other was a non-designer using AI.

If you can ignore the title (and the fact that Dan still actively posts on Twitter; something I find very hard to ignore), then there’s a really thoughtful analysis in there.

It’s less about one platform or tool vs. another more than it is a commentary on how design happens, and whether or not that’s changing in a significant way.

In particular, there’s a very revealing graph that shows the pros and cons of both approaches.

There’s no doubt about it, using a generative large language model helped a non-designer to get past the blank page. But it was less useful in subsequent iterations that rely on decision-making:

I’ve said it before and I’ll say it again: design is deciding. The best designers are the best deciders.

Dan finishes by saying that what he’d really like to see is an experienced designer/decider using these tools to turbo-boost their process:

AI raises the floor for non-designers. But can it raise the ceiling for designers?

Meanwhile, Matt has been writing about Vibe-designing. Matt is an experienced designer, but he’s not experienced with Figma. He’s found that he can work around that using a large language model:

Where in the past 30 years I might have had to cajole a more technically adept colleague into making something through sketches, gesticulating and making sound effects – I open up a Claude window and start what-iffing.

The “vibe” part of the equation often defaults to the mean, which is not a surprise when you think about what you’re asking to help is a staggeringly-massive machine for producing generally-unsurprising satisfactory answers quickly. So, you look at the output as a basis for the next sketch, and the next sketch and quickly, together, you move to something more novel as a result.

Interesting! Just as Dan insisted, the important work is making the decision and moving on to the next stage. If the actual outputs at each stage are mediocre, that seems to be okay, as long as they’re just good enough to inform a go/no-go decision.

This certainly seems more centaur-like than the usual boring uses of large language models to simply do what people are already doing.

Rich gets at something similar when he talks about using large language models for prototyping, where it’s okay if the code is kind of shitty:

If all you need is crappy code to try out a concept or a solution, then an LLM might well enable you (the designer) to do that.

Mind you, even if you do end up finding useful and appropriate ways to use these tools, you’re still using a tool built on exploitation and unfairness:

It’s hard (and reckless) to ignore the heartfelt and cogent perspective laid out by Miriam on the role of AI companies in the current geopolitical crisis:

When eugenics-obsessed billionaires try to sell me a new toy, I don’t ask how many keystrokes it will save me at work. It’s impossible for me to discuss the utility of a thing when I fundamentally disagree with the purpose of it.

Another uncalled-for blog post about the ethics of using AI | Clagnut by Richard Rutter

This is a really thoughtful piece by Rich, who’s got conflicted feelings about large language models in the design process. I suspect a lot of people can relate to this.

What I do know is that I find LLMs useful on occasion, but every time I use one I die a little inside.

In the way

This sums up my experience of companies and products trying to inject AI in to the products I use to communicate with other people. It’s always just in the way, making stupid suggestions.

Build It Yourself | Armin Ronacher’s Thoughts and Writings

We’re at a point in the most ecosystems where pulling in libraries is not just the default action, it’s seen positively: “Look how modular and composable my code is!” Actually, it might just be a symptom of never wanting to type out more than a few lines.

It always amazes me when people don’t view dependencies as liabilities. To me it feels like the coding equivalent of going to a loan shark. You are asking for technical debt.

There are entire companies who are making a living of supplying you with the tools needed to deal with your dependency mess. In the name of security, we’re pushed to having dependencies and keeping them up to date, despite most of those dependencies being the primary source of security problems.

But there is a simpler path. You write code yourself. Sure, it’s more work up front, but once it’s written, it’s done.

“Wait, not like that”: Free and open access in the age of generative AI

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.

Hallucinations in code are the least dangerous form of LLM mistakes

The moment you run LLM generated code, any hallucinated methods will be instantly obvious: you’ll get an error. You can fix that yourself or you can feed the error back into the LLM and watch it correct itself.

Compare this to hallucinations in regular prose, where you need a critical eye, strong intuitions and well developed fact checking skills to avoid sharing information that’s incorrect and directly harmful to your reputation.

With code you get a powerful form of fact checking for free. Run the code, see if it works.

Severance Is the Future Tech Bros Want - Reactor

The tech bros advocating for generative AI to take over art are at the same level of cultural refinement as the characters in Severance. They’re creating apps to summarize books to people, tweeting from accounts with Greek statue profile pictures.

GenAI would automate Lumon’s cultural mission, allowing humans to sever themselves from the production of art and culture.

Anchor position tool

This is a great little helper in understanding anchor positioning in CSS.

Chrome-only for now.

Generative AI use and human agency

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.

The Gist: AI, a talking dog for the 21st Century.

My main problem with AI is not that that it creates ugly, immoral, boring slop (which it does). Nor even that it disenfranchises artists and impoverishes workers, (though it does that too).

No, my main problem with AI is that its current pitch to the public is suffused with so much unsubstantiated bullshit, that I cannot banish from my thoughts the sight of a well-dressed man peddling a miraculous talking dog.

Also, trust:

They’ve also managed to muddy the waters of online information gathering to the point that that even if we scrubbed every trace of those hallucinations from the internet – a likely impossible task - the resulting lack of trust could never quite be purged. Imagine, if you will, the release of a car which was not only dangerous and unusable in and of itself, but which made people think twice before ever entering any car again, by any manufacturer, so long as they lived. How certain were you, five years ago, that an odd ingredient in an online recipe was merely an idiosyncratic choice by a quirky, or incompetent, chef, rather than a fatal addition by a robot? How certain are you now?

The Generative AI Con

I Feel Like I’m Going Insane

Everywhere you look, the media is telling you that OpenAI and their ilk are the future, that they’re building “advanced artificial intelligence” that can take “human-like actions,” but when you look at any of this shit for more than two seconds it’s abundantly clear that it absolutely isn’t and absolutely can’t.

Despite the hype, the marketing, the tens of thousands of media articles, the trillions of dollars in market capitalization, none of this feels real, or at least real enough to sustain this miserable, specious bubble.

We are in the midst of a group delusion — a consequence of an economy ruled by people that do not participate in labor of any kind outside of sending and receiving emails and going to lunches that last several hours — where the people with the money do not understand or care about human beings.

Their narrative is built on a mixture of hysteria, hype, and deeply cynical hope in the hearts of men that dream of automating away jobs that they would never, ever do themselves.

Generative AI is a financial, ecological and social time bomb, and I believe that it’s fundamentally damaging the relationship between the tech industry and society, while also shining a glaring, blinding light on the disconnection between the powerful and regular people. The fact that Sam Altman can ship such mediocre software and get more coverage and attention than every meaningful scientific breakthrough of the last five years combined is a sign that our society is sick, our media is broken, and that the tech industry thinks we’re all fucking morons.

The hardest working font in Manhattan – Aresluna

This is absolutely wonderful!

There’s deep dives and then there’s Marcin’s deeeeeeep dives. Sit back and enjoy this wholesome detective work, all beautifully presented with lovely interactive elements.

This is what the web is for!

AI is Stifling Tech Adoption | Vale.Rocks

Want to use all those great features that have been in landing in browsers over the past year or two? View transitions! Scroll-driven animations! So much more!

Well, your coding co-pilot is not going to going to be of any help.

Large language models, especially those on the scale of many of the most accessible, popular hosted options, take humongous datasets and long periods to train. By the time everything has been scraped and a dataset has been built, the set is on some level already obsolete. Then, before a model can reach the hands of consumers, time must be taken to train and evaluate it, and then even more to finally deploy it.

Once it has finally released, it usually remains stagnant in terms of having its knowledge updated. This creates an AI knowledge gap. A period between the present and AI’s training cutoff. This gap creates a time between when a new technology emerges and when AI systems can effectively support user needs regarding its adoption, meaning that models will not be able to service users requesting assistance with new technologies, thus disincentivising their use.

So we get this instead:

I’ve anecdotally noticed that many AI tools have a ‘preference’ for React and Tailwind when asked to tackle a web-based task, or even to create any app involving an interface at all.