What I’ve learned about writing AI apps so far | Seldo.com

LLMs are good at transforming text into less text

Laurie is really onto something with this:

This is the biggest and most fundamental thing about LLMs, and a great rule of thumb for what’s going to be an effective LLM application. Is what you’re doing taking a large amount of text and asking the LLM to convert it into a smaller amount of text? Then it’s probably going to be great at it. If you’re asking it to convert into a roughly equal amount of text it will be so-so. If you’re asking it to create more text than you gave it, forget about it.

Depending how much of the hype around AI you’ve taken on board, the idea that they “take text and turn it into less text” might seem gigantic back-pedal away from previous claims of what AI can do. But taking text and turning it into less text is still an enormous field of endeavour, and a huge market. It’s still very exciting, all the more exciting because it’s got clear boundaries and isn’t hype-driven over-reaching, or dependent on LLMs overnight becoming way better than they currently are.

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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.

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Tech continues to be political | Miriam Eric Suzanne

Being “in tech” in 2025 is depressing, and if I’m going to stick around, I need to remember why I’m here.

This. A million times, this.

I urge you to read what Miriam has written here. She has articulated everything I’ve been feeling.

I don’t know how to participate in a community that so eagerly brushes aside the active and intentional/foundational harms of a technology. In return for what? Faster copypasta? Automation tools being rebranded as an “agentic” web? Assurance that we won’t be left behind?

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AI wants to rule the World, but it can’t handle dairy.

AI has the same problem that I saw ten year ago at IBM. And remember that IBM has been at this AI game for a very long time. Much longer than OpenAI or any of the new kids on the block. All of the shit we’re seeing today? Anyone who worked on or near Watson saw or experienced the same problems long ago.

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What happens to what we’ve already created? - The History of the Web

We wonder often if what is created by AI has any value, and at what cost to artists and creators. These are important considerations. But we need to also wonder what AI is taking from what has already been created.

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Is it okay?

Robin takes a fair and balanced look at the ethics of using large language models.

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