Link tags: complete

Dynamic Datalist: Autocomplete from an API :: Aaron Gustafson

Great minds think alike! I have a very similar HTML web component on the front page of The Session called input-autosuggest.

AI doesn’t need to think. We do! - craigabbott.co.uk

A good overview of how large language models work:

The words flow together because they’ve been seen together many times. But that doesn’t mean they’re right. It just means they’re coherent.

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.

Fine-tuning Text Inputs

Garrett talks through some handy HTML attributes: spellcheck, autofocus, autocapitalize, autocomplete, and autocorrect:

While they feel like small details, when we set these attributes on inputs, we streamline things for visitors while also guiding the browser on when it should just get out of the way.

Vibe Shift

Forget every article you’ve read that tries to explain large language models. Just read this post by Peter and feel it.