Snippets

Training data are construction materials for a language models. A language model can never be inspired. It is itself a cultural artefact derived from other cultural artefacts.

The machine learning process is loosely based on decades-old grossly simplified models of how brains work.

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It’s important to remember this so that we don’t fall for marketing claims that constantly imply that these tools are fully functioning assistants.

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Who was the first man on the moon?

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What you’re likely to get back from that prompt would be something like:

On July 20, 1969, Neil Armstrong became the first human to step on the moon.

This is NASA’s own phrasing. Most answers on the web are likely to be variations on this, so the answer from a language model is likely to be so too.

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The prompt we provided is strongly associated in the training data set with other sentences that are all variations of NASA’s phrasing of the answer. The model won’t answer with just “Neil Armstrong” because it isn’t actually answering the question, it’s responding with the text that correlates with the question. It doesn’t “know” anything.

A core aspect of the theory-building model of software development is code that developers don’t understand is a liability. It means your mental model of the software is inaccurate which will lead you to create bugs as you modify it or add other components that interact with pieces you don’t understand.

Language model tools for software development are specifically designed to create large volumes of code that the programmer doesn’t understand. They are liability engines for all but the most experienced developer. You can’t solve this problem by having the “AI” understand the codebase and how its various components interact with each other because a language model isn’t a mind. It can’t have a mental model of anything. It only works through correlation.

A great interactive demo showcasing just about all the major obnoxious dark patterns a lot of larger websites use to make our lives miserable. Not the dystopian future we were promised.

Instead of attaching tooltips directly to document.body, attach them to a predefined div in document.body.

BAD

GOOD

Introduction

Tooltips in our app were taking >80ms. And during this time, the main thread was blocked, you couldn’t interact with anything.

Other components like modal, popover, dropdown had similar performance issues. In some cases, a modal took more than 1 second to appear while making the UI unresponsive.

Read on in the source link for details.

js;dr is JavaScript required; Didn’t Read.

Pages that are empty without JS: dead to history (archive-org), unreliable for search results (despite any search engine claims of JS support, check it yourself), and thus ignorable. No need to waste time reading or responding.

Also known as, if it’s not curlable, it’s not on the web.

I wish more people understood that “I want the computer to generate a natural language text that sounds like a plausible answer to a question about x” and “I want the computer to answer a question about x” are two very different problems.

Visually-hidden styles are used to hide content from most users, while keeping it accessible to assistive technology users.

It works because the content is technically visible and displayed — it appears in the accessibility tree and the render tree, both of which are used by assistive technologies — it’s just that the rendered size is zero.

Our industry has largely settled on a standard CSS pattern for this, refined over years of testing and iteration, by many people. This pattern:

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This article is not about when or why you would use visually-hidden content. There’s a number of excellent articles that discuss these questions in detail, notably Scott O’Hara’s Inclusively Hidden. But most of them don’t go into much detail about the specific CSS involved — why do we use this particular pattern, with these specific properties? So today I’m going to dissect it, looking at each of the properties in turn, why it’s there, and why it isn’t something else.