For the majority of these, a simple google search would have lead to an existing program/website that does the same thing.
We're past the POC stage. LLMs can generate code for simple programs. It's when you try to tweak the requirements and point how a program introduces a bug that you eventually realize they still fail to take you through the last mile just as they did year and a half ago.
"For the majority of these, a simple google search would have lead to an existing program/website that does the same thing."
That's what's so wild about this: that's true, and yet in most of those cases it's still faster and more productive for me to ask Claude to build me a brand new tool _from scratch_ than it is for me to try and find an existing one via Google.
The problem with trying to Google for these kinds of things is that you have to evaluate the results that come back and figure out which one of them correctly solves your problem. That's a few extra steps.
It's genuinely faster to prompt something like this instead:
> Build an artifact (no react) where I can paste text into a textarea and it will return that text with all HTML entities - single and double quotes and less than greater than ampersand - correctly escaped. The output should be in a textarea accompanied by a "Copy to clipboard" button which changes text to "Copied!" for 1.5s after you click it. Make it mobile friendly
In this case I knew exactly what I wanted: it had to do less than, greater than, ampersand, double quotes AND single quotes. I know from past experience that many tools like this forget about single quotes, so I'd have to evaluate any tools I found to check that they do that. And I was on my phone so I wanted a "copy to clipboard" button.
Simon, all these example are great and on a side note I've learned a lot through your writing. However, as so many other AI for low/no code examples, it starts feeling like trying to perform a dexterity intensive task such as knotting while wearing thick gloves. All these examples are great on their own. What to me feels so wrong about seeing so many of these on HN front page is that my experience has been that even after almost 2 years from when ChatGPT was initially introduced, all models that I've been using improved a lot in accuracy, speed, input/output size but still more often than not fail to be useful when used with even a small _existing_ code base and an evolving set of tasks that aren't the busy work / boiler plate type.
I'm working on a longer piece of writing about this, but I think a lot of this comes down to the fact that my personal style of developing code is extremely compatible with LLMs.
I have hundreds of projects which are small single page HTML apps, single purpose command line, utilities or plugins for my larger projects.
All of these are short enough to fit into the context window of an LLM.
If all I worked on were 1-2 hundred thousand or million line applications I would get significantly less value out of LLMs.
We're past the POC stage. LLMs can generate code for simple programs. It's when you try to tweak the requirements and point how a program introduces a bug that you eventually realize they still fail to take you through the last mile just as they did year and a half ago.