AI is bad at figuring out what to do, but fantastic at actually doing it.
I’ve totally transformed how I write code from writing it to myself to writing detailed instructions and having the AI do it.
It’s so much faster and less cognitively demanding. It frees me up to focus on the business logic or the next change I want to make. Or to go grab a coffee.
I would say from my experience there's a high variability in AI's ability to actually write code unless you're just writing a lot of scripts and basic UI components.
The AI version of that Kent Beck mantra is probably "Make the change tedious but trivial (warning: this may be hard). Then make the AI do the tedious and trivial change."
AI's advantage is that it has infinite stamina, so if your
can make your hard problem a marathon of easy problems it becomes doable.
It can still fuck it up. And you need to actually read the code. But still a time saver for certain trivial tasks. Like if I'm going to scrape a web page as a cron job I can pretty much just tell it here's the URL, here's the XPath for the elements I want, and it'll take it from there. Read over the few dozen lines of code, run it and we're done in a few minutes.
You might want to ask ChatGPT what that is referencing. Specifically, Steve Jobs telling everyone it was their fault that Apple put the antenna right where people hold their phones and it was their fault they had bad reception.
The issue is really that LLMs are impossible to deterministically control, and no one has any real advice on how to deterministically get what you want from them.
I recognized the reference. I just don’t think it applies here.
The iPhone antenna issue was a design flaw. It’s not reasonable to tell people to hold a phone in a certain way. Most phones are built without a similar flaw.
LLMs are of course nondeterministic. That doesn’t mean they can’t be useful tools. And there isn’t a clear solution similar to how there was a clear solution to the iPhone problem.
"Antennagate" is the gift that keeps on giving. Great litmus test for pundits and online peanut galleries.
You are technically correct: it was a design flaw.
But folks usually incorrectly trot it out as an example of a manufacturer who arrogantly blamed users for a major product flaw.
The reality is that that essentially nobody experienced this issue in real life. The problem disappeared as long as you used a cell phone case. Which is how 99.99% of people use their phones. To experience the issue in real life you had to use the phone "naked", hold it a certain way, and have slightly spotty reception to begin with.
So when people incorrectly trot this one out I can just ignore the rest of what they're saying...
It will make a mess but if you drop a console.log into the browser debug console to show the AI what it should be looking for after it spent 3 hours failing to help understand and debug the problem, it will do 1 week of work in 2 hours.
I've noticed this too. The latest cursor version has a @browser command which will launch a browser with playwright, and call tools to inspect the html and inject JavaScript to debug in real-time.
When it has access to the right tools, it does a decent job, especially for fixing CSS issues.
But when it can't see the artifacts it's debugging, it starts guessing, confident that it knows the root cause.
A recent example: I was building up a html element out of the DOM and exporting to PNG using html2canvas. The element was being rendered correctly in the DOM, but the exported image was incorrect, and it spent 2 hours spinning it's wheels and repeating the same fixes over and over.
I’ve totally transformed how I write code from writing it to myself to writing detailed instructions and having the AI do it.
It’s so much faster and less cognitively demanding. It frees me up to focus on the business logic or the next change I want to make. Or to go grab a coffee.