So the author claims he's getting $2000 per month worth of frontier AI free of charge. Ok. If he's been doing that for 6 months that's $12k. What has this produced concretely? For $12k you can find a used car in decent condition. Heck for $1200 (his actual out-of-pocket spend) you get a brand new ebike! (on which you could put a pelican and make a photo of both if that's your fancy). But here it's unclear what has come of it.
(It's mostly open source, you're welcome to dig around in https://github.com/simonw and https://github.com/datasette if you like.)
My time as an experienced software engineer is worth a lot of money - a whole lot more than $12,000 for the past six months.
(I have a feeling if I could say "and I closed $2m in sales with the software I wrote!" people would find a way to say that didn't mean anything anyway, because how can I prove I wouldn't have made those sales writing it by hand?)
Given the audience you are reaching, that is actually the expectation. Github stars is not a great metric.
As you might suspect, this is what I have an issue with. Without LLMs, isn't it possible or even likely that that code wouldn't have been written at all, and wouldn't have been missed? If LLMs are mostly used to produce throwaway prototypes then it's a stretch to say that's money well spent.
If indeed it let you advance your main product much faster then sure it's a different story. You're the judge of that. It's hard to see the impact from the consumer side; everything is still broken and no extraordinary app seems to be emerging. Maybe it's just a question of time. We'll see.
And that is likely a fair assessment, though I understand perfectly the feeling that you have that you are accomplishing great(er) things thanks to AI.
I take some reassurance from knowing that they are indeed used by real people to solve real problems though.
How long would it taken you to do it yourself? How much longer will the next task take you, compared to when you would’ve written the code yourself. How is the mental model compared to when you would’ve written it yourself?.
I’m not saying you’re wrong, again there are use cases. But the calculation is not plain and simple it goes deep into our perception, perceived productivity versus actual productivity.
I’ve 2 months maxxed out all 6k of Claude Code and bought Antigravity on top. My codebase became 140k lines. I introduced tons of bugs and spent another 2 months, deleting 80k of code. I wish I would’ve just chatted with AI and not let agents touch my codebase. I would’ve saved approx 300$ subscription prices a month and 2 months of my life.
It's taken me the best part of this year to readjust and find a pace and level of ambition that fits.
New language, infrastructure, general level of understanding of something I barely have an idea of.
Rubber duck debugging, if i dont know the correct solution
Checking my code for issues and bugs.
But not for:
- writing my code - agentic coding (help me)
The inference has reduced drastically. It’s basically just chatting. I don’t let it write anything, but sometimes I purposely use the browser window instead of them sitting in my codebase, because I know it gets things subtly wrong and migth focus on the wrong things.
The same way people used to say don’t copypaste code at least write it out I think it’s still true. It helps to buidl the mental model and to find the right abstractions.
But yes, it's likely that the ease of which code can now be outout lets us produce lots of unnecessary code just because we can, and the author says as much in a below comment
Open source software changed the world. AI that will cheaply write whatever you want in a few days will also change the world.
From this I assume you think that what the llm has generated is as valuable as your own work generally is. How do you even calculate this?