[trenchpilgrim]

Same. On many days 90% of my code output by lines is Claude generated and things that took me a day now take well under an hour.

Also, a good chunk of my personal OSS projects are AI assisted. You probably can't tell from looking at them, because I have strict style guides that suppress the "AI style", and I don't really talk about how I use AI in the READMEs. Do you also expect I mention that I used Intellisense and syntax highlighting too?

[droidjj]

The author’s main point is that there hasn’t been an uptick in total code shipped, as you would expect if people are 10x-ing their productivity. Whether folks admit to using AI in their workflow is irrelevant.

[trenchpilgrim]

The bottleneck on how much I ship has never been how fast I can write and deploy code :)

[larve]

Their main point is "AI coding claims don't add up", as shown by the amount of code shipped. I personally do think some of the more incredible claims about AI coding add up, and am happy to talk about it based on my "evidence", ie the software I am building. 99.99% of my code is ai generated at this point, with the occasional one line I fill in because it'd be stupid to wait for an LLM to do it.

For example, I've built 5-6 iphone apps, but they're kind of one-offs and I don't know why I would put them up on the app store, since they only scratch my own itches.

[Gormo]

I'd suspect that a very large proportion of code has always been "private code" written for personal or intra-organizational purposes, and which never get released publicly.

But if we expect the ratio of this sort of private code to publicly-released code to remain relatively stable, which I think is a reasonable expectation, then we'd expect there to be a proportional increase in both private and public code as a result of any situation that increased coding productivity generally.

So the absence of a notable increase in the volume of public code either validates the premise that LLMs are not actually creating a general productivity boost for software development, or instead points to its productivity gains being concentrated entirely in projects that never do get released, which would raise the question of why that might be.

[trenchpilgrim]

Oh yeah, I love building one off tools with it. I am working on a game mod with a friend, we are hand writing the code that runs when you play it, but we vibe code all sorts of dev tools to help us test and iterate on it faster.

Do internal, narrow purpose dev tools count as shipped code?

[daxfohl]

This seems to be a common thread. For personal projects where most details aren't important, they are good at meeting the couple things that are important to you and filling in the rest with reasonable, mostly-good-enough guesses. But the more detailed the requirements are, the less filler code there is, and the more each line of code matters. In those situations it's probably faster to type the line of code than to type the English equivalent and hand-hold the assistant through the editing process.

[larve]

I don't think so, although I think at that point experience heavily comes into play. With GPT-5 especially, I can basically point cursor/codex at a repo and say "refactor this to this pattern" and come back 25 minutes later to a pretty much impeccable result. In fact that's become my favourite past time lately.

I linked some examples higher up, but I've been maintaining a lot of packages that I started slightly before chatgpt and then refactored and worked on as I progressively moved to the "entirely AI generated" workflow I have today.

I don't think it's an easy skill (not saying that to make myself look good, I spent an ungodly amount of time exploring programming with LLMs and still do), akin to thinking at a strategic level vs at a "code" level.

Certain design patterns also make it much easier to deal with LLM code: state reducers (redux/zustand for example), event-driven architectures, component-based design systems, building many CLI tools that the agent can invoke to iterate and correct things, as do certain "tools" like sqlite/tmux (by that I mean just telling the LLM "btw you can use tmux/sqlite", you allow it to pass hurdles that would otherwise just make it spiral into slop-ratatouille).

I also think that a language like go was a really good coincidence, because it is so amenable to LLM-ification.

[Aeolun]

I don’t think this is necessarily true. People that didn’t ship before still don’t ship. My ‘unshipped projects’ backlog is still nearly as large. It’s just got three new entries in the past two months instead of one.

[warkdarrior]

Maybe people are working less and enjoying life more, while shipping the same amount of code as before.

If someone builds a faster car tomorrow, I am not going to go to the office more often.

[leoc]

"In this economy?", as the saying goes.

[jplusequalt]

Jevon's paradox.

[jplusequalt]

>Do you also expect I mention that I used Intellisense and syntax highlighting too?

No, but I expect my software to have been verified for correctness, and soundness by a human being with a working mental model of how the code works. But, I guess that's not a priority anymore if you're willing to sacrifice $2400 a year to Anthropic.

[trenchpilgrim]

$2400? Mate, I have a free GitHub Copilot subscription (Microsoft hands them out to active OSS developers), and work pays for my Claude Code via our cloud provider backend (and it costs less per working day than my morning Monster can). LLM inference is _cheap_ and _getting cheaper every month_.

> No, but I expect my software to have been verified for correctness, and soundness by a human being with a working mental model of how the code works.

This is not exclusive with AI tools:

- Use AI to write dev tools to help you write and verify your handwritten code. Throw the one-off dev tools in the bin when you're done.

- Handwrite your code, generate test data, review the test data like you would a junior engineer's work.

- Handwrite tests, AI generate an implementation, have the agent run tests in a loop to refine itself. Works great for code that follows a strict spec. Again, review the code like you would a junior engineer's work.

[jplusequalt]

Writing the tests by hand, but letting the AI write the code sounds horribly dull.

[trenchpilgrim]

I'm an infrastructure/platform engineer. If the code is boring, that probably means I'm doing my job well. This isn't hobby coding.