bigstrat2003 4 days ago

I don't understand people who say this. My knee jerk reaction (which I rein in because it's incredibly rude) is always "wow, that person must really suck at programming then". And I try to hold to the conviction that there's another explanation. For me, the vast, vast majority of the time I try to use it, AI slows my work down, it doesn't speed it up. As a result it's incredibly difficult to understand where these supposed 10x improvements are being seen.

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loandbehold 4 days ago | parent | next [-]

For me, most of the value comes from Claude Code's ability to 1. research codebase and answer questions about it 2. Perform adhoc testing on the code. Actually writing code is icing on the cake. I work on large code base with more than two million lines of code. Claude Code's ability to find relevant code, understand its purpose, history and interfaces is very time saving. It can answer in minutes questions that would take hours of digging through the code base. Ad hoc testing is another thing. E.g. I can just ask it to test an API endpoint. It will find correct data to use in the database, call the endpoint and verify that it returned correct data and e.g. everything was updated in db correctly.

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libraryofbabel 4 days ago | parent | prev | next [-]

Usually the "10x" improvements come from greenfield projects or at least smaller codebases. Productivity improvements on mature complex codebases are much more modest, more like 1.2x.

If you really in good faith want to understand where people are coming from when they talk about huge productivity gains, then I would recommend installing Claude Code (specifically that tool) and asking it to build some kind of small project from scratch. (The one I tried was a small app to poll a public flight API for planes near my house and plot the positions, along with other metadata. I didn't give it the api schema at all. It was still able to make it work.) This will show you, at least, what these tools are capable of -- and not just on toy apps, but also at small startups doing a lot of greenfield work very quickly.

Most of us aren't doing that kind of work, we work on large mature codebases. AI is much less effective there because it doesn't have all the context we have about the codebase and product. Sometimes it's useful, sometimes not. But to start making that tradeoff I do think it's worth first setting aside skepticism and seeing it at its best, and giving yourself that "wow" moment.

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mattmanser 4 days ago | parent | next [-]

So, I'm doing that right now. You do get wow moments, but then you rapidly hit the WTF are you doing moments.

One of the first three projects I tried was a spin on a to-do app. The buttons didn't even work when clicked.

Yes, I keep it iterating, give it a puppeteer MCP, etc.

I think you're just misunderstanding how hard it is to make a greenfield project when you have a super-charged stack overflow that AI is.

Greenfield projects aren't hard, what's hard is starting them.

What AI has helped me immensely with is blank page syndrome. I get it to spit out some boilerplate for a SINGLE page, then boom, I have a new greenfield project 95% my own code in a couple of days.

That's the mistake I think you 10x ers are making.

And you're all giddy and excited and are putting in a ton of work without realising you're the one doing the work, not the AI.

And you'll eventually burn out on that.

And those of us who are a bit more skeptical are realising we could have done it on our own, faster, we just wouldn't normally have bothered. I'd have gone done some gardening with that time instead.

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libraryofbabel 4 days ago | parent [-]

I'm not a 10x-er. My job is working on a mature codebase. The results of AI in that situation are mixed, 1.2x if you're lucky.

My recommendation was that it's useful to try the tools on greenfield projects, since they you can see them at their best.

The productivity improvements of AI for greenfield projects are real. It's not all bullshit. It is a huge boost if you're at a small startup trying to find product market fit. If you don't believe that and think it would be faster to do it all manually I don't know what to tell you - go talk to some startup founders, maybe?

	▲	mattmanser 3 days ago \| parent [-]
		That 1.2x is suspiciously familiar to the recent study showing AI harmed productivity. 1.2x was self-reported, but when measured, developers were actually 0.85x ers using AI.

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loandbehold 4 days ago | parent | prev [-]

I was able to realize huge productivity gains working on a 20 years old codebase with 2+ million loc, as I mentioned in the sister post. So I disagree that big productivity gains are only on greenfield projects. Realizing productivity gains on mature code based requires more skill and upfront setup. You need to put some work in your claude.md and give Claude tools for accessing necessary data, logs, build process. It should be able to test your code autonomously as much as possible. In my experience, people who say they are not able to realize productivity gains don't put enough effort to understand these new tools and setup them properly for their project.

	▲	libraryofbabel 4 days ago \| parent [-]
		You should write a blog post on this! We need more discussion of how to get traction on mature codebases and less of the youtube influencers making toy greenfield apps. Of course at a high level it's all going to be "give the model the right context" (in Claude.md etc.) but the devil is in the details.

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bentcorner 4 days ago | parent | prev [-]

It depends on what kind of code you're working on and what tools you're using. There's a sliding scale of "well known language + coding patterns" combined with "useful coding tools that make it easy to leverage AI", where AI can predict what you're going to type, and also you can throw problems at the AI and it is capable of solving "bigger" problems.

Personally I've found that it struggles if you're using a language that is off the beaten path. The more content on the public internet that the model could have consumed, the better it will be.