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ninkendoHNSource author
Lessons learnedExperience excerpt · English

My personal anecdote: I used an LLM recently to basically vibe code a password manager

… My personal anecdote: I used an LLM recently to basically vibe code a password manager. Now, I’ve been a software engineer for 20 years. I’m very familiar with the process of code review and how to dive in to someone else’s code and get a feel for what’s happening, and how to spot issues. So when I say the LLM produced thousands of lines of working code in a very short time (probably at least 10 times faster than I would have done it), you could easily point at me and say “ha, look at ninkendo, he thinks more lines of code equals better!” And walk away feeling smug. Like, in your mind perhaps you think the result is an unmaintainable mess, and that the only thing I’m gushing about is the LOC count. But here’s the thing: it actually did a good job. I was personally reviewing the code the whole time. And believe me when I say, the resulting product is actually good . The code is readable and obvious, it put clean separation of responsibilities into different crates (I’m using rust) and it wrote tons of tests, which actually validate behavior. …

Original discussionDon't trust AI agents196 source comments · at import
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badsectoraculaHN

> OpenClaw has nearly half a million lines of code, 53 config files, and over 70 dependencies. This breaks the basic premise of open source security. Chromium has 35+ million lines, but you trust Google’s review processes. Most open source projects work the other way: they stay small enough that many eyes can actually review them. Nobody has reviewed OpenClaw’s 400,000 lines. This reminds me of a very common thing posted here (and elsewhere, e.g. Twitter) to promote how good LLMs are and how they're going to take over programming: the number of lines of code they produce. As if every competent programmer suddenly forgot the whole idea of LoC being a terrible metric to measure productivity or -even worse- software quality. Or the idea that software is meant to written to be readable (to water down "Programs are meant to be read by humans and only incidentally for computers to execute" a b…

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bee_riderHN

If you measure the productivity of the system that is “you, using an LLM” in terms of the rate at which you can get actually-reviewed code completed (which, based on your comment, seems to be what you were doing) that seems like a totally reasonable way of doing things. But in that case the bottleneck is probably you reviewing code, right? Which, I bet, is faster than writing code. But you probably won’t get the truly absurd superhuman speed ups. What would you say is your multiplier, in terms of throughly reviewing code vs writing it from scratch?

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FEELmyAGIHN

This whole reply, and every other "anecdote" reply is more worthless than the pixels its printed on, without a link to your "actually did a good job" password manager. (wow funny how these vibe code apps always are copies of something theres many open source versions of already)

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badsectoraculaHN

I don't know if it is incompetence - if anything i doubt it, someone else pointed out that pg also used that metric and i don't think pg is incompetent. However at the same time i think it is misleading at best. My impression is that, as someone else wrote, we do not have an actual metric for such things as productivity or quality or what have you, but some people do want to communicate that they feel (regardless of if that matches reality) using an LLM is better/faster/easier and they latch to the (wrong) assumption about more LoC == better/faster that non-programmers already believed for years (intentionally or not, they may also deluding themselves) as that is an easy path to convince them that the new toys have value that applies to the non-programmers too (note that i explicitly ignore the perspective of the "toymakers" as those have further incentives to promote their products). Pe…

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halnine9000HN

>this tech is here to stay How can you say that when all these models are externally sourced by companies that actively make a loss per token? When they finally need to make a profit, how can we be sure these models as well as their owners will remain as reliable and not enshittified? Anthropic has been blacklisted in the last 24 hours so its a turbulent industry to say the least

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