alt Hacker NewsHonest question: why is this not enough?
If the code passes tests, and also works at the functionality level - what difference does it make if you’ve read the code or not?
You could come up with pathological cases like: it passed the tests by deleting them. And the code written by it is extremely messy.
But we know that LLMs are way smarter than this. There’s very very low chance of this happening and even if it does - it quick glance at code can fix it.
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You can't test everything. The input space may be infinite. The app may feel janky. You can't even be sure you're testing all that can be tested.
The code may seem to work functionally on day 1. Will it continue to seem to work on day 30? Most often it doesn't.
And in my experience, the chances of LLMs fucking up are hardly very very low. Maybe it's a skill issue on my part, but it's also the case that the spec is sometimes discovered as the app is being built. I'm sure this is not the case if you're essentially summoning up code that exists in the test set, even if the LLM has to port it from another language, and they can be useful in parts here and there. But turning the controls over to the infinite monkey machine has not worked out for me so far.
It depends on the scale of complexity you’re working at and who your users are going to be. I’ve found that it’s trivial to have Claude Code spit out so much functionality that even just proper manually verifying it becomes a gargantuan task. I end up just manually testing the pieces I’m familiar with which is fine if there’s a QA department who can do a full run through of the feature and are prepared to deal with vibe coding pitfalls, but not so much on open source projects where slop gets shipped and unfamiliar users get stuck with bugs they can’t possibly troubleshoot. Writing the code from scratch The Old Way™ leaves a lot less room for shipping convincing but non functional slop because the dev has to work through it before shipping.
The most immediate example I can think of is the beans LLM workflow tracker. It’s insane that its measured in the 100s of thousands of LoC and getting that thing setup in a repo is a mess. I had to use Github copilot to investigate the repo to get the latest method. This wouldn’t fly at my employer but a lot of projects are going to be a lot less scrupulous.
You can see the effects in popular consumer facing apps too: Anthropic has drunk way too much of its own koolaid and now I get 10-50% failure rates on messages in their iOS app depending on the day. Some of their devs have publicly said that Claude writes 100% of their code and its starting to show. Intermittent network failures and retries have been a solved problem for decades, ffs!
> If the code passes tests, and also works at the functionality level
Why doesn’t outsourcing work if this is all that is needed?
Good question. Several reasons.
1. Since the same AI writes both the code and the unit tests, it stands to reason that both could be influenced by the same hallucinations.
2. Having a dev on call reduces time to restore service because the dev is familiar with the code. If developers stop reviewing code, they won't be familiar with it and won't be as effective. I am currently unaware of any viable agentic AI substitute for a dev on call capability.
3. There may be legal or compliance standards regarding due diligence which won't get met if developers are no longer familiar with the code.
I have blogged about this recently at https://www.exploravention.com/blogs/soft_arch_agentic_ai/