Reading the code does not mean you understand the code. One lesson that experience in software gave me: I never understood the code. You think it works a certain way, until you find out that it doesn't.
What LLMs make possible is for me to say: find out all the ways this thing works. Analyze the different ways we can run this software, build a fuzzer, build property tests, and run this software in every scenario possible. Log full traces. Log all the outputs. Now, analyze each scenario for bugs. You can't do that by hand.
If we are committed to it, if we put the resources towards it and dedicate the time to it (and we could do this just by saying: it will take half as long as it used to take!), software built by llms in healthcare, finance, automotive, defense, power plans, aviation, manufacturing can all be made MORE reliable and better with LLMs... without ever reading a single line of code. The LLMS are very good at logic, by the way.
Anyway all of this reads like someone who is not actually using LLMs to build software or hasn't tried them in a while. I felt the same way in 2025. I've written 100s of thousands of lines of difficult code. You, the person reading this, has probably interacted with software I've written. For a time you would've interacted with it every time you made a debit card transaction in the united states, for example. I understand code, and care about quality, and that's why I'm all in on LLMs for code.
I agree, I think this false dichotomy between using LLMs and caring about quality/reliability needs to stop. All these mission critical industries listed in the article rely on extensive testing for quality assurance, with human code review being a layer on top of all that, but far from the most critical one.
Interpretability is the same, our abilities to do that have increased rather than decreased. I think a codebase generated by AI is actually more understandable than one generated by humans at this point, and you can ask clarifying questions whenever you get stuck.
TFA's points only make sense if the mental model the author has in mind is someone who writes a prompt then immediately puts an app into production without any thought behind it.
If you can quantify quality/reliability/understandability, you can tell LLM what kind of code do you expect. If not, you get whatever.
At my current place we not only have automated tests, static analysis and static rector (linting, but also automatic pattern matcher for problematic code) but also:
- architecture tests that define relationships between application layers
- ADRs that guide developers (and agents as well) that communicate how new code should be written and how existing code should be treated
I find that "how code should look like"/"what code should do" is an ambiguous idea that always is preached, but never defined = everyone's idea of quality is slightly different and only looking at existing code you tend to align. Everyone's idea of what the product does/should do is kept within their heads. If we define this knowledge in writing LLMs can not only write code according to the patterns that are thus defined, review existing code based on these documents, but also actually read acceptance criteria documents to check if the code does what it's intended to do (gherkin)
Same goes for understandability - if LLM applies one pattern this time, another pattern another time, if you have multiple coding patterns then that hurts clarity. Sometimes LLMs work as common denominator thus achieving clarity, but I find that actually giving LLMs reference works.
efficax · · focus · HN ↗
What LLMs make possible is for me to say: find out all the ways this thing works. Analyze the different ways we can run this software, build a fuzzer, build property tests, and run this software in every scenario possible. Log full traces. Log all the outputs. Now, analyze each scenario for bugs. You can't do that by hand.
If we are committed to it, if we put the resources towards it and dedicate the time to it (and we could do this just by saying: it will take half as long as it used to take!), software built by llms in healthcare, finance, automotive, defense, power plans, aviation, manufacturing can all be made MORE reliable and better with LLMs... without ever reading a single line of code. The LLMS are very good at logic, by the way.
Anyway all of this reads like someone who is not actually using LLMs to build software or hasn't tried them in a while. I felt the same way in 2025. I've written 100s of thousands of lines of difficult code. You, the person reading this, has probably interacted with software I've written. For a time you would've interacted with it every time you made a debit card transaction in the united states, for example. I understand code, and care about quality, and that's why I'm all in on LLMs for code.
pu_pe · · focus · HN ↗
Interpretability is the same, our abilities to do that have increased rather than decreased. I think a codebase generated by AI is actually more understandable than one generated by humans at this point, and you can ask clarifying questions whenever you get stuck.
TFA's points only make sense if the mental model the author has in mind is someone who writes a prompt then immediately puts an app into production without any thought behind it.
eithed · · focus · HN ↗
At my current place we not only have automated tests, static analysis and static rector (linting, but also automatic pattern matcher for problematic code) but also: - architecture tests that define relationships between application layers - ADRs that guide developers (and agents as well) that communicate how new code should be written and how existing code should be treated
I find that "how code should look like"/"what code should do" is an ambiguous idea that always is preached, but never defined = everyone's idea of quality is slightly different and only looking at existing code you tend to align. Everyone's idea of what the product does/should do is kept within their heads. If we define this knowledge in writing LLMs can not only write code according to the patterns that are thus defined, review existing code based on these documents, but also actually read acceptance criteria documents to check if the code does what it's intended to do (gherkin)
Same goes for understandability - if LLM applies one pattern this time, another pattern another time, if you have multiple coding patterns then that hurts clarity. Sometimes LLMs work as common denominator thus achieving clarity, but I find that actually giving LLMs reference works.