> My answer to this is to often get the LLMs to do multiple rounds of code review
So I am supposed to trust the machine, that I know I cannot trust to write the initial code correctly, to somehow do the review correctly? Possibly multiple times? Without making NEW mistakes in the review process?
Sorry no sorry, but that sounds like trying to clean a dirty floor by rubbing more dirt over it.
It sounds to me like you may not have used a lot of these tools yet, because your response sounds like pushback around theoreticals.
Please try the tools (especially either Claude Code with Opus 4.5, or OpenAI Codex 5.2). Not at all saying they're perfect, but they are much better than you currently think they might be (judging by your statements).
AI code reviews are already quite good, and are only going to get better.
Why is the go-to always "you must not have used it" in lieu of the much more likely experience of having already seen and rejected first-hand the slop that it churns out? Synthetic benchmarks can rise all they want; Opus 4.5 is still completely useless at all but the most trivial F# code and, in more mainstream affairs, continues to choke even on basic ASP.NET Core configuration.
> It sounds to me like you may not have used a lot of these tools yet
And this is more and more becoming the default answer I get whenever I point out obvious flaws of LLM coding tools.
Did it occur to you that I know these flaws precisely because I work a lot with, and evaluate the performance of, LLM based coding tools? Also, we're almost 4y into the alleged "AI Boom" now. It's pretty safe to assume that almost everyone in a development capacity has spent at least some effort evaluating how these tools do. At this point, stating "you're using it wrong" is like assuming that people in 2010 didn't know which way to hold a smartphone.
Sorry no sorry, but when every criticism towards a tool elecits the response that people are not using it well, then maybe, just maybe, the flaw is not with all those people, but with the tool itself.
> Spending 4 years evaluating something that’s changing every month means almost nothing, sorry.
No need to be sorry. Because, if we accept that premise, you just countered your own argument.
If me evaluating these things for the past 4 years "means almost nothing" because they are changing sooo rapidly...then by the same logic, any experience with them also "means almost nothing". If the timeframe to get any experience with these models befor said experience becomes irelevant is as short as 90 days, then there is barely any difference between someone with experience and someone just starting out.
Meaning, under that premise, as long as I know how to code, I can evaluate these models, no matter how little I use them.
Luckily for me though, that's not the case anyway because...
> It’s about “if you last tried it more than 3 months ago,
...guessss what: I try these almost every week. It's part of my job to do so.
Implementation -> review cycles are very useful when iterating with CC. The point of the agent reviewer is not to take the place of your personal review, but to catch any low hanging fruit before you spend your valuable time reviewing.
> but to catch any low hanging fruit before you spend your valuable time reviewing.
And that would be great, if it wern't for the fact that I also have to review the reviewers review. So even for the "low hanging fruit", I need to double-check everything it does.
That is not my perspective. I don't review every review, instead use a review agent with fresh context to find as much as possible. After all automated reviews pass, I then review the final output diff. It saves a lot of back and forth, especially with a tight prompt for the review agent. Give the reviewer specific things to check and you won't see nearly as much garbage in your review.
Well, you can review its reasoning. And you can passively learn enough about, say, Rust to know if it's making a good point or not.
Or you will be challenged to define your own epistemic standard: what would it take for you to know if someone is making a good point or not?
For things you don't understand enough to review as comfortably, you can look for converging lines of conclusions across multiple reviews and then evaluate the diff between them.
I've used Claude Code a lot to help translate English to Spanish as a hobby. Not being a native Spanish speaker myself, there are cases where I don't know the nuances between two different options that otherwise seem equivalent.
Maybe I'll ask 2-3 Claude Code to compare the difference between two options in context and pitch me a recommendation, and I can drill down into their claims infinitely.
At no point do I need to go "ok I'll blindly trust this answer".
Humans do have capacity for deductive reasoning and understanding, at least. Which helps. LLMs do not. So would you trust somebody who can reason or somebody who can guess?
People work different than llms they fond things we don't and the reverse is also obviously true. As an example, a stavk ise after free was found in a large monolithic c++98 codebase at my megacorp. None of the static analyzers caught it, even after modernizing it and getting clang tidy modernize to pass, nothing found it. Asan would have found it if a unit test had covered that branch. As a human I found it but mostly because I knew there was a problem to find. An llm found and explained the bug succinctly. Having an llm be a reviewer for merge requests males a ton of sense.
So I am supposed to trust the machine, that I know I cannot trust to write the initial code correctly, to somehow do the review correctly? Possibly multiple times? Without making NEW mistakes in the review process?
Sorry no sorry, but that sounds like trying to clean a dirty floor by rubbing more dirt over it.