With not much more effort you can get a much better review by additionally concatenating the touched files and sending them as context along with the diff. It was the work of about five minutes to make the scaffolding of a very basic bot that does this, and then somewhat more time iterating on the prompt. By the way, I find it's seriously worth sucking up the extra ~four minutes of delay and going up to GPT-5 high rather than using a dumber model; I suspect xhigh is worth the ~5x additional bump in runtime on top of high, but at that point you have to start rearchitecting your workflows around it and I haven't solved that problem yet.
(That's if you don't want to go full Codex and have an agent play around with the PR. Personally I find that GPT-5.2 xhigh is incredibly good at analysing diffs-plus-context without tools.)
Alternative twist on this that I find works very well (and that I posted about a month ago https://news.ycombinator.com/item?id=45959846) - instead of concat&sending touched files, checkout the feature branch and the prompt becomes "help me review this pr, diff attached, we are on the feature branch" with an AI that has access to the codebase (I like Cursor).
I've been using gemini-3-flash the last few days and it is quite good, I'm not sure you need the biggest models anymore. I have only switched to pro once or twice the last few days
Depends what you mean by "need", of course, but in my experience the curves aren't bending yet; better model still means better-quality review (although GPT-5.0 high was still a reasonably competent reviewer)!
Do you do any preprocessing of diffs to replace significant whitespace with some token that is easier to spot? In my experience, some LLMs cannot tell unchanged context from the actual changes. That's especially annoying with -U99999 diffs as a shortcut to provide full file context.
I've only ever had that problem when supplying a formatted diff alone. Once I moved to "provide the diff, and then also provide the entire contents of the file after the change", I've never had the problem. (I've also only seriously used GPT-5.0 high or more powerful models for this.)
I still dont get the idea about AI code reviews.
A code review (at least in my opinion) is for your peers to check if the changes will have a positive or negative effect on the overall code + architecture.
I have yet to see an LLM being good at this.
Sure, they will leave comments about common made errors (your editor should already warn about this before you even commit it) etc. But to notify about this weird thing that was done to make sure something a lot of customers wanted is made reality.
also, PR's are created to share knowledge. Questions and answers on them are to spread knowledge in the team. AI does not do that.
Sure, AI code reviews aren't a replacement for an architecture review on a larger team project.
But they're fantastic at spotting dumb mistakes or low-hanging fruit for improvements!
And having the AI spot those for you first means you don't waste your team's valuable reviewing time on the simple stuff that you could have caught early.
My experience with AI code reviews has been very mixed and more on the negative side than the positive one. In particular, I've had to disable the AI reviewer on some projects my team manages because it was so chatty that it caused meaningful notifications from team members to be missed.
In most of the repos I work with, it tends to make a large number of false positive or inappropriate suggestions that are just plain wrong for the code base in question. Sometimes these might be ok in some settings, but are generally just wrong. About 1 in every 10~20 comments is actually useful or something novel that hasn't been caught elsewhere etc. The net effect is that the AI reviewer we're effectively forced to use is just noise that get's ignored because it's so wrong so often.
one person proved the uselessness of ai reviews for our entire company.
He'd make giant, 100+ file changes, 1000+ worded PRs. Impossible to review. eventually he just modified the permissions to require a single approval, approves his changes and merges. This is still going on, but he's isolated to repos he made himself
He'd copy/paste the output from AI on other people's reviews. Often they were false positives or open ended questions. So he automated his side, but doubled or tripled the work of the person requesting the review. not to mention the ai's comments were 100-300 words with formatting and emojis.
The contractors refused to address any comments made by him. Some felt it was massively disrespectful as they put tons of time and effort into their changes and he can't even bother to read it himself.
It got to the CTO. And AI reviews have been banned.
But it HAS helped the one Jr guy on the team prepare for reviews and understand review comments better. It's also helped us write better comments, since I and some others can be really bad at explaining something
What model were you using? This is true for e.g. the best Qwen model, which I believe to be too dumb to be useful, but not GPT-5.0 or above in my experience (and I consider myself to be a pretty demanding customer).
When you say "structurally incapable", what do you mean? They can certainly output the words (e.g. https://github.com/Smaug123/WoofWare.Zoomies/pull/165#issuec... ); do you mean something more along the lines of "testing can prove the presence of bugs, but not their absence", like "you can't know whether you've got a false or true negative"? Because that's also a problem with human reviewers.
The solution to this is to get agents to output an importance scale (0-1) then just filter anything below whatever threshold you prefer before creating comments/change reqs.
I love having to hit Resolve Conversation umpteen times before I can merge because somebody added Copilot and it added that many dumb questions/suggestions
those AI checks, if you insist in getting them, should be part of your pre-commit, not part of your PR review flow.
they are at best (if they even reach this level) as good as a local run of a linter or static type checker
If you are running them as a PR check, the PR is out there. So people will spend time on that PR. no matter if you are fixing the AI comments or not.
Best to fix those things BEFORE you provide your code to the team.
My team uses draft PRs and goes through a process, including AI review, before removing the draft status thereby triggering any remaining human review.
A PR is also a decent UI for getting the feedback but especially so for documenting/discussing the AI review suggestions with the team, just like human review.
AI review is also not equivalent to linter and static checks. It can suggest practices appropriate for the language and appropriate for your code base. Like a lot of my AI experiences it's pretty hit or miss and it's non-deterministic but it doesn't have much cost to disregard the misses and I appreciate the hits.
I'm starting to repeat myself here:
If you need/want the opinion of an AI on the changes you make, you should run the robot locally BEFORE you make a pr. I would even say: before you even push commits to the central repository.
This just sounds like you haven’t worked in a team environment in the last 12 months.
The ergonomics of doing this in pre-commit make no sense.
Spin up a PR in GitHub and get Cursor and/or Claude to do a code review — it’s amazing.
It’ll often spot bugs (not only obvious ones), it’ll utilise your agent.md to spot mismatched coding style, missing documentation, it’ll check sentry to see if this part of the code touches a hotspot or a LOC that’s been throwing off errors … it’s an amazing first pass.
Once all the issues are resolved you can mark the PR as ready for review and get a human to look big picture.
It’s unquestionably a huge time saver for reviewers.
And having the AI and human review take place with the same UX (comments attached to lines of code, being able to chat to the AI to explain decisions, having the AI resolve the comment when satisfied) just makes sense and is an obvious time saver for the submitter.
Stuff like coding style and missing documentation is what your basic dumb formatter and linter are supposed to do, using a LLM for such things is hilarious overkill and waste of electricity.
I think what has happened is that there are a whole lot of younger programmers who have not used tooling like linters and static analyzers. For them, when AI does the same thing it is a revelation.
When your linter or static analyser says something, you know exactly what it means. When AI says something - many a time, it doesnt make sense. And I use AI code review tools every day.
why not have AI review your code BEFORE you share it with the team ?
that shows so much more respect to the rest of the team then just throwing your code into the wild, only to change it because some robot tells you that X could be Y
This question is surprising to me, because I consider AI code review the single most valuable aspect of AI-assisted software development today. It's ahead of line/next-edit tab completion, agentic task completion, etc.
AI code review does not replace human review. But AI reviewers will often notice little things that a human may miss. Sometimes the things they flag are false positives, but it's still worth checking in on them. If even one logical error or edge case gets caught by an AI reviewer that would've otherwise made it to production with just human review, it's a win.
Some AI reviewers will also factor in context of related files not visible in the diff. Humans can do this, but it's time consuming, and many don't.
AI reviews are also a great place to put "lint" like rules that would be complicated to express in standard linting tools like Eslint.
We currently run 3-4 AI reviewers on our PRs. The biggest problem I run into is outdated knowledge. We've had AI reviewers leave comments based on limitations of DynamoDB or whatever that haven't been true for the last year or two. And of course it feels tedious when 3 bots all leave similar comments on the same line, but even that is useful as reinforcement of a signal.
If you have an architecture document, readmes for related services, relevant code from related services and such assembled for a LLM, it can do a pretty solid review even on microservices. It can catch parameter mismatches/edge cases, instrument logging end to end, do some reasonable flow modeling, etc. It can also point out when uncovered code is a risk, and do a sanity check on tests.
In order to be time efficient, human review should focus on the 'what' rather than the 'how' in most cases.
I work alone (in a medium sized company). No peers, no code review. AI code review is invaluable.
AI is a mixed bag. I'm the type of person who is compelled to have a deep understanding of the code they write. Writing my own code vs fixing AI-generated code is a wash timewise, and the AI generated code is so limited (assuming you pared down the uselessly elaborate code and fixed all the critical runtime bugs) as to restrict further iterations. And I'm talking about uploading an architectural blueprint with every function a documented but otherwise empty stub.
AI is a great bellwether. I bounce ideas off AI for a consensus. The closest equivalent is reading StackOverflow comments. I once offhandedly complained that python had no equivalent for setattr at class scope (as __class__ is not defined until after __new__), but AI showed me how to provide a closure in __prepare__ over the class namespace, which was introduced in 3.3 (?) and to which I paid little attention. What a gem.
AI is great for learning. If you follow a textbook or blog or paper and don't understand, AI can clarify. But be careful with less structured learning - it is important to build a full mental model accounting for every possible outcome and explanation, otherwise you're susceptible to hallucinations. I remember my first derivative in which the end result could be obtained via two separate proofs, one of which would imply an incorrect calculus. You've got to play with it until you're satisfied your mental model accounts for all the facts.
Because AI facilitates learning so easily I feel the best skills for a future generation are those pertaining to memory and retention. Ya know, assuming we don't develop individualized and personalized AI that can model your next word and act as a personal memex.
AI is great as a search assistant. I have much better recall when rereading content. Thus, I prefer to ask AI to search for links to content I vaguely recall, rather than ask AI for it's own summary or recollection.
Despite being terrible at writing decent code, AI provides fantastic code review. It catches everything from subtle high-level errors - even potential errors that haven't yet occured - to api mismatches to syntax errors. I actually wish I was as fluent at namespaces and cgroups as AI, and I'm well versed.
AI is interesting at comments. It can be hard to provide germane comments while wading through the weeds. I feel the best comments are written a few days after the code is complete. AI provides that fresh perspective instantly. And if AI can't understand your code, you had better improve your comments.
I'm about to try AI for unit tests. I prefer hypothesis tests. I took a quick glance at the generated code, and it seemed overly complicated. So I'm not optimistic.
Outside of software, AI is great for things you don't really care about. Yeah it might hallucinate, but my back-and-forth with AI is more about holding up a mirror to myself, revealing inner biases. Especially useful for interior decoration / remodeling.
Of course, take everything I said with a grain of salt as security at my company actively discourages AI. So, everything I said applies to free/cheap plans only. And I haven't tried skills yet.
As for PR reviews, assuming you've got linting and static analysis out the way, you'd need to enter a sufficiently reasonable prompt to truly catch problems or surface reviews that match your standard and not generic AI comments.
My company uses some automatic AI PR review bots, and they annoy me more than they help. Lots of useless comments
I would just put a PR_REVIEW.md file in the repo an have a CI agent run it on the diff/repo and decide pass or reject. In this file there are rules the code must be evaluated against. It could be project level policy, you just put your constraints you cannot check by code testing. Of course any constraint that can be a code test, better be a code test.
My experience is you can trust any code that is well tested, human or AI generated. And you cannot trust any code that is not well tested (what I call "vibe tested"). But some constraints need to be in natural language, and for that you need a LLM to review the PRs. This combination of code tests and LLM review should be able to ensure reliable AI coding. If it does not, iterate on your PR rules and on tests.
`gh pr diff num` is an alternative if you have the repo checked out. One can then pipe the output to one's favorite llm CLI and create a shell alias with a default review prompt.
> My company uses some automatic AI PR review bots, and they annoy me more than they help. Lots of useless comments
One way to make them more useful is to ask to list the topN problems found in the change set.
Hum? I just tell claude to review pr #123 and it uses 'gh' to do everything, including responding to human comments! Feedback from coleagues has been awesome.
Not my experience. Most Claude reviews are horrible and if I catch you replying with Claude (any AI really) under your own name you are gonna get two earfulls. Don't get me wrong, if you have an AI bot that I can have a convo with on the PR, sure. But you passing their stuff off as you: do that twice and you're dead to me.
Now, I use it as well to review, just like you mention it pulls it via gh, has all the source to reference and then tells me what it thinks. But it can't be left alone.
Similarly people have been trying to pass root cause analyses off as true and they sound confident but have holes like a good Swiss cheese.
Good thing I work on an old C++ code base where it's impossible for AI to go through the millions of lines that all interact horribly in unpredictable ways.
Funny you mention that, I have very recently just came back from a one-shot prompt which fixed a rather complex template instantiation issue in a relatively big very convoluted low-level codebase (lots of asm, SPDK / userspace nvme, unholy shuffling of data between numa domains into shared l3/l2 caches). That codebase maybe isn't in millions of lines of code but definitely is complex enough to need a month of onboarding time. Or you know, just give Claude Opus 4.5 a lldb backtrace with 70% symbols missing due to unholy linker gymnastics and get a working fix in 10 mins.
And those are the worst models we will have used from now on.
Template instantiation is relatively simple and can be resolved immediately. Trying to figure out how 4 different libraries interact with undefined behavior to boot is not going to be easy for AI for a while.
I recently started using LLMs to review my code before asking for a more formal review from colleagues. It's actually been surprisingly useful - why waste my colleagues time with small obvious things? But it's also gone much further than that sometimes with deeper reviews points. Even when I don't agree with them it's great having that little bit more food for thought - if anything it helps seed the review
"Diff to master and review the changes. Branch designed to address <problem statement>. Write output to d:\claudeOut in typst (.typ) format."
It'll do the diffs and search both branch and master versions of files.
I prefer reading PDFs than markdown, but it'll default to markdown unprompted if you prefer.
I have almost all my workspaces configured with /add-dir to add d:/claudeOut and d:/claudeIn as general scratch folders for temporary in/out file permissions so it can read/write outside the context of the workspace for things like this.
You might get better results using a better crafted prompt (or code review skill?). In general I find claude code reviews are:
- Overly fussy about null checking everything
- Completely miss on whether the PR has properly distilled the problem down to its essence
- Are good at catching spelling mistakes
- Like to pretend they know if something is well architectured, but doesn't
So it's a bit of a mixed bag, I find it focuses on trivia but it's still useful as a first pass before letting your teammates have to catch that same trivia.
It will absolutely assume too much from naming, so it's kind of a good spot if it's making wrong kind of assumptions about how parts work, to think how to name things more clearly.
e.g. If you write a class called "AddingFactory", it'll go around assuming that's what it does, even if the core of it returns (a, b) -> a*b.
You have to then work hard to get it to properly examine the file and convince itself that it is actually a multiplier.
Obviously real-world examples are more subtle than that, but if you're finding yourself arguing with it, it's worth sometimes considering whether you should rename things.
This one's served fairly well:
"Review this diff - detect top 10 problem-causers, highlight 3 worst - I'm talking bugs with editing,saving etc. (not type errors or other minor aspects) [your diff]". The bit on "editing, saving" would vary based on goal of diff.
Not who you're replying to but working at a small small company, I didn't have anyone to give my code for review to so have used AI to fill in that gap. I usually go with a specific then general pass, where for example if I'm making heavy use of async logic, I'll ask the LLM to pay particular attention to pitfalls that can arise with it.
We're a Haskell shop, so I usually just say "review the current commit. You're an experienced Haskell programmer and you value readable and obvious code" (because that it is indeed what we value on the team). I'll often ask it to explicitly consider testing, too
while this approach is useful, i think the diff is too small to catch a lot of bugs.
i use https://www.coderabbit.ai/ and it tends to be aware of files that aren't in the diff, and definitely can see the rest of the file your are editing (not just the lines in the diff)
I have been using Codex as a code review step and it has been magnificent, truly. I don’t like how it writes code, but as a second line of defence I’m getting better code reviews out of it than I’ve ever had from a human.
With not much more effort you can get a much better review by additionally concatenating the touched files and sending them as context along with the diff. It was the work of about five minutes to make the scaffolding of a very basic bot that does this, and then somewhat more time iterating on the prompt. By the way, I find it's seriously worth sucking up the extra ~four minutes of delay and going up to GPT-5 high rather than using a dumber model; I suspect xhigh is worth the ~5x additional bump in runtime on top of high, but at that point you have to start rearchitecting your workflows around it and I haven't solved that problem yet.
(That's if you don't want to go full Codex and have an agent play around with the PR. Personally I find that GPT-5.2 xhigh is incredibly good at analysing diffs-plus-context without tools.)
Alternative twist on this that I find works very well (and that I posted about a month ago https://news.ycombinator.com/item?id=45959846) - instead of concat&sending touched files, checkout the feature branch and the prompt becomes "help me review this pr, diff attached, we are on the feature branch" with an AI that has access to the codebase (I like Cursor).
I've been using gemini-3-flash the last few days and it is quite good, I'm not sure you need the biggest models anymore. I have only switched to pro once or twice the last few days
Here are the commits, the tasks were not trivial
https://github.com/hofstadter-io/hof/commits/_next/
Social posts and pretty pictures as I work on my custom copilot replacement
https://bsky.app/profile/verdverm.com
Depends what you mean by "need", of course, but in my experience the curves aren't bending yet; better model still means better-quality review (although GPT-5.0 high was still a reasonably competent reviewer)!
Yes, it's my new daily driver for light coding and the rest. Also great at object recognition and image gen
Do you do any preprocessing of diffs to replace significant whitespace with some token that is easier to spot? In my experience, some LLMs cannot tell unchanged context from the actual changes. That's especially annoying with -U99999 diffs as a shortcut to provide full file context.
I've only ever had that problem when supplying a formatted diff alone. Once I moved to "provide the diff, and then also provide the entire contents of the file after the change", I've never had the problem. (I've also only seriously used GPT-5.0 high or more powerful models for this.)
I still dont get the idea about AI code reviews. A code review (at least in my opinion) is for your peers to check if the changes will have a positive or negative effect on the overall code + architecture. I have yet to see an LLM being good at this.
Sure, they will leave comments about common made errors (your editor should already warn about this before you even commit it) etc. But to notify about this weird thing that was done to make sure something a lot of customers wanted is made reality.
also, PR's are created to share knowledge. Questions and answers on them are to spread knowledge in the team. AI does not do that.
[edit] Added the part about knowledge sharing
Sure, AI code reviews aren't a replacement for an architecture review on a larger team project.
But they're fantastic at spotting dumb mistakes or low-hanging fruit for improvements!
And having the AI spot those for you first means you don't waste your team's valuable reviewing time on the simple stuff that you could have caught early.
My experience with AI code reviews has been very mixed and more on the negative side than the positive one. In particular, I've had to disable the AI reviewer on some projects my team manages because it was so chatty that it caused meaningful notifications from team members to be missed.
In most of the repos I work with, it tends to make a large number of false positive or inappropriate suggestions that are just plain wrong for the code base in question. Sometimes these might be ok in some settings, but are generally just wrong. About 1 in every 10~20 comments is actually useful or something novel that hasn't been caught elsewhere etc. The net effect is that the AI reviewer we're effectively forced to use is just noise that get's ignored because it's so wrong so often.
one person proved the uselessness of ai reviews for our entire company.
He'd make giant, 100+ file changes, 1000+ worded PRs. Impossible to review. eventually he just modified the permissions to require a single approval, approves his changes and merges. This is still going on, but he's isolated to repos he made himself
He'd copy/paste the output from AI on other people's reviews. Often they were false positives or open ended questions. So he automated his side, but doubled or tripled the work of the person requesting the review. not to mention the ai's comments were 100-300 words with formatting and emojis.
The contractors refused to address any comments made by him. Some felt it was massively disrespectful as they put tons of time and effort into their changes and he can't even bother to read it himself.
It got to the CTO. And AI reviews have been banned.
But it HAS helped the one Jr guy on the team prepare for reviews and understand review comments better. It's also helped us write better comments, since I and some others can be really bad at explaining something
This seems like quite a dramatic organisational response to a single incompetent employee!
What model were you using? This is true for e.g. the best Qwen model, which I believe to be too dumb to be useful, but not GPT-5.0 or above in my experience (and I consider myself to be a pretty demanding customer).
Sometimes the only review a PR needs is "LGTM" - something today's LLMs are structurally incapable of.
When you say "structurally incapable", what do you mean? They can certainly output the words (e.g. https://github.com/Smaug123/WoofWare.Zoomies/pull/165#issuec... ); do you mean something more along the lines of "testing can prove the presence of bugs, but not their absence", like "you can't know whether you've got a false or true negative"? Because that's also a problem with human reviewers.
The solution to this is to get agents to output an importance scale (0-1) then just filter anything below whatever threshold you prefer before creating comments/change reqs.
I love having to hit Resolve Conversation umpteen times before I can merge because somebody added Copilot and it added that many dumb questions/suggestions
those AI checks, if you insist in getting them, should be part of your pre-commit, not part of your PR review flow. they are at best (if they even reach this level) as good as a local run of a linter or static type checker If you are running them as a PR check, the PR is out there. So people will spend time on that PR. no matter if you are fixing the AI comments or not. Best to fix those things BEFORE you provide your code to the team.
[edit] Added part about wasting your teams time
My team uses draft PRs and goes through a process, including AI review, before removing the draft status thereby triggering any remaining human review.
A PR is also a decent UI for getting the feedback but especially so for documenting/discussing the AI review suggestions with the team, just like human review.
AI review is also not equivalent to linter and static checks. It can suggest practices appropriate for the language and appropriate for your code base. Like a lot of my AI experiences it's pretty hit or miss and it's non-deterministic but it doesn't have much cost to disregard the misses and I appreciate the hits.
I'm starting to repeat myself here: If you need/want the opinion of an AI on the changes you make, you should run the robot locally BEFORE you make a pr. I would even say: before you even push commits to the central repository.
This just sounds like you haven’t worked in a team environment in the last 12 months.
The ergonomics of doing this in pre-commit make no sense.
Spin up a PR in GitHub and get Cursor and/or Claude to do a code review — it’s amazing.
It’ll often spot bugs (not only obvious ones), it’ll utilise your agent.md to spot mismatched coding style, missing documentation, it’ll check sentry to see if this part of the code touches a hotspot or a LOC that’s been throwing off errors … it’s an amazing first pass.
Once all the issues are resolved you can mark the PR as ready for review and get a human to look big picture.
It’s unquestionably a huge time saver for reviewers.
And having the AI and human review take place with the same UX (comments attached to lines of code, being able to chat to the AI to explain decisions, having the AI resolve the comment when satisfied) just makes sense and is an obvious time saver for the submitter.
Stuff like coding style and missing documentation is what your basic dumb formatter and linter are supposed to do, using a LLM for such things is hilarious overkill and waste of electricity.
I think what has happened is that there are a whole lot of younger programmers who have not used tooling like linters and static analyzers. For them, when AI does the same thing it is a revelation.
Your linter can tell if a comment exists. AI can tell if it’s up to date.
When your linter or static analyser says something, you know exactly what it means. When AI says something - many a time, it doesnt make sense. And I use AI code review tools every day.
It makes as much sense to use AI in pre-commit as it does to use a linter.
why not have AI review your code BEFORE you share it with the team ? that shows so much more respect to the rest of the team then just throwing your code into the wild, only to change it because some robot tells you that X could be Y
We have AI code reviews enabled for some PR reviews and we discuss them from time to time on the PR to see if it’s worth doing it.
I completely agree.
This question is surprising to me, because I consider AI code review the single most valuable aspect of AI-assisted software development today. It's ahead of line/next-edit tab completion, agentic task completion, etc.
AI code review does not replace human review. But AI reviewers will often notice little things that a human may miss. Sometimes the things they flag are false positives, but it's still worth checking in on them. If even one logical error or edge case gets caught by an AI reviewer that would've otherwise made it to production with just human review, it's a win.
Some AI reviewers will also factor in context of related files not visible in the diff. Humans can do this, but it's time consuming, and many don't.
AI reviews are also a great place to put "lint" like rules that would be complicated to express in standard linting tools like Eslint.
We currently run 3-4 AI reviewers on our PRs. The biggest problem I run into is outdated knowledge. We've had AI reviewers leave comments based on limitations of DynamoDB or whatever that haven't been true for the last year or two. And of course it feels tedious when 3 bots all leave similar comments on the same line, but even that is useful as reinforcement of a signal.
(And in my experience, if GPT-5 misunderstands something, then that thing is either async Python, modern F#, or in need of better comments.)
If you have an architecture document, readmes for related services, relevant code from related services and such assembled for a LLM, it can do a pretty solid review even on microservices. It can catch parameter mismatches/edge cases, instrument logging end to end, do some reasonable flow modeling, etc. It can also point out when uncovered code is a risk, and do a sanity check on tests.
In order to be time efficient, human review should focus on the 'what' rather than the 'how' in most cases.
I work alone (in a medium sized company). No peers, no code review. AI code review is invaluable.
AI is a mixed bag. I'm the type of person who is compelled to have a deep understanding of the code they write. Writing my own code vs fixing AI-generated code is a wash timewise, and the AI generated code is so limited (assuming you pared down the uselessly elaborate code and fixed all the critical runtime bugs) as to restrict further iterations. And I'm talking about uploading an architectural blueprint with every function a documented but otherwise empty stub.
AI is a great bellwether. I bounce ideas off AI for a consensus. The closest equivalent is reading StackOverflow comments. I once offhandedly complained that python had no equivalent for setattr at class scope (as __class__ is not defined until after __new__), but AI showed me how to provide a closure in __prepare__ over the class namespace, which was introduced in 3.3 (?) and to which I paid little attention. What a gem.
AI is great for learning. If you follow a textbook or blog or paper and don't understand, AI can clarify. But be careful with less structured learning - it is important to build a full mental model accounting for every possible outcome and explanation, otherwise you're susceptible to hallucinations. I remember my first derivative in which the end result could be obtained via two separate proofs, one of which would imply an incorrect calculus. You've got to play with it until you're satisfied your mental model accounts for all the facts.
Because AI facilitates learning so easily I feel the best skills for a future generation are those pertaining to memory and retention. Ya know, assuming we don't develop individualized and personalized AI that can model your next word and act as a personal memex.
AI is great as a search assistant. I have much better recall when rereading content. Thus, I prefer to ask AI to search for links to content I vaguely recall, rather than ask AI for it's own summary or recollection.
Despite being terrible at writing decent code, AI provides fantastic code review. It catches everything from subtle high-level errors - even potential errors that haven't yet occured - to api mismatches to syntax errors. I actually wish I was as fluent at namespaces and cgroups as AI, and I'm well versed.
AI is interesting at comments. It can be hard to provide germane comments while wading through the weeds. I feel the best comments are written a few days after the code is complete. AI provides that fresh perspective instantly. And if AI can't understand your code, you had better improve your comments.
I'm about to try AI for unit tests. I prefer hypothesis tests. I took a quick glance at the generated code, and it seemed overly complicated. So I'm not optimistic.
Outside of software, AI is great for things you don't really care about. Yeah it might hallucinate, but my back-and-forth with AI is more about holding up a mirror to myself, revealing inner biases. Especially useful for interior decoration / remodeling.
Of course, take everything I said with a grain of salt as security at my company actively discourages AI. So, everything I said applies to free/cheap plans only. And I haven't tried skills yet.
TIL: you could add a ".diff" to a PR URL. Thanks!
As for PR reviews, assuming you've got linting and static analysis out the way, you'd need to enter a sufficiently reasonable prompt to truly catch problems or surface reviews that match your standard and not generic AI comments.
My company uses some automatic AI PR review bots, and they annoy me more than they help. Lots of useless comments
I would just put a PR_REVIEW.md file in the repo an have a CI agent run it on the diff/repo and decide pass or reject. In this file there are rules the code must be evaluated against. It could be project level policy, you just put your constraints you cannot check by code testing. Of course any constraint that can be a code test, better be a code test.
My experience is you can trust any code that is well tested, human or AI generated. And you cannot trust any code that is not well tested (what I call "vibe tested"). But some constraints need to be in natural language, and for that you need a LLM to review the PRs. This combination of code tests and LLM review should be able to ensure reliable AI coding. If it does not, iterate on your PR rules and on tests.
`gh pr diff num` is an alternative if you have the repo checked out. One can then pipe the output to one's favorite llm CLI and create a shell alias with a default review prompt.
> My company uses some automatic AI PR review bots, and they annoy me more than they help. Lots of useless comments
One way to make them more useful is to ask to list the topN problems found in the change set.
> TIL: you could add a ".diff" to a PR URL. Thanks!
You can also append ".patch" and get a more useful output
Hum? I just tell claude to review pr #123 and it uses 'gh' to do everything, including responding to human comments! Feedback from coleagues has been awesome.
We are sooo gonna get replaced soon...
Not my experience. Most Claude reviews are horrible and if I catch you replying with Claude (any AI really) under your own name you are gonna get two earfulls. Don't get me wrong, if you have an AI bot that I can have a convo with on the PR, sure. But you passing their stuff off as you: do that twice and you're dead to me.
Now, I use it as well to review, just like you mention it pulls it via gh, has all the source to reference and then tells me what it thinks. But it can't be left alone.
Similarly people have been trying to pass root cause analyses off as true and they sound confident but have holes like a good Swiss cheese.
> Feedback from colleagues has been awesome
Colleague's feedback:
Claude> Address comments on PR #123
Good thing I work on an old C++ code base where it's impossible for AI to go through the millions of lines that all interact horribly in unpredictable ways.
Funny you mention that, I have very recently just came back from a one-shot prompt which fixed a rather complex template instantiation issue in a relatively big very convoluted low-level codebase (lots of asm, SPDK / userspace nvme, unholy shuffling of data between numa domains into shared l3/l2 caches). That codebase maybe isn't in millions of lines of code but definitely is complex enough to need a month of onboarding time. Or you know, just give Claude Opus 4.5 a lldb backtrace with 70% symbols missing due to unholy linker gymnastics and get a working fix in 10 mins.
And those are the worst models we will have used from now on.
Template instantiation is relatively simple and can be resolved immediately. Trying to figure out how 4 different libraries interact with undefined behavior to boot is not going to be easy for AI for a while.
Maybe LLMs can't fix all of the human deficiencies in the past.
Is all marketing, it does not even work with js frontend frameworks.
I recently started using LLMs to review my code before asking for a more formal review from colleagues. It's actually been surprisingly useful - why waste my colleagues time with small obvious things? But it's also gone much further than that sometimes with deeper reviews points. Even when I don't agree with them it's great having that little bit more food for thought - if anything it helps seed the review
Yes, exactly!
The objective of this initial review is to catch those low-hanging fruit that your colleagues would waste cycles on.
LLMs can catch syntax and basic semantics. Peers can spend time on more interesting things like design and relevant biz context.
Are you using a particularly well crafted prompt or just something off the cuff?
Personally, this is what I use in claude code:
"Diff to master and review the changes. Branch designed to address <problem statement>. Write output to d:\claudeOut in typst (.typ) format."
It'll do the diffs and search both branch and master versions of files.
I prefer reading PDFs than markdown, but it'll default to markdown unprompted if you prefer.
I have almost all my workspaces configured with /add-dir to add d:/claudeOut and d:/claudeIn as general scratch folders for temporary in/out file permissions so it can read/write outside the context of the workspace for things like this.
You might get better results using a better crafted prompt (or code review skill?). In general I find claude code reviews are:
So it's a bit of a mixed bag, I find it focuses on trivia but it's still useful as a first pass before letting your teammates have to catch that same trivia.It will absolutely assume too much from naming, so it's kind of a good spot if it's making wrong kind of assumptions about how parts work, to think how to name things more clearly.
e.g. If you write a class called "AddingFactory", it'll go around assuming that's what it does, even if the core of it returns (a, b) -> a*b.
You have to then work hard to get it to properly examine the file and convince itself that it is actually a multiplier.
Obviously real-world examples are more subtle than that, but if you're finding yourself arguing with it, it's worth sometimes considering whether you should rename things.
This one's served fairly well: "Review this diff - detect top 10 problem-causers, highlight 3 worst - I'm talking bugs with editing,saving etc. (not type errors or other minor aspects) [your diff]". The bit on "editing, saving" would vary based on goal of diff.
Not who you're replying to but working at a small small company, I didn't have anyone to give my code for review to so have used AI to fill in that gap. I usually go with a specific then general pass, where for example if I'm making heavy use of async logic, I'll ask the LLM to pay particular attention to pitfalls that can arise with it.
We're a Haskell shop, so I usually just say "review the current commit. You're an experienced Haskell programmer and you value readable and obvious code" (because that it is indeed what we value on the team). I'll often ask it to explicitly consider testing, too
This is exactly the right approach IMO. You find the signal amongst the slop, and all your colleagues see is a better PR.
https://cursor.com/bugbot
I didn't see this mentioned, but we've been running bugbot for a while now and it's very good. It catches so many subtle bugs.
It's definitely caught some subtle things that I've completely missed
while this approach is useful, i think the diff is too small to catch a lot of bugs.
i use https://www.coderabbit.ai/ and it tends to be aware of files that aren't in the diff, and definitely can see the rest of the file your are editing (not just the lines in the diff)
I have been using Codex as a code review step and it has been magnificent, truly. I don’t like how it writes code, but as a second line of defence I’m getting better code reviews out of it than I’ve ever had from a human.
In CC or Codex (or whichever) — “run git diff and review”
Yes please upload your code to our LLM so we can train on it to help your competitors
gh pr diff [num]
also works if you have the GitHub cli installed. Would setup an AGENTS.md or SKILL.md to instruct an agent on how to use gh too.
Yeah a terrible review presumably. It has zero context.
[dead]
How to do agentic workflow like 2 years ago.
What would SOA be?