At 30k commits a day, is there anything that can meaningfully be called review?
And so the wildly unprofitable AI company explains how it token maxes it's code and review process.
Authors should really try and view this article from the perspective of someone who has to think about cost to use the models.
Anthropic has been profitable for the past two quarters.
Profitable as in after expenses.
Source? Only reports I’ve seen was they were profitable for one month in last quarter due to xAI discount of renting out colossus . Be interested to learn otherwise.
We're talking about being actually profitable, not fake profitable.
Yes when you subtract expenses and capex
Claude Code is great, but do you really push that much (new) code every day?
What I’m more worried about is feature identification and how to monitor them. How can you get the product to the next level?
Honestly I’m not worried about code anymore in my codebase I’ve not had bug problems for a while and incidents get fixed automatically.
I monitor new features with an agent who checks recordings.
Most actually good features need still 20-30 iterations to design the userflow to actually be interactive, but then there is also another challenge to translate usage patterns into features and how to present functionality with the least amount of friction as possible.
How to efficiently run monitors without wasting your token spends. A best 'monitor' best practice would be nice.
Still this article was very helpful and gave me a lot of new ideas.
How is the ability of a human to understand the code via hands-on familiarity not a direct dependency of their ability to do manual code review? I don't get it. Having claude write an explainer (something I do a lot!) doesn't get you to the point where you can confidently assess a complex diff. And you've funneled your priors on what the diff says and does through the same system that already reviewed and approved it. This article isn't bad but it doesn't give enough weight to the human context-building part of the problem.
It looks like they are specifically optimizing for incidents/reverts and shipping faster
> The goal hasn't changed: less human time per change, and no more reverts than before.
Is this Goodhart's law in action? Less reverts and less incidents was measured and became the target. Is this then still a good measure? I guess Revenue is a more direct measure, but we don't have access to that one.
From my perspective as a power user, the product has improved substantially over the past six months. It has gained new features that are actually useful, lots of UI/UX/perf improvements, and bug fixes as well.
New features have actually been remarkable. For me they are almost always in one of two categories: things I've always wanted (but never actually bothered communicating to the devs), and things I didn't know I needed but did. The product team is clearly very tuned in.
All in all, I'd say their approach is working.
Here I am still unable to stop myself from doing line-by-line code review. I notice too many things (redundant code, duplicate code, overly complex architecture, etc.) by doing it that I feel it would be careless not to.
My advice is to start a totally vibe coded side project. You will never gain confidence until you experiment, find the failure cases, learn to deal with them, etc.
Choose something you want to do but don't really care that much about. Be more ambitious than you think you could be, have a high bar on the outcome but purposefully choose to completely ignore the actual code. Make it write tests, dashboards, monitoring - whatever you need other than code to feel confident.
Now is the cheapest time it will ever be to experiment.
[flagged]
Please don't do this here.
(You're welcome to make your substantive points thoughtfully, of course.)
Issue: I am still unable to stop myself from doing line-by-line code review
Response: Try experimenting in a low stakes environment
From Google: "Exposure therapy is a psychological treatment designed to help people confront their fears, break cycles of avoidance, and reduce anxiety."
I think the intended takeaway is to learn to solve those problems without line by line review. Start by being hands off on a low stakes project, and see how the model fails. Then, come up with, and try, solutions that aren't you doing archeology.
This is why I like GitHub Copilot's autocompletion, I'm right there guiding and policing the code as it appears.
No one cares about good code. It’s a means to an end. Your code doesn’t need to look pretty or be well documented or any of that stuff anymore because people are not looking at it. The only thing people care about is performant and error free code.
People saying this are telling on themselves that they have no idea what quality code is. It's not about looking pretty or nice variable names.
Code quality matters to AI just as much as it did to people
Do you have any data backing this statement? Or is it just your experience?
Yep.
The worst part about this is that if you aren't watching it, the AI will "solve" this with a thousand assert_3_plus_5_equals_8() test cases that just further clutter the workspace and the context window. Churn baby churn.
The models are pretty good at finding code duplication and eliminating it though, and it's usually part of my AI review process at this point
If you let it go buck wild on a massive change, there are those times where it sort of paints itself into a corner but rewrites are cheap (for now), so it's just not that big of a deal, imho
Personal experience:
AI tends to add new wheels for a new feature in a large codebase, because finding, understanding, and generalizing the existing code to integrate a new feature will likely break existing features, cost more effort (and token) to implement and test. Human reviewers also tend to like these code since they are clean, easy to review, and get the job done, especially when they are not familiar with existing codebase.
The issue is that wheels like these accumulate in the codebase, and they inevitably entangle. When an upstream specification changes, when a new feature that works with many existing features are introduced, all these wheels need to be revisited, and in many cases they are not because the new test cases do not cover all the paths. The code base got larger and more complex, and contains more bugs than the unit tests can cover, especially when unit tests are often "bent" to fit the wrong behavior unnoticed.
Literally 50 years of engineering history and culture cares about whether code is good. There are libraries worth of books about it, not as a means to an app but for its own beauty. Just read “The Elements of Programming Style”, for example.
Do what you want, write apps with AI and don’t review code, it doesn’t impact me. The claim that no one cares about good code is demonstrably false, and that’s all I’m objecting to.
> it doesn’t impact me
so long as his software isn't used for anything that can affect you
Not true, LLM’s will struggle with badly organized and designed large codebases just the same, though likely with less errors and profanities.
The biggest concern is that AI can doxx you and find where you live if your code sucks. If that doesn't scare you, nothing will.
Good code is a means to the end of the code being error (and vulnerability) free, maintainable and performant.
And if people are not looking it, those people don't care about their customers.
The way you phrase it makes me realize its almost like a GPL contamination. Eg, once you pull in GPL code, your project is infected and can never use a BSD (or other GPL incompatible) license.
Similarly, once you pull in terrible AI slop code, the project becomes unmaintainable by humans and you need to maintain it with AI.
There’s a little irony about the example you picked of the GPL, just because in the US the output of an LLM cannot be covered under copyright. The GPL works as an assignment of rights under copyright.
This is, of course, tangential to your point.
Code that isn't duplicated in 15 places, that doesn't read the whole database to fetch just 1 record, that doesn't do 15 database reads where one would suffice, that uses an optimized algorithm instead of an exponential one recreated badly from scratch... all that is also good code, and is just as important in LLM-slop era.
You're unable to stop being responsible? Tragic!
Is it responsible to move slowly when your competitors are moving quickly? Depends on how your competitors perform. If they perform well, then your "responsibility" suddenly becomes your extinction.
> Security gets its own automated review too. ... You either fix it or close the thread with a short reason, which is how you clear a false positive.
I hope this is only for low-risk false positives. Dismissing a critical finding as a false positive should still involve AppSec.
You could improve revert rates while simultaneously introducing more security vulnerabilities.
And the linked "claude-code-security-review" explicitly states in the README
> This action is not hardened against prompt injection attacks
That's a significant limitation for an AI security review control, no?
I'm somewhat surprised they would say Claude is writing 90% of their code at this point. Did they get tired of reporting new milestones, or has there been a plateau?
> Low - Docs, copy, internal-only features, additive changes behind a flag
> None required once automated review and security checks are green
How anybody can let an LLM write docs and not even read them is pure insanity.
everything is shipped with auto reviews behind a flag. then you need a human approver to launch
good model
what are people using for automated code review these days? the claude code review product originally mentioned it cost "$15-25 per review" (which is absurd), but it seems like they've removed that from their docs with the launch yesterday. we've looked at various products in the space (coderabbit, greptile, etc.) but have gripes with each.
honestly i'm thinking it might be easier (and cheaper) to launch our own agent from GH webhooks and trigger `/code-review`..
> the claude code review product originally mentioned it cost "$15-25 per review" (which is absurd)
If an engineer paid $150,000 a year reviews some code manually for about 30 minutes, the cost of their time is about $36.
in a vacuum, sure. the average pull request will have multiple commits once opened (i.e. to address feedback or perhaps it was opened too early), each triggering a review. it's very easy to rack up $100+ per pr (and an eng may open a dozen prs in a single day)
Poorly I'm afraid, given how many new bugs make their way to the claude code CLI every update.
Remind me to invest more in the clouds they are burning those tokens in.
FWIW, Pangram identifies several sections as 100% AI-written.
Well, that explains the Github repo with 5000 issues, maybe about a third of which are random regressions of previous behavior.
I just learned that manually invoked skills MUST be the _first_ thing in the prompt, e.g. you can't have `disable-model-invocation: true` and say "Hi, /grill-me on X".
Worse, everything after the /grill-me is injected a second time into the prompt as a <command-args> so your prompt is literally added to the context TWICE. (first time in the original user prompt.)
I swear Anthropic is intentionally bloating your context so you burn tokens on useless bullshit like this.
Oh /loop was the crowning achievement. Basically guarantees you'll run a prompt at max context without caching over and over.
I was running into something similar where I had it working on a single Python file. Maybe part of the issue is that I had memory turned off, and the conversation might have spilled into two conversations. I wasn't using version control either, but since it was a single file with about 1000 lines of code, it didn't seem too hard to eventually get all of my features back.
I snorted laughing at this. "We are experiencing tremendous growth" and it's just a codebase with 1,000 agents endlessly adding markdown documentation for every 1 line change.
That explains why the UI keeps changing.
How is this a proper product? Every time you open the desktop app the buttons may be on a completely random place
I wonder how they maintain the quality of tests for such a massive codebase and frequent commits.
That's the trick: they don't
30 thousand changes merged per day? no. like just no. what are we doing? I'm trying to understand
There was a post some months back called "Lines of Code Got a Better Publicist" or something along those lines. This is just more of that. I am not impressed by 30 thousand "changes" per day in the slightest without significant added context. When I logged in and prompted Claude today, do I feel like I got +600,000 change value from when I logged in a month ago?
I’m horrified by it. There is no possible justification for it but it does explain their buggy products.
I, too, can write and merge 30000 changes per day. Even 300000 if I've up to.
Of course, just like "line of code" is a worthless metric, "change merge per day" is;
for i in {1..10000000}
do
echo "I am working !" >> README.md
git add README.md
git commit -s -m "feat: working with AI"
done
The premise is clear, is one agent can do what developer can, but quicker, why not speed up the whole workflow to match it?
Have you used Claude Code?
Have you seen the benefit of the churn of 1000 PRs a day?
It’s only about 10 per engineer. I’m not at Anthropic and before 2026 I was regularly hitting 1400 commits / year on my GitHub. If agents made me 2x faster (they have, review is still slow; I currently have 40 PRs open) that’d be the same rate.
Right, but why do things this fast to begin with, especially for a product that's already publicly available?
I don't want to use a product that shifts under me as I use it several times a day. I want a product that actually moves slowly and works very reliably.
As an end user: why would I care about how many hundreds of releases a month are being pushed if maybe 1% of them have features I care about?
Speed is not the product.
Humans don't operate at AI speeds. We're making the car go from 0 to 5000 mph in 1 second and the drivers are turning to goo in the seat.
More code output isn't a metric of success per se. Goodhart's law.
And yet they can't fix the janky bugs when you scroll or show/hide chain of though, commands executed, configuring plug-ins from the options menu etc.
That's a shockingly low number.
1000/day in what sounds like a single repo is a low number?
I am not sure what it is you are saying. What I am saying: I would have supposed that Claude writes 99.x% of Claude's Code code, and that ~no code at Anthropic is written by humans anymore.
I agree finding out who is writing that 10% of the code in the AI company is important. Maybe time to identity them and put them on PIP since they are slowing down everyone else and giving a bad name to the AI company
to achieve what?
Honestly not sure that there are that many effective changes a day given how little complexity there is to their public facing product.
No. 90% sounds like a low number. That implies something else is writing 10% of the code? People? Other models? What on earth is writing that other 10%?
90% ?
A random twitter said that, 6 month ago, it was 100% !
DECLINE ! SELL THE STOCK OPTIONS ! (wait, there are none, confused!)
Title is: How the Claude Code team does code review
I'm thinking about high volume mass manufacturing as an example here. I like those "How it is made" type YouTube videos. You see a production line that has some kind of QA process where cameras + automated systems do spot checks at high speeds and these crazy actuators are punching out bad examples. These processes often also involve spot checks where some statistical sample is selected and viewed carefully.
I don't think we've gotten to that level of reliable review with code, but I don't see any reason we can't continue moving in that direction.