I gave Codex a task of porting an OpenCV tracking algorithm (CSRT) from C++ to Rust, so that I can directly use it in my project without having to cross-compile

It one-shot the task perfectly in 1hr, and even developed a GUI on top of it. All I did was to provide the original source and algo paper

I’ve spent years getting specialized in writing numerical code (computational mechanics, fem), and now AI can automate 95% of the low-level grunt work

Acquiring these skills involved highly difficult, excruciating intellectual labor spanning many years, very similar to ML research. Doing tensor math, writing out the solver code, wondering why your solution is not converging, finally figuring out it was a sign typo after 2 days

Kids these days both have it easy and hard. They can fast forward large chunks of the work, but then they will never understand things as deeply as someone who wrote the whole thing by hand

I guess the more valuable skill now is being able to zoom in and out of abstraction levels quickly when needed. Using AI, but recognizing fast when it fails, learning what needs to be done, fixing it, zooming back out, repeat. Adaptive learning, a sort of “depth-on-demand”. The quicker you can pick up new skills and knowledge, the more successful you will be

A simple documentation workflow for AI agents.

For setup instructions, skip to the How to setup SimpleDoc in your repo section.

If you have used AI agents such as Anthropic’s Claude Code, OpenAI’s Codex, etc., you might have noticed their tendency to create markdown files at the repository root:

...
├── API_SPEC.md
├── ARCHITECTURE.md
├── BACKLOG.md
├── CLAUDE.md
├── CODE_REVIEW.md
├── DECISIONS.md
├── ENDPOINTS.md
├── IMPLEMENTATION_PLAN.md
├── NOTES.md
├── QA_CHECKLIST.md
├── SECURITY_PLAN.md
└── src/
    └── ...
├── TEST_COVERAGE.md
├── TEST_REPORTS.md
├── TEST_RESULTS.md
...

The default behavior for models as of writing this in December 2025 is to create capitalized Markdown files at the repository root. This is of course very annoying, when you accidentally commit them and they accumulate over time.

The good news is, this problem is 100% solvable, by using a simple instruction in your AGENTS.md file:

**Attention agent!** Before creating ANY documentation, read the docs/HOW_TO_DOC.md file first. It contains guidelines on how to create documentation in this repository.

But what should be in docs/HOW_TO_DOC.md file and why is it a separate file? In my opinion, the instructions for solving this problem are too specific to be included in the AGENTS.md file. It’s generally a good idea to not inject them into every context.

To solve this problem, I developed a lightweight standard over time, for organizing documentation in a codebase. It is framework-agnostic, unopinionated and designed to be human-readable/writable (as well as agents). I was surprised to be not able to find something similar enough online, crystallized the way I wanted it to be. So I created a specification myself, called SimpleDoc.

Basically, it tells the agent to

1. Create documentation files in the docs/ folder, with YYYY-MM-DD prefixes and lowercase filenames, like 2025-12-22-an-awesome-doc.md, so that they will by default be chronologically sorted.
2. Always include YAML frontmatter with author, so that you can identify who created it without checking git history, if you are working in a team.
3. The exception here are timeless and general files like README.md, INSTALL.md, AGENTS.md, etc. which can be capitalized. But these are much rarer, so we can just follow the previous rules most of the time.

Here is your call to action to check the spec itself: SimpleDoc.

How to setup SimpleDoc in your repo

Run the following command from your repo root:

npx -y @simpledoc/simpledoc migrate

This starts an interactive wizard that will:

1. Migrate existing Markdown docs to SimpleDoc conventions (move root docs into docs/, rename to YYYY-MM-DD-… using git history, and optionally insert missing YAML frontmatter with per-file authors).
2. Ensure AGENTS.md contains the reminder line and that docs/HOW_TO_DOC.md exists (created from the bundled SimpleDoc template).

If you just want to preview what it would change:

npx -y @simpledoc/simpledoc migrate --dry-run

If you run into issues with the workflow or have suggestions for improvement, you can email me at onur@solmaz.io.

Happy documenting!

Below: Why agentic coding tools like Cursor, Claude Code, OpenAI Codex, etc. should implement more ways of letting users queue messages.

See Peter Steinberger’s tweet where he queues continue 100 times to nudge the GPT-5-Codex model to not stop while working on a predictable, boring and long-running refactor task:

Tweet embed disabled to avoid requests to X.

This is necessary while working with a model like GPT-5-Codex. The reason is that the model has a tendency to stop generating at certain checkpoints, due to the way it has been trained, even when you instruct it to FINISH IT UNTIL COMPLETION!!1!. So the only way to get it to finish something is to use the message queue.¹

But this isn’t the only use case for queued messages. For example, you can use the model to retrieve files into its context, before starting off a related task. Say you want to find the root cause of a <bug in component X>. Then you can queue

1. Explain how <component X> works in plain language. Do not omit any details.
2. Find the root cause of <bug> in <component X>.

This will generally help the model to find the root cause easier, or make more accurate predictions about the root cause, by having the context about the component.

Another example: After exploring a design in a dialogue, you can queue the next steps to implement it.

<Prior conversation exploring how to design a new feature>

1. Create an implementation plan for that in the docs/ folder. Include all the details we discussed
2. Commit and push the doc
3. Implement the feature according to the plan.
4. Continue implementing the feature until it is done. Ignore this if the task is already completed.
5. Continue implementing the feature until it is done. Ignore this if the task is already completed.

… you get the idea.

I generally queue like this when the feature is specified enough in the conversation already. If it’s underspecified, then the model will make up stuff.

When I first moved from Claude Code to Codex, the way it implemented queued messages was annoying (more on the difference below). But as I grew accustomed to it, it started to feel a lot like something I saw elsewhere before: chess premoves.

Chess???

A premove is a relatively recent invention in chess which is made possible by digital chess engines. When the feature is turned on, you don’t need to wait for your opponent to finish their move, and instead can queue your next move. It then gets executed automatically if the queued move is valid after your opponent’s move:

If you are fast enough, this let’s you move without using up your time in bullet chess, and even lets you queue up entire mate-in-N sequences, resulting in highly entertaining cases like the video above.

I tend to think of message queueing as the same thing: when applied effectively, it saves you a lot of time, when you can already predict the next move.

In other words, you should queue (or premove) when your next choice is decision-insensitive to the information you will receive in the next turn—so waiting wouldn’t change what you do, it would only delay doing it.

With this perspective, some obvious candidates for queuing in agentic codeing are rote tasks that come before and after “serious work”, e.g:

• making the agent explain the codebase,
• creating implementation plans,
• fixing linting errors,
• updating documentation during work before starting off a subsequent step,
• committing and pushing,
• and so on.

Different ways CLI agents implement queued messages

As I have mentioned above, Claude Code implements queued messages differently from OpenAI Codex. In fact, there are three main approaches that I can think of in this design space, which is based on when a user’s new input takes effect:

1. Post-turn queuing (FIFO²): User messages wait until the current action finishes completely before they’re handled. Example: OpenAI Codex CLI.

2. Boundary-aware queuing (Soft Interrupt): New messages are inserted at natural breakpoints, like after finishing a tool call, assistant reply or a task in the TODO list. This changes the model’s course of action smoothly, without stopping ongoing generation. Example: Claude Code, Cursor.

3. Immediate queuing (Hard Interrupt): New user messages immediately stop the current action/generation, discarding ongoing work and restarting the assistant’s generation from scratch. I have not seen any tool that implements this yet, but it could be an option for the impatient.

Why not implement all of them?

And here is my title-sake argument: When I move away from Claude Code, I miss boundary-aware queuing. When I move away from OpenAI Codex, I miss FIFO queueing.

I don’t see a reason why we could not implement all of them in all agentic tools. It could be controlled by a key combo like Ctrl+Enter, a submenu, or a button, depending on whether you are in the terminal or not.

Having the option would definitely make a difference in agentic workflows where you are running 3-4 agents in parallel.

So if you are reading this and are implementing an agentic coding tool, I would be happy if you took all this into consideration!

This is an adaptation of the original Google’s Code Review Guidelines, to use GitHub specific terminology. Google has their own internal tools for version control (Piper) and code review (Critique). They have their own terminology, like “Change List” (CL) instead of “Pull Request” (PR) which most developers are more familiar with. The changes are minimal and the content is kept as close to the original as possible. The hope is to make this gem accessible to a wider audience.

I also combined the whole set of documents into a single file, to make it easier to consume. You can find my fork here. If you notice any mistakes, please feel free to submit a PR to the fork.

Introduction

A code review is a process where someone other than the author(s) of a piece of code examines that code.

At Google, we use code review to maintain the quality of our code and products.

This documentation is the canonical description of Google’s code review processes and policies.

This page is an overview of our code review process. There are two other large documents that are a part of this guide:

• How To Do A Code Review: A detailed guide for code reviewers.
• The PR Author’s Guide: A detailed guide for developers whose PRs are going through review.

What Do Code Reviewers Look For?

Code reviews should look at:

• Design: Is the code well-designed and appropriate for your system?
• Functionality: Does the code behave as the author likely intended? Is the way the code behaves good for its users?
• Complexity: Could the code be made simpler? Would another developer be able to easily understand and use this code when they come across it in the future?
• Tests: Does the code have correct and well-designed automated tests?
• Naming: Did the developer choose clear names for variables, classes, methods, etc.?
• Comments: Are the comments clear and useful?
• Style: Does the code follow our style guides?
• Documentation: Did the developer also update relevant documentation?

See How To Do A Code Review for more information.

Picking the Best Reviewers

In general, you want to find the best reviewers you can who are capable of responding to your review within a reasonable period of time.

The best reviewer is the person who will be able to give you the most thorough and correct review for the piece of code you are writing. This usually means the owner(s) of the code, who may or may not be the people in the CODEOWNERS file. Sometimes this means asking different people to review different parts of the PR.

If you find an ideal reviewer but they are not available, you should at least CC them on your change.

In-Person Reviews (and Pair Programming)

If you pair-programmed a piece of code with somebody who was qualified to do a good code review on it, then that code is considered reviewed.

You can also do in-person code reviews where the reviewer asks questions and the developer of the change speaks only when spoken to.

See Also

• How To Do A Code Review: A detailed guide for code reviewers.
• The PR Author’s Guide: A detailed guide for developers whose PRs are going through review.

How to do a code review

The pages in this section contain recommendations on the best way to do code reviews, based on long experience. All together they represent one complete document, broken up into many separate sections. You don’t have to read them all, but many people have found it very helpful to themselves and their team to read the entire set.

• The Standard of Code Review
• What to Look For In a Code Review
• Navigating a PR in Review
• Speed of Code Reviews
• How to Write Code Review Comments
• Handling Pushback in Code Reviews

See also the PR Author’s Guide, which gives detailed guidance to developers whose PRs are undergoing review.

The Standard of Code Review

The primary purpose of code review is to make sure that the overall code health of Google’s code base is improving over time. All of the tools and processes of code review are designed to this end.

In order to accomplish this, a series of trade-offs have to be balanced.

First, developers must be able to make progress on their tasks. If you never merge an improvement into the codebase, then the codebase never improves. Also, if a reviewer makes it very difficult for any change to go in, then developers are disincentivized to make improvements in the future.

On the other hand, it is the duty of the reviewer to make sure that each PR is of such a quality that the overall code health of their codebase is not decreasing as time goes on. This can be tricky, because often, codebases degrade through small decreases in code health over time, especially when a team is under significant time constraints and they feel that they have to take shortcuts in order to accomplish their goals.

Also, a reviewer has ownership and responsibility over the code they are reviewing. They want to ensure that the codebase stays consistent, maintainable, and all of the other things mentioned in “What to look for in a code review.”

Thus, we get the following rule as the standard we expect in code reviews:

In general, reviewers should favor approving a PR once it is in a state where it definitely improves the overall code health of the system being worked on, even if the PR isn’t perfect.

That is the senior principle among all of the code review guidelines.

There are limitations to this, of course. For example, if a PR adds a feature that the reviewer doesn’t want in their system, then the reviewer can certainly deny approval even if the code is well-designed.

A key point here is that there is no such thing as “perfect” code—there is only better code. Reviewers should not require the author to polish every tiny piece of a PR before granting approval. Rather, the reviewer should balance out the need to make forward progress compared to the importance of the changes they are suggesting. Instead of seeking perfection, what a reviewer should seek is continuous improvement. A PR that, as a whole, improves the maintainability, readability, and understandability of the system shouldn’t be delayed for days or weeks because it isn’t “perfect.”

Reviewers should always feel free to leave comments expressing that something could be better, but if it’s not very important, prefix it with something like “Nit: “ to let the author know that it’s just a point of polish that they could choose to ignore.

Note: Nothing in this document justifies merging PRs that definitely worsen the overall code health of the system. The only time you would do that would be in an emergency.

Mentoring

Code review can have an important function of teaching developers something new about a language, a framework, or general software design principles. It’s always fine to leave comments that help a developer learn something new. Sharing knowledge is part of improving the code health of a system over time. Just keep in mind that if your comment is purely educational, but not critical to meeting the standards described in this document, prefix it with “Nit: “ or otherwise indicate that it’s not mandatory for the author to resolve it in this PR.

Principles

• Technical facts and data overrule opinions and personal preferences.

• On matters of style, the style guide is the absolute authority. Any purely style point (whitespace, etc.) that is not in the style guide is a matter of personal preference. The style should be consistent with what is there. If there is no previous style, accept the author’s.

• Aspects of software design are almost never a pure style issue or just a personal preference. They are based on underlying principles and should be weighed on those principles, not simply by personal opinion. Sometimes there are a few valid options. If the author can demonstrate (either through data or based on solid engineering principles) that several approaches are equally valid, then the reviewer should accept the preference of the author. Otherwise the choice is dictated by standard principles of software design.

• If no other rule applies, then the reviewer may ask the author to be consistent with what is in the current codebase, as long as that doesn’t worsen the overall code health of the system.

Resolving Conflicts

In any conflict on a code review, the first step should always be for the developer and reviewer to try to come to consensus, based on the contents of this document and the other documents in The PR Author’s Guide and this Reviewer Guide.

When coming to consensus becomes especially difficult, it can help to have a face-to-face meeting or a video conference between the reviewer and the author, instead of just trying to resolve the conflict through code review comments. (If you do this, though, make sure to record the results of the discussion as a comment on the PR, for future readers.)

If that doesn’t resolve the situation, the most common way to resolve it would be to escalate. Often the escalation path is to a broader team discussion, having a Technical Lead weigh in, asking for a decision from a maintainer of the code, or asking an Eng Manager to help out. Don’t let a PR sit around because the author and the reviewer can’t come to an agreement.

Next: What to look for in a code review

What to look for in a code review

Note: Always make sure to take into account The Standard of Code Review when considering each of these points.

Design

The most important thing to cover in a review is the overall design of the PR. Do the interactions of various pieces of code in the PR make sense? Does this change belong in your codebase, or in a library? Does it integrate well with the rest of your system? Is now a good time to add this functionality?

Functionality

Does this PR do what the developer intended? Is what the developer intended good for the users of this code? The “users” are usually both end-users (when they are affected by the change) and developers (who will have to “use” this code in the future).

Mostly, we expect developers to test PRs well-enough that they work correctly by the time they get to code review. However, as the reviewer you should still be thinking about edge cases, looking for concurrency problems, trying to think like a user, and making sure that there are no bugs that you see just by reading the code.

You can validate the PR if you want—the time when it’s most important for a reviewer to check a PR’s behavior is when it has a user-facing impact, such as a UI change. It’s hard to understand how some changes will impact a user when you’re just reading the code. For changes like that, you can have the developer give you a demo of the functionality if it’s too inconvenient to patch in the PR and try it yourself.

Another time when it’s particularly important to think about functionality during a code review is if there is some sort of parallel programming going on in the PR that could theoretically cause deadlocks or race conditions. These sorts of issues are very hard to detect by just running the code and usually need somebody (both the developer and the reviewer) to think through them carefully to be sure that problems aren’t being introduced. (Note that this is also a good reason not to use concurrency models where race conditions or deadlocks are possible—it can make it very complex to do code reviews or understand the code.)

Complexity

Is the PR more complex than it should be? Check this at every level of the PR—are individual lines too complex? Are functions too complex? Are classes too complex? “Too complex” usually means “can’t be understood quickly by code readers.” It can also mean “developers are likely to introduce bugs when they try to call or modify this code.”

A particular type of complexity is over-engineering, where developers have made the code more generic than it needs to be, or added functionality that isn’t presently needed by the system. Reviewers should be especially vigilant about over-engineering. Encourage developers to solve the problem they know needs to be solved now, not the problem that the developer speculates might need to be solved in the future. The future problem should be solved once it arrives and you can see its actual shape and requirements in the physical universe.

Tests

Ask for unit, integration, or end-to-end tests as appropriate for the change. In general, tests should be added in the same PR as the production code unless the PR is handling an emergency.

Make sure that the tests in the PR are correct, sensible, and useful. Tests do not test themselves, and we rarely write tests for our tests—a human must ensure that tests are valid.

Will the tests actually fail when the code is broken? If the code changes beneath them, will they start producing false positives? Does each test make simple and useful assertions? Are the tests separated appropriately between different test methods?

Remember that tests are also code that has to be maintained. Don’t accept complexity in tests just because they aren’t part of the main binary.

Naming

Did the developer pick good names for everything? A good name is long enough to fully communicate what the item is or does, without being so long that it becomes hard to read.

Comments

Did the developer write clear comments in understandable English? Are all of the comments actually necessary? Usually comments are useful when they explain why some code exists, and should not be explaining what some code is doing. If the code isn’t clear enough to explain itself, then the code should be made simpler. There are some exceptions (regular expressions and complex algorithms often benefit greatly from comments that explain what they’re doing, for example) but mostly comments are for information that the code itself can’t possibly contain, like the reasoning behind a decision.

It can also be helpful to look at comments that were there before this PR. Maybe there is a TODO that can be removed now, a comment advising against this change being made, etc.

Note that comments are different from documentation of classes, modules, or functions, which should instead express the purpose of a piece of code, how it should be used, and how it behaves when used.

Style

We have style guides at Google for all of our major languages, and even for most of the minor languages. Make sure the PR follows the appropriate style guides.

If you want to improve some style point that isn’t in the style guide, prefix your comment with “Nit:” to let the developer know that it’s a nitpick that you think would improve the code but isn’t mandatory. Don’t block PRs from being merged based only on personal style preferences.

The author of the PR should not include major style changes combined with other changes. It makes it hard to see what is being changed in the PR, makes merges and rollbacks more complex, and causes other problems. For example, if the author wants to reformat the whole file, have them send you just the reformatting as one PR, and then send another PR with their functional changes after that.

Consistency

What if the existing code is inconsistent with the style guide? Per our code review principles, the style guide is the absolute authority: if something is required by the style guide, the PR should follow the guidelines.

In some cases, the style guide makes recommendations rather than declaring requirements. In these cases, it’s a judgment call whether the new code should be consistent with the recommendations or the surrounding code. Bias towards following the style guide unless the local inconsistency would be too confusing.

If no other rule applies, the author should maintain consistency with the existing code.

Either way, encourage the author to file a bug and add a TODO for cleaning up existing code.

Documentation

If a PR changes how users build, test, interact with, or release code, check to see that it also updates associated documentation, including READMEs, repository docs, and any generated reference docs. If the PR deletes or deprecates code, consider whether the documentation should also be deleted. If documentation is missing, ask for it.

Every Line

In the general case, look at every line of code that you have been assigned to review. Some things like data files, generated code, or large data structures you can scan over sometimes, but don’t scan over a human-written class, function, or block of code and assume that what’s inside of it is okay. Obviously some code deserves more careful scrutiny than other code—that’s a judgment call that you have to make—but you should at least be sure that you understand what all the code is doing.

If it’s too hard for you to read the code and this is slowing down the review, then you should let the developer know that and wait for them to clarify it before you try to review it. At Google, we hire great software engineers, and you are one of them. If you can’t understand the code, it’s very likely that other developers won’t either. So you’re also helping future developers understand this code, when you ask the developer to clarify it.

If you understand the code but you don’t feel qualified to do some part of the review, make sure there is a reviewer on the PR who is qualified, particularly for complex issues such as privacy, security, concurrency, accessibility, internationalization, etc.

Exceptions

What if it doesn’t make sense for you to review every line? For example, you are one of multiple reviewers on a PR and may be asked:

• To review only certain files that are part of a larger change.
• To review only certain aspects of the PR, such as the high-level design, privacy or security implications, etc.

In these cases, note in a comment which parts you reviewed. Prefer giving Approve with comments .

If you instead wish to grant Approval after confirming that other reviewers have reviewed other parts of the PR, note this explicitly in a comment to set expectations. Aim to respond quickly once the PR has reached the desired state.

Context

It is often helpful to look at the PR in a broad context. Usually the code review tool will only show you a few lines of code around the parts that are being changed. Sometimes you have to look at the whole file to be sure that the change actually makes sense. For example, you might see only four new lines being added, but when you look at the whole file, you see those four lines are in a 50-line method that now really needs to be broken up into smaller methods.

It’s also useful to think about the PR in the context of the system as a whole. Is this PR improving the code health of the system or is it making the whole system more complex, less tested, etc.? Don’t accept PRs that degrade the code health of the system. Most systems become complex through many small changes that add up, so it’s important to prevent even small complexities in new changes.

Good Things

If you see something nice in the PR, tell the developer, especially when they addressed one of your comments in a great way. Code reviews often just focus on mistakes, but they should offer encouragement and appreciation for good practices, as well. It’s sometimes even more valuable, in terms of mentoring, to tell a developer what they did right than to tell them what they did wrong.

Summary

In doing a code review, you should make sure that:

• The code is well-designed.
• The functionality is good for the users of the code.
• Any UI changes are sensible and look good.
• Any parallel programming is done safely.
• The code isn’t more complex than it needs to be.
• The developer isn’t implementing things they might need in the future but don’t know they need now.
• Code has appropriate unit tests.
• Tests are well-designed.
• The developer used clear names for everything.
• Comments are clear and useful, and mostly explain why instead of what.
• Code is appropriately documented (generally in repository docs).
• The code conforms to our style guides.

Make sure to review every line of code you’ve been asked to review, look at the context, make sure you’re improving code health, and compliment developers on good things that they do.

Next: Navigating a PR in Review

Navigating a PR in review

Summary

Now that you know what to look for, what’s the most efficient way to manage a review that’s spread across multiple files?

1. Does the change make sense? Does it have a good description?
2. Look at the most important part of the change first. Is it well-designed overall?
3. Look at the rest of the PR in an appropriate sequence.

Step One: Take a broad view of the change

Look at the PR description and what the PR does in general. Does this change even make sense? If this change shouldn’t have happened in the first place, please respond immediately with an explanation of why the change should not be happening. When you reject a change like this, it’s also a good idea to suggest to the developer what they should have done instead.

For example, you might say “Looks like you put some good work into this, thanks! However, we’re actually going in the direction of removing the FooWidget system that you’re modifying here, and so we don’t want to make any new modifications to it right now. How about instead you refactor our new BarWidget class?”

Note that not only did the reviewer reject the current PR and provide an alternative suggestion, but they did it courteously. This kind of courtesy is important because we want to show that we respect each other as developers even when we disagree.

If you get more than a few PRs that represent changes you don’t want to make, you should consider re-working your team’s development process or the posted process for external contributors so that there is more communication before PRs are written. It’s better to tell people “no” before they’ve done a ton of work that now has to be thrown away or drastically re-written.

Step Two: Examine the main parts of the PR

Find the file or files that are the “main” part of this PR. Often, there is one file that has the largest number of logical changes, and it’s the major piece of the PR. Look at these major parts first. This helps give context to all of the smaller parts of the PR, and generally accelerates doing the code review. If the PR is too large for you to figure out which parts are the major parts, ask the developer what you should look at first, or ask them to split up the PR into multiple PRs.

If you see some major design problems with this part of the PR, you should send those comments immediately, even if you don’t have time to review the rest of the PR right now. In fact, reviewing the rest of the PR might be a waste of time, because if the design problems are significant enough, a lot of the other code under review is going to disappear and not matter anyway.

There are two major reasons it’s so important to send these major design comments out immediately:

• Developers often mail a PR and then immediately start new work based on that PR while they wait for review. If there are major design problems in the PR you’re reviewing, they’re also going to have to re-work their later PR. You want to catch them before they’ve done too much extra work on top of the problematic design.
• Major design changes take longer to do than small changes. Developers nearly all have deadlines; in order to make those deadlines and still have quality code in the codebase, the developer needs to start on any major re-work of the PR as soon as possible.

Step Three: Look through the rest of the PR in an appropriate sequence

Once you’ve confirmed there are no major design problems with the PR as a whole, try to figure out a logical sequence to look through the files while also making sure you don’t miss reviewing any file. Usually after you’ve looked through the major files, it’s simplest to just go through each file in the order that the code review tool presents them to you. Sometimes it’s also helpful to read the tests first before you read the main code, because then you have an idea of what the change is supposed to be doing.

Next: Speed of Code Reviews

Speed of Code Reviews

Why Should Code Reviews Be Fast?

At Google, we optimize for the speed at which a team of developers can produce a product together, as opposed to optimizing for the speed at which an individual developer can write code. The speed of individual development is important, it’s just not as important as the velocity of the entire team.

When code reviews are slow, several things happen:

• The velocity of the team as a whole is decreased. Yes, the individual who doesn’t respond quickly to the review gets other work done. However, new features and bug fixes for the rest of the team are delayed by days, weeks, or months as each PR waits for review and re-review.
• Developers start to protest the code review process. If a reviewer only responds every few days, but requests major changes to the PR each time, that can be frustrating and difficult for developers. Often, this is expressed as complaints about how “strict” the reviewer is being. If the reviewer requests the same substantial changes (changes which really do improve code health), but responds quickly every time the developer makes an update, the complaints tend to disappear. Most complaints about the code review process are actually resolved by making the process faster.
• Code health can be impacted. When reviews are slow, there is increased pressure to allow developers to merge PRs that are not as good as they could be. Slow reviews also discourage code cleanups, refactorings, and further improvements to existing PRs.

How Fast Should Code Reviews Be?

If you are not in the middle of a focused task, you should do a code review shortly after it comes in.

One business day is the maximum time it should take to respond to a code review request (i.e., first thing the next morning).

Following these guidelines means that a typical PR should get multiple rounds of review (if needed) within a single day.

Speed vs. Interruption

There is one time where the consideration of personal velocity trumps team velocity. If you are in the middle of a focused task, such as writing code, don’t interrupt yourself to do a code review. Research has shown that it can take a long time for a developer to get back into a smooth flow of development after being interrupted. So interrupting yourself while coding is actually more expensive to the team than making another developer wait a bit for a code review.

Instead, wait for a break point in your work before you respond to a request for review. This could be when your current coding task is completed, after lunch, returning from a meeting, coming back from the breakroom, etc.

Fast Responses

When we talk about the speed of code reviews, it is the response time that we are concerned with, as opposed to how long it takes a PR to get through the whole review and be merged. The whole process should also be fast, ideally, but it’s even more important for the individual responses to come quickly than it is for the whole process to happen rapidly.

Even if it sometimes takes a long time to get through the entire review process, having quick responses from the reviewer throughout the process significantly eases the frustration developers can feel with “slow” code reviews.

If you are too busy to do a full review on a PR when it comes in, you can still send a quick response that lets the developer know when you will get to it, suggest other reviewers who might be able to respond more quickly, or provide some initial broad comments. (Note: none of this means you should interrupt coding even to send a response like this—send the response at a reasonable break point in your work.)

It is important that reviewers spend enough time on review that they are certain their “Approve” means “this code meets our standards.” However, individual responses should still ideally be fast.

Cross-Time-Zone Reviews

When dealing with time zone differences, try to get back to the author while they have time to respond before the end of their working hours. If they have already finished work for the day, then try to make sure your review is done before they start work the next day.

Approve With Comments (LGTM)

In order to speed up code reviews, there are certain situations in which a reviewer should Approve even though they are also leaving unresolved comments on the PR. This should be done when at least one of the following applies:

• The reviewer is confident that the developer will appropriately address all the reviewer’s remaining comments.
• The comments don’t have to be addressed by the developer.
• The suggestions are minor, e.g. sort imports, fix a nearby typo, apply a suggested fix, remove an unused dep, etc.

The reviewer should specify which of these options they intend, if it is not otherwise clear.

Approve With Comments is especially worth considering when the developer and reviewer are in different time zones and otherwise the developer would be waiting for a whole day just to get approval.

Large PRs

If somebody sends you a code review that is so large you’re not sure when you will be able to have time to review it, your typical response should be to ask the developer to split the PR into several smaller PRs that build on each other, instead of one huge PR that has to be reviewed all at once. This is usually possible and very helpful to reviewers, even if it takes additional work from the developer.

If a PR can’t be broken up into smaller PRs, and you don’t have time to review the entire thing quickly, then at least write some comments on the overall design of the PR and send it back to the developer for improvement. One of your goals as a reviewer should be to always unblock the developer or enable them to take some sort of further action quickly, without sacrificing code health to do so.

Code Review Improvements Over Time

If you follow these guidelines and you are strict with your code reviews, you should find that the entire code review process tends to go faster and faster over time. Developers learn what is required for healthy code, and send you PRs that are great from the start, requiring less and less review time. Reviewers learn to respond quickly and not add unnecessary latency into the review process. But don’t compromise on the code review standards or quality for an imagined improvement in velocity—it’s not actually going to make anything happen more quickly, in the long run.

Emergencies

There are also emergencies where PRs must pass through the whole review process very quickly, and where the quality guidelines would be relaxed. However, please see What Is An Emergency? for a description of which situations actually qualify as emergencies and which don’t.

Next: How to Write Code Review Comments

How to write code review comments

Summary

• Be kind.
• Explain your reasoning.
• Balance giving explicit directions with just pointing out problems and letting the developer decide.
• Encourage developers to simplify code or add code comments instead of just explaining the complexity to you.

Courtesy

In general, it is important to be courteous and respectful while also being very clear and helpful to the developer whose code you are reviewing. One way to do this is to be sure that you are always making comments about the code and never making comments about the developer. You don’t always have to follow this practice, but you should definitely use it when saying something that might otherwise be upsetting or contentious. For example:

Bad: “Why did you use threads here when there’s obviously no benefit to be gained from concurrency?”

Good: “The concurrency model here is adding complexity to the system without any actual performance benefit that I can see. Because there’s no performance benefit, it’s best for this code to be single-threaded instead of using multiple threads.”

Explain Why

One thing you’ll notice about the “good” example from above is that it helps the developer understand why you are making your comment. You don’t always need to include this information in your review comments, but sometimes it’s appropriate to give a bit more explanation around your intent, the best practice you’re following, or how your suggestion improves code health.

Giving Guidance

In general it is the developer’s responsibility to fix a PR, not the reviewer’s. You are not required to do detailed design of a solution or write code for the developer.

This doesn’t mean the reviewer should be unhelpful, though. In general you should strike an appropriate balance between pointing out problems and providing direct guidance. Pointing out problems and letting the developer make a decision often helps the developer learn, and makes it easier to do code reviews. It also can result in a better solution, because the developer is closer to the code than the reviewer is.

However, sometimes direct instructions, suggestions, or even code are more helpful. The primary goal of code review is to get the best PR possible. A secondary goal is improving the skills of developers so that they require less and less review over time.

Remember that people learn from reinforcement of what they are doing well and not just what they could do better. If you see things you like in the PR, comment on those too! Examples: developer cleaned up a messy algorithm, added exemplary test coverage, or you as the reviewer learned something from the PR. Just as with all comments, include why you liked something, further encouraging the developer to continue good practices.

Label comment severity

Consider labeling the severity of your comments, differentiating required changes from guidelines or suggestions.

Here are some examples:

Nit: This is a minor thing. Technically you should do it, but it won’t hugely impact things.

Optional (or Consider): I think this may be a good idea, but it’s not strictly required.

FYI: I don’t expect you to do this in this PR, but you may find this interesting to think about for the future.

This makes review intent explicit and helps authors prioritize the importance of various comments. It also helps avoid misunderstandings; for example, without comment labels, authors may interpret all comments as mandatory, even if some comments are merely intended to be informational or optional.

Accepting Explanations

If you ask a developer to explain a piece of code that you don’t understand, that should usually result in them rewriting the code more clearly. Occasionally, adding a comment in the code is also an appropriate response, as long as it’s not just explaining overly complex code.

Explanations written only in the code review tool are not helpful to future code readers. They are acceptable only in a few circumstances, such as when you are reviewing an area you are not very familiar with and the developer explains something that normal readers of the code would have already known.

Next: Handling Pushback in Code Reviews

Handling pushback in code reviews

Sometimes a developer will push back on a code review. Either they will disagree with your suggestion or they will complain that you are being too strict in general.

Who is right?

When a developer disagrees with your suggestion, first take a moment to consider if they are correct. Often, they are closer to the code than you are, and so they might really have a better insight about certain aspects of it. Does their argument make sense? Does it make sense from a code health perspective? If so, let them know that they are right and let the issue drop.

However, developers are not always right. In this case the reviewer should further explain why they believe that their suggestion is correct. A good explanation demonstrates both an understanding of the developer’s reply, and additional information about why the change is being requested.

In particular, when the reviewer believes their suggestion will improve code health, they should continue to advocate for the change, if they believe the resulting code quality improvement justifies the additional work requested. Improving code health tends to happen in small steps.

Sometimes it takes a few rounds of explaining a suggestion before it really sinks in. Just make sure to always stay polite and let the developer know that you hear what they’re saying, you just don’t agree.

Upsetting Developers

Reviewers sometimes believe that the developer will be upset if the reviewer insists on an improvement. Sometimes developers do become upset, but it is usually brief and they become very thankful later that you helped them improve the quality of their code. Usually, if you are polite in your comments, developers actually don’t become upset at all, and the worry is just in the reviewer’s mind. Upsets are usually more about the way comments are written than about the reviewer’s insistence on code quality.

Cleaning It Up Later

A common source of push back is that developers (understandably) want to get things done. They don’t want to go through another round of review just to get this PR in. So they say they will clean something up in a later PR, and thus you should Approve this PR now. Some developers are very good about this, and will immediately write a follow-up PR that fixes the issue. However, experience shows that as more time passes after a developer writes the original PR, the less likely this clean up is to happen. In fact, usually unless the developer does the clean up immediately after the present PR, it never happens. This isn’t because developers are irresponsible, but because they have a lot of work to do and the cleanup gets lost or forgotten in the press of other work. Thus, it is usually best to insist that the developer clean up their PR now, before the code is in the codebase and “done.” Letting people “clean things up later” is a common way for codebases to degenerate.

If a PR introduces new complexity, it must be cleaned up before merge unless it is an emergency. If the PR exposes surrounding problems and they can’t be addressed right now, the developer should file a bug for the cleanup and assign it to themselves so that it doesn’t get lost. They can optionally also write a TODO comment in the code that references the filed bug.

General Complaints About Strictness

If you previously had fairly lax code reviews and you switch to having strict reviews, some developers will complain very loudly. Improving the speed of your code reviews usually causes these complaints to fade away.

Sometimes it can take months for these complaints to fade away, but eventually developers tend to see the value of strict code reviews as they see what great code they help generate. Sometimes the loudest protesters even become your strongest supporters once something happens that causes them to really see the value you’re adding by being strict.

Resolving Conflicts

If you are following all of the above but you still encounter a conflict between yourself and a developer that can’t be resolved, see The Standard of Code Review for guidelines and principles that can help resolve the conflict.

The PR author’s guide to getting through code review

The pages in this section contain best practices for developers going through code review. These guidelines should help you get through reviews faster and with higher-quality results. You don’t have to read them all, but they are intended to apply to every Google developer, and many people have found it helpful to read the whole set.

• Writing Good PR Descriptions
• Small PRs
• How to Handle Reviewer Comments

See also How to Do a Code Review, which gives detailed guidance for code reviewers.

Writing good PR descriptions

A PR description is a public record of change, and it is important that it communicates:

1. What change is being made? This should summarize the major changes such that readers have a sense of what is being changed without needing to read the entire PR.

2. Why are these changes being made? What contexts did you have as an author when making this change? Were there decisions you made that aren’t reflected in the source code? etc.

The PR description will become a permanent part of our version control history and will possibly be read by hundreds of people over the years.

Future developers will search for your PR based on its description. Someone in the future might be looking for your change because of a faint memory of its relevance but without the specifics handy. If all the important information is in the code and not the description, it’s going to be a lot harder for them to locate your PR.

And then, after they find the PR, will they be able to understand why the change was made? Reading source code may reveal what the software is doing but it may not reveal why it exists, which can make it harder for future developers to know whether they can move Chesterton’s fence.

A well-written PR description will help those future engineers – sometimes, including yourself!

First Line

• Short summary of what is being done.
• Complete sentence, written as though it was an order.
• Follow by empty line.

The first line of a PR description should be a short summary of specifically what is being done by the PR, followed by a blank line. This is what appears in version control history summaries, so it should be informative enough that future code searchers don’t have to read your PR or its whole description to understand what your PR actually did or how it differs from other PRs. That is, the first line should stand alone, allowing readers to skim through code history much faster.

Try to keep your first line short, focused, and to the point. The clarity and utility to the reader should be the top concern.

By tradition, the first line of a PR description is a complete sentence, written as though it were an order (an imperative sentence). For example, say "Delete the FizzBuzz RPC and replace it with the new system.” instead of "Deleting the FizzBuzz RPC and replacing it with the new system.” You don’t have to write the rest of the description as an imperative sentence, though.

Body is Informative

The first line should be a short, focused summary, while the rest of the description should fill in the details and include any supplemental information a reader needs to understand the change holistically. It might include a brief description of the problem that’s being solved, and why this is the best approach. If there are any shortcomings to the approach, they should be mentioned. If relevant, include background information such as bug numbers, benchmark results, and links to design documents.

If you include links to external resources consider that they may not be visible to future readers due to access restrictions or retention policies. Where possible include enough context for reviewers and future readers to understand the PR.

Even small PRs deserve a little attention to detail. Put the PR in context.

Bad PR Descriptions

“Fix bug” is an inadequate PR description. What bug? What did you do to fix it? Other similarly bad descriptions include:

• “Fix build.”
• “Add patch.”
• “Moving code from A to B.”
• “Phase 1.”
• “Add convenience functions.”
• “kill weird URLs.”

Some of those are real PR descriptions. Although short, they do not provide enough useful information.

Good PR Descriptions

Here are some examples of good descriptions.

Functionality change

Example:

RPC: Remove size limit on RPC server message freelist.

Servers like FizzBuzz have very large messages and would benefit from reuse. Make the freelist larger, and add a goroutine that frees the freelist entries slowly over time, so that idle servers eventually release all freelist entries.

The first few words describe what the PR actually does. The rest of the description talks about the problem being solved, why this is a good solution, and a bit more information about the specific implementation.

Refactoring

Example:

Construct a Task with a TimeKeeper to use its TimeStr and Now methods.

Add a Now method to Task, so the borglet() getter method can be removed (which was only used by OOMCandidate to call borglet’s Now method). This replaces the methods on Borglet that delegate to a TimeKeeper.

Allowing Tasks to supply Now is a step toward eliminating the dependency on Borglet. Eventually, collaborators that depend on getting Now from the Task should be changed to use a TimeKeeper directly, but this has been an accommodation to refactoring in small steps.

Continuing the long-range goal of refactoring the Borglet Hierarchy.

The first line describes what the PR does and how this is a change from the past. The rest of the description talks about the specific implementation, the context of the PR, that the solution isn’t ideal, and possible future direction. It also explains why this change is being made.

Small PR that needs some context

Example:

Create a Python3 build rule for status.py.

This allows consumers who are already using this as in Python3 to depend on a rule that is next to the original status build rule instead of somewhere in their own tree. It encourages new consumers to use Python3 if they can, instead of Python2, and significantly simplifies some automated build file refactoring tools being worked on currently.

The first sentence describes what’s actually being done. The rest of the description explains why the change is being made and gives the reviewer a lot of context.

Using tags

Tags are manually entered labels that can be used to categorize PRs. These may be supported by tools or just used by team convention.

For example:

• “[tag]”
• “[a longer tag]”
• “#tag”
• “tag:”

Using tags is optional.

When adding tags, consider whether they should be in the body of the PR description or the first line. Limit the usage of tags in the first line, as this can obscure the content.

Examples with and without tags:

Good:

// Tags are okay in the first line if kept short.
[banana] Peel the banana before eating.

// Tags can be inlined in content.
Peel the #banana before eating.

// Tags are optional.
Peel the banana before eating.

// Multiple tags are acceptable if kept short.
#banana #apple: Assemble a fruit basket.

// Tags can go anywhere in the PR description.
> Assemble a fruit basket.
>
> #banana #apple

Bad:

// Too many tags (or tags that are too long) overwhelm the first line.
//
// Instead, consider whether the tags can be moved into the description body
// and/or shortened.
[banana peeler factory factory][apple picking service] Assemble a fruit basket.

Generated PR descriptions

Some PRs are generated by tools. Whenever possible, their descriptions should also follow the advice here. That is, their first line should be short, focused, and stand alone, and the PR description body should include informative details that help reviewers and future code searchers understand each PR’s effect.

Review the description before merging the PR

PRs can undergo significant change during review. It can be worthwhile to review a PR description before merging the PR, to ensure that the description still reflects what the PR does.

Next: Small PRs

Small PRs

Why Write Small PRs?

Small, simple PRs are:

• Reviewed more quickly. It’s easier for a reviewer to find five minutes several times to review small PRs than to set aside a 30 minute block to review one large PR.
• Reviewed more thoroughly. With large changes, reviewers and authors tend to get frustrated by large volumes of detailed commentary shifting back and forth—sometimes to the point where important points get missed or dropped.
• Less likely to introduce bugs. Since you’re making fewer changes, it’s easier for you and your reviewer to reason effectively about the impact of the PR and see if a bug has been introduced.
• Less wasted work if they are rejected. If you write a huge PR and then your reviewer says that the overall direction is wrong, you’ve wasted a lot of work.
• Easier to merge. Working on a large PR takes a long time, so you will have lots of conflicts when you merge, and you will have to merge frequently.
• Easier to design well. It’s a lot easier to polish the design and code health of a small change than it is to refine all the details of a large change.
• Less blocking on reviews. Sending self-contained portions of your overall change allows you to continue coding while you wait for your current PR in review.
• Simpler to roll back. A large PR will more likely touch files that get updated between the initial PR submission and a rollback PR, complicating the rollback (the intermediate PRs will probably need to be rolled back too).

Note that reviewers have discretion to reject your change outright for the sole reason of it being too large. Usually they will thank you for your contribution but request that you somehow make it into a series of smaller changes. It can be a lot of work to split up a change after you’ve already written it, or require lots of time arguing about why the reviewer should accept your large change. It’s easier to just write small PRs in the first place.

What is Small?

In general, the right size for a PR is one self-contained change. This means that:

• The PR makes a minimal change that addresses just one thing. This is usually just one part of a feature, rather than a whole feature at once. In general it’s better to err on the side of writing PRs that are too small vs. PRs that are too large. Work with your reviewer to find out what an acceptable size is.
• The PR should include related test code.
• Everything the reviewer needs to understand about the PR (except future development) is in the PR, the PR’s description, the existing codebase, or a PR they’ve already reviewed.
• The system will continue to work well for its users and for the developers after the PR is merged.
• The PR is not so small that its implications are difficult to understand. If you add a new API, you should include a usage of the API in the same PR so that reviewers can better understand how the API will be used. This also prevents checking in unused APIs.

There are no hard and fast rules about how large is “too large.” 100 lines is usually a reasonable size for a PR, and 1000 lines is usually too large, but it’s up to the judgment of your reviewer. The number of files that a change is spread across also affects its “size.” A 200-line change in one file might be okay, but spread across 50 files it would usually be too large.

Keep in mind that although you have been intimately involved with your code from the moment you started to write it, the reviewer often has no context. What seems like an acceptably-sized PR to you might be overwhelming to your reviewer. When in doubt, write PRs that are smaller than you think you need to write. Reviewers rarely complain about getting PRs that are too small.

When are Large PRs Okay?

There are a few situations in which large changes aren’t as bad:

• You can usually count deletion of an entire file as being just one line of change, because it doesn’t take the reviewer very long to review.
• Sometimes a large PR has been generated by an automatic refactoring tool that you trust completely, and the reviewer’s job is just to verify and say that they really do want the change. These PRs can be larger, although some of the caveats from above (such as merging and testing) still apply.

Writing Small PRs Efficiently

If you write a small PR and then you wait for your reviewer to approve it before you write your next PR, then you’re going to waste a lot of time. So you want to find some way to work that won’t block you while you’re waiting for review. This could involve having multiple projects to work on simultaneously, finding reviewers who agree to be immediately available, doing in-person reviews, pair programming, or splitting your PRs in a way that allows you to continue working immediately.

Splitting PRs

When starting work that will have multiple PRs with potential dependencies among each other, it’s often useful to think about how to split and organize those PRs at a high level before diving into coding.

Besides making things easier for you as an author to manage and organize your PRs, it also makes things easier for your code reviewers, which in turn makes your code reviews more efficient.

Here are some strategies for splitting work into different PRs.

Stacking Multiple Changes on Top of Each Other

One way to split up a PR without blocking yourself is to write one small PR, send it off for review, and then immediately start writing another PR based on the first PR. Most version control systems allow you to do this somehow.

Splitting by Files

Another way to split up a PR is by groupings of files that will require different reviewers but are otherwise self-contained changes.

For example: you send off one PR for modifications to a protocol buffer and another PR for changes to the code that uses that proto. You have to merge the proto PR before the code PR, but they can both be reviewed simultaneously. If you do this, you might want to inform both sets of reviewers about the other PR that you wrote, so that they have context for your changes.

Another example: you send one PR for a code change and another for the configuration or experiment that uses that code; this is easier to roll back too, if necessary, as configuration/experiment files are sometimes pushed to production faster than code changes.

Splitting Horizontally

Consider creating shared code or stubs that help isolate changes between layers of the tech stack. This not only helps expedite development but also encourages abstraction between layers.

For example: You created a calculator app with client, API, service, and data model layers. A shared proto signature can abstract the service and data model layers from each other. Similarly, an API stub can split the implementation of client code from service code and enable them to move forward independently. Similar ideas can also be applied to more granular function or class level abstractions.

Splitting Vertically

Orthogonal to the layered, horizontal approach, you can instead break down your code into smaller, full-stack, vertical features. Each of these features can be independent parallel implementation tracks. This enables some tracks to move forward while other tracks are awaiting review or feedback.

Back to our calculator example from Splitting Horizontally. You now want to support new operators, like multiplication and division. You could split this up by implementing multiplication and division as separate verticals or sub-features, even though they may have some overlap such as shared button styling or shared validation logic.

Splitting Horizontally & Vertically

To take this a step further, you could combine these approaches and chart out an implementation plan like this, where each cell is its own standalone PR. Starting from the model (at the bottom) and working up to the client:

Separate Out Refactorings

It’s usually best to do refactorings in a separate PR from feature changes or bug fixes. For example, moving and renaming a class should be in a different PR from fixing a bug in that class. It is much easier for reviewers to understand the changes introduced by each PR when they are separate.

Small cleanups such as fixing a local variable name can be included inside of a feature change or bug fix PR, though. It’s up to the judgment of developers and reviewers to decide when a refactoring is so large that it will make the review more difficult if included in your current PR.

Keep related test code in the same PR

PRs should include related test code. Remember that smallness here refers the conceptual idea that the PR should be focused and is not a simplistic function on line count.

Tests are expected for all Google changes.

A PR that adds or changes logic should be accompanied by new or updated tests for the new behavior. Pure refactoring PRs (that aren’t intended to change behavior) should also be covered by tests; ideally, these tests already exist, but if they don’t, you should add them.

Independent test modifications can go into separate PRs first, similar to the refactorings guidelines. That includes:

• Validating pre-existing, merged code with new tests.
  • Ensures that important logic is covered by tests.
  • Increases confidence in subsequent refactorings on affected code. For example, if you want to refactor code that isn’t already covered by tests, merging test PRs before merging refactoring PRs can validate that the tested behavior is unchanged before and after the refactoring.
• Refactoring the test code (e.g. introduce helper functions).
• Introducing larger test framework code (e.g. an integration test).

Don’t Break the Build

If you have several PRs that depend on each other, you need to find a way to make sure the whole system keeps working after each PR is merged. Otherwise you might break the build for all your fellow developers for a few minutes between your PR merges (or even longer if something goes wrong unexpectedly with your later PR merges).

Can’t Make it Small Enough

Sometimes you will encounter situations where it seems like your PR has to be large. This is very rarely true. Authors who practice writing small PRs can almost always find a way to decompose functionality into a series of small changes.

Before writing a large PR, consider whether preceding it with a refactoring-only PR could pave the way for a cleaner implementation. Talk to your teammates and see if anybody has thoughts on how to implement the functionality in small PRs instead.

If all of these options fail (which should be extremely rare) then get consent from your reviewers in advance to review a large PR, so they are warned about what is coming. In this situation, expect to be going through the review process for a long time, be vigilant about not introducing bugs, and be extra diligent about writing tests.

Next: How to Handle Reviewer Comments

How to handle reviewer comments

When you’ve sent a PR out for review, it’s likely that your reviewer will respond with several comments on your PR. Here are some useful things to know about handling reviewer comments.

Don’t Take it Personally

The goal of review is to maintain the quality of our codebase and our products. When a reviewer provides a critique of your code, think of it as their attempt to help you, the codebase, and Google, rather than as a personal attack on you or your abilities.

Sometimes reviewers feel frustrated and they express that frustration in their comments. This isn’t a good practice for reviewers, but as a developer you should be prepared for this. Ask yourself, “What is the constructive thing that the reviewer is trying to communicate to me?” and then operate as though that’s what they actually said.

Never respond in anger to code review comments. That is a serious breach of professional etiquette that will live in the review history. If you are too angry or annoyed to respond kindly, then walk away from your computer for a while, or work on something else until you feel calm enough to reply politely.

In general, if a reviewer isn’t providing feedback in a way that’s constructive and polite, explain this to them in person. If you can’t talk to them in person or on a video call, then send them a private email. Explain to them in a kind way what you don’t like and what you’d like them to do differently. If they also respond in a non-constructive way to this private discussion, or it doesn’t have the intended effect, then escalate to your manager as appropriate.

Fix the Code

If a reviewer says that they don’t understand something in your code, your first response should be to clarify the code itself. If the code can’t be clarified, add a code comment that explains why the code is there. If a comment seems pointless, only then should your response be an explanation in the code review tool.

If a reviewer didn’t understand some piece of your code, it’s likely other future readers of the code won’t understand either. Writing a response in the review tool doesn’t help future code readers, but clarifying your code or adding code comments does help them.

Think Collaboratively

Writing a PR can take a lot of work. It’s often really satisfying to finally send one out for review, feel like it’s done, and be pretty sure that no further work is needed. It can be frustrating to receive comments asking for changes, especially if you don’t agree with them.

At times like this, take a moment to step back and consider if the reviewer is providing valuable feedback that will help the codebase and Google. Your first question to yourself should always be, “Do I understand what the reviewer is asking for?”

If you can’t answer that question, ask the reviewer for clarification.

And then, if you understand the comments but disagree with them, it’s important to think collaboratively, not combatively or defensively:

Bad: "No, I'm not going to do that."

Good: "I went with X because of [these pros/cons] with [these tradeoffs]
My understanding is that using Y would be worse because of [these reasons].
Are you suggesting that Y better serves the original tradeoffs, that we should
weigh the tradeoffs differently, or something else?"

Remember, courtesy and respect should always be a first priority. If you disagree with the reviewer, find ways to collaborate: ask for clarifications, discuss pros/cons, and provide explanations of why your method of doing things is better for the codebase, users, and/or Google.

Sometimes, you might know something about the users, codebase, or PR that the reviewer doesn’t know. Fix the code where appropriate, and engage your reviewer in discussion, including giving them more context. Usually you can come to some consensus between yourself and the reviewer based on technical facts.

Resolving Conflicts

Your first step in resolving conflicts should always be to try to come to consensus with your reviewer. If you can’t achieve consensus, see The Standard of Code Review, which gives principles to follow in such a situation.

Emergencies

Sometimes there are emergency PRs that must pass through the entire code review process as quickly as possible.

What Is An Emergency?

An emergency PR would be a small change that: allows a major launch to continue instead of rolling back, fixes a bug significantly affecting users in production, handles a pressing legal issue, closes a major security hole, etc.

In emergencies we really do care about the speed of the entire code review process, not just the speed of response. In this case only, the reviewer should care more about the speed of the review and the correctness of the code (does it actually resolve the emergency?) than anything else. Also (perhaps obviously) such reviews should take priority over all other code reviews, when they come up.

However, after the emergency is resolved you should look over the emergency PRs again and give them a more thorough review.

What Is NOT An Emergency?

To be clear, the following cases are not an emergency:

• Wanting to launch this week rather than next week (unless there is some actual hard deadline for launch such as a partner agreement).
• The developer has worked on a feature for a very long time and they really want to get the PR in.
• The reviewers are all in another timezone where it is currently nighttime or they are away on an off-site.
• It is the end of the day on a Friday and it would just be great to get this PR in before the developer leaves for the weekend.
• A manager says that this review has to be complete and the PR merged today because of a soft (not hard) deadline.
• Rolling back a PR that is causing test failures or build breakages.

And so on.

What Is a Hard Deadline?

A hard deadline is one where something disastrous would happen if you miss it. For example:

• Submitting your PR by a certain date is necessary for a contractual obligation.
• Your product will completely fail in the marketplace if not released by a certain date.
• Some hardware manufacturers only ship new hardware once a year. If you miss the deadline to submit code to them, that could be disastrous, depending on what type of code you’re trying to ship.

Delaying a release for a week is not disastrous. Missing an important conference might be disastrous, but often is not.

Most deadlines are soft deadlines, not hard deadlines. They represent a desire for a feature to be done by a certain time. They are important, but you shouldn’t be sacrificing code health to make them.

If you have a long release cycle (several weeks) it can be tempting to sacrifice code review quality to get a feature in before the next cycle. However, this pattern, if repeated, is a common way for projects to build up overwhelming technical debt. If developers are routinely merging PRs near the end of the cycle that “must get in” with only superficial review, then the team should modify its process so that large feature changes happen early in the cycle and have enough time for good review.

My >10 year old programming habits have changed since Claude Code launched. Python is less likely to be my go-to language for new projects anymore. I am managing projects in languages I am not fluent in—TypeScript, Rust and Go—and seem to be doing pretty well.

It seems that typed, compiled, etc. languages are better suited for vibecoding, because of the safety guarantees. This is unsurprising in hindsight, but it was counterintuitive because by default I “vibed” projects into existence in Python since forever.

Paradoxically, after a certain size of project, I can move faster and safer with e.g. Claude Code + Rust, compared to Claude Code + Python, despite the low-levelness of the code¹. This is possible purely because of AI tools.

For example, I refactored large chunks of our TypeScript frontend code at TextCortex. Claude Code runs tsc after finishing each task and ensures that the code compiles before committing. This let me move much faster compared to how I would have done it in Python, which does not provide compile-time guarantees. I am amazed every time how my 3-5k line diffs created in a few hours don’t end up breaking anything, and instead even increase stability.

LLMs are leaky abstractions, sure. But they now work well enough so that they solve the problem Python solved for me (fast prototyping), without the disadvantages of Python (lower safety guarantees, slowness, ambiguity²).

Because of this, I predict a decrease in Python adoption in companies, specifically for production deployments, even though I like it so much.

A certain characteristic of legacy desktop apps, like Microsoft Office, Autodesk AutoCAD, Adobe Photoshop and so on, are that they have crappy proprietary file formats. In 2025, we barely have reliable, fully-supported open source libraries to read and write .DOCX, .XLSX, .PPTX,¹ .DWG, .PSD and so on, even though related products keep making billions in revenue.

The reason is simple: Moat through obfuscation.

The business model for these products when they first appeared in the 1980s and 1990s was to sell the compiled binaries for a one-time fee. This was pre-internet, before Software-as-a-Service (SaaS) could provide a reliable revenue stream. Having a standardized file format would have meant giving competitors a chance to develop a superior product and take over the market. So they went the other way and made sure their file formats would only be read by their own products, for example by changing the specifications in each new version. To keep their businesses safe, they prevented interoperability of entire modalities of human work, and by doing so, they harmed the entire world’s economy for decades.²

Can you blame them? The only thing they could monetize was the editor. Office 365 and Adobe Creative Cloud has since implemented a SaaS model to capitalize even more, but the file formats are still crap—a vestige of the old business model.³

But finally, a revolution is underway. This might all change.

None of these products were designed to be used by developers. They were designed to be used by the “End User”. According to Microsoft, the End User does not care about elegance or consistency in design.⁴ The End User could never understand version control. The End User sends emails back and forth with extensions such as v1.final.docx, v1.final.final.docx. Until recently, the End User was the main customer of software.

However, we have a new customer in the market: AI. The average AI model is very different than Microsoft’s stereotypical End User. They can code. In fact, models have to code, or at least encode structured data like a function call JSON, in order to have agency. Yes, we will also have AIs using computers directly like OpenAI’s Operator, but it is generally more straightforward to use an API for an AI model than to use an emulated desktop.

We will soon witness AI models surpass the human End User in terms of economic production. Tyler Cowen⁵, Andrej Karpathy⁶ and others are convinced that we should plan for a future where AIs are major economic actors.

“The models, they just want to learn”. The models also want intuitive APIs and simple file formats. The models abhor unnecessary complexity. If you have developed a RAG pipeline for Excel files, you know what I mean.

If AI creates pressure to replace legacy file formats, then what can companies monetize if not the editor? The answer is the AI itself. Serve a proprietary model, serve an open source model, charge per tokens, charge for inference, charge for kilowatt-hours, charge for agent-hours/days. The business model will differ from industry to industry, but the trend is clear: value will be more and more linked to AI compute, and less and less to Software 1.0⁷.

There is now a huge opportunity in the market to create better software, that follow the File over App philosophy:

if you want to create digital artifacts that last, they must be files you can control, in formats that are easy to retrieve and read. Use tools that give you this freedom.

We already observe that AI systems work drastically more efficiently if they are granted such freedom. There is a reason why OpenAI based ChatGPT’s Code Interpreter on Python and not on Visual Basic, or why it chose to render equations using LaTeX instead of Office Math Markup Language (OMML)⁸. Open and widespread formats are more represented in the datasets, and the models can output them more correctly.

There is going to be an AI-native “Microsoft Office”, and it will not be created by Microsoft. Copilot is not it, and Microsoft knows it. Boiling tar won’t turn it into sugar. Same for other Adobe, Autodesk and other creators of clutter.

Internet Explorer’s 2009 YouTube moment is coming for legacy desktop apps, and it will be glorious.

Cultures can be categorized across many axes, and one of them is whether you can call an older male stranger uncle or female stranger auntie. For example, calling a shopkeeper uncle might be sympathetic in Singapore, whereas doing the same in Germany (Onkel) might get a negative reaction: “I’m not your uncle”.

This is similar to calling a stranger bro. In social science, this is called fictive kinship, social ties that are not based on blood relations. For readers which come from such cultures, this does not need an explanation. But for other readers, this might be a weird concept. Why would you call a stranger uncle or auntie?

Hover over the countries below to see which ones use uncle/auntie terms:

Note that fictive kinship can also have different levels:

1. Level 0: Blood relatives only. “Uncle”/”Auntie” is strictly for real uncles/aunts (by blood or marriage). No fictive use.

2. Level 1: Close non-relatives. Used for family friends, “uncle” or “auntie” is an honorary title but not for random people.

3. Level 2: Casual acquaintances. Used more widely for neighbors, family friends, or community members you vaguely know, but typically not for an absolute stranger.

4. Level 3: Total strangers. Used even for someone you’ve just met: a shopkeeper, taxi driver, or older passerby.

Many cultures fall somewhere between these levels and it’s not always black and white. Where possible, I’ve simplified it to the most typical usage.

Ommerism and social cohesion

The thought first occurred to me when I visited Singapore and heard people use uncle and auntie. Here were people speaking English, but it felt like they were speaking Turkish (my mother tongue).

The cultural difference is apparent to me as well since I started living in Germany. People here are more lonely, strangers distrust each other more, and there are no implicit social ties. I guess this holds for the entire Anglo/Germanic culture, including the US and the commonwealth.

Don’t get me wrong, people in Turkey distrust each other as well, probably even more. It is a more dangerous country than Germany. But those dangerous strangers are still uncles. It’s weird, I know.

As far as I could tell, the phenomenon is not even sociologically that much recognized or studied. There is no specific name for it, other than being a specific form of fictive kinship. Therefore, I will name it myself: ommerism. It derives from a recently popularized gender-neutral term for an uncle or auntie, ommer.

Lack of ommerism is an indicator for a weak collective culture. Such cultures are more individualistic, familial ties are weaker and people are overall more lonely. People from such cultures could for example tweet:

It is extra ironic that ex-colonies like Singapore (ex-British), Indonesia (ex-Dutch), Philippines (ex-Spanish) etc. took their colonizers’ words for uncle/auntie and started using it this way, whereas the original cultures still do not.

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