can produce an incredible amount of content in a short period of time. This could be creating new features, updating production logs, or correcting a bug report.
The bottleneck in software engineering and data science has shifted from developing code to reviewing what coding agents are building. In this article, I discuss how I successfully update Claude's output to become an even more efficient developer using Claude Code.
Why are you preparing for an exit review?
You may wonder why you need to develop code for review and output. A few years ago, the biggest bottleneck (so far) was writing code to generate results. Now, however, we can generate code by simply telling a code agent like Claude Code.
Generating code is no longer just a bottleneck
So, as developers are constantly striving to identify and mitigate issues, we move on to the next bottleneck, which is updating Claude's Code release.
Of course, we need to update the code it generates with pull requests. However, there is a lot of output to review if you use Claude Code to solve all possible tasks (which you should be doing). You need to update:
- Report by Claude Code
- Errors detected by Code Claude in your production logs
- Claude Code emails are meant for your access
You should be trying to use coding agents for all the work you do, not just editing jobs, but all your commercial work, making presentations, reviewing logs, and everything in between. So, we need to use special techniques to update this content quickly.
In my next section, I'll cover some of the techniques I use to review Claude code releases.
Output review techniques
The review method I use varies by job, but I'll cover some examples in the following paragraphs. I will keep it as clear as possible to my specific use cases, and then you can try to make this your own tasks.
It updates the code
Obviously, reviewing code is one of the most common tasks you do as a developer, especially now that coding agents are faster and more efficient at generating code.
In order to do a successful code review, I did two important things:
- Set up a custom code review capability with a full overview of how to perform code reviews effectively, what to look for and more.
- Have the OpenClaw agent automatically use this capability whenever I'm tagged in a pull request.
So, whenever someone tags me in a voting request, my agent automatically sends me a message with a code review I've done and proposes to post that code review message to GitHub. All I need to do at that point is just look at the summary of the voting request, and if I want to, just press submit on the proposed voting request update. This presents many problems that could reach production if not detected.
This is probably the most important or critical review method I use, and I would argue that effective code review is probably one of the most important things that companies can focus on now to increase speed, considering the increase in code output by coding agents.
Reviews generated emails
Another common task I do is to generate emails that I send through a cold outreach tool or emails to get people to respond. Often times I want to revise these emails and formatting. For example, if they have links in them or other bold letters and so on.
Updating this in a text-only interface like Slack is not a good situation. First, it creates a lot of mess in the Slack channel and Slack also can't format correctly all the time.
So, one of the most efficient ways to review generated emails and general formatted text I've found is to ask Claude Code to generate an HTML file and open it in your browser.
This allows Claude Code to quickly generate formatted content, making it much easier for you to review. Claude not only shows formatted emails but also shows in a very nice way, which person receives which email, and also if you send a sequence of emails, it is very easy to format.
Using HTML to update output is one of my secret life hacks, saving hours of time each week.
Updates production log reports
Another very common task I use for Cloud Code is reviewing production log reports. I usually use a daily query where I analyze the production logs, looking for errors and things to know about, or just log warnings in the code.
This is incredibly useful because reporting tools that send error alerts are often noisy, and you end up with a lot of false alarms.
So, instead, I prefer to be sent a daily report, which I can analyze. This report is sent with the OpenClaw agent, but the way I preview the results is incredibly important, and this is where the HTML file formatting comes in as well.
When you review these production logs, there is a lot of information. First, you have different error messages to see. Second, you have the number of times each error message occurred. You may have different IDs that refer to each error message that you want to display in a simple way. All this information is very difficult to provide in a good way in txt formatting, like loose for example, but it is amazingly good to preview in an HTML file.
So, after my agent reviews the production logs, I ask it to generate a report and present it in an HTML file, which makes it very easy for me to review all the output and quickly get an overview of what's important, what they can skip, and so on.
Another professional tip here is not only to generate an HTML file but also to ask Claude Code to open it in your particular browser, which it does automatically, and you immediately get an overview. And be notified whenever the agent is finished because the browser appears on your computer with a new tab that holds the generated HTML file.
The conclusion
In this article, I've covered some of the techniques I use to review the output of Claude's code. I discussed why it is so important to improve the results of the review, highlighting how the bottleneck in software engineering has changed from generating code to analyzing the results of the code. So, since the bottle is now part of the review, we want to make that work as well as possible, which is the topic we've been discussing here today. I talked about the different use cases I used for Cloud Code and how I analyzed the results. Further improving the way you analyze the output of your coding agents will be very important going forward, and I urge you to spend time optimizing this process and thinking about how you can make reviewing the output of the coding agent more efficient. I've covered some techniques that I use every day, but of course, there are many other techniques that you can use, and the fact that you'll have your own set of tests will require their own set of techniques that are different from mine.
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