Our Blog Posts

In this blog post, I’ll give an overview of how Akita works, what’s hard about getting Akita to work with non-network integration tests, and how our test client integration works. We’ll dig into the details of extending Flask’s test client. This post may be interesting to anyone looking to add request logging or pre- or post-processing to REST framework test clients.

This blog post is about our vision for API observability, why inferring models of API behavior is the way to achieve this vision, and the work that we need to do to get from here to there.

From the beginning, our goal at Akita has been to make software more robust and trustworthy. This past year, our learnings led us to significantly shift our strategy for how to get there. Here are the lessons we learned that led to our pivot, that may be helpful to anyone else who thinks a lot about how programming tools get adopted.

As a developer who has also worked in devops, I understand the importance of finding and fixing bugs early in the development cycle. In this blog post, I’m going to show a bug that’s hard to catch with source diffs, linters, or static analysis. Then I’ll show how to use Akita to catch this bug, describing the entire setup from start to finish. Finally, I’ll explain how Akita works under the hood.

This blog post is about a new feature that my team and I built after I spent a painful couple of days figuring out how to script against Notion. After my experience trying to learn the undocumented Notion API, we decided to automate the process of learning web APIs so that nobody would have to suffer like this again.

Akita operates by watching network traffic. I wanted to try Akita out on our tests in CI. But the problem is that our tests don’t actually generate network traffic. This post describes the workaround I found for our Flask integration tests, based on automatically creating real HTTP traffic from the tests, and sending it to a dummy server.

In our last blog post, we talked about how to catch cross-service bugs by watching API traffic. In this blog post, we show specifically how checking data formats across APIs can catch some nasty bugs.

In this blog post, we’ll show you a bug that slips through the cracks today, explain why it’s nobody’s fault, and show you how we diff API behaviors to catch bugs (while generating API specs along the way!!).

It's no secret that APIs are a blessing and a curse. In this post, I talk about our approach for taming this complexity.