Our Blog Posts

In this post, Kevin Ku covers a couple of advanced packet filtering topic. Kevin first talks about how to filter packets with packet capture filters (cBPF). Then Kevin provides a quick start on how to use tcpdump under the common scenarios you might encounter with docker containers.

The Akita agent works by passively monitoring API traffic, so that we don’t need to be on the data path or add any performance overhead. But the trade-off is that pointing Akita at the right traffic can be error-prone. Mark Gritter talks about the latest addition to our CLI: network diagnostics to smoother Akita setup.

tcpdump and Wireshark are great, but what if you want to programmatically analyze network traffic? In this post, Kevin Ku walks through how to use GoPacket to programmatically capture and analyze network traffic.

In this blog post, Mark Gritter talks about why API performance matters, how tracking performance at the endpoint level can help, and how this looks with Akita’s new endpoint performance feature.

In this blog post, Cole Schlesinger gives 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. 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, Kevin Ku understands the importance of finding and fixing bugs early in the development cycle. In this blog post, Kevin shows a bug that’s hard to catch with source diffs, linters, or static analysis. He shows how to use Akita to catch this bug and explains how Akita works under the hood.

This blog post is about a new feature that we built at Akita after Chris Corcoran spent a painful couple of days figuring out how to script against Notion. After Chris's 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. Akita user Sébastien Portebois wanted to try Akita out on CI tests, but the problem is that the tests don’t actually generate network traffic. This post describes a workaround 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, Jean Yang talks about our approach for taming this complexity.