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The pull request looked perfect. Two approvals, clean diff, all checks green. The team shipped it Thursday afternoon. By Friday morning, support tickets were coming in. On a popular mid-range Android device, the new checkout button rendered behind a promotional banner: visually present but physically untappable. Every reviewer read that code, yet none of them could have caught it, because the defect was never visible in the diff. It only ever appeared on the device.

Android app crashes are a nightmare for developers. But they rarely come out of the blue. Most crashes stem from structural causes like leaky memory or sloppy resource management, and most failures will have a root cause that surfaces in either logcat or the stack trace. This post will give you a true Android crash course, showing you the tell-tale signs and the tried-and-tested fixes that work across devices, territories and app utilities.

At its simplest, AI debugging automates repetitive coding tasks like searching logs, reading stack traces and comparing sessions. But good AI debugging is a much more challenging concept that relies on focused context, runtime evidence and structured investigation paths. In this post we’ll show you how to debug with AI, not just productively but also responsibly. Let’s get into it.

‍ Every CFO has looked at the headcount report and asked the same question. Why does it take twelve people and four tools to do something that feels, on paper, like it should take three? The honest answer is usually not laziness or bloat. It is a coordination tax. Someone has to read an email, decide what it means, open three other systems to check context, type a response, update a tracker, and notify two other teams. None of that is hard work.

The most useful piece of software we came across last quarter never left someone's laptop. An operations lead at one of our enterprise clients built herself an inventory reconciliation dashboard. No ticket, no sprint, no engineer involved. She described the workflow to an AI agent one evening and had something working by the next morning. A problem her team had been raising for over a year, solved by someone who has never written production code in her life.

Model Context Protocol allows AI models like Claude to communicate with the outside world. But MCP debugging has been one of our steepest learning curves at Bugfender. Several different layers need to work together at the same time and if one thing breaks, it can scupper the whole workflow. That’s why we’re here today. To pass on hard-won knowledge, so you can jump the curve.