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At the moment, we see a lot of discussions revolving ChatGPT and other modern AI tools like, e.g., GitHub Copilot. Many managers praise them as the new silver bullet to beat the (often self-made) skills shortage that will make software developers redundant while driving software development efficiency to unprecedented heights.

In the previous post, we started with the observation that companies (still) want to break up their monoliths into microservices. If you ask them what they expect from this measure, they typically expect to cure the “big ball of mud” issue with microservices or to improve their time to market with them.

Time and again clients approach my colleagues and me with the request that they want to break up their monolith into microservices and they ask us how to do this best. Apparently, they are convinced that breaking up the monolith into microservices will solve some big problems they had for a long time.

Recently, I had two experiences within a few days that made me think regarding system dependability. In both situations, the systems acted detached from their surrounding reality and thus became confusing or even annoying – even if it would have been easy for them to detect their reality detachment.

About a decade ago, Jeffrey Dean and Luiz André Barroso published their IMO great article “The tail at scale” in the Communications of the ACM ¹. The article dives into the topic of latency tail-tolerance.