I come from a world where strategy is best kept secret. Whether it be from a company who has a codename for literally everything, or the competitive culture of playing and coaching D1 athletics, confidentiality became a required skill. Meetings, trainings, code reviews, scouting reports… anything of significance happened behind closed doors. In other words, definitely not open source.

We’ve all been there — you’re on-call, fast asleep at 3 AM when suddenly, in comes the alerts–in overdrive. Your system is notifying you of some sort of abnormal behavior, but with all the alerts and data coming through, its difficult to figure out what your system is trying to tell you. Is there potential malicious behavior? Did someone write faulty code? Is it an important issue or can it wait? Is it nothing at all?

Every single app — large or small, open source or not — has room for improvement when it comes to performance. This is why we created Skylight for Open Source to give open source contributors the tools they need to find these issues. Over the next week, we'll show you three different open source apps running on Skylight, each with their own unique performance challenges, varying in complexity.

When developing mobile apps it’s important to monitor errors so that you can understand your user’s experience. You need deeper insight than just a crash report because errors could cause a degraded user experience or a drop in key behavioral metrics. Your team needs to know quickly when there are production problems either with the app itself or with your backend services so you can fix the issue before more customers are impacted.

Open source has come a long way. Open source components are the building blocks of arguably every organization’s software. According to Stack Overflow’s 2018 developer survey results, nearly half of professional developers contribute to open source projects, and 40% listed contribution to open source software as part of their non-formal learning background.

If you think about it, mobile apps are among the production services most in need of real observability: nearly countless hardware platforms and operating systems in combination with your app’s code result in a dizzying matrix of possible sources for any given issue, which means you need the power of true high-cardinality search to solve your problems.

Netflix recommends Stranger Things to you because it knows you like watching sci-fi thrillers. Tinder lets you swipe right into your next date because it’s learned your interests and partner preferences. Amazon keeps showing you Fitbit because you’ve spent a considerable amount of time browsing through the fitness and wellness category.

Computer programs that talk to people aren’t new. The natural language processing program ELIZA , which played the role of a digital psychologist, first debuted way back in 1966. This early chatbot was capable of “listening” to you as you shared your life story, delivering mostly coherent, yet vague canned responses to whatever you typed in.