Chapter 1: What is the main topic discussed in this episode?
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Today, we are dropping another episode in our series, Developer Chats, hearing from the large-scale system builders themselves. In this episode, we are talking with Oleksandr Pekota, Principal Software Engineer at Teaching Strategies, LLC.
Oleksandr helps to show us at what point of scale platform approaches are required, when to run experiments, when to stop, and perhaps more importantly, engineering ownership beyond the code. Alexander, thank you for being on the show today. Thank you for being on CodeStory. Thanks for having me. Absolutely.
Really excited to dive into all of your experience today as moving into more technical leadership as a principal engineer and platform versus feature thinking and all the things we're going to dive into. Before we do, tell me in my audience a little bit about you. So basically, I am currently working as a principal software engineer at Teaching Strategies.
I can count like 14 years in production engineering. So I think I have at least of some understanding like what I'm talking about. My background is mostly focused about PHP and Golang. I do have some extra experience in different languages, but that is not that rich to say that I'm strong in it.
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Chapter 2: At what point does engineering shift from features to system thinking?
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To see how active telemetry works in practice, get a demo at mesmo.com slash codestory. That's M-E-Z-M-O dot com slash codestory. This episode is sponsored by Unblocked. Your coding agents have access to your code base. Maybe you even connected other tools via MCPs. But access doesn't mean context. Agents can't reason across MCPs.
They don't know your architectural decisions, your team's patterns, or why the API was shaped the way it is. So agents look in the wrong place and deliver bad outputs. Then you spend time correcting. More loops, more tokens. Unblocked is the context layer your agents are missing. It synthesizes your PRs, docs, Slack, and tickets into organizational context that agents actually understand.
So they make better plans, write higher quality code, use fewer tokens, and require fewer correction loops. If you're running cloud code, cursor, or any agentic workflow, Unblocked is worth a look. Learn more at getunblocked.com slash codestory. If you look across your projects, I'm curious how you recognize a truly mature engineering system. Is it based on code quality? Is it based on process?
Or how teams respond when things go wrong? How do you measure that? To be honest, my thinking about it is going to be maybe funny in some way. And maybe some people won't agree with my vision. But I think a mature system is the system that makes basically money. and where customers have their use cases covered. This is like the base, this is like the foundation.
If you have this, everything else is secondary. So, for example, it can be like something with a terrible code base, very bad design practices, no architecture at all. But unless it makes money and solves the problem, Who cares? So on the other hand, we can have a strong team. They can do everything solid, dry, Kubernetes, microservices, cloud.
And their code is written like in Kotlin, Golang, Scala. It's great from the engineering perspective, like it's just some kind of like a Ruby. And at some point that
the project has no funding or like it's difficult to change product perspective make a quick shift like simple cost to pay for the infrastructure and the team like actually cost a lot of money itself and funniest part like when somebody is leaving the product You can't find replacement due to the fact how complex or like how mature your particular architecture is.
So basically, like summarizing all of this together, I can say that like mature system is like a balance of everything. And the highest priority, I would still say that is going to be like making money. If you make money and you have a nice, friendly, responsible team that can do trade-offs and keep it working and still keep it yet simple.
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