Anton Kazakov

Probably the most noticeable change engineers experience when they become leaders is the increase of day-to-day chaos. The moment you become responsible for the work of a team or a large area, tasks start coming at you. Dozens of them! Sometimes, per day. And also, questions. From everyone and their dog! And finally, meetings. Lots of them, long and short, exciting and boring... Does it sound familiar?
To me, very. And it took me quite some time to figure out what to do with that. I'm going to share my recipe for taming the chaos with you. I'll talk about the components of that chaos, of reasons that they occur, and about solutions for avoiding it.

Probably the most noticeable change engineers experience when they become leaders is the increase of day-to-day chaos. The moment you become responsible for the work of a team or a large area, tasks start coming at you. Dozens of them! Sometimes, per day. And also, questions. From everyone and their dog! And finally, meetings. Lots of them, long and short, exciting and boring... Does it sound familiar?
To me, very. And it took me quite some time to figure out what to do with that. I'm going to share my recipe for taming the chaos with you. I'll talk about the components of that chaos, of reasons that they occur, and about solutions for avoiding it.

Hardly any people in Tech like when there's a lot of tech debt. And most of us would like when there's not too much of it. But how do we understand how much exactly we have of it? Where exactly does it sit? Which part of it is actually the most annoying? What would be the benefit for us if we spend time getting rid of it? When it comes to planning how you tackle your tech debt, all these questions deserve answers. Especially when we're asked about the ROI on our efforts to eliminate some annoying legacy stuff and build a new shiny module or service. Also, when we work on tech debt, we do want to tackle the most impactful parts of it first, don't we? This talk is about all of that: how we measure our tech debt, how we interpret the results of these measurements so that they give us the answers to the right questions, and how we guide our decision making with those answers.