Lectures & courses

Education

Courses, lectures, and lecture series. Conference talks live in the talks section.

HSE University / proposed syllabus

Distributed Systems

A six-month course for HSE University: 10 lectures, 4 labs, and an end-to-end distributed service project. From failure models to reliability testing.

6 months10 lectures4 labs

Explore the course

Learning goal: Design a distributed service and test its guarantees through failure experiments.
Useful preparation: One programming language, HTTP and database basics, Git, and basic container usage.
Format: Web slides, exercises, four labs, and an end-to-end project.

Start with: Start with failure

HSE University2026

Cloud, Data, and AI Platforms for AI Business

A guest lecture on resources for AI projects and a seven-lecture course: cloud technology basics, data platforms, DataOps/MLOps, AI platforms, AIOps, and a hands-on lab.

Learning goal: Connect an AI project's needs to cloud services, a data platform, and operational tools.
Useful preparation: Choose an AI project whose resource needs you want to assess. The first course lecture introduces cloud basics.
Format: Web slides for a guest lecture and seven sessions, including a lab on building an agent.

Start with: Cloud Technology Basics

Central University2024–2026

Lectures on Software Development and Distributed Systems

The evolution of development approaches and distributed systems trade-offs: CAP, PACELC, consistency models, and Cassandra.

Learning goal: Explain how architecture and data storage change an application's constraints, including CAP and PACELC trade-offs.
Useful preparation: Review a familiar web application: its requests, data, and release process.
Format: Two standalone lectures with web slides: a development overview and a distributed systems deep dive.

Start with: Evolution of Development Approaches

ITMO University2025

Lecture on the Future of Software Development

Software development in 2030: AI agents, AlphaEvolve, SE 3.0, and engineering metrics in the agent era.

Learning goal: Separate observed AI capabilities from future scenarios and define how to test a forecast.
Useful preparation: Choose a familiar development task and identify where AI helps and where human review remains necessary.
Format: Web slides exploring future scenarios, with research references.

Start with: Software Development in 2030: Hypotheses About a (Not So) Bright Future

Dec 6, 2025Software Development in 2030: Hypotheses About a (Not So) Bright Future
#AI

Strugatskys Lecture2025

The Strugatsky Brothers for Engineers

Pair programming, the Noon Universe, NIICHAVO, and engineering ethics through the lens of the Strugatsky brothers.

Learning goal: Connect literary themes to engineering work and choose a Strugatsky book to start with.
Useful preparation: Recall an example of collaboration or an ethical choice in engineering to compare with the lecture's themes.
Format: Web slides with literary examples and a reading path.

Start with: The Strugatsky Brothers for Engineers

Sep 9, 2025The Strugatsky Brothers for Engineers
#Leadership

PM Course2020

Project Management Course

A short intro for engineers and managers: PMI, IPMA, PRINCE2, project vs product vs operations, programs, and portfolios.

Learning goal: Distinguish projects, products, and operations by their goals, constraints, and management approach.
Useful preparation: Choose a familiar work initiative and describe its outcome, timeframe, and participants.
Format: An introductory 2020 lecture with web slides: foundational concepts and an overview of approaches from that period.

Start with: Project Management — Short Intro

Apr 1, 2020Project Management — Short Intro
#Leadership

tellmeabout.tech2026

AI-Native Development Lecture Series

Nine lectures on how AI reshapes the engineering process: from classic PDLC to AI-native organization, platform, measurement, observability, and context design.

Learning goal: Map the changes needed for AI in development: organization, platform, measurement, observability, and context.
Useful preparation: Describe how a task moves from idea to release and one constraint you want to examine through the lectures.
Format: Nine lectures with web slides in the series order.

Start with: From Classic PDLC to AI-Native Development