Schedule

We will analyze several typical cases in which our agent and platform use LLM to solve "fuzzy" tasks, and after the talk, the audience will grasp specific approaches based on our production cases.

We'll take a look at where the FFM API (Project Panama) stands in 2026, drawing on our experience with both using large native libraries like Catboost and more granular — but no less practical — examples, such as our own bindings to libc.

We will tell and show how the hybrid JIT + AOT code compilation model works under the hood in the Android Runtime and what benefits it brings to real applications. We will compare the Google solution with the Leyden project in the big Java.

In this talk, we will analyze Java virtual streams not as a feature, but as an engineering solution.

Why in our architecture supporting billions of objects in one bucket does not yet mean being ready to support thousands of buckets.

How Java 25 with FFM API learned to manage hardware without a single line of C: we analyze the evolution of the architecture of the open-source Pi4J project: from JNI/JNA to pure Java, the boundaries of applicability, timings and memory — with benchmarks and demos on real sensors.

We will look into different models of concurrent programming.

Elasticsearch as a search engine can be debated for a long time. But it turned out to be unexpectedly good for storing metrics: I'll tell you how we launched TSDS under a load of more than a million metrics per second, what problems we encountered and why we eventually had to go inside Elasticsearch and Lucene.

Let's look at the evolution of the JVM through the lens of closing architectural holes. Using the one-nio serialization framework as an example, we will conduct an archaeological dig from Unsafe, MagicAccessorImpl, and synthetic accessors to modern mechanisms such as VarHandles, Nestmates, and Hidden Classes.

The E2E benchmark, beautiful graphics, impressive RPS and the first place in the table easily create the feeling that the technology is objectively faster than competitors. But what exactly does the E2E benchmark show: the actual stack performance, the quality of the selected scenario, or the ability to prepare the test correctly?

AI has significantly changed software development: our dependencies have more vulnerabilities, and AI-generated software in production has become less stable and lower in quality. We’ll discuss the challenges facing Java ecosystems and a solution we already have for many of these problems.

...but so that nobody notices it.

At the JVM Language Summit 2025, Brian Goetz talked about plans to add type classes to the Java language. The talk is based on Brian's story, as there is very little other information on this topic, but it is not just a retelling.

If you want to understand what lies behind the beautiful hitmaps, or if you want to build profiles in your production environment, then this talk is definitely for you.

How I sketched out a Kotlin Script with a simple agent on Koog. It's not particularly smart, but it's simple and manageable: sometimes it's easier to explain a task with code than with words.

In Russian classics, there’s a character known as the “little man.” A large system is moving forward, great decisions are being made, and somewhere down below, there’s a person who just wants to do their job properly. With AI, we’re now in roughly the same situation: there’s a big race going on around us, and we, the little people, need to somehow adapt to a world where AI has already become part of development — and still manage not to end up in a psych ward.

Why Context Engineering is becoming a new engineering discipline, how AI is changing the entire development lifecycle (AI PDLC), and what expertise will define a strong engineer in the coming years.

I decided to check: what would change in my work if I gave the AI agent the same tools that the developer himself uses: an IDE, LSP, and the project’s knowledge base?

Using the composite case, we will analyze a situation in which an LLM agent dramatically accelerated the preparation of specifications, but almost did not shorten the total product release period.

We’ll look a few years ahead: how AI agents will change the developer profession, why DDD and modeling will become more important, how the roles of product and platform developers will be divided, and what will remain within a person’s responsibility when writing code yourself will no longer be necessary.

I'll tell you how to make agents create UI based on guides.

In an hour, the agent does the work that I used to do in a week. And for the first time in my career, I caught myself asking a question that’s silly to say out loud: am I an engineer?

The talk will provide an overview of the theory and apply it to specific cases that I have heard in dozens of presentations on the topic, highlighting the common elements. Come to get a ready-made theoretical foundation and a set of practical templates!

What part of the work does management take on and how can the delivery process really be accelerated?

We will perform the Sharikov Blues (perhaps literally!) to reveal the complex aspects of the modern understanding of how technological systems are shaped by social, economic, and cultural factors.

In reality, only engineers will survive with AI: those who do not fully surrender their thinking to machines and remain decision-makers rather than interface operators.

The dominant paradigm of LLM development assumes that the growth of solution quality is linearly related to the growth of the power (and cost) of the underlying model.

The talk challenges this assumption by showing that with a well-thought-out engineering architecture — the distribution of tasks among short-lived agents, the accumulation of structured state, and strict symbolic control — comparable-quality results can be achieved at a fraction of the cost of models.