JetBrains Research

It is a well-known problem by now that training LLMs on sensitive data raises serious privacy concerns. In a recent blog post, we talked about membership inference attacks and our research on mitigating them.  At JetBrains Research, we are deeply concerned about user privacy and continually …

Every week there's at least one new announcement about what AI will do to software development. Most of the conversation moves fast and stays shallow, focusing on productivity gains, job displacement, or which model scored highest on the latest benchmark. We’re more interested in the questions un…

How many developers use AI coding agents (Claude Code, Codex, Cursor, JetBrains Junie, and others)? Evidence from the Developer Ecosystem Survey 2026.

This post relates findings about language migration from the 2025 State of Developer Ecosystem survey. In general, project requirements are still the most common reasons for switching languages.

TL;DR KotlinLLM is now public. It's a research prototype for delegating runtime logic to an LLM from Kotlin code. Instead of calling an LLM on every request or running a separate agent, you can write an explicit Kotlin call. Its body is generated Kotlin source code, and that code is updated as yo…

The main message Advances in AI are already single-handedly changing how we interact with technologies. This moment might become the first interaction revolution in roughly 60 years, since the mouse-and-windowing paradigm crystallized in the 1970s and 1980s. For us at JetBrains, the most interest…

A few years back, the Kotlin team figured it was time their programming language had a mascot – something fun and friendly to make developers feel more at home. After all, so many other programming languages have their own characters – Gofer, elePHPant, Dart’s Dash, or Rust’s Ferris, for example. …

Leaderboard scores don’t hold up when we look more closely at benchmarks that are considered standards. The models dramatically improved on the exact tasks we trained them on, yet those gains often did not show up on other benchmarks or on slightly different tasks in the same codebase. This blog post is about our research on that meaning gap between what coding benchmarks measure and what we wish they measured – and how to improve these benchmarks.

Imagine there’s a stranger out there who has nothing but API access to your chatbot. They are interested in knowing whether a specific patient, employee, or customer appears in the data you trained it on. Without breaching the database or stealing backups, this person can theoretically figure out th…

Our ROI (return on investment) calculator was designed to help estimate the potential gains of JetBrains IDEs and AI Ultimate subscriptions.

In training agents, we toss the whole run if the final outcome is imperfect, missing out on valuable info. To fix this, we developed Step Rejection Fine-Tuning.

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