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Jetbrains
Software development company that builds smart programming tools, especially integrated development environments (IDEs), to help developers write and fix code efficiently
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Google Summer of Code 2026 With the Kotlin Foundation: Recap
The Kotlin Foundation wrapped up its fourth Google Summer of Code program in 2026, supporting two projects by Ayako Hayasaka and Mark Seif Khalil. Ayako implemented tail-call support in the Kotlin/Wasm backend, using return_call and return_call_ref instructions to avoid stack growth in recursive programs, and then found and fixed a missing optimization for return_call in the V8 engine that caused a performance regression. Mark built a database and automated pipeline to collect and structure scattered information about where Kotlin is taught worldwide, covering courses, curricula, repositories, and learning platforms. In Ayako’s benchmarks, several recursive patterns achieved 1.7 to 4.3 times faster execution at a recursion depth of 10,000, and at a depth of one million, the same programs crashed without tail calls but completed successfully with them. The education landscape project now provides a reusable foundation for monitoring Kotlin’s presence in education and identifying trends and gaps.
IntelliJ IDEA’s Java and Kotlin for Cursor and VS Code Hits 1.0
IntelliJ IDEA’s Java and Kotlin language support extension has reached its 1.0 stable release, now available for VS Code and Cursor editors. The extension is powered by IntelliJ IDEA’s language server and debug adapter, providing smart code completion, navigation, code analysis, refactoring, and build tool support for Maven, Gradle, and Bazel. It also offers powerful debugging capabilities including emulated breakpoints and async stack traces, along with JUnit test runner support. The 1.0 release resulted from close collaboration with the Cursor team, making Java and Kotlin a first-class experience in Cursor’s Agents Window with dedicated support for nullability and Spring Framework specifics. Starting with 1.0, the extension requires an IntelliJ IDEA Ultimate subscription but provides a 30-day free trial, and future updates will enable agents to call the LSP directly for faster AI-assisted development, with announcements pending for purely agentic licensing.
How Netflix Made AI Coding Agents More Efficient with JetBrains IDE Intelligence
Netflix integrated JetBrains’ headless code-intelligence engine into coding agents Claude Code and Codex to address agents’ struggles navigating Java/Kotlin code across compiled JAR boundaries. The engine provides deterministic indexing of project source files and libraries, and Netflix built a daemon and CLI to make the LSP callable by agents, keeping the index warm across repeated calls. In benchmarks on two real codebases—an internal Spring Boot service and the open-source DGS Framework—agents using JetBrains intelligence were typically 20-30% more efficient in tokens and time, with up to 65% improvement for stronger models like Opus 5 at high effort. Correctness held steady or improved across all configurations, and the gains were consistent across every model and agent tested. Netflix plans to extend the same benchmark methodology to Python, TypeScript, and Go, as coding agents for those languages face similar problems.
MPS 2026.2 Release Candidate Available
JetBrains has released the MPS 2026.2 Release Candidate, a language workbench for creating domain-specific languages, with several key improvements over the previous EAP1 build. The RC adds Linux ARM64 support with a bundled JetBrains Java Runtime and the ability to build standalone MPS-based applications for that architecture. Build Language now supports importing files from TAR archives, preserving symbolic links and file permissions, which is useful for packaging platform-specific plugins and native libraries. The Projectional Agent Toolkit receives a major performance boost with token usage reductions exceeding 50% in tests using Claude Code, plus new MCP tools for typesystem debugging, intentions, and quick-fixes. Archive packaging preserves symbolic links and uses .tar.gz for macOS to avoid duplicate native libraries, while the Import dialog now respects user selections and the Diff dialog has visual improvements.
What Makes a Great KotlinConf Proposal?
The KotlinConf 2027 Call for Papers is open, and the post offers guidance for crafting a strong proposal, emphasizing that a valuable talk can come from a simple lesson learned rather than a groundbreaking discovery. Reviewers first notice the overall care, including a clear title and focused abstract, and they can detect AI-generated content, so authenticity and a personal voice are crucial. A good proposal starts with a central story that identifies a problem, what was tried, and what was learned, even from a small project, and it should be tested with colleagues. The title should communicate the topic and benefit, avoiding vagueness, while the abstract should provide two to three paragraphs of meaningful detail that outline the talk’s journey and include concrete takeaways. The post advises choosing the right format between lightning and regular sessions, and reviewing the proposal for clarity and authenticity before submitting.
Boost Your Pipeline: Top 6 CI/CD Integrations for Qodana
Qodana brings IntelliJ IDEA’s inspection engine into CI/CD pipelines, automating code quality checks on every commit to catch issues before they reach production. The tutorial covers six integrations—GitHub Actions with its v2025.2 marketplace action and pull request integration, GitLab’s native CI/CD component that removes Docker complexity, TeamCity’s GUI-based native build runner with no YAML required, Jenkins via Docker-based Jenkinsfile for complex workflows, Azure Pipelines’ QodanaScan@2025 task displaying results in the Extensions tab, and CircleCI with a container-optimized orb. Each integration supports quality gates, baseline comparisons, and automated feedback, enabling teams to enforce consistent coding standards without disrupting existing workflows. Teams can select the integration that matches their current platform without requiring new tools or retraining. This shift-left approach reduces rework by catching defects early and transforms the pipeline into a quality guardian.
How Cloud-Based Agent Sandboxing Works in Air
AI agents require sandboxing to restrict file, network, and system access, preventing unintended modifications and data leakage. The post categorizes five agent execution modes into local (workspace, git worktree) and remote (SSH, Docker, Cloud), with remote modes providing stronger isolation by default. In JetBrains Air, each cloud agent task runs in its own VM with the agent inside a Docker container, and all network connections are routed through a proxy that allows only trusted domains by default. Secrets are stored encrypted and exposed as environment variables, and the agent operates within the developer’s repository permissions. Cloud-based execution reduces local setup requirements and gives organizations control over network access policies, with cloud agents currently available to organizational users via Air in IDEs and Air Teams.
KotlinConf Recap: Lessons from Verifone’s Architect on Kotlin in Mission-Critical Payments
Verifone architect Yuri Geronimus presented at KotlinConf 2026 on using Kotlin’s type system to enforce payment specifications at compile time. He described six incident patterns where a single misused word caused outages, compliance failures, or financial loss, including a terminal fleet outage from a nullable mandatory field. His method uses value classes for spec nouns, validate factories at boundaries, non-null types, masked sensitive data by default, and state-specific classes to make illegal states unrepresentable. Geronimus advocates incremental migration from legacy Java and C++ systems using four parallel tracks rather than a rewrite, noting that payment terminals must operate offline for up to 40 days by regulation. The approach shifts reliability from developer discipline to compiler enforcement, enabling engineers to speak the business language and propose regulatory changes.
Lauterbach and JetBrains Announce TRACE32 Integration in CLion IDE
Lauterbach and JetBrains have integrated TRACE32 debugging tools with CLion, enabling embedded developers to debug applications on target hardware directly from the IDE. The integration uses the Debug Adapter Protocol (DAP) to connect CLion to TRACE32 through the TRACE32 Debug Adapter, allowing developers to control execution, step through code, and inspect variables while TRACE32 handles target hardware communication. Two workflows are supported: launching a new TRACE32 PowerView session from CLion with scripts to configure the connection, or attaching to an existing TRACE32 instance. The integration requires CLion 2026.2.4 or later, TRACE32 PowerView 09.2026 (or build 195402), and TRACE32 Debug Adapter version 0.1.2 or later, with detailed configuration examples in the Lauterbach knowledge base. This brings code editing, navigation, and hardware debugging into one workflow, and a free webinar on October 27, 2026 will demonstrate the integration in action.
CI/CD Pipeline Observability Tools: How to Choose the Right Setup
CI/CD pipeline observability tools help teams understand why builds fail or slow down by connecting data from version control, build agents, tests, and deployment tools. The JetBrains Developer Ecosystem Survey 2026 found that 44% of 20,416 respondents cited debugging cloud infrastructure as a top challenge, while 16% named observability. Pipeline observability covers queue time, duration, success rates, flaky tests, cache performance, and platform-level metrics like agent utilization and server health. Built-in CI/CD dashboards are used by 76% of respondents, but many teams also use Prometheus, Grafana, Elastic, or OpenTelemetry for metrics, logs, and traces. The post compares built-in dashboards, Prometheus and Grafana, Elastic and OpenSearch, OpenTelemetry, commercial platforms like Datadog, and custom in-house solutions, recommending teams start with the questions their current setup cannot answer.
Python vs Go: A Practical Comparison for Developers
The post compares Python and Go across language design, performance, tooling, and developer profiles, drawing on quantitative data from the JetBrains Developer Ecosystem 2026 survey of 28,945 respondents and qualitative interviews with developers who migrated production workloads from Python to Go. Python favors expressive code and rapid iteration, while Go emphasizes explicit, standardized code that is easy to maintain across teams. Go excels in raw performance and concurrency and is widely used in cloud infrastructure, whereas Python remains stronger for machine learning, data science, and scientific computing. Survey data shows that Go developers are typically more infrastructure-focused and senior, and that Go is rarely a first language, while Python is beginner-friendly with 10% of its developers coding for less than a year. Most teams use both languages, with Go for services and infrastructure and Python for AI and data workloads, and moving to Go rarely means abandoning Python.
Coding, Math, and AI: What’s Ahead for School Students in 2026–2027
JetBrains Academy is offering free Youth Clubs in mathematics, AI, and coding for school students in 2026-2027, with registration open until October 31 for the Math and AI clubs and the Coding Club starting October 22. In-person JetBrains Coding Cups will be held in Cyprus, Poland, and Serbia for students aged 13-19, and the Cyprus Programming Challenge returns for its fourth edition on November 8. The Youth Coding Challenge on November 29 features Junior and Senior leagues, with winners potentially invited to advanced programs like the Youth Coding Club or the ACTS camp. The ACTS 2027.1 camp in Cyprus from March 15-25 offers tracks in Competitive Programming, Software Engineering, and Artificial Intelligence, with the AI track by invitation only for national teams from five countries and 20 students from Cyprus. Participants develop skills through practical assignments and challenging problems alongside like-minded peers.
Build Your Game Challenge: Join in and Compete for Prizes
JetBrains Academy announces the Build Your Game Challenge for October, where participants complete the Build a Game With an AI Coding Agent course, add custom features, and submit their projects for prizes. The challenge runs from October 12 to October 23, with community voting and a JetBrains jury evaluating submissions on creativity, originality, gameplay, and player experience. Prizes include $400 and $250 for Community Choice, $150 for a Jury Award, and four $50 Jury Awards for standout projects, all as digital gift cards. Participants can use Junie Lite in JetBrains Air for free to build their game, or switch to a more capable model for complex mechanics. Submissions and voting open on October 19 via Discord, and winners will be announced on October 28.
UI freeze reports are now available in JetBrains Marketplace
JetBrains Marketplace now provides UI freeze reports for plugin authors, accessible from the Freezes tab after enabling Marketplace reporting via the JetBrainsMarketplaceErrorReportSubmitter handler in plugin.xml. Reports are grouped by error type, with each pile showing a representative report and a thread-dump excerpt that identifies threads involved, such as the event dispatch thread (EDT), helping spot recurring patterns. Full thread dumps captured every five seconds during the freeze are available, along with optional CPU snapshots and performance metrics, and comparing dumps across samples can reveal persistent operations. The analyze-freeze skill guides AI agents through investigating IntelliJ Platform thread dumps, focusing on the EDT, coroutine behavior, and common patterns like non-cancellable read actions blocking the UI, with suggestions for fixes. Before fixing, authors should check the plugin version, keep blocking I/O outside read actions, and test changes by repeating the operation and examining fresh diagnostic evidence.
Mellum2.1 Gets to Work: A Fast Open Model for Coding Agents
JetBrains open-sourced Mellum2.1, a compact 12B mixture-of-experts model with only 2.5B active parameters, released under the Apache 2.0 license. The architecture is unchanged from Mellum2, so all improvements come from post-training through reinforcement learning now the main training phase. The model trained on millions of sandboxed runs across thousands of real environments to develop agentic coding skills like exploring codebases, editing files, and checking its own changes. Performance evaluations show substantial gains in agentic coding, general coding, competitive programming, math, tool calling, and general knowledge compared to Mellum2 and similar open models like Qwen3.5-9B and Gemma 4 E4B. Mellum2.1 matches Mellum2’s speed and with multi-token prediction serves almost twice as many tokens as Qwen3.5-9B under heavy load.
Code Quality Never Changes – Or Is AI Challenging This Notion?
The post examines whether AI-generated code challenges traditional notions of code quality, especially in game development where performance, stability, and security are critical. It defines code quality across six dimensions: correctness, performance, stability, security, maintainability, and reusability. The author notes that while 95% of Unity developers use AI, AI code produces 1.7x more logic errors and more security issues than human code, though the overall issue rate is comparable. The proposed solution is to pair AI code generation with deterministic static analysis tools like Qodana, which can catch issues across all six quality dimensions. Studies cited show that combining static analysis with LLM calls reduces token usage by 72–92% and leads to 33% fewer vulnerabilities in master branches. The post demonstrates this workflow using Qodana integrated with GitHub Actions and IDE hooks that force AI agents to reformat and lint generated code.
New Report: The State of CI/CD in 2026
JetBrains released a report on the state of CI/CD in 2026, drawing on four surveys including the Developer Ecosystem Survey 2026 and the State of CI/CD Tools Survey 2026. Among DevOps respondents, 83.4% run CI/CD pipelines. Self-hosting remains strong: 72.8% run their primary system on premises or self-managed cloud, and 30.5% use on-premises installations, a five-year high. AI is used by 48.8% of organizations for at least one CI/CD task, but only 8% integrate AI steps directly into builds; 28% interact via MCP or agent skills versus 10% via built-in assistants. The report covers popular tools, reasons for multi-platform use, switching factors, and barriers to AI adoption.
Introducing Warm Agents, a TeamCity Plugin
The Warm Agents plugin for TeamCity reduces build queue time by maintaining a target number of idle, pre-started cloud agents, addressing the delay when builds wait for a starting agent. When idle agents drop below the target, TeamCity automatically provisions a new instance from the cloud image, ensuring builds start immediately; instances start in batches with short delays. The plugin works with any cloud provider TeamCity supports, requires TeamCity 2024.12.3 or later, and respects the cloud image’s hard limit on total running agents. Administrators configure warm agents via REST API calls, setting a target count per cloud image, and can schedule target changes using build triggers to scale down overnight and restore in the morning. The plugin trades cost for latency, as each idle agent consumes a build license and cloud instance cost, and exposes utilization and saturation metrics in Prometheus format.
Faster Developers Don’t Make a Faster Team
Discovery calls with engineering organizations reveal that widespread use of AI agents has not translated into faster team delivery. Individual developers gain speed, but three structural bottlenecks stall team-level progress. Teams lack shared prompts, rules, and configurations, forcing every agent to start cold and re-explain conventions. Code review processes designed for human-paced change become the new constraint, with some teams measuring increased pull request time. Automating routine work like library upgrades creates its own operational burden, as teams struggle to maintain and distribute automation pipelines across projects. Without shared infrastructure, coordination breaks down and the gap between proficient users and the rest widens.
Start Your Next KMP App With Kotlin Toolchain 0.13
The Kotlin Toolchain 0.13 provides a unified CLI entry point for building JVM, Android, iOS, multiplatform, and server-side applications with declarative configuration. It introduces the kotlin new command for interactive project creation, improved iOS onboarding with automatic simulator setup, and Android SDK auto-provisioning on IDE import. New features include Swift Package Manager dependency support for Apple platforms, multiplatform library publication with Compose Resources and SwiftPM metadata, and Kotlin/Native compiler caching for faster rebuilds. The toolchain is designed for both developers and AI agents, with a Hot Reload MCP server and dedicated AI agent skills included in official Kotlin agent skills. The team recommends the toolchain for new KMP projects and plans future milestones like brew installation, out-of-the-box AI skills, and streamlined app store publishing.
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