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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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Learning to Code in the Age of AI: Advice From a Top Udemy Instructor
An interview with Python educator Ardit Sulce examines how beginners should learn programming as AI changes development and teaching. Sulce says AI now handles many small questions, while teachers provide curated learning paths, judgment, and context; he also argues that students should preserve productive struggle while outsourcing setup friction. He recommends evaluating understanding through code explanation, review, and debugging rather than treating working code as proof, and identifies reading unfamiliar code, debugging, and system understanding as important junior skills. His proposed three-stage path moves from writing syntax by hand in a professional IDE, to using AI as a tutor, and finally to using it as a co-worker while reviewing increasingly complex projects.
Kotlin Toolchain 0.12: Multiplatform Library Publishing, Wasm Apps, and More
Kotlin Toolchain 0.12.0 extends its multiplatform build and publishing capabilities with multiplatform library publication, preview support for Wasm applications, command-line Compose Hot Reload, and broader IDE and terminal improvements. Libraries use the existing publishing configuration and produce a common API, per-platform artifacts, sources, module metadata, plus commonized and platform-specific cinterop bindings; Compose Multiplatform resources are not yet included. wasm-js/app modules can be built into ready-to-use web applications, run with kotlin run, customized through resources, and supplied transitive npm dependencies from Kotlin Multiplatform libraries. The release also reduces default Maven Central files, supports Compose Hot Reload through an MCP server, and recommends project-root-relative // dependencies while retaining the older syntax. It raises minimum compilation requirements to JDK 17 and Kotlin 2.2.20, updates bundled versions including Kotlin 2.4.10 and JDK 25, and recommends IntelliJ IDEA 2026.2.1 or newer for the latest features.
Register Now for JetBrains GameDev Day 2026
JetBrains GameDev Day 2026 is scheduled for September 28 as a free online event for people building games, engines, tools, and production pipelines. The program covers Unity, Unreal Engine, Godot, proprietary engines, and engine-neutral infrastructure, with sessions on AI-assisted Figma-to-Unity integration, procedural content generation, ECS migration, cross-runtime C# and C++ debugging, Unreal performance profiling, and CI troubleshooting. The livestream starts at 10:00 am UTC, and recordings will be available on demand afterward; the full schedule and speaker details are still forthcoming. Watch parties are planned in Belgrade, alongside DevGAMM x FTW! attendee check-in, and at the JetBrains office in Limassol, with separate local timings and registration requirements.
TeamCity 2026.2: Pipelines General Availability, BYOK for AI Assistant, and More
TeamCity 2026.2 makes pipelines generally available on TeamCity Cloud and On-Premises, moving them beyond the Early Access Program and adding branch-aware editing, failure-tolerant job execution, promotion, job debugging, and VCS-unbound workflows. The release also expands AI capabilities: AI Assistant supports Bring Your Own Key for Anthropic, OpenAI, Google Gemini, other supported models, or a local LLM, while MCP adds pipeline management tools and OAuth/PKCE authorization. Additional changes include configurable retries for failed dependencies, IDEA plugin improvements, GitHub pull-request matching by source branch, and a choice of server- or agent-side Kotlin DSL compilation. The pipeline MCP post and delete tools require manually enabled Brave mode, and DSL compilation on agents is described as less restricting and more secure but subject to trade-offs. Bug-fix updates 2025.11.8 and 2026.1.4 also ship.
The MPS 2026.2 Early Access Program Has Started
MPS 2026.2 EAP1 introduces improvements to editor test reporting, usage-result grouping, node selection, and startup configuration, alongside bug fixes. Editor tests can now use node-matching rules or per-node markers to ignore irrelevant containment roles, properties, references, or other fixture differences, while failures identify the responsible node and cell with presentation, concept name, and node ID. Find Usages can group results by virtual package, including one tree level per segment of dotted names, and this option is enabled by default. Select Up now treats lists of sibling nodes as an intermediate editing unit, supporting more predictable repeated selection and group copy-paste. Build tooling separates the IntelliJ Platform-provided boot classpath from MPS-specific extensions, exposes additional JVM arguments, and applies shared and operating-system-specific --add-opens options more precisely, reducing launcher maintenance and stale configuration risks.
Stop Guessing at Hard Faults
CLion 2026.2.2 adds an AI skill for diagnosing ARM Cortex-M hard faults through dedicated MCP tooling. Embedded debugging typically requires decoding CFSR and HFSR, matching the faulting program counter to disassembly, and reproducing crashes because default handlers often report only that a fault occurred. When triggered by a stopped hard-fault-family handler or relevant fault evidence, the skill provides decoded status registers, the hardware-stacked exception frame, SVD-decoded peripheral registers, disassembly, and nearby memory to an agent. It supports any debugging tool, including Lauterbach TRACE32, Segger J-Link, and ST-LINK, and can produce a bug location and fix, with optional rerunning to verify the program. The skill is enabled by default but requires CLion’s MCP server; it works in AI chat and CLI mode for Claude Code and Codex, while GitHub Copilot supports CLI mode only.
How to Handle Errors in Go
Go treats errors as values rather than moving error handling outside normal control flow: functions return an error alongside other results, and callers check it. The guide presents the built-in error interface, returning errors with errors.New, wrapping failures with fmt.Errorf and %w, and distinguishing nil success from non-nil failure. It also explains panic and recover for unexpected failures, including deferred recovery in services that must continue handling other requests, while warning that panics should not replace ordinary error handling. Its best practices emphasize adding context, choosing informative custom error types, inspecting wrapped errors with errors.Is, errors.As, and errors.AsType, and logging errors when they are handled or cannot be returned. The conclusion recommends balancing early crashes with recovery when restarting is costly or when independent goroutines can continue safely.
The Grails Plugin Has a New Home: Apache Grails
The Grails plugin for IntelliJ IDEA, providing framework-aware coding assistance, GSP tooling, and GORM integration, has moved to the Apache Grails project. The Apache Software Foundation now owns its source code, while the Apache Grails Project Management Committee oversees development, releases, and compatibility rather than IntelliJ IDEA release cycles. The Grails team builds, signs, and publishes updates to JetBrains Marketplace under the Apache Grails vendor listing; build 262.0.0 supports IntelliJ IDEA 2026.2. Existing users do not need to migrate, and the plugin ID remains unchanged for now, but beginning with 2026.2 it is no longer bundled by default with IntelliJ IDEA Ultimate. Users who cannot find it in the IDE can install it from Marketplace, where JetBrains will continue featuring the plugin.
Ensuring Code Compliance in Public Sector Software Projects
Public sector software must satisfy data protection, security, accessibility, procurement, supply-chain, and infrastructure requirements while protecting sensitive citizen data. Compliance failures can expose organizations to breaches, penalties, legal costs, contract disputes, and reputational damage. The brief organizes common risks around secure development, contractual and procurement obligations, audit evidence, long-term supportability, and IT governance, noting examples such as hardcoded credentials, weak cryptography, vulnerable dependencies, license conflicts, unsupported libraries, and unchecked quality issues. It recommends embedding automated SAST, secret detection, cryptography and dependency scans, license checks, quality gates, technical debt metrics, freshness tracking, testing, and time-stamped reports into IDE and CI/CD workflows, plus self-hosted or air-gapped deployment where policy requires it. These controls are presented as ways to catch problems before production, create audit trails, support handovers, and maintain compliance over long software lifecycles.
Authenticating TeamCity Builds to External Services With OIDC
Static CI/CD credentials can leak through logs or artifacts, expose uncertainty over who copies them, and require rotation, so the article presents OIDC as an alternative for authenticating TeamCity builds. It explains the workload flow: an identity provider issues a signed JWT whose claims include validity, audience, and issuer, while the consuming service retrieves metadata and JWKS, verifies the signature, and checks those claims. The TeamCity OIDC JWT plugin makes the server an identity provider, supports RSA- or ECDSA-based signing, key rotation, configurable public issuer hosting, and extensions for HSMs or key-management services. After installation from JetBrains Marketplace, build configurations can issue tokens at startup or on demand, with default or fixed five-minute lifetimes, for direct presentation or token exchange with AWS, Google Cloud, and other OIDC services.
Fine-Tuning SOTA Object Detection Models on Real-World Datasets
This practical comparison examines how YOLO12, YOLO26, and RF-DETR behave when moved from COCO to specialized real-world object-detection datasets, and how fine-tuning changes the outcome. The authors first evaluate six pretrained checkpoints on COCO val2017, then use cable-damage, bone-fracture, and soda-bottles datasets from RF100-VL for zero-shot testing, fine-tuning, and accuracy/latency comparison. Because the model implementations require incompatible dependencies, the workflow uses one PyCharm project with three isolated uv environments and remote GPU execution; the reported latency stays in native frameworks rather than TensorRT. All models perform well on COCO, but direct performance on specialized data falls close to zero, while 10 fine-tuning epochs adapt the detectors well to cable damage and soda bottles. Bone-fracture remains difficult despite fine-tuning, with RF-DETR performing best, indicating that domain shift may require more data, longer training, or domain-specific pretraining.
From Leaderboards to Model Profiles: A Deep Dive Evaluation of LLMs for Agentic Coding
Resolve rate alone can hide how coding agents reach the same outcome. This evaluation compares Claude Opus 4.7, Gemini 3.5 Flash, GPT-5.5, and Qwen 3.6 27B FP8 through a Junie pipeline across four benchmark datasets, combining test results with trajectory, cost, efficiency, patch quality, and process measures. In one private benchmark, Opus and Gemini solved the same number of tasks, but Opus averaged 184 steps at USD 2.79 per run versus Gemini's 271 steps at USD 1.24. Across 523 tasks, Opus resolved 267 (51.1%) and Gemini 254 (48.6%); shared outcomes masked differences in navigation, validation, redundancy, hallucination, and patch scope. The wider comparison found GPT-5.5 had the highest resolve rate and always ran an executable check, Opus led patch-quality metrics, and Qwen delivered lower cost with lower resolution, while the authors caution that profiles depend on the Junie scaffold and LLM judges are diagnostic rather than ground truth.
Sunsetting of the JetBrains Teacher Pack for Bootcamps
JetBrains is sunsetting the Teacher Pack for Bootcamps, citing the expansion of separate education programs for teachers, students, classrooms, and independent learners. Bootcamps can submit one final application by September 30, 2026, at 11:59 pm CET; applications will no longer be accepted from October 1. Approved applications submitted by the deadline receive one final set of student coupons valid for up to six months, while previously issued coupons and instructor IDE access remain active until their expiration dates, with no additional coupons or free instructor access after October 1. The program will be fully retired on April 1, 2027, when all bootcamp-provided coupons will have expired and the graduation discount will end; alternatives include Teacher Packs for accredited institutions, the Course Creators Program, the Student Pack, and free or non-commercial IDE options.
The State of Django 2026: Boring is so back
The fifth annual Django Developers Survey, produced by Django Software Foundation and PyCharm, draws on responses from nearly 3,500 developers in more than 40 countries. It reports that Django’s core remains stable, with PostgreSQL used by 76–79% of respondents across five years, templates around 80%, and 43% already on Django 6.0. At the same time, AI is routine but fragmented: 58% of AI users work with it daily, while 56% use it only for chat and advice and just 27% use autonomous multi-step tasks. The survey also describes rapid consolidation around uv and Ruff, growing adoption of type hints, and a testing and CI/CD base that can verify agent-generated changes, although 19% write no automated tests. Django supports both server-rendered and API/frontend patterns, while its stable foundation lets developers change surrounding tools and workflows without replacing the framework.
Security Incident Affecting JetBrains Cadence
JetBrains reports a security incident affecting Cadence, its hosted service that integrates with PyCharm through an optional plugin and runs projects on cloud compute resources. Cadence used TeamCity to orchestrate workloads, and attackers exploited the environment through critical vulnerability CVE-2026-63077. The attackers accessed personal data, compromised a 2024 server backup and Cadence-related AWS IAM credentials, and accessed files in JetBrains AWS S3 buckets; the scope remains under investigation. JetBrains discovered the exploitation on August 23, took the server offline on August 24, invalidated plugin access tokens, and urges users to rotate secrets, investigate connected systems, review repositories, and treat executions and synchronized files as untrusted.
Project Loom in IntelliJ IDEA: Virtual Threads, Scoped Values, and Structured Concurrency
Project Loom in IntelliJ IDEA introduces Java developers to three cohesive concurrency features: Virtual Threads (JEP 444, stable since Java 21), Scoped Values (JEP 506, stable since Java 25), and Structured Concurrency (JEP 533, seventh preview in Java 27). The post identifies pain points such as CompletableFuture ignoring interrupt flags, manual ExecutionException unwrapping chains, duplicated cancel() calls across catch blocks, mutable ThreadLocal pitfalls, and flat thread dumps lacking request hierarchy. A CustomerProfileService example is refactored from CompletableFuture.allOf to StructuredTaskScope.ShutdownOnFailure, where sibling tasks are cancelled automatically and a single timeout covers the whole scope. IntelliJ IDEA debugger support from version 2026.1 groups virtual threads forked within a StructuredTaskScope into containers representing their scopes, and the post walks through downloading JDK 27 (EA) and enabling the experimental language level.
Differential Privacy for Hugging Face Trainers – Without Rewriting Your Training Loop
Training large language models on sensitive data risks memorization and membership inference attacks, yet standard high-level transformer tools lack native differential privacy capabilities. JetBrains Research created and open-sourced DPTrainer, a library that integrates PyTorch's Opacus framework with the Hugging Face Trainer ecosystem. The library provides differential privacy through differentially private stochastic gradient descent without requiring manual training loops or source code modifications. DPTrainer extends base training arguments with a PrivacyArguments dataclass, managing per-sample gradient calculation, gradient clipping, noise injection, and Poisson-sampled data loading. A dynamic patching mechanism injects differential privacy into specialized subclasses like DPOTrainer and SFTTrainer, while built-in privacy accountants track the cumulative privacy budget across training steps and checkpoints.
The CLion Roadmap: What’s Coming Between Now and Late 2026
The CLion development roadmap outlines planned updates for minor releases and the stable 2026.3 release. Development priorities center on agentic workflows, embedded development support, and debugger enhancements. Planned AI features include agent-assisted project setup and a specialized skill for ARM Cortex-M HardFault debugging with register, memory, and disassembly access. Debugger enhancements introduce JSON-based debug profiles, import capabilities from tools such as VS Code, a dedicated Lauterbach TRACE32 profile, and LLDB 21 for Windows MSVC users. Additional additions span QNX project support, default resolve configurations for unlinked files, ISPC language support, bundled toolchain upgrades, and improvements to IDE responsiveness and Dev Container workflows.
How Much Code Do Developers Really Let Agents Write?
JetBrains surveyed over 15,000 professional developers globally in 2026 to measure how much production code is generated by AI agents versus manual authoring. On average, respondents report that agents fully write approximately 47% of their code, while 38% involves AI assistance and 27% is written manually. Adoption varies across ecosystems, with Go, JavaScript, and TypeScript developers reporting the highest agentic code generation at 54% to 55%, while C and C++ developers retain the highest manual share. Senior developers lead adoption over juniors, with about a quarter delegating more than 80% of code generation to agents. Cluster analysis categorizes the developer population into agentic coders (31%), AI-assisted coders (47%), and manual coders (23%).
Compose Multiplatform 1.12.0 Released
JetBrains has announced the release of Compose Multiplatform 1.12.0 with targeted capabilities for artificial intelligence integrations, web rendering, and desktop application layouts. The update incorporates an experimental Model Context Protocol server inside Compose Hot Reload, allowing AI agents to trigger reloads, capture screenshots, inspect the semantic tree, and simulate user interactions directly. For web deployments, the framework now provides automatic font fallback by downloading necessary Noto font subsets on demand when unresolved characters occur during rendering. Desktop developers receive an experimental v2 window and dialog application programming interface in the androidx.compose.ui.window.v2 package, which distinguishes requested states from actual window states and permits precise control over positioning, sizing constraints, and multi-screen placement. These combined enhancements improve developer workflows and runtime flexibility across multiple target platforms.
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