# Jetbrains
> Software development company that builds smart programming tools, especially integrated development environments (IDEs), to help developers write and fix code efficiently

## Articles

### [Compose Multiplatform 1.12.0 Released](https://yomu.fyi/post/compose-multiplatform-1-12-0-released.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Elvira Mustafina
- Published: Aug 26, 2026

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.


### [How Ubuntu Is Using Rust to Rebuild Core System Tools](https://yomu.fyi/post/how-ubuntu-is-using-rust-to-rebuild-core-system-tools.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Irina Mihajlovic
- Published: Aug 26, 2026

Canonical is selectively adopting Rust to rewrite core system utilities across Ubuntu releases to improve memory safety, security, and long-term resilience. Starting in Ubuntu 26.04 LTS, uutils coreutils shipped as the default implementation targeting complete GNU compatibility, alongside sudo-rs, which introduced intentional behavior changes like asterisk display on password entry. Canonical is also funding ntpd-rs to consolidate NTP, NTS, and PTP into a single utility, while collaborating on UPKI to bring certificate revocation to system utilities. To manage Rust packaging at distribution scale, Canonical vendors dependencies per package rather than creating individual Debian packages for crates and embeds software bills of materials into binaries using cargo auditable. Future plans include exploring Rust implementations of compression libraries like bzip2-rs, zlib-rs, and zstd-rs.


### [OpenTelemetry Comes to IntelliJ IDEA, GoLand, PyCharm, and WebStorm](https://yomu.fyi/post/opentelemetry-comes-to-intellij-idea-goland-pycharm-and-webstorm.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Egor Klimov
- Published: Aug 26, 2026

With the 2026.2 release, JetBrains expanded its OpenTelemetry plugin from Rider to IntelliJ IDEA, GoLand, PyCharm, and WebStorm. The tool captures logs, metrics, traces, and service maps locally without requiring an external observability backend. Developers can search structured log records, plot metric values over time, inspect distributed spans, and verify communication paths across databases and message queues during local runs. To ingest telemetry, the plugin automatically configures OpenTelemetry Protocol environment variables for supported run configurations and terminal sessions, or accepts forwarded data from existing collectors. Additionally, experimental Model Context Protocol support allows AI coding agents to query gathered logs, spans, and service topology.


### [Ideas Worth a Longer Conversation: The JetBrains Research Podcast](https://yomu.fyi/post/ideas-worth-a-longer-conversation-the-jetbrains-research-podcast.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Katie Fraser
- Published: Aug 25, 2026

The JetBrains Research Podcast investigates foundational software development and computer science questions through discussions with industry researchers. In team psychology, Cat Hicks demonstrates that collaborative culture and organizational belonging reduce developer anxiety around automated tooling while curbing the risks of overproduction pressure. Historical analysis by Tomáš Petříček shows that programming spans five competing cultures rather than pure specification implementation, explaining why automated code generation historically encounters limits. Regarding artificial intelligence evaluation, Ibragim Badertdinov details how SWE-rebench isolates model capability through dataset decontamination, leveraging the strict verifiability of software test execution for training feedback. Additionally, conversations with Alexander Kulikov and Anna Kogan examine how foundational mathematical understanding aids AI oversight and address adoption hurdles for open-source computer vision infrastructure.


### [Help AI Coding Agents Write Up-To-Date Code With Modern Golang Skills](https://yomu.fyi/post/help-ai-coding-agents-write-up-to-date-code-with-modern-golang-skills.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Artem Pronichev
- Published: Aug 24, 2026

AI coding agents often generate outdated Go patterns because older syntax dominates training data and new features fall past model cutoffs. To address this gap, the GoLand team released Modern Go Guidelines, an open-source set of skills and CLI tools covering Go 1.0 through Go 1.27. The tool inspects a project's go.mod file or a specific version flag to supply only the language features and standard library additions compatible with that environment. Using a progressive disclosure approach, the CLI provides concise rule identifiers via a list command and expands into before-and-after examples through an explain command. This mechanism minimizes token consumption while helping agents produce up-to-date, compilable code without modifying project files.


### [How We Optimized the Qwen 3.6 Model for Our Junie Agent](https://yomu.fyi/post/how-we-optimized-the-qwen-3-6-model-for-our-junie-agent.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Stanislav Erokhin
- Published: Aug 24, 2026

Deploying the Junie coding agent locally on Apple MacBook M5 hardware requires overcoming steep performance bottlenecks during model execution. Engineers optimized the entire stack using Qwen3.6-27B rather than Qwen3.8-27B, which severely degrades without token-heavy reasoning enabled. At the agent layer, Junie appends requests directly into a rolling context and caches initial prompt prefixes to maximize KV-cache reuse across tasks. For model inference, the team disabled reasoning, selected 4-bit quantization, and patched MLX-VLM to execute self-attention prefill matrix operations using faster 8-bit instructions on the M5 processor. Combining these prefill improvements with simultaneous multi-token prediction and n-gram speculative decoding delivered up to a 2x generation speedup.


### [Junie Can Now Run Entirely on Your Mac – No Credits, No Cloud](https://yomu.fyi/post/junie-can-now-run-entirely-on-your-mac-no-credits-no-cloud.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Dmitry Savelev
- Published: Aug 24, 2026

JetBrains has introduced Junie Local, a feature that runs the Junie coding agent entirely on local hardware without cloud dependencies, subscriptions, or credit meters. Users execute a single command inside Junie to download a 4-bit quantized Qwen3.6-27B model, requiring an Apple M5 Mac with 64 GB of RAM. The team prioritized prefill throughput over pure generation speed by utilizing 8-bit arithmetic instructions on the M5 Neural Accelerator, which increased prefill throughput by approximately 40%. Performance optimizations also include KV-cache reuse, speculative decoding to double generation speed, and disabling reasoning to preserve performance. In JetBrains' evaluations, the local configuration performed comparably to cloud-based Sonnet 4.5 on everyday tasks while ensuring source code, diffs, and prompts never leave the local environment.


### [Spring Boot Configuration Management Best Practices](https://yomu.fyi/post/spring-boot-configuration-management-best-practices.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Siva Katamreddy
- Published: Aug 21, 2026

Spring Boot externalizes application configuration across files, environment variables, system properties, and command-line arguments to allow a single artifact to run across multiple environments. A resilient setup classifies values into application defaults, deployment configurations, and externalized secrets stored in dedicated vaults. Binding properties to type-safe Java records via @ConfigurationProperties ensures immutability, supports relaxed property mapping, and eliminates the discovery issues common to scattered @Value annotations. Enforcing validation with @Validated and Jakarta Bean Validation constraints triggers fail-fast startup behavior when required settings are absent or malformed. Property precedence determines the effective runtime values, which must be adapted appropriately whether deploying a monolith, containerized workloads in Kubernetes, or a microservices cluster.


### [PyCharm for AI-assisted Django Workflows](https://yomu.fyi/post/pycharm-for-ai-assisted-django-workflows.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Will Vincent
- Published: Aug 20, 2026

According to the 2026 Django Developers Survey, ninety percent of respondents incorporate artificial intelligence into their weekly or daily workflows. While agents generate code rapidly, developers remain accountable for understanding, evaluating, and shipping changes within their applications. PyCharm addresses these demands by integrating support for external agents, local models via Ollama and LM Studio, and customizable agent skills across codebases. The IDE maintains version-specific framework assistance, including auto-completion for Django 6.0 template partials, alongside architectural inspection tools and visual diffs. Furthermore, developers can trace application components, inspect API endpoints, execute HTTP requests, and query database migrations directly through built-in tooling.


### [Ready for Go 1.27 on Day One](https://yomu.fyi/post/ready-for-go-1-27-on-day-one.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Artem Pronichev
- Published: Aug 20, 2026

GoLand 2026.2 introduces day-one support for Go 1.27 language features, tooling improvements, and profiling capabilities. The Go 1.27 release adds generic methods, promoted field names in struct composite literals, improved function type inference, and a dedicated profile for detecting goroutine leaks. To assist with code maintenance, GoLand integrates official go fix modernizers directly into editor inspections, the Problems tool window, and optional pre-commit checks. Developers can capture, visualize, and analyze goroutine leak profiles alongside existing CPU, memory, and mutex profiles within the IDE. Additionally, updated Modern Go Code Guidelines provide AI coding agents with Go 1.27 context and API changes aligned with the version specified in go.mod.