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title: Best JetBrains posts — May 2026 | daily.dev
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# Best of JetBrains — May 2026

1. 1  
[](https://daily.dev/posts/faster-rust-tests-with-cargo-nextest-lymw73raz "Faster Rust Tests With cargo-nextest")  
Article  
![Avatar of jetbrains](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/53ecf0c676f34b3896ee109609d91efa)JetBrains · 19w  
Faster Rust Tests With cargo-nextest  
A recap of a JetBrains livestream with Rain, the creator of cargo-nextest, covering what cargo-nextest is, when to use it over cargo test, and its key features. cargo-nextest can be up to 3x faster than cargo test and adds observability features like run recording, Perfetto trace output, and live test status reporting. It runs each test in its own process for better isolation, supports flaky test retries, and includes debugger integration that preserves the normal execution environment. RustRover 2026.1 now includes native cargo-nextest support with structured results in the Test tool window. Additional Rust tools recommended include cargo-hack, cargo-expand, and cargo-semver-checks.  
50
2. 2  
[](https://daily.dev/posts/how-rust-ides-understand-code-yfytlpwlu "How Rust IDEs Understand Code")  
Article  
![Avatar of jetbrains](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/53ecf0c676f34b3896ee109609d91efa)JetBrains · 15w  
How Rust IDEs Understand Code  
A deep dive into how Rust IDEs understand code, based on a RustRover livestream with Lukas Wirth (rust-analyzer lead, Zed) and Vlad Beskrovny (JetBrains). Key topics include why IDEs must reimplement the compiler frontend for low-latency responses, the evolution from RLS to rust-analyzer, the challenges of Rust's cyclic module graph and name resolution, why procedural macros are isolated into separate processes to prevent IDE crashes, the complexity of replicating Rust's type inference rules, and how RustRover uses PSI stubs and MapReduce-style indexing while rust-analyzer uses Salsa-based query-driven memoization. The post also covers how RustRover integrates semantic analysis into debugging via a custom LLDB integration with MIR evaluation.  
46  
15
3. 3  
[](https://daily.dev/posts/the-road-to-name-based-destructuring-c4j1di6l4 "The Road to Name-Based Destructuring")  
Article  
![Avatar of jetbrains](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/53ecf0c676f34b3896ee109609d91efa)JetBrains · 18w  
The Road to Name-Based Destructuring  
Kotlin is introducing name-based destructuring as a replacement for the current position-based approach. A new syntax using \`val\` inside parentheses (e.g., \`(val name, val age) = person\`) extracts properties by name rather than position, eliminating bugs from property reordering and improving refactoring safety. A new square-bracket syntax handles positional destructuring for cases like Pairs and collections. Both syntaxes are currently Experimental in Kotlin 2.3.20 via compiler flags. The migration timeline targets Stable status in 2.5.0 (end of 2026), with the old parentheses syntax fully switching to name-based behavior by 2.7.0 (end of 2027). Migration tooling including compiler warnings and IDE quick-fixes will assist the transition.  
20
4. 4  
[](https://daily.dev/posts/intellij-idea-2025-3-5-is-out--thk1bmvui "IntelliJ IDEA 2025.3.5 is Out!")  
Article  
![Avatar of jetbrains](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/53ecf0c676f34b3896ee109609d91efa)JetBrains · 19w  
IntelliJ IDEA 2025.3.5 is Out!  
IntelliJ IDEA 2025.3.5 has been released with a targeted performance fix for Spring projects. The key change prevents Spring bean declaration searches from triggering during typing or code completion, resulting in smoother completion behavior in Spring-based projects. This patch is specifically relevant for users who have not yet upgraded to v2026.1\. The update is available via the IDE itself, JetBrains Toolbox App, Ubuntu snaps, or direct download.  
20
5. 5  
[](https://daily.dev/posts/a-practical-guide-to-profiling-in-go-fvzkksvyy "A Practical Guide to Profiling in Go")  
Article  
![Avatar of jetbrains](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/53ecf0c676f34b3896ee109609d91efa)JetBrains · 17w  
A Practical Guide to Profiling in Go  
A comprehensive guide to Go profiling using pprof, covering CPU, memory (heap/allocs), block, mutex, and goroutine profiles. Explains how each profile type works, when to use them, and how to interpret results. Covers three collection methods: runtime/pprof for code-controlled profiling, net/http/pprof for long-running services, and GoLand's new built-in profiler tool (v2026.1.2+). Also walks through inspecting profiles via terminal commands, the pprof web interface, and GoLand's flame graph, call graph, top, tree, and line profiler views.  
16  
1
6. 6  
[](https://daily.dev/posts/deprecating-dotmemory-unit-d8cbwv0jv "Deprecating dotMemory Unit")  
Article  
![Avatar of jetbrains](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/53ecf0c676f34b3896ee109609d91efa)JetBrains · 15w  
Deprecating dotMemory Unit  
JetBrains is deprecating dotMemory Unit, its unit testing framework for detecting memory issues in .NET code. The tool will no longer receive maintenance, compatibility updates, or security fixes. Key reasons include lack of support for modern .NET versions, incompatibility with recent dotMemory workspace formats, and outdated dependencies with known security vulnerabilities. The deprecation was marked on NuGet.org on May 20, 2026, with the official notice posted May 28, 2026\. JetBrains acknowledges there is currently no direct replacement for in-test memory profiling workflows.  
13
7. 7  
[](https://daily.dev/posts/friction-maxxing-failure-and-learning-to-code-x2erwmp74 "“Friction-maxxing”, Failure, and Learning to Code")  
Article  
![Avatar of jetbrains](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/53ecf0c676f34b3896ee109609d91efa)JetBrains · 18w  
“Friction-maxxing”, Failure, and Learning to Code  
Explores the concept of 'friction-maxxing' as applied to learning programming, arguing that productive struggle and failure are neurologically essential for deep learning. Drawing on education research, it explains how active neuroplasticity requires challenge and stress hormones to encode new knowledge, why debugging builds deep understanding, and why AI tools can create an illusion of learning without actual retention. It also highlights the underteaching of self-regulation strategies in computing curricula and points to research-backed interventions for managing the discomfort of learning.  
10  
1

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