Last updated: July 2026 · Covers GoLand, IntelliJ, Android Studio, and VS Code workflows
Go, Java, and Kotlin share a structural fact that changes the AI tool calculus compared to Python or React: a large share of professional development in these languages happens on JetBrains IDEs — GoLand, IntelliJ IDEA, and Android Studio — not VS Code. That single fact rules out Cursor and Windsurf for a meaningful portion of this audience before any other criterion even applies.
This guide covers what actually matters for these three languages: JetBrains IDE reality, language-specific patterns (goroutines and error handling in Go, Spring and enterprise patterns in Java, null-safety and coroutines in Kotlin), and which tools handle each well.
Before evaluating any tool on capability, answer one question: what IDE do you actually use?
Go developers are split — GoLand is the JetBrains-native option, but a substantial share of the Go community works in VS Code, particularly for cloud-native and DevOps-adjacent work where lightweight tooling is valued. This means Go is the one language of the three where Cursor and Windsurf are genuinely viable options for a large fraction of developers, not ruled out by IDE choice the way they are for Java.
Java developers are overwhelmingly on IntelliJ IDEA. It is the de facto standard for professional Java development — Spring Boot tooling, enterprise application servers, and Java-specific refactoring support in IntelliJ are not matched by VS Code's Java extensions. For most Java developers, Cursor and Windsurf are not realistic options regardless of their AI capability.
Kotlin developers split into two very different populations: Android developers on Android Studio (which is IntelliJ under the hood, so the same constraints apply), and backend Kotlin developers (Ktor, Spring Boot with Kotlin) who have somewhat more IDE flexibility but still gravitate toward IntelliJ for the same tooling reasons as Java.
The practical upshot: for Java and Kotlin, start your evaluation with JetBrains AI Assistant and GitHub Copilot, not Cursor. For Go, you have a genuine three-way choice depending on your specific IDE preference.
For Go developers already in VS Code — common in cloud-native, Kubernetes-adjacent, and DevOps-heavy teams — Cursor and Windsurf are both fully viable, unlike for Java or Kotlin. Cursor's codebase indexing handles Go's package-based structure well, and its multi-file agent mode is effective at Go's common refactoring patterns — extracting an interface, updating all implementations, and adjusting call sites consistently.
Go's explicit error handling (if err != nil) is a pattern both tools reproduce reliably, since it is the language's dominant idiom and appears constantly in training data. Where these tools show more variance is in idiomatic goroutine and channel usage for concurrent code — reviewing AI-suggested concurrency patterns more carefully than straightforward business logic is a reasonable practice regardless of tool.
See Cursor vs Windsurf for the general comparison; the considerations there apply to Go work without much Go-specific adjustment.
GitHub Copilot has a mature GoLand plugin, making it the primary AI option for Go developers who prefer JetBrains tooling — GoLand's debugger, Go modules management, and test runner integration are meaningful reasons to stay on GoLand even though Go itself does not require it the way enterprise Java does.
If you already pay for GoLand, JetBrains AI Assistant is worth evaluating before adding a second subscription. It understands GoLand's project structure, run configurations, and Go-specific inspections in ways a generic extension does not replicate.
Cline (VS Code) and Aider (terminal, any editor) both handle Go well as BYOK agents. Aider's repository map is a good fit for Go's convention of many small files organized by package — it identifies related files across package boundaries effectively without the manual context-building that a flatter file structure might require elsewhere. See the Aider Rules guide for Go-specific CONVENTIONS.md patterns (error wrapping conventions, table-driven test structure, and similar Go idioms worth encoding explicitly).
For the large majority of professional Java developers on IntelliJ IDEA, JetBrains AI Assistant is where to start, for the simple reason that it is already included with your IntelliJ subscription and requires no additional evaluation cost. It understands Spring annotations, dependency injection patterns, Maven/Gradle build configuration, and IntelliJ's own project structure in ways that a generic AI extension does not.
GitHub Copilot's IntelliJ plugin is feature-complete and widely deployed at Java shops, particularly larger organizations that have already standardized on GitHub Copilot Business for its admin controls and IP indemnity (see AI Coding Tools for Teams: Enterprise Guide). Copilot's training data includes a long history of Java and Spring patterns, and its autocomplete for verbose Java boilerplate — constructors, builders, standard getter/setter patterns where records are not applicable — is reliable.
For teams choosing between JetBrains AI Assistant and Copilot specifically, see Cursor vs JetBrains AI for the broader framework, even though that comparison is written from a Cursor-switching angle — the underlying JetBrains-vs-Copilot tradeoffs it describes apply directly here.
Continue.dev has a genuine JetBrains plugin, making it one of the few ways to get BYOK model flexibility (including local models via Ollama) inside IntelliJ without leaving the IDE. For Java teams with specific privacy requirements or a preference for a particular model provider, Continue.dev is worth evaluating alongside the two options above. See the Continue.dev Rules guide for JetBrains-specific configuration.
A significant share of professional Java work is on codebases that are 10+ years old, running older Spring versions or pre-Spring Boot configurations. This intersects directly with the considerations in Best AI Coding Tools for Legacy Codebases — conservative, approval-gated agent behavior and characterization testing before refactoring apply as much to legacy Java as to any other legacy stack.
For Kotlin Android development specifically, JetBrains AI Assistant in Android Studio has no serious competitor. Android Studio's Jetpack Compose preview, layout tools, and Android-specific run configurations are deeply integrated with JetBrains' own AI tooling in ways that no external AI coding tool replicates, since Cursor and Windsurf do not support Android Studio at all.
Kotlin's null-safety system (?, !!, safe calls) and coroutine-based concurrency are well-represented in JetBrains AI Assistant's suggestions, reflecting JetBrains' own stewardship of the Kotlin language and correspondingly strong training emphasis on idiomatic Kotlin patterns.
For backend Kotlin work (Ktor, Spring Boot with Kotlin) on IntelliJ IDEA rather than Android Studio, GitHub Copilot is a strong alternative with the same IntelliJ plugin used for Java work. Kotlin's more concise syntax compared to Java (data classes, sealed classes, extension functions) is well-represented in Copilot's completions.
Kotlin-specific patterns — sealed class hierarchies for representing state, structured concurrency with coroutines and CoroutineScope, and Kotlin Multiplatform (KMP) considerations for teams sharing code across Android and iOS — are areas where AI suggestions benefit from an explicit rules file describing your project's specific conventions, since these patterns have more variation across codebases than Java's more standardized enterprise patterns. See the Cursor Rules guide or equivalent for your tool for the general pattern of encoding this.
- Always wrap errors with context using fmt.Errorf("%w", err) or errors.Wrap,
never return bare errors from internal functions.
- Use table-driven tests as the default test structure.
- Prefer explicit error returns over panics, except in truly unrecoverable
situations (e.g., in main() during startup).
- Goroutines: always ensure a clear cancellation path (context.Context)
for any goroutine that is not guaranteed to complete quickly.
- Use constructor injection for dependencies, never field injection.
- Prefer Java records for immutable data carriers where the project's
Java version supports them (17+).
- Follow existing package-by-feature or package-by-layer convention —
check the existing structure before creating new packages.
- All public API methods require Javadoc comments.
- Never use the not-null assertion operator (!!) — use safe calls (?.)
with explicit handling, or requireNotNull() with a clear message.
- Use sealed classes for representing a fixed set of states, not enums
with associated data.
- All coroutine-launching functions must accept a CoroutineScope or
be suspend functions — never launch a global, unscoped coroutine.
- Prefer data classes for simple value holders; avoid Java-style
getter/setter boilerplate.
Go microservices deployed to Kubernetes, VS Code-based team — Cursor or Windsurf for day-to-day work; either is a legitimate primary choice, unlike for Java/Kotlin.
Go CLI tools and DevOps tooling — Aider or Claude Code for terminal-native workflows that match how this kind of Go code is typically developed and tested.
Java Spring Boot enterprise application — JetBrains AI Assistant first (already included), GitHub Copilot Business if your organization needs the broader enterprise admin features covered in the Enterprise Guide.
Legacy Java on an older Spring version — Apply the conservative, test-first approach from Best AI Coding Tools for Legacy Codebases; JetBrains AI Assistant or GitHub Copilot for the IDE-level work, paired with careful characterization testing before any refactor.
Kotlin Android app — JetBrains AI Assistant in Android Studio, without a serious second option to evaluate — no external tool matches the native Android tooling integration.
Kotlin backend service (Ktor/Spring) — Same guidance as Java: JetBrains AI Assistant or GitHub Copilot on IntelliJ.
Kotlin Multiplatform (KMP) sharing code with iOS — JetBrains AI Assistant, given JetBrains' own stewardship of KMP tooling; be explicit in your rules file about which code is shared-multiplatform versus platform-specific, since this distinction is not always obvious from file location alone.
It depends on your IDE. For VS Code Go developers, Cursor or Windsurf are both fully viable — unlike Java or Kotlin, Go does not have the same JetBrains-dominance constraint. For GoLand users, GitHub Copilot or JetBrains AI Assistant. For terminal-first Go development (CLI tools, DevOps scripting), Aider or Claude Code fit the workflow well.
JetBrains AI Assistant is the correct starting point for the large majority of Java developers, since it is already included with an IntelliJ IDEA subscription. GitHub Copilot is the mature alternative, particularly for organizations already standardized on Copilot Business for its admin controls. Cursor and Windsurf are not realistic options for most professional Java development, since they do not support IntelliJ IDEA.
No — Cursor is a VS Code fork with no IntelliJ IDEA support, and IntelliJ is the professional standard for Java development. While VS Code has Java extensions, they do not match IntelliJ's Java-specific tooling depth (refactoring, Spring support, application server integration), which is why most professional Java developers remain on IntelliJ regardless of AI tool preference.
JetBrains AI Assistant in Android Studio, with no serious competing option. Android Studio's Jetpack Compose tooling, layout preview, and Android-specific run configurations are deeply integrated with JetBrains' AI features in ways no external tool — including Cursor, which does not support Android Studio at all — can replicate.
Yes, particularly for backend Kotlin development (Ktor, Spring Boot with Kotlin) on IntelliJ IDEA. Copilot's Kotlin completions handle the language's concise syntax (data classes, sealed classes, extension functions) well. For Android-specific Kotlin work, JetBrains AI Assistant's native Android Studio integration is still the stronger choice due to the IDE-specific tooling involved.
It is an area that benefits from extra review rather than a fundamental blocker. Straightforward Go patterns — error handling, standard library usage, common web/API patterns — are reliably generated by all major tools. Concurrent code using goroutines and channels has more room for subtle correctness issues (race conditions, unclosed channels, missing cancellation paths), so treating AI-suggested concurrent code with the same scrutiny you would apply to a human-written first draft is a reasonable practice.
Start with JetBrains AI Assistant since it costs nothing beyond your existing IntelliJ subscription. Evaluate GitHub Copilot specifically if you find AI Assistant's agent capabilities or specific model options insufficient for your workflow, or if your organization has already standardized on GitHub Copilot Business for other reasons (admin controls, IP indemnity, existing GitHub-centric workflows).