JetBrains AI + Real-time App

Built a real-time app with JetBrains AI?
We'll make it production-ready.

Real-time apps — chat, live collaboration, multiplayer features, live dashboards — have fundamentally different technical requirements than standard request-response web apps. Users expect sub-second updates, offline handling, and reliable message delivery. AI tools can set up a basic WebSocket connection, but production real-time features need connection management, message ordering, conflict resolution, and infrastructure that scales with concurrent users.

JavaKotlinPythonTypeScriptPHP

Real-time App challenges in JetBrains AI apps

Building a real-time app with JetBrains AI is a great start — but these challenges need attention before launch.

Connection management and reconnection

WebSocket connections drop constantly — network switches, phone sleep, server deploys. AI tools open a connection but don't handle disconnection, reconnection, or missed messages during downtime. Users see stale data or lose messages without knowing it.

Message ordering and delivery guarantees

Messages must arrive in order and must not be lost. Network issues can deliver messages out of sequence or duplicate them. AI-generated real-time code has no message ordering, no deduplication, and no delivery confirmation — which means lost messages and confused users.

Conflict resolution in collaborative editing

When two users edit the same content simultaneously, whose changes win? AI tools implement 'last write wins,' which silently discards one user's work. Production collaboration needs operational transforms or CRDTs to merge concurrent edits correctly.

Scaling concurrent connections

Each WebSocket connection holds server resources. A single server can handle hundreds of connections, but thousands require load balancing with sticky sessions, a pub/sub layer (like Redis), and horizontal scaling. AI tools don't implement any of this.

Presence and typing indicators

Showing who's online, who's typing, and who's viewing a document requires frequent presence updates that can overwhelm your server. These updates need throttling, batching, and efficient broadcast — details AI tools skip entirely.

Message persistence and history

Real-time messages need to be stored for later retrieval — chat history, edit history, activity logs. AI tools send messages through WebSockets but don't persist them, so refreshing the page loses the entire conversation.

Security in real-time channels

Every WebSocket message needs authentication and authorization. Who can send messages to this channel? Who can read them? AI tools often create open WebSocket endpoints where anyone can listen to or inject messages into any conversation.

What we check in your JetBrains AI real-time app

Connection lifecycle — reconnection logic, heartbeat, graceful degradation
Message delivery — ordering guarantees, deduplication, acknowledgments
Authentication — WebSocket connections verified, channel-level authorization
Scaling — connection pooling, pub/sub architecture, horizontal scaling readiness
Presence system — efficient online/typing indicators with throttling
Message persistence — chat history, search, pagination of historical messages
Conflict resolution — handling simultaneous edits without data loss
Offline support — message queuing, sync on reconnect
Performance — latency under 100ms for message delivery
Monitoring — connection counts, message throughput, error tracking

Common JetBrains AI issues we fix

Beyond real-time app-specific issues, these are JetBrains AI patterns we commonly fix.

highCode Quality

Over-engineered enterprise patterns generated for simple startup use cases

JetBrains AI is trained on enterprise Java and Kotlin patterns, so it tends to generate verbose factory patterns, abstract base classes, and interface hierarchies for problems that could be solved with a simple function in a startup context.

highSecurity

Generated Spring Boot code includes unnecessary security exposure in default configurations

Spring Boot applications generated by JetBrains AI may include actuator endpoints, management ports, or H2 console access enabled in configurations that should be disabled or secured before production deployment.

mediumCode Quality

Verbose boilerplate code increases bundle size and maintenance overhead

Java code generation in particular produces verbose getter/setter patterns, checked exception hierarchies, and XML configuration that modern Kotlin or Lombok-based approaches would handle with a fraction of the code.

mediumTesting

Generated unit tests use JUnit 4 patterns in projects that have moved to JUnit 5

JetBrains AI sometimes generates JUnit 4 annotations (@Test from org.junit, @Before, @After) in projects configured for JUnit 5, causing compilation errors and requiring annotation migration.

Start with a self-serve audit

Get a professional review of your JetBrains AI real-time app at a fixed price.

External Security Scan

Black-box review of your public-facing app. No code access needed.

$19
  • OWASP Top 10 vulnerability check
  • SSL/TLS configuration analysis
  • Security header assessment
  • Expert review within 24h
Get Started

Code Audit

In-depth review of your source code for security, quality, and best practices.

$19
  • Security vulnerability analysis
  • Code quality review
  • Dependency audit
  • Architecture review
  • Expert + AI code analysis
Get Started
Best Value

Complete Bundle

Both scans in one package with cross-referenced findings.

$29$38
  • Everything in both products
  • Cross-referenced findings
  • Unified action plan
Get Started

100% credited toward any paid service. Start with an audit, then let us fix what we find.

Frequently asked questions

Can I build a real-time app with JetBrains AI?

JetBrains AI is a great starting point for a real-time app. It handles the initial scaffolding well, but real-time apps have specific requirements — connection management and reconnection and message ordering and delivery guarantees — that need professional attention before launch.

What issues does JetBrains AI leave in real-time apps?

Common issues include: over-engineered enterprise patterns generated for simple startup use cases, generated spring boot code includes unnecessary security exposure in default configurations, verbose boilerplate code increases bundle size and maintenance overhead. For a real-time app specifically, these issues are compounded by the need for connection management and reconnection.

How do I make my JetBrains AI real-time app production-ready?

Start with our code audit ($19) to get a clear picture of what needs fixing. For most JetBrains AI-built real-time apps, the critical path is: security review, then fixing core flow reliability, then deployment. We provide a fixed quote after the audit.

How much does it cost to fix a JetBrains AI-built real-time app?

Our code audit is $19 and gives you a complete report of issues. Fixes start at $199 with our Fix & Ship plan. For larger real-time app projects, we provide a custom fixed quote after the audit — no hourly billing.

Get your JetBrains AI real-time app production-ready

Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.

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