How to Use KotlinLLM and Hermes Agent for Pair Programming in 2026

Why AI pair programming is essential today

Emerging AI tools that are redefining pair programming

JetBrains' KotlinLLM: a new standard for developer macros

KotlinLLM, open-sourced by JetBrains Research, is now available as a plugin for IntelliJ IDEA. It offers intelligent macros likeasLlmandmockLlm, enabling developers to generate Kotlin code in real time and reload it via JDI. This integration drastically reduces boilerplate writing time, allowing programmers to focus on business logic.

• LLM-based macros that work directly in the editor
• Code generation and hot reloads via JDI
• Integration with existing JetBrains tools

Nous Research's Hermes Agent: continuous human-AI collaboration

With recent support for Buzz (Block’s open-source Nostr workspace), Hermes Agent takes human-AI collaboration to new heights. Developers can now share prompts, execute tools, and collaborate in real time within a self-hostable workspace, where AI acts as a constantly active pair programmer.

• Real-time prompt sharing via Buzz
• No service costs and fully user-controlled
• Workflows optimized for pair programming

How to integrate KotlinLLM into your IDE

Following a guided process ensures quick adoption and increased productivity. Here are the essential steps:

1. Install the KotlinLLM plugin from JetBrains Marketplace.
2. Enable theasLlm and mockLlm> macros in your configuration.li>
3. Create a prompt template that includes style constraints, test notes, and security guidelines.li>
4. Try generating a small function using the built-in chat interface.li> ol>

   Example prompt for generating a factorial calculation helper:p>

   val prompt = \"\"\"
   Write a function that calculates the factorial of a non-negative integer.
   Provide a concise implementation with Kotlin-style docstring and tests.
   \"\"\";
   val generatedCode = asLlm(prompt);
   code>pre>
   Leveraging Hermes Agent for continuous collaborationh2>
   Once you have a local AI assistant, the next step is to keep it active throughout the entire development cycle. Configure Hermes Agent to:p>
   Monitor log files for changes to detect new requirements.li>
   Send automatic notifications when a block of code is modified.li>
   Offer proactive suggestions based on context while writing.li>
   ul>
   This integration creates a seamless pair programming experience that works both offline and online.p>
   Effective prompt engineering for pair programmingh2>
   The quality of generated code depends heavily on prompt clarity. Follow these best practices:p>
   Be specificstrong>: include function signatures, return types, and exception constraints.li>
   Define style conventionsstrong>: e.g., idiomatic Kotlin, formatting rules (Kotlin style guide), test comments.li>
   Add security constraintsstrong>: handle sensitive data, validate inputs, and consider API limits.li>
   Use reusable templatesstrong>: create a repository of prompts that can be adapted for different use cases.li>
   ul>
   Practical example: Writing a Kotlin function with KotlinLLMh2>
   Below is a complete example demonstrating how to combine KotlinLLM and Hermes Agent to implement a word filter for a list.p>
   // Prompt sent to KotlinLLM
   val prompt = \"\"\"
   Create a function called `filterWords` that accepts a `List` and an `Int minLength`.
   Return a new list containing only the strings whose length is greater than or equal to `minLength`.
   Follow Kotlin style conventions, include a docstring, and a test with JUnit5.
   \"\"\";
   
   // Generated code (example)
   val filtered = filterWords(listOf("apple", "banana", "cherry", "date"), 5);
   // filtered contains ["banana", "cherry"]
   code>pre>
   Best practices for seamless collaborationh2>
   Always test AI-generated codestrong> with the existing test suite.li>
   Document your promptsstrong> in a shared repository for team reuse.li>
   Use pull request-style reviewsstrong> to ensure quality checks.li>
   Keep a performance logstrong> to monitor the speed and accuracy of generated code.li>
   ul>
   Metrics and tests to evaluate the effectiveness of AI pair programmingh2>
   Monitor these key indicators to assess impact:p>
   Code coveragestrong>: % of lines covered by tests after AI generation.li>
   Cycle timestrong>: hours needed to go from idea to a working component.li>
   Number of bugsstrong>: defects discovered in later stages compared to the previous period.li>
   Developer satisfactionstrong>: quick surveys to gauge ease of use.li>
   ul>
   Conducting periodic reviews helps refine prompts and optimize workflows.p>
   Common mistakes to avoidh2>
   Relying blindly on generated codestrong> without review.li>
   Neglecting security in promptsstrong>, leading to loss or exposure of sensitive data.li>
   Maintaining multiple unsynchronized versionsstrong> of Hermes Agent or KotlinLLM.li>
   Ignoring team guidelinesstrong> on coding conventions.li>
   ul>
   Future trends: Gemini Robotics and beyondh2>
   As software tools continue to evolve, hardware is following suit. Google DeepMind’s recent release of Gemini Robotics 2 introduces physical control models and multi-robot collaboration that promise to automate hardware development processes. While not yet directly integrated into pair programming, these advancements suggest a future where software and hardware development will be unified in AI-driven collaborative environments.p>
   Conclusionh2>
   Start experimenting with KotlinLLM today, configure Hermes Agent in your Buzz workspace, and watch your programming productivity transform.p>
   
   IMPORTANT: We are in 2026. All temporal references must be current. Do not mention past years such as 2024, 2023, etc. The content must be fresh and relevant to the present.
   
   CURRENT CONTEXT (July 2026):
   Here are some recent trends and news stories to inspire you:
   - Zuckerberg details Meta’s personal AI superintelligence strategy: Mark Zuckerberg has published a WSJ op-ed that argues superintelligence must reach individuals, not just a handful of institutions. The Meta chief pub... [2026-07-30]
   - OpenAI report links coding agents to faster science software builds: OpenAI has released a new field report tracking eight scientific computing projects where coding agents cut runtimes. The report documents projects th... [2026-07-29]
   - Google AI Overviews become more common in search: Google’s AI-generated summaries appeared in 43% of US searches measured by Similarweb, up from 15% a year earlier, according to the market intelligence... [2026-07-29]
   
   Use this current information as inspiration to create an original and relevant prompt for 2026.