Forem: Lynn Mikami

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Lynn Mikami — Fri, 19 Dec 2025 10:21:23 +0000

Streamlining API Development with Apidog MCP Claude Code

pipipi-dev ・ Sep 7

#apidog #mcp #claudecode #api

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Lynn Mikami — Tue, 21 Oct 2025 07:14:18 +0000

My Top 7 Postman Offline Alternatives Every Developer Should Try

Emmanuel Mumba ・ Oct 21

#webdev #programming #ai #javascript

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Lynn Mikami — Mon, 13 Oct 2025 09:13:46 +0000

Top 5 AI Test Case Generation Tools to Boost Your API Testing in 2025

Emmanuel Mumba ・ Oct 13

#webdev #programming #ai #javascript

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Lynn Mikami — Fri, 05 Sep 2025 10:48:40 +0000

Top 10 Free Frontend UI Libraries & Frameworks for Developers in 2025

Emmanuel Mumba ・ Sep 5

#webdev #programming #javascript

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Lynn Mikami — Fri, 22 Aug 2025 06:59:37 +0000

How to Set Up a CI/CD Pipeline Using GitHub Actions

Emmanuel Mumba ・ Aug 21

#webdev #programming #ai

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Lynn Mikami — Fri, 22 Aug 2025 06:14:49 +0000

My 2025 Roundup of Must-Know MCP Services

Emmanuel Mumba ・ Aug 22

#webdev #programming #ai #javascript

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Lynn Mikami — Tue, 19 Aug 2025 08:37:02 +0000

Top 10 Free Tools Every Web Developer Should Know

Emmanuel Mumba ・ Aug 19

#webdev #programming #ai #javascript

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Lynn Mikami — Fri, 15 Aug 2025 01:46:03 +0000

The 12 Best AI Tools for Project Management in 2025

Emmanuel Mumba ・ Aug 14

#webdev #programming #ai

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Lynn Mikami — Fri, 08 Aug 2025 08:10:53 +0000

Top 10 Public APIs Every Developer Should Know in 2025

Emmanuel Mumba ・ Aug 8

#webdev #programming #ai

Opik: The Open-Source Compass for Navigating the Complex World of LLM Development

Lynn Mikami — Sun, 13 Jul 2025 12:28:09 +0000

The world of software development has been irrevocably transformed by the rise of Large Language Models (LLMs). From sophisticated RAG chatbots that converse with your data to powerful agentic pipelines that automate complex tasks, developers are now building applications that were once the stuff of science fiction. But this new frontier comes with a new set of challenges, a "Wild West" of development where success is often hard to measure and failures can be both subtle and catastrophic.

How do you know if your chatbot is hallucinating? Is your RAG system retrieving the right context, or is it just confidently wrong? How can you systematically improve your prompts and agentic logic without resorting to guesswork? These are the critical questions that keep developers up at night. The answer lies not in building more complex models, but in building better systems to understand, evaluate, and optimize them.

Enter Opik, an open-source LLM evaluation platform built by the team at Comet. Opik is designed to be a comprehensive, end-to-end solution that addresses the entire lifecycle of an LLM application. It’s more than just a logger or a tracer; it's a complete toolkit that helps you build, evaluate, and optimize LLM systems that run better, faster, and cheaper. By providing deep observability, powerful evaluation metrics, and proactive optimization tools, Opik is positioning itself as the essential compass for any developer navigating the complex terrain of LLM-powered applications.

Tired of Postman? Want a decent postman alternative that doesn't suck?

Apidog is a powerful all-in-one API development platform that's revolutionizing how developers design, test, and document their APIs.

Unlike traditional tools like Postman, Apidog seamlessly integrates API design, automated testing, mock servers, and documentation into a single cohesive workflow. With its intuitive interface, collaborative features, and comprehensive toolset, Apidog eliminates the need to juggle multiple applications during your API development process.

Whether you're a solo developer or part of a large team, Apidog streamlines your workflow, increases productivity, and ensures consistent API quality across your projects.

What is Opik? A Deep Dive into the Core Pillars

At its heart, Opik is designed to bring order and engineering rigor to the often-chaotic process of LLM development. It accomplishes this through three core pillars that cover the journey from initial development to production monitoring.

Pillar 1: Comprehensive Observability and Tracing

The foundational principle of any robust system is observability: you cannot fix what you cannot see. Opik provides a best-in-class tracing system that illuminates the inner workings of your LLM applications.

Deep Tracing: Opik captures every step of your application's execution. For a RAG application, this means you can see the user's initial query, the embedding process, the documents retrieved from the vector store, the exact context passed to the LLM, and the final generated answer—all in a single, unified view. This level of detail is invaluable for debugging why a system is behaving in an unexpected way.
Vast Integrations: A tool is only as good as its ability to fit into your existing workflow. Opik shines here with an extensive and rapidly growing list of native integrations. Whether you're using popular frameworks like LangChain, LlamaIndex, and OpenAI, or cutting-edge agentic tools like Autogen, CrewAI, and Google's Agent Development Kit (ADK), Opik can be integrated with just a few lines of code. This seamless integration means you spend less time on instrumentation and more time gaining insights.
Prompt Playground: Experimentation is key. Opik includes a Prompt Playground, allowing you to tweak prompts, test different models, and see the results immediately within the context of your application's traces, making prompt engineering a more scientific and less speculative process.

Pillar 2: Robust Evaluation and Experimentation

Once you can see what your application is doing, the next logical step is to measure how well it's doing it. This is arguably Opik's most powerful pillar.

LLM-as-a-Judge: Traditional metrics like BLEU or ROUGE are often insufficient for evaluating the nuanced quality of LLM outputs. Opik embraces the "LLM-as-a-Judge" paradigm, using a powerful LLM to score your application's performance on complex, subjective criteria. It comes with pre-built metrics for critical tasks like hallucination detection, content moderation, and in-depth RAG assessment (evaluating answer relevance and context precision).
Structured Experiments: Opik formalizes the evaluation process through "Datasets" and "Experiments." You can create a dataset of test cases (e.g., questions and ground-truth answers) and then run experiments to systematically compare the performance of different models, prompts, or retrieval strategies. The results are displayed in clear, intuitive dashboards, making it easy to see which changes lead to genuine improvements.
CI/CD Integration: To bring LLM evaluation into a modern DevOps workflow, Opik offers a PyTest integration. This allows you to define performance thresholds and run your evaluation suite as part of your continuous integration pipeline, preventing regressions and ensuring that only high-quality models and prompts make it to production.

Pillar 3: Production Monitoring and Proactive Optimization

A tool's utility shouldn't end at the development stage. Opik is built for production scale, capable of handling over 40 million traces per day.

Production Dashboards: Once your application is live, Opik provides dashboards to monitor key metrics over time, including trace counts, token usage, latency, and user feedback scores. This helps you understand user engagement and spot performance degradation.
Online Evaluation Rules: Using the same powerful LLM-as-a-Judge metrics from the testing phase, you can create "Online Evaluation Rules" to automatically flag problematic interactions in production. If a response is flagged for potential hallucination or toxicity, you can be alerted immediately.
Opik Agent Optimizer & Guardrails: This is where Opik moves from being a passive observer to an active partner. The Opik Agent Optimizer is a dedicated SDK and set of tools designed to help you systematically enhance your agents' performance. Opik Guardrails provide the features needed to implement responsible AI practices, helping to secure your applications against misuse and ensure they behave safely and ethically.

Getting Started with Opik: Flexibility for Every Developer

Opik understands that development teams have different needs and constraints, offering a flexible installation model that caters to everyone from individual hobbyists to large enterprises.

Option 1: Comet.com Cloud (Easiest & Recommended): For those who want to get started immediately with zero setup, Comet offers a free and generous cloud-hosted version of Opik. You simply create an account, get an API key, and you can start logging traces in minutes. This is the ideal path for quick starts, individual developers, and teams who prefer a fully managed solution.
Option 2: Self-Host for Full Control: For teams with strict data privacy requirements or those who want complete control over their infrastructure, Opik can be fully self-hosted.
- Docker Compose: The simplest way to run Opik locally for development and testing. By cloning the GitHub repository and running a single script (./opik.sh), you can have a full Opik instance running on your local machine in minutes.
- Kubernetes & Helm: For scalable, production-grade deployments, Opik provides a Helm chart to easily deploy the platform on any Kubernetes cluster. This ensures high availability and scalability to handle massive production workloads.

Once the server is running, connecting your code is as simple as installing the Python SDK (pip install opik) and running a configuration command (opik configure).

Opik in Action: The Lifecycle of a RAG Chatbot

Let's walk through a practical example of how Opik would be used to build and deploy a RAG chatbot.

Development & Tracing: You're building your chatbot using LangChain. You add the Opik integration, which is as simple as setting a few environment variables. Immediately, every time you test your chatbot, the full trace appears in the Opik UI. You can clearly see the documents retrieved and the exact prompt sent to the LLM, making it easy to debug why the bot gave a strange answer.
Evaluation: You create a Dataset in Opik with 50 challenging questions and their ideal answers. You then run an Experiment to compare your current gpt-3.5-turbo model against gpt-4o. Opik runs your chatbot against the entire dataset for both models and automatically scores each response for Answer Relevance and Hallucination. The dashboard clearly shows that gpt-4o provides more relevant answers and hallucinates less, justifying the higher cost.
Optimization: You notice in the traces that your retrieval step sometimes pulls in irrelevant document chunks. You go to the Prompt Playground and tweak your system prompt to instruct the LLM to ignore irrelevant information in the context. You re-run the experiment and see a measurable improvement in your scores.
Deployment & Monitoring: You're confident in your application and deploy it to production. You set up an Online Evaluation Rule to send you a Slack alert whenever a user interaction is flagged by the Moderation metric with a score above 0.8. You also implement Opik Guardrails to prevent the chatbot from discussing sensitive topics. Your dashboard monitors token usage to ensure costs stay within budget.

Conclusion: An Indispensable Tool for the Modern AI Developer

The era of "prompt-and-pray" is over. Building reliable, scalable, and safe LLM applications requires a new class of tools built on the principles of engineering rigor. Opik stands out as a leader in this new category. By seamlessly integrating comprehensive tracing, deep evaluation, and proactive optimization into a single, open-source platform, it empowers developers to move beyond guesswork and start making data-driven decisions.

Whether you are a solo developer building your first RAG application or a large enterprise deploying complex agentic systems, Opik provides the clarity and control needed to succeed. It is more than just a tool; it is a comprehensive platform that guides you through the entire LLM lifecycle, ensuring that the applications you build are not only powerful but also predictable, reliable, and ready for the real world.

Claude Code vs Gemini CLI: Who's the Better AI Coding CLI Tool?

Lynn Mikami — Sun, 13 Jul 2025 12:24:45 +0000

The developer's toolkit is in the midst of a seismic shift. For decades, the Integrated Development Environment (IDE) has been the undisputed king—a graphical palace of features, debuggers, and extensions. But a new paradigm is rapidly gaining ground, one that takes us back to the raw power and efficiency of the command line, supercharged with the intelligence of large language models (LLMs). This is the dawn of the AI-powered CLI, a new class of tool that promises to turn natural language into the ultimate programming interface.

At the forefront of this revolution are two titans: Anthropic's Claude Code and Google's Gemini CLI. Both are brilliant, agentic coding assistants that live in your terminal, capable of understanding complex requests, manipulating files, and writing sophisticated code. Yet, they are not interchangeable. They are born from different philosophies, excel in different areas, and cater to different developer needs.

This detailed comparison will dissect their core differences to help you decide which AI co-pilot is right for you.

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Unlike traditional tools like Postman, Apidog seamlessly integrates API design, automated testing, mock servers, and documentation into a single cohesive workflow. With its intuitive interface, collaborative features, and comprehensive toolset, Apidog eliminates the need to juggle multiple applications during your API development process.

Whether you're a solo developer or part of a large team, Apidog streamlines your workflow, increases productivity, and ensures consistent API quality across your projects.

Try APIDog Now

Claude Code vs Gemini CLI: The Decisive Factor of Price

For most developers, students, and hobbyists, cost is the single most important factor. Here, the difference between the two tools could not be more stark, and the choice is incredibly clear.

Gemini CLI: Gemini CLI is completely free for personal use. All you need is a standard Google account. This gives you a very generous free tier, including up to 1,000 requests per day and 60 requests per minute, powered by the highly capable Gemini 1.5 Flash model. You do not need to enter a credit card or set up any form of billing. You can install it and start using it within minutes.
Claude Code: Claude Code is a commercial, paid product. To use it, you must have an active, paid subscription to Anthropic's services, such as the Claude Pro or Claude Team plan. Alternatively, you can get access via API keys through cloud platforms like Amazon Bedrock or Google Cloud's Vertex AI, which also operate on a pay-per-use basis. This model is designed for professionals and businesses who can absorb the cost and require enterprise-level support and security.

Who is better for the price-sensitive developer?
Gemini CLI is the undisputed winner. For any individual developer, student, open-source contributor, or anyone who does not have a budget for development tools, Gemini CLI is the obvious choice. Its powerful free offering removes any financial barrier to entry.

Claude Code vs Gemini CLI: Context Window and Project Scale

A model's context window is its working memory. It determines how much information (code, text, history) it can consider at once, which is critical for understanding large and complex projects.

Gemini CLI: Boasts a colossal 1 million token context window (roughly 750,000 words). This allows it to ingest entire small-to-medium-sized projects in a single prompt. You can point it at a whole directory and ask architectural questions, perform project-wide refactoring, or find dependencies across dozens of files.
Claude Code: Features a smaller, yet still very large, 200,000 token context window. This is more than enough for deeply analyzing single large files, understanding complex functions, or maintaining context within a specific module. Its strength is in the depth of analysis of the context it's given.

Who is better for project scale?
Gemini CLI is better for large-scale project analysis and understanding entire codebases. If your primary use case involves asking questions about how your whole project fits together, Gemini CLI's massive context window is a clear advantage. Claude Code is better for focused, deep analysis of specific, large files or modules where extreme precision within that context is required.

Claude Code vs Gemini CLI: A Battle of Code Quality vs. Speed

This is the classic trade-off: do you want a perfect answer slowly, or a good answer instantly?

Claude Code: Prioritizes quality and precision. Users consistently report that Claude Code generates more polished, idiomatic, and production-ready code. It excels at error handling and often considers edge cases you might not have thought of. It might take a few extra seconds to respond, but that time is spent on a deeper "thought" process, often resulting in a superior, more secure, and maintainable final output.
Gemini CLI: Prioritizes speed and responsiveness. It generates code and answers remarkably quickly, making it an exceptional tool for brainstorming, rapid prototyping, and generating boilerplate code in seconds. The code quality is generally high, but it can sometimes be more of a direct, literal translation of your prompt, occasionally requiring manual refinement.

Who is better for quality vs. speed?
Claude Code is better for generating high-quality, production-ready code where correctness is critical. For building core business logic or mission-critical features, Claude's meticulous approach is superior. Gemini CLI is better for speed, rapid prototyping, and quickly generating initial code drafts. When you need to get ideas down fast or scaffold a new feature, Gemini's velocity is unmatched.

Claude Code vs Gemini CLI: Multimodal Features and Capabilities

A modern AI assistant is expected to do more than just process text. The ability to understand other forms of data, like images, is a significant advantage.

Gemini CLI: Is the multimodal champion. Its ability to process images and documents opens up revolutionary workflows. You can provide it with a UI sketch drawn on a napkin (as a PNG or JPEG) and ask it to generate the HTML and CSS. You can feed it a PDF design document and have it create the application's data models. This is a powerful, unique capability.
Claude Code: Is a text-and-code focused specialist. Its entire feature set is honed to be the absolute best at understanding and generating code and natural language. It does not currently support multimodal inputs like images.

Who is better for features and capabilities?
Gemini CLI is unequivocally better for multimodal workflows. For frontend developers, UI/UX designers, or anyone who wants to bridge the gap between visual concepts and functional code, Gemini CLI's capabilities are a game-changer.

Claude Code vs Gemini CLI: Platform Support and User Experience

A great tool needs to work seamlessly on your machine and be easy to set up.

Gemini CLI: Was built with cross-platform parity in mind. It provides a consistent, high-quality experience across Windows, macOS, and Linux. The authentication process is incredibly simple: a single gemini auth login command opens your web browser to securely log in with your Google account.
Claude Code: Was initially optimized for macOS. While it now runs perfectly well on Linux and Windows, some users feel the experience is still slickest on a Mac. The setup process reflects its professional, API-first model, requiring you to manually find your API key and set it via a claude config set api-key YOUR_API_KEY command.

Who is better for support and user experience?
Gemini CLI is better for users seeking a seamless, consistent experience across all major operating systems with a simpler authentication process. Its ease of setup makes it more approachable for beginners.

The Ultimate Workflow: Why Not Both?

The smartest developers won't see this as a zero-sum game. The ultimate workflow involves leveraging both tools for their unique strengths, creating a development process that is both fast and robust.

Broad Strokes & Scaffolding (Gemini CLI): Begin your work with Gemini CLI. Point it at your entire project directory to get a high-level architectural overview. Use its incredible speed to generate boilerplate for new components and its multimodal features to scaffold a UI from a design image. Use it for free, rapid experimentation.
Critical Logic & Refinement (Claude Code): When it's time to build the core, mission-critical logic, switch to your paid Claude Code instance. Feed it specific functions and modules. Ask it to perform a deep refactoring for security and maintainability, write comprehensive unit tests that cover all edge cases, and debug a piece of truly complex logic.

Conclusion: Final Verdict in the Claude Code vs Gemini CLI Showdown

The choice between these two exceptional tools is a clear reflection of your personal or organizational priorities.

The final verdict is this:

Choose Gemini CLI if your priorities are cost, speed, large-scale context, and multimodal capabilities. It is the perfect AI co-pilot for students, individual developers, open-source contributors, and anyone who needs a powerful, versatile, and free tool for rapid development.
Choose Claude Code if your priorities are absolute code quality, security, and enterprise-level precision. It is the superior choice for professional developers and teams building mission-critical applications where the cost of a bug far outweighs the cost of the tool.

The conversational command line is here to stay. Whether you choose the accessible power of Gemini CLI, the professional precision of Claude Code, or a combination of both, one thing is certain: the developers who learn to have a conversation with their code will be the ones who build the future.

How to Use Kimi K2 with Claude Code

Lynn Mikami — Sun, 13 Jul 2025 12:11:18 +0000

Kimi K2, a state-of-the-art language model from Moonshot AI, and Claude Code Router, a versatile open-source tool for routing large language model (LLM) requests. While both are powerful in their own right, their true potential is unlocked when used in tandem. This combination allows developers to leverage the exceptional coding and reasoning capabilities of Kimi K2 within a highly customizable and locally-controlled environment.

This comprehensive guide will walk you through the process of setting up and using Kimi K2 with Claude Code Router. We will cover everything from obtaining the necessary API keys to configuring the router and running your first code generation requests. By the end of this article, you will have a powerful, personalized coding assistant at your fingertips, ready to tackle your most complex development challenges.

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Apidog is a powerful all-in-one API development platform that's revolutionizing how developers design, test, and document their APIs.

Unlike traditional tools like Postman, Apidog seamlessly integrates API design, automated testing, mock servers, and documentation into a single cohesive workflow. With its intuitive interface, collaborative features, and comprehensive toolset, Apidog eliminates the need to juggle multiple applications during your API development process.

Whether you're a solo developer or part of a large team, Apidog streamlines your workflow, increases productivity, and ensures consistent API quality across your projects.

Try APIDog Now

Why Kimi K2 and Claude Code Router?

Before we dive into the technical details, let's briefly explore what makes this combination so compelling.

Kimi K2: A New Frontier in Code Intelligence

Kimi K2 is not just another large language model. Developed by Moonshot AI, it is a Mixture-of-Experts (MoE) model with a staggering one trillion parameters. This sophisticated architecture allows Kimi K2 to excel in a wide range of tasks, but it truly shines in the domain of code. Its key strengths include:

Exceptional Code Generation: Kimi K2 has been trained on a massive dataset of code, enabling it to generate high-quality, contextually-aware code in various programming languages.
Advanced Reasoning: The MoE architecture allows Kimi K2 to reason through complex problems, making it an invaluable tool for debugging, algorithm design, and architectural planning.
Long-Context Understanding: With a context window of up to 128,000 tokens, Kimi K2 can process and understand vast amounts of code, enabling it to work effectively with large codebases and complex project structures.
Agentic Capabilities: Kimi K2 is designed for agentic workflows, meaning it can break down complex tasks into smaller, manageable steps and execute them autonomously.

Claude Code Router: Your Personal AI Gateway

Claude Code Router is an open-source tool that acts as a local proxy for your LLM requests. It allows you to intercept, modify, and route requests to different models based on your specific needs. This offers several key advantages:

Flexibility and Customization: You are not locked into a single model or provider. You can easily switch between models, experiment with different settings, and even create custom routing rules.
Cost-Effectiveness: By routing requests to the most cost-effective model for a given task, you can significantly reduce your API usage costs.
Privacy and Security: Since the router runs locally, you have complete control over your data. Your code and prompts are not sent to any third-party services unless you explicitly configure them to be.
Offline Capabilities (with local models): While we are focusing on Kimi K2 via an API, Claude Code Router can also be configured to work with locally-hosted models, providing a completely offline AI coding assistant.

By combining Kimi K2's raw power with Claude Code Router's flexibility, you can create a coding assistant that is perfectly tailored to your workflow and preferences.

Prerequisites

Before we begin, you will need to have the following:

An OpenRouter API Key: OpenRouter provides a unified API for accessing a wide range of LLMs, including Kimi K2. You can sign up for a free account and obtain an API key from their website.
Node.js and npm (or yarn): Claude Code Router is a Node.js application. You will need to have Node.js and a package manager (npm or yarn) installed on your system.
Git: You will need Git to clone the Claude Code Router repository from GitHub.
A Text Editor: You will need a text editor to modify the configuration files. Visual Studio Code is a popular choice, but any text editor will do.

Setting Up Your Environment

Now, let's get our hands dirty and set up the necessary tools.

Step 1: Get Your OpenRouter API Key

Navigate to the OpenRouter website.
Sign up for a free account or log in if you already have one.
Once you are logged in, navigate to your account settings or the API section to generate a new API key.
Copy the API key and store it in a safe place. We will need it later.

Step 2: Install Claude Code Router

Open your terminal or command prompt.
Clone the Claude Code Router repository from GitHub using the following command:
```
git clone https://github.com/musistudio/claude-code-router.git
```
Navigate into the newly created directory:
```
cd claude-code-router
```

Install the necessary dependencies using npm or yarn:

# Using npm
npm install

# Or using yarn
yarn install

Configuring Claude Code Router for Kimi K2

With the setup complete, it's time to configure Claude Code Router to use Kimi K2 via the OpenRouter API.

Step 1: Set Your OpenRouter API Key

Claude Code Router needs to know your OpenRouter API key to authenticate with the API. The recommended way to do this is by setting an environment variable.

On macOS and Linux:

export OPENROUTER_API_KEY="YOUR_API_KEY"

On Windows:
```
set OPENROUTER_API_KEY="YOUR_API_KEY"
```

Replace "YOUR_API_KEY" with the actual API key you obtained from OpenRouter.

Step 2: Configure the Model

Next, we need to tell Claude Code Router to use Kimi K2 as the default model. To do this, we will modify the configuration file.

Open the claude-code-router directory in your text editor.
Locate the configuration file. This is typically a config.json or a similar file. If one doesn't exist, you may need to create it based on a provided template (e.g., config.example.json).

In the configuration file, you will need to specify the model you want to use. The model name for Kimi K2 on OpenRouter is moonshotai/kimi-k2. You will also need to specify the API endpoint. The configuration might look something like this:

{
  "default_model": "kimi-k2",
  "models": {
    "kimi-k2": {
      "api_key_env": "OPENROUTER_API_KEY",
      "api_base": "https://openrouter.ai/api/v1",
      "model_name": "moonshotai/kimi-k2"
    }
  }
}

This configuration tells Claude Code Router to use a model named "kimi-k2" by default, and it provides the necessary details to connect to the OpenRouter API.

Using Your Kimi K2-Powered Coding Assistant

With everything configured, you are now ready to start using your new coding assistant.

Step 1: Start the Router

In your terminal, from the claude-code-router directory, start the router with the following command:

npm start

This will start the local proxy server. You should see a message in your terminal indicating that the server is running.

Step 2: Interact with Your Assistant

Claude Code Router provides a command-line interface (CLI) for interacting with the configured model. You can now start sending prompts to Kimi K2.

Here are a few examples of what you can do:

Generate a Python script to fetch data from an API:

claude-code "Write a Python script that fetches the latest posts from the JSONPlaceholder API and saves them to a CSV file."

Debug a JavaScript function:

claude-code "I have this JavaScript function that is not working as expected. Can you help me debug it?

```javascript
function calculateTotalPrice(items) {
  let total = 0;
  for (let i = 0; i < items.length; i++) {
    total += items[i].price;
  }
  return total;
}
```

Write a unit test for a Go function:

claude-code "Write a unit test in Go for a function that calculates the factorial of a number."

Kimi K2 will process your requests and provide you with intelligent, code-aware responses directly in your terminal.

Advanced Usage and Customization

The true power of Claude Code Router lies in its customizability. Here are a few ideas for taking your setup to the next level:

Add More Models: You are not limited to just Kimi K2. You can add other models from OpenRouter or other providers to your configuration file and create custom routing rules to use the best model for each task.
Customize System Prompts: You can create custom system prompts to fine-tune the behavior of Kimi K2. For example, you can create a prompt that instructs the model to always provide code in a specific style or to explain its reasoning in detail.
Integrate with Your IDE: Many IDEs have extensions that allow you to integrate with custom scripts and tools. You can configure your IDE to send code snippets and prompts to your local Claude Code Router instance, creating a seamless in-editor coding assistance experience.

Conclusion

By combining the cutting-edge capabilities of Kimi K2 with the flexibility of Claude Code Router, you can create a powerful and personalized AI coding assistant that will revolutionize your development workflow. This setup empowers you to harness the full potential of large language models while maintaining control over your tools and data.

The steps outlined in this guide provide a solid foundation for getting started. We encourage you to experiment with different models, prompts, and configurations to create a setup that is perfectly tailored to your needs. The future of software development is here, and with tools like Kimi K2 and Claude Code Router, you are at the forefront of this exciting new era.