This article is primarily a self-note that I keep updating as I learn more about Claude Code. The AI tooling landscape evolves rapidly, so some information might be outdated by the time you read this. If you find something that needs updating, feel free to reach out!

Whether you’re a junior developer just getting started or a senior developer looking to boost your workflow, Claude Code has something for everyone.

Why Should You Care?

If you’ve read my Solutionist Mindset talk, you know I believe in using AI as a copilot, not a pilot. Claude Code embodies this philosophy perfectly—it’s a CLI tool that sits alongside your existing workflow, helping you move faster while keeping you in control.

For Android and Compose Multiplatform (KMP) developers like us, having a tool that understands our codebase context is game-changing. Gradle configurations, multi-module architectures, platform-specific implementations—Claude Code can navigate all of this.

What is Claude Code?

Claude Code is Anthropic’s official CLI tool that brings Claude directly into your terminal. Unlike the web interface, it:

• Has full access to your codebase (with your permission)
• Can read, write, and edit files directly
• Runs shell commands for you
• Understands project context across multiple files
• Integrates with Git for version control operations

Think of it as having a senior developer sitting next to you who can:

• Explore your codebase instantly
• Write and refactor code
• Debug issues by reading logs and stack traces
• Create commits and PRs
• Explain complex code sections

Latest Updates (January 2026)

Update available for Claude Code users (v: 2.1.2)!

Model Selection

Opus 4.5 is now available in model selection. While Sonnet is superior for general coding tasks, Opus is amazing for complex features and crazy bugs. You can use -m flag for selecting a specific model for particular task sessions.

[!WARNING] Token Usage Warning: Continuous usage of Opus models will consume your rate limits and quota significantly faster (approx. 5-10x) than Sonnet. Use Opus only for complex debugging or architectural tasks.

# Run with a specific model
claude -m claude-opus-4-20250514

Official Plugins

Check out the official plugins:

• All Plugins
• Code Review Plugin

Getting Started

Installation

# Using npm
npm install -g @anthropic-ai/claude-code

# Using Homebrew (macOS)
brew install claude-code

After installation, run claude in your terminal to start an interactive session. You’ll need to authenticate with your Anthropic API key or use the claude --login command to login via the browser.

Basic Usage

Navigate to your project directory and simply run:

claude

This starts an interactive session where you can ask questions, request code changes, or explore your codebase.

Essential Commands Every Developer Should Know

1️⃣ Slash Commands

Claude Code has built-in slash commands that trigger specific workflows:

2️⃣ The CLAUDE.md File

One of the most powerful features for multi-module Android/KMP projects is the CLAUDE.md file. Create this at your project root:

# Project: MyKMPApp

## Architecture
- Multi-module KMP project
- Shared module: commonMain, androidMain, iosMain
- Android app module with Jetpack Compose UI
- iOS app using SwiftUI

## Key Conventions
- Use Koin for dependency injection
- Room for local database (Android), SQLDelight for shared
- Ktor for networking
- Kotlin Coroutines + Flow for async operations

## Build Commands
- `./gradlew assembleDebug` - Build Android debug
- `./gradlew :shared:build` - Build shared module only
- `./gradlew connectedAndroidTest` - Run instrumented tests

## Module Structure
- :app - Android application entry point
- :shared - KMP shared code
- :feature:home - Home feature module
- :feature:settings - Settings feature module
- :core:network - Networking utilities
- :core:database - Database layer

Claude reads this file and uses it as persistent context for every conversation. This is incredibly useful for:

• Multi-module navigation - Claude knows your module structure
• Consistent coding patterns - Follows your conventions
• Faster builds - Knows the right Gradle commands

3️⃣ Vim-Style Keybindings

For terminal enthusiasts, Claude Code supports vim keybindings:

Practical Use Cases for Android/KMP Developers

Use Case 1: Exploring Unfamiliar Codebases

When you join a new project or inherit legacy code:

You: How is the authentication flow implemented in this app?
     Show me the key files involved.

Claude will search through your codebase, identify relevant files (ViewModels, Repositories, API services), and explain the flow.

Use Case 2: Writing Compose UI Components

You: Create a bottom sheet component for filtering products.
     It should have checkboxes for categories and a price range slider.
     Follow our existing design system in :core:designsystem module.

Claude will:

1. Look at your existing design system
2. Match the patterns and naming conventions
3. Create the component following your architecture

Use Case 3: Debugging Build Issues

You: I'm getting this Gradle error when building the shared module:
     [paste error here]

     Help me understand and fix it.

Claude can read your build.gradle.kts files, understand the dependency graph, and suggest fixes.

Use Case 4: Writing Platform-Specific Implementations

You: I need to implement biometric authentication.
     Create the expect/actual declarations for Android and iOS
     in the :core:auth module.

Claude understands KMP’s expect/actual mechanism and generates appropriate platform-specific code.

Use Case 5: Creating Git Commits

You: /commit

Claude will:

1. Analyze your staged changes
2. Understand the context of modifications
3. Generate a meaningful commit message
4. Create the commit

Use Case 6: Room Database Migrations

You: I need to add a 'lastSyncedAt' column to the UserEntity.
     Create the migration and update the entity.

Claude handles the boilerplate of Room migrations, which can be error-prone manually.

Best Practices for Effective Usage

✅ DO

1. Be specific with context - Instead of “fix this bug”, say “fix the crash in UserRepository.kt when the token expires” make sure you add the file and line-number of the function or scope.

2. Review generated code - Always understand what Claude writes. Don’t blindly accept suggestions.

3. Use it for exploration - Ask Claude to explain complex parts of your codebase or third-party libraries

4. Leverage for boilerplate - Let Claude handle repetitive tasks like:

   • Creating data classes from API responses
   • Writing Room entities and DAOs
   • Setting up Hilt/Koin modules
   • Creating navigation graphs
   • Writing unit test boilerplate

5. Maintain your CLAUDE.md - Keep it updated as your project evolves

6. Use the right model for the task - Haiku for quick questions, Sonnet for general coding, Opus for complex debugging

❌ DON’T

1. Don’t share sensitive data - Avoid passing API keys, secrets, or user data through Claude

2. Don’t skip the review - Especially for security-critical code (authentication, payment processing, encryption etc)

3. Don’t use it as a crutch - You should still understand the fundamentals. AI is a multiplier, not a replacement.

4. Don’t expect perfection - Claude can make mistakes. Treat its output as a starting point.

5. Don’t ignore Gradle sync - After Claude modifies build.gradle.kts, sync manually in Android Studio

Cost Management Tips

Claude Code uses API tokens, which cost money. Here’s how to optimize:

1️⃣ Choose the Right Model

2️⃣ Use /compact Regularly

Long conversations consume more tokens. Use /compact to summarize and reduce context.

3️⃣ Start Fresh for New Tasks

Use /clear when switching to unrelated tasks. No need to carry previous context.

4️⃣ Be Concise

Instead of:

"Hey Claude, I was wondering if you could maybe help me
understand how the user authentication works in this app,
like when someone logs in, what happens step by step?"

Try:

"Explain the login flow. Start from LoginViewModel."

6️⃣ Offload Tasks to Other Models/Tools (Save those Tokens!)

Not everything requires Claude Code’s deep context awareness. Save your tokens by routing tasks to the right tool:

• Use Gemini for Quick Concepts: Need to understand “How LruCache works internally” or “Explain the Builder pattern”? Use Gemini. It’s fast, free/cheap, and great for general knowledge that doesn’t need your private codebase context.
• Use ChatGPT for High-Level Project Questions: If you need advice on “Best practices for modularizing a KMP project” or architecture discussions where providing full code access isn’t necessary, ChatGPT is a great option.
• Use CLI Tools for Quick Answers: If you’re a terminal power user (using tmux, dia, etc.), tools like ddgr (DuckDuckGo from terminal) or Ollama (local models) are fantastic for quick lookups without leaving your flow.

[!TIP] Pro Tip: Reserve Claude Code for tasks that specifically need to read your files, understand your project structure, or perform edits. For everything else, cheaper (or free) alternatives often work just as well!

7️⃣ Check Costs with /cost

Regularly run /cost to monitor your usage.

Integration with Development Workflow

IDE Integration

Claude Code works alongside your IDE, not inside it. A typical workflow:

1. Have your IDE open (Android Studio / Fleet / IntelliJ)
2. Run Claude in a terminal (split screen works great)
3. Ask Claude to make changes
4. Review changes in IDE (they appear instantly)
5. Test and iterate

Git Workflow Integration

Claude Code integrates nicely with Git:

# Start a session
claude

# Create meaningful commits
You: /commit

# Create a pull request
You: Create a PR for these changes. The target branch is develop.

# Review code before pushing
You: Review the changes in the last commit for any issues.

Common Gotchas for Android/KMP Projects

1️⃣ Gradle Sync After Changes

When Claude modifies build.gradle.kts files, you’ll need to sync in Android Studio manually. Claude can’t trigger this for you.

2️⃣ Resource Files

Claude can create/modify XML resources (layouts, strings, drawables), but be careful with:

• Generated resources (R class) - These regenerate on build
• Vector drawables - Complex paths might need manual tweaking

3️⃣ Compose Preview

Claude-generated Compose components might need @Preview annotations added for visibility in Android Studio’s preview pane.

4️⃣ iOS-Specific Code

For KMP projects, Claude can write Swift/Objective-C code for iOS implementations, but:

• You’ll need Xcode to verify it compiles
• Swift interop with Kotlin can be tricky

5️⃣ Version Catalog

If you use libs.versions.toml, make sure Claude knows about it in your CLAUDE.md. Otherwise, it might use hardcoded versions.

My Personal Workflow

Here’s how I typically use Claude Code for Android development:

1. Morning exploration - “What did I work on yesterday? Show me recent changes.”

2. Feature development - Start with asking Claude to explore existing patterns, then implement following those patterns

3. Code review helper - “Review this ViewModel for potential memory leaks or coroutine issues”

4. Documentation - “Generate KDoc comments for the public methods in NetworkClient.kt”

5. Refactoring - “Migrate this callback-based API to use Kotlin Coroutines with Flow”

6. Test writing - “Write unit tests for UserRepository using MockK”

Advanced: MCP Servers

Claude Code supports Model Context Protocol (MCP) servers, which extend its capabilities. For Android developers, interesting MCPs include:

• File system access - Already built-in
• Git operations - Built-in
• Web search - For documentation lookups
• Custom tools - You can create your own MCP servers

Check out the MCP Documentation for more details.

Useful One-Liners

Quick commands I use frequently:

# Start with a specific model for complex tasks
claude -m claude-opus-4-20250514

# Resume last conversation
claude --resume

# Check installation health
claude /doctor

# Quick code review
claude "Review my staged changes for issues"

What’s Next?

This article covers the fundamentals, but Claude Code is constantly evolving. I plan to update this note as I discover:

• New features and capabilities
• Better workflows for KMP development
• Integration patterns with CI/CD
• Team collaboration strategies

Final Thoughts

Claude Code is a powerful addition to the Android/KMP developer toolkit. It’s not about replacing your skills—it’s about amplifying them. Use it to handle boilerplate, explore unfamiliar code, and move faster on repetitive tasks.

But remember: You are still the pilot. Claude is your copilot. Understand what it generates, review its suggestions, and keep learning the fundamentals.

The developers who thrive in the AI age won’t be those who code the fastest—they’ll be the ones who solve problems thoughtfully while leveraging every tool at their disposal.

Resources

• Official Claude Code Documentation
• Claude Code GitHub
• Official Plugins
• Model Context Protocol
• My Solutionist Mindset Talk

Feel free to connect with me on: 📩 Email 🌍 Website

I wish to keep this article as a living document. Last updated: January 2026

Make sure you are using a capable system — ideally with a powerful CPU, GPU, and adequate cooling — before running large language models locally. LLMs can consume significant resources, generate substantial heat, and may cause system instability or damage if your hardware isn’t up to the task. Please proceed with caution!

This guide walks you through setting up Ollama (with deepseek-r1-goose) and Goose CLI.

Step 1: Download & Install Ollama

curl -fsSL https://ollama.com/install.sh | sh

Or, if you prefer to download manually then checkout:https://ollama.com/download Ollama-SetUpGuide

Step 2:Pull and Run the model

The following model is optimized for the agent we are going to install next so lets pull and run the model:

ollama run michaelneale/deepseek-r1-goose

more info about the model is available here https://www.ollama.com/michaelneale/deepseek-r1-goose

Step 3: Download And configure Goose-cli

Download the goose via HomeBrew if you don’t have it installed please check this article HomeBrew-SetUpGuide

brew install block-goose-cli

now you can run the goose

goose 

Running after the first installation, the configure menu will be shown, make sure you select Ollama as the model provider, you can navigate by up and down arrow and hit return/enter to select the option.

After the model provider, next comes the model selection option, just type michaelneale/deepseek-r1-goose and hit return/enter.

Later if you want to change the model you can always run bash goose configure, i would recommend you use this model others are not working as expected this is already slow.

You can stop goose by given /exit command.

Where Offline Agents Work Best

• The task is narrow, well-defined, and focused.
• You want fast, private processing without sending data to the cloud.
• You are working with small to moderate inputs and outputs, as local models may struggle with large contexts or long conversations on limited hardware.
• Context limit required is less than 32K tokens

Again, always monitor your system’s health, and don’t hesitate to stop the model if things heat up!

RNNs were a significant milestone because they addressed sequential data processing but had major limitations. These limitations led to the birth of attention mechanisms and transformers. Interestingly, before GPT became a household name, Google extensively used RNNs in their products, including predictive text, speech recognition, and early recommendation systems.

What is a Recurrent Neural Network (RNN)?

Have you ever wondered how your keyboard predicts the next word while typing? Before transformers took over, RNNs were the secret behind word suggestions and other sequential tasks.

Definition:

An RNN (Recurrent Neural Network) is a type of artificial neural network designed specifically for processing sequential data. Unlike traditional feed-forward networks, RNNs have loops that allow information to persist, making them well-suited for tasks where context and order matter.

Common Use Cases of RNNs:

👉 Natural Language Processing (NLP) (e.g., speech recognition, text generation, machine translation)
👉 Time Series Prediction (e.g., stock price forecasting, weather prediction)
👉 Sequence Modeling (e.g., handwriting recognition, music composition)

How Does an RNN Work?

Let’s use the word prediction example to understand the key stages of an RNN. When you type a sentence, the RNN processes it in three key stages:

1️⃣ Input Processing Stage

This is the data collection stage, where the model gathers input to make predictions.

🔹 When you first start using a social media app, its recommendations may seem random or inaccurate. Over time, as you interact more, the app starts to understand your preferences. This happens because it continuously collects data from you to make better predictions.

🔹 In the keyboard analogy, as you type more, the model stores your words in memory, helping it predict what you might type next.

2️⃣ Hidden State Update (Memory Stage)

At this stage, the RNN updates its hidden state based on the new input and previous memory.

🔹 In social media, the model remembers what you’ve previously engaged with and adjusts recommendations accordingly. If you keep watching travel vlogs, it will prioritize showing more similar content.

🔹 In keyboard predictions, the model updates its memory with each word you type, continuously refining its contextual understanding.

3️⃣ Output Generation Stage

Now, the model makes a decision and produces an output based on its learning.

🔹 In social media, this means recommending the next video or post that best matches your interests.
🔹 In typing, this means predicting and suggesting the next word in your sentence.

4️⃣ Backpropagation Through Time (BPTT) – Training Stage

This is where the model learns from past mistakes and improves over time. Think of it like a student reviewing mistakes from past exams to perform better in the next test.

🔹 In social media, if you suddenly stop engaging with travel vlogs and switch to fitness videos, the model realizes its past predictions were wrong and adjusts itself to reflect your new interests.

🔹 In keyboard predictions, if you frequently delete a suggested word and type something else, the model adapts to improve its future suggestions.

How Does BPTT Work?

1️⃣ The model compares its predictions to actual user behavior.
2️⃣ If there’s an error, it adjusts its internal weights to improve accuracy.
3️⃣ This process repeats across multiple iterations, constantly fine-tuning the model.

This stage is crucial because it ensures that the model evolves and adapts based on real-world interactions.

Final Thoughts

RNNs played a critical role in AI’s evolution, powering early NLP applications, recommendations, and predictive text systems. However, due to limitations like vanishing gradients and slow processing, they were eventually replaced by transformers and attention-based models.

Yet, understanding RNNs is essential because they paved the way for modern AI breakthroughs. Without RNNs, we wouldn’t have reached the transformer era of GPT! 🚀

Your feedbacks are welcome, Thanks for Reading.