How Hyperleap Uses Claude Code for AI-Assisted Development

Q: What is Claude Code and how does it differ from other AI coding tools?

Claude Code is a CLI-based AI coding assistant built by Anthropic that runs directly in your terminal. Unlike IDE plugins like GitHub Copilot that focus on autocompletion, or chat interfaces like ChatGPT that operate outside your codebase, Claude Code has full agentic capabilities—it can read your project files, make multi-file edits, run commands, and iterate based on results, all from the command line.

Q: What types of tasks work best with Claude Code?

The highest-impact tasks are multi-file operations like refactoring, renaming across a codebase, content migrations, and feature implementation. Bug investigation and code review preparation also benefit significantly. Short, single-line code changes are typically faster to do manually.

TL;DR: Hyperleap AI uses Claude Code as its primary AI coding assistant because of its CLI-first design, full codebase understanding, and agentic multi-step execution. Key productivity gains include faster onboarding to unfamiliar code, batch refactoring across dozens of files in minutes, and comprehensive test suite generation that follows existing conventions.

How Hyperleap Uses Claude Code for AI-Assisted Development

AI coding assistants have moved from novelty to necessity. At Hyperleap, we've been building AI products since before the current wave of AI tools—which gives us perspective on what works and what's hype. Claude Code has become a core part of our development workflow, and the productivity impact has been substantial.

Why We Chose Claude Code

The Landscape of AI Coding Tools

We evaluated multiple AI coding assistants:

IDE plugins (Copilot, Codeium): Good for autocompletion, limited for larger tasks
Chat interfaces (ChatGPT, Claude.ai): Powerful but disconnected from codebase
Agent tools (Cursor, Windsurf): IDE-integrated but heavyweight
CLI tools (Claude Code): Terminal-native with full codebase access

Each approach has merits. Our choice came down to workflow fit and capability depth.

What Made Claude Code Stand Out

Several factors aligned Claude Code with how we work:

Terminal-native: We live in the terminal. A CLI tool integrates seamlessly with existing workflows—no context switching to a different application.

Codebase understanding: Claude Code reads your project files, understands structure, and makes changes that fit your existing patterns.

Agentic execution: Instead of suggesting code snippets, Claude Code can execute multi-step tasks—reading files, making edits, running commands, and iterating based on results.

Model quality: Claude's reasoning capabilities translate directly to better code suggestions and fewer hallucinations.

Key Difference

Most AI coding tools are reactive—they respond to prompts. Claude Code is agentic—it can plan and execute multi-step tasks autonomously while keeping you informed.

Our Claude Code Workflow

Development Tasks

Bug Investigation: When something breaks, we describe the symptom. Claude Code searches the codebase, identifies likely causes, and often proposes fixes.

Feature Implementation: We describe what we want to build. Claude Code analyzes existing patterns, creates new files, modifies existing ones, and maintains consistency with our codebase style.

Refactoring: Renaming, restructuring, and improving code quality across multiple files is where agentic capabilities shine. One prompt can trigger dozens of coordinated changes.

Code Review Preparation: Before submitting PRs, we ask Claude Code to review changes, identify potential issues, and suggest improvements.

Common Commands

Our most frequent use patterns:

# Start a session in the project directory
claude

# Ask about existing code
> How does the authentication flow work in this project?

# Request implementation
> Add a new API endpoint for user preferences

# Fix issues
> The build is failing with this error: [paste error]

# Refactor
> Rename the UserService class to AccountService across the codebase

Pro Tip

Start sessions in your project root. Claude Code automatically reads project structure and configuration files like package.json, tsconfig.json, and CLAUDE.md to understand context.

The CLAUDE.md Pattern

We maintain a CLAUDE.md file in our project root that provides context about:

Project architecture and conventions
Key directories and their purposes
Common commands and scripts
Patterns to follow for new code

This file acts as a persistent prompt, ensuring Claude Code understands our project-specific context without repeating it every session.

Practical Examples

Example: Adding a New Component

Prompt: "Create a new comparison table component for the blog that takes a list of products and features"

What happens:

Claude Code examines existing components for patterns
Identifies the styling approach (Tailwind)
Checks for existing UI primitives to build on
Creates the component following established conventions
Adds proper TypeScript types
Suggests where to export it

The component arrives ready to use, consistent with our codebase.

Example: Debugging a Build Error

Prompt: "The build fails with 'Cannot find module @/lib/utils'"

What happens:

Claude Code checks the import path configuration
Verifies the file exists at the expected location
Examines tsconfig.json for path aliases
Identifies the mismatch
Proposes the fix

Often this takes under a minute, versus the 10-15 minutes of manual investigation.

Example: Content Migration

Prompt: "All blog posts need a new frontmatter field 'lastUpdated'. Add it to each post with today's date."

What happens:

Claude Code identifies all MDX files in content/blog/
Parses each file's frontmatter
Adds the new field consistently
Preserves existing formatting
Reports what was changed

A task that would take an hour manually completes in minutes.

Scale Multiplier

Agentic capabilities mean Claude Code handles tedious-but-important tasks that would otherwise be skipped or done inconsistently.

Results and Impact

Development Velocity

Quantifying productivity gains is difficult, but observable patterns include:

Faster onboarding: New code areas become accessible quickly through Q&A
Reduced context switching: No jumping between docs, code, and search
Fewer stuck moments: When blocked, describing the problem often unsticks progress
More consistent code: Generated code follows existing patterns

Code Quality

Perhaps surprisingly, code quality has improved:

Aspect	Impact
Consistency	Higher—follows existing patterns
Type coverage	Better—comprehensive typing by default
Edge cases	More considered—often suggests error handling
Documentation	Improved—generates comments where appropriate

Non-Engineering Uses

Claude Code isn't limited to traditional engineering tasks:

Marketing site updates: This blog post workflow includes Claude Code. New pages, component updates, and content changes flow through the same interface.

Documentation: README updates, API documentation, and internal guides benefit from codebase-aware generation.

Configuration: Environment setup, CI/CD changes, and infrastructure-as-code modifications work well with agentic execution.

Advanced Use Cases

The basic workflow—ask a question, get code—only scratches the surface. Here are the advanced patterns we've developed that deliver the most value.

GitHub PR Review Automation

Before merging any pull request, we run Claude Code against the diff. The prompt is straightforward: "Review this PR for bugs, security issues, performance concerns, and consistency with our codebase patterns." Claude Code reads the changed files, cross-references them with related code, and produces a structured review covering potential issues, suggestions for improvement, and confirmation of what looks correct. This doesn't replace human review, but it catches issues that tired eyes miss—unused imports, inconsistent error handling, missing type annotations—and it does so in seconds rather than minutes. The review quality improves further because Claude Code has access to our CLAUDE.md file, so it evaluates changes against our documented conventions rather than generic best practices.

Writing and Maintaining Test Suites

Test writing is one of the highest-ROI uses of Claude Code. We describe the function or component under test, specify the edge cases we care about, and Claude Code generates comprehensive test suites that follow our existing testing patterns. For a React component, this means tests covering rendering states, user interactions, error boundaries, and accessibility. For API endpoints, it generates tests covering valid inputs, validation errors, authentication failures, and rate limiting. The generated tests follow our Jest/Testing Library conventions because Claude Code reads our existing test files to match patterns. When a refactor breaks tests, Claude Code updates them in batch—a task that would take an hour manually completes in minutes.

Debugging Production Issues

When a production bug is reported, we paste the error trace and relevant logs into Claude Code. It traces the error through the codebase, identifies the root cause, and proposes a fix. For stack traces that span multiple files and libraries, this is significantly faster than manual investigation. Claude Code can also search for similar patterns elsewhere in the codebase—"Are there other places where we make this same API call without error handling?"—which helps us fix classes of bugs rather than individual instances.

Architectural Planning and Documentation

For larger changes—adding a new feature area, refactoring a data model, introducing a new service—we use Claude Code as a planning partner. We describe the goal and constraints, and Claude Code analyzes the current architecture to propose implementation approaches. It identifies files that would need to change, dependencies that would be affected, and migration steps required. This isn't a replacement for human architecture decisions, but it accelerates the analysis phase by surfacing code dependencies and patterns that would take time to trace manually.

Integration with Our Development Stack

Claude Code connects with our broader development toolchain in ways that compound its standalone value.

GitHub: Claude Code operates directly on git repositories. It reads commit history, understands branch state, and can create commits with well-structured messages. For PR preparation, it generates detailed descriptions of changes, explains the reasoning behind modifications, and flags breaking changes. When working on feature branches, it maintains awareness of the main branch state to avoid conflicts.

CI/CD Pipelines: After making changes, Claude Code can run our build and test commands directly—pnpm build, pnpm lint, pnpm test—and iterate on failures. If a build fails after a change, we describe the error and Claude Code fixes it in the same session. This tight loop between code change, build verification, and fix eliminates the context switching that normally happens between editor, terminal, and CI dashboard.

Slack Notifications: Our development workflow includes Slack notifications for build status and deployment events. When a CI failure appears in Slack, we can immediately open Claude Code, paste the error, and begin investigation. The time between "build failed" notification and "fix committed" is often under 5 minutes for straightforward issues.

Notion and Documentation: Claude Code generates and updates technical documentation as part of implementation tasks. When we add a new API endpoint, the prompt includes "update the API documentation in the README." Code changes and documentation updates happen in the same session, which eliminates the documentation drift that occurs when docs are updated separately from code.

Comparing Alternatives We Evaluated

We tested several AI coding assistants before standardizing on Claude Code. Each tool has legitimate strengths, and the right choice depends on team workflow and priorities.

Cursor: An AI-native IDE built on VS Code with deep AI integration. Cursor provides inline completions, chat, and agentic editing within a full IDE environment. For teams that prefer GUI-based development, Cursor is an excellent choice. We found its IDE-based approach heavier than we needed—our workflow is terminal-centric, and Cursor's value proposition centers on the IDE experience. Teams that live in VS Code should seriously evaluate Cursor; teams that live in the terminal will prefer Claude Code.

GitHub Copilot: The most widely adopted AI coding tool, focused on inline autocompletion. Copilot excels at predicting the next line of code as you type, which accelerates routine coding tasks. However, it lacks agentic capabilities—it can't read multiple files, execute commands, or perform multi-step tasks. For autocompletion, Copilot is strong. For the broader set of tasks we need (investigation, refactoring, multi-file changes, testing), Claude Code's agentic approach is more capable.

Aider: Another CLI-based AI coding tool that shares Claude Code's terminal-native philosophy. Aider supports multiple LLM backends and provides a chat-based interface for code editing. We found Aider's multi-file editing less reliable than Claude Code's, and the model quality of Claude (the underlying LLM) produced better reasoning and fewer hallucinations than alternative backends. Aider is a capable tool that may suit teams committed to open-source tooling or specific model providers.

The deciding factors for Claude Code were terminal-native workflow, agentic multi-step execution, codebase-aware context through CLAUDE.md, and the quality of Claude's underlying reasoning capabilities.

Lessons Learned

Context is everything. The CLAUDE.md pattern—providing persistent project context—dramatically improves output quality. Invest time in maintaining this file.
Describe intent, not implementation. Better prompts describe what you want to achieve, not step-by-step instructions. Let Claude Code figure out the how.
Trust but verify. AI-generated code should be reviewed like any other code. The review tends to be faster because the code is often cleaner than quick manual implementation.
Iterate naturally. If the first output isn't right, describe what's wrong. Claude Code refines based on feedback rather than starting over.
Use it for the tedious stuff. The biggest wins come from tasks that are straightforward but time-consuming—migrations, renaming, consistency fixes.

Who Should Consider Claude Code

Claude Code fits teams that:

Work in the terminal and want AI without IDE lock-in
Value codebase context over generic code generation
Need agentic capabilities for multi-step tasks
Appreciate model quality and thoughtful reasoning
Build with modern stacks (TypeScript, React, Node, Python, etc.)

Consideration

Claude Code requires comfort with command-line interfaces. If your team prefers GUI-based tools, IDE-integrated options like Cursor may be a better fit.

Getting Started

If you're evaluating Claude Code:

Install and configure following the official documentation
Create a CLAUDE.md file with project context
Start with exploratory questions about your codebase
Progress to small changes before larger implementations
Build the habit of reaching for Claude Code when you'd normally search or context-switch

The learning curve is minimal if you're comfortable in the terminal. The productivity gains compound as you develop patterns for your specific workflow.

"As an AI company, we evaluate AI tools with the same rigor we apply to building them. Claude Code earned its place in our stack not because of hype, but because it measurably reduced the time between 'I need this feature' and 'it's deployed.' The CLAUDE.md pattern alone changed how we think about project documentation," says Gopi Krishna Lakkepuram, Founder & CEO of Hyperleap AI and former Microsoft engineer.

The Meta-Observation

We're an AI company using AI tools to build AI products. This creates useful perspective: we evaluate AI tools with the same rigor we apply to building them.

Claude Code passes that evaluation. It's not magic—it's a productivity multiplier that requires thoughtful use. The teams that integrate it effectively will ship faster than those that don't.

See What We're Building

Claude Code helps us ship Hyperleap AI Agents faster. See the AI chatbot platform we build with these tools—deploy customer-facing AI on your website, WhatsApp, and Instagram.

Explore AI Agents

Frequently Asked Questions

What is Claude Code and how does it differ from other AI coding tools?

Claude Code is a CLI-based AI coding assistant built by Anthropic that runs directly in your terminal. Unlike IDE plugins like GitHub Copilot that focus on autocompletion, or chat interfaces like ChatGPT that operate outside your codebase, Claude Code has full agentic capabilities—it can read your project files, make multi-file edits, run commands, and iterate based on results, all from the command line.

How does Claude Code understand your existing codebase?

Claude Code reads your project structure, configuration files (package.json, tsconfig.json), and a special CLAUDE.md file that provides persistent project context. This means it understands your architecture, coding conventions, and patterns before making suggestions, resulting in code that fits naturally with existing work.

Is Claude Code suitable for non-engineers?

Claude Code requires comfort with command-line interfaces, making it best suited for developers and technical team members. However, anyone who works in the terminal—including DevOps engineers, technical writers, and data engineers—can benefit from its capabilities. For GUI-preferred workflows, IDE-integrated tools like Cursor may be a better fit.

What types of tasks work best with Claude Code?

The highest-impact tasks are multi-file operations like refactoring, renaming across a codebase, content migrations, and feature implementation. Bug investigation and code review preparation also benefit significantly. Short, single-line code changes are typically faster to do manually.

Does Claude Code work with all programming languages?

Claude Code works with any language or framework, though it performs best with widely-used stacks including TypeScript, JavaScript, Python, React, Node.js, and Go. Its effectiveness depends on the model's training data coverage for a given language, so mainstream languages produce the best results.

How does Claude Code handle large refactoring tasks across many files?

Claude Code excels at multi-file refactoring because of its agentic architecture. You describe the desired change—"rename UserService to AccountService across the codebase" or "migrate all API calls to use the new error handling pattern"—and Claude Code scans every relevant file, makes coordinated edits, and reports what was changed. It maintains consistency across files because it reads the full project context before making changes. For large codebases, this turns hour-long manual refactoring sessions into minutes of automated work with human review.

Can Claude Code write tests for existing code?

Yes, and this is one of the highest-value use cases. Describe the function or component you want tested, and Claude Code generates comprehensive test suites that follow your existing testing conventions. It reads your current test files to match naming patterns, assertion styles, and setup/teardown approaches. For React components, it covers rendering states, user interactions, and edge cases. For API endpoints, it generates tests for valid inputs, validation failures, and error conditions. When refactors break existing tests, Claude Code updates them in batch.

How does Claude Code compare to using ChatGPT or Claude.ai for coding tasks?

Chat interfaces like ChatGPT and Claude.ai are powerful for discussing code concepts and generating snippets, but they operate outside your codebase. You copy-paste code in and out, manually apply suggestions, and provide context about your project structure with each conversation. Claude Code eliminates this friction by reading your files directly, making edits in place, running commands, and iterating on results—all within your terminal. The difference is between discussing code and operating on code.

This is part of our "Tools We Use" series, where we share the software and workflows that power Hyperleap AI. These are genuine recommendations based on our experience—we have no affiliate relationship with the tools we discuss.