Proactive: Building a CoderPad-Style Environment for Practicing TypeScript and React Interviews
Preparing for software engineering interviews often feels oddly disconnected from the work many of us actually do.
Most interview preparation platforms emphasize classic data structures and algorithms problems. While those skills are important, they rarely reflect the kinds of problems many frontend or full-stack engineers spend their time solving: designing React components, reasoning about state, structuring APIs, or debugging real application logic.
At the same time, many of these platforms are expensive and closed systems. You practice problems in their environment, on their terms, with limited ability to adapt the exercises or explore variations. For someone who enjoys building tools as part of the learning process, that constraint can feel limiting.
So I built Proactive, a small application designed to simulate a CoderPad-style interview environment, focused specifically on practicing TypeScript and React problems.
The goal was not to build a massive learning platform. Instead, I wanted a tight, intentionally scoped tool that solves a specific problem: giving engineers a realistic environment to practice the kinds of coding exercises they might encounter in a modern frontend interview.
The Core Idea
At its heart, Proactive is a browser-based coding environment.
It provides:
- a Monaco editor, similar to the experience of VS Code
- a set of pre-built TypeScript and React exercises
- commands to run tests, request hints, analyze solutions, and progress to new exercises
The goal is to mimic the rhythm of a real interview session. You are given a prompt, you write code in an editor, and you iteratively refine the solution.
This is similar to tools like CoderPad or CodeSignal, but optimized for a very specific use case: frontend-focused interview practice.
Why Build This Instead of Using Existing Tools?
Three things pushed me toward building my own solution.
1. Frontend Interviews Aren’t Algorithm Interviews
Many interview preparation platforms heavily emphasize algorithmic problems. In practice, however, many frontend or full-stack interviews look very different.
They often involve tasks like:
- implementing a small React component
- managing state correctly
- structuring TypeScript types
- debugging existing code
- reasoning about UI behavior
Practicing those types of problems requires a different environment than a typical algorithm sandbox.
2. Learning Tools Should Be Extensible
Another limitation of many platforms is that they are closed systems. You practice their problems, but you can’t easily create your own exercises or extend the environment.
I wanted something different: a system where exercises are simply templates and test suites that can be modified, extended, or replaced.
In other words, the tool itself becomes part of the learning process.
3. Interview Preparation Platforms Are Expensive
Many popular interview preparation tools operate on subscription models.
While these services provide value, they can become costly over time, particularly if you’re only interested in practicing a narrow category of problems.
Building a small, focused tool provided a free alternative while also serving as a learning project.
Architecture at a Glance
Proactive is intentionally simple.
The application consists of three primary pieces:
Frontend
- Next.js application
- Monaco editor integration
- exercise navigation and state management
Exercise Templates
- predefined TypeScript or React coding challenges
- associated test harnesses
- progressive difficulty
Server Layer
- handles evaluation requests
- manages interactions with the analysis tools
The system is deliberately lightweight. Rather than building a full online judge or distributed execution system, the application focuses on providing the minimum environment necessary for realistic practice.
Simulating the Interview Workflow
A typical session with Proactive follows a simple flow.
- Open an exercise
- Read the prompt
- Implement the solution in the Monaco editor
- Run the test suite
- Iterate based on results
- Optionally request hints or analysis
- Move to the next exercise
This mirrors the cadence of many real technical interviews.
The goal is not simply to arrive at the correct answer, but to practice the process of reasoning, iteration, and communication that interviewers are evaluating.
Why Keep the Scope Small
One of the easiest traps in building developer tools is scope creep.
It would be tempting to add features like:
- large exercise libraries
- full user accounts
- social leaderboards
- advanced scoring systems
But none of those features were necessary for the core goal of the project.
Proactive was intentionally designed as a focused tool rather than a platform.
Keeping the scope tight allowed the project to remain:
- understandable
- extensible
- and easy to evolve.
A Side Benefit: Building With Agentic Coding Tools
Another interesting aspect of the project was using agentic coding tools during development.
Because the project was intentionally small and well-defined, it turned out to be a great candidate for experimenting with AI-assisted development workflows.
The constraints of the project — clear boundaries, well-scoped features, and modular components — made it particularly well suited for iterative collaboration with coding agents.
In that sense, Proactive became both:
- a tool for practicing interviews, and
- a small experiment in how developers might build software alongside AI systems.
Where This Might Go Next
There are several directions the project could evolve:
- generating new exercises dynamically
- expanding the test harness for more complex problems
- tracking progress across sessions
- adding collaborative interview simulations
But for now, Proactive is doing exactly what it was designed to do: provide a simple, realistic environment for practicing modern TypeScript and React interview problems.
If there’s interest, I may open source the project and continue evolving it.
In the meantime, it has already served its original purpose: making interview preparation feel a little closer to real engineering work.
Check out Proactive here: https://proactive-three.vercel.app/