Build Interview Skills with a Local-First IDE Using Gemma 4

A growing number of developers are turning to AI-powered tools to sharpen their technical interview skills. A recent entrant in this space is Structure, a macOS desktop IDE that blends local-first execution with a locally hosted Gemma 4 model to simulate authentic interview experiences. Unlike many AI coding assistants, Structure avoids acting as an autocomplete engine. Instead, it uses Gemma 4 to provide context-aware feedback after the user writes, tests, and submits their solution.

Structure introduces a structured four-panel workflow designed to mirror real interview conditions:

• A problem statement panel with examples, constraints, and complexity targets
• A C++ or Python editor for writing solutions
• A local execution panel for visible and hidden test cases
• A tutor panel powered by a locally running Gemma 4 model

The application supports multiple practice modes—Learn, Interview, Debug, Speedrun, and Review—each adjusting the behavior of the AI tutor accordingly. In Learn mode, the tutor prompts users with Socratic questions and progressive hints. Interview mode enforces brevity and focus, simulating a live interviewer. Debug mode isolates the first concrete failure, while Review mode helps users convert their attempts into durable recall cards for spaced repetition.

All code, test results, review cards, settings, and session history are stored locally in a SQLite database. Code execution runs on the user’s machine using standard compilers like clang++ or python3. Gemma 4 is accessed through Ollama, enabling full offline operation so no code or learning data ever leaves the device.

Setting Up Structure for Local Practice

Getting started with Structure is straightforward. Users clone the repository and build the app using CMake and Qt 6. The setup process includes fetching the local Gemma 4 model and preparing the environment:

git clone 
cd structure-ide
cmake -S . -B build -DCMAKE_PREFIX_PATH="$(brew --prefix qt)"
cmake --build build
scripts/setup-gemma4-local.sh
open build/Structure.app

Once open, users can choose from a built-in set of data structures and algorithms problems. After writing a solution, they run visible sample tests and hidden local tests directly within the app. Upon submission, Structure streams grounded feedback from Gemma 4, grounding its analysis in the problem statement, submitted code, test results, and the user’s stated time and space complexity.

The project includes smoke tests that verify the build integrity. Running cmake --build build followed by ctest --test-dir build --output-on-failure confirms all core components are functioning correctly. The project is licensed under the MIT license, making it accessible for both personal and educational use.

How Gemma 4 Powers Real-Time Interview Coaching

Structure treats Gemma 4 not as a code generation engine, but as an intelligent tutor that responds to real execution outcomes. The application collects the full context of the user’s attempt—including code, test results, and performance claims—before invoking the model. This ensures feedback is grounded, relevant, and delivered at the right moment in the user’s workflow.

The app defaults to the lightweight gemma4:e2b model for rapid, low-latency responses ideal for interview practice. This model stays resident in memory, streams feedback quickly, and preserves the privacy of local inference. Users with more powerful hardware can manually override the model or let the setup script select a larger Gemma 4 variant when system resources allow.

Behind the scenes, Structure uses a modular architecture to support this design:

• src/ai/LocalLLMClient.cpp handles communication with Ollama, switching between OpenAI-compatible endpoints and Ollama’s native API for streaming, model control, and response budgeting
• src/ai/PromptBuilder.cpp constructs mode-specific prompts that include the problem statement, test outputs, constraints, and the user’s submitted complexity
• src/ai/TutorEngine.cpp manages the feedback loop, streaming chat responses, limiting context length, and falling back to deterministic feedback if the model endpoint is unavailable
• scripts/setup-gemma4-local.sh automates model setup, warm-up, and benchmarking
• src/storage/Database.cpp stores model preferences and ensures consistent behavior across sessions

This separation of concerns ensures the AI’s role remains focused: to coach, not to complete. The prompt design reinforces this by instructing the model to ground correctness claims in actual test outputs, avoid speculation when tests pass, and provide concrete C++ best practices only when relevant.

Beyond Code Completion: AI as a Coach

The team behind Structure emphasizes intentional AI use over quick fixes. By running Gemma 4 locally and integrating it tightly with the IDE’s execution environment, the app delivers real-time, context-aware coaching without compromising privacy or performance. This approach aligns with the needs of developers preparing for technical interviews, where quick thinking, clear communication, and correct implementation are essential.

As local AI models continue to improve, tools like Structure demonstrate how thoughtful integration can transform practice into mastery. Whether preparing for coding rounds, system design interviews, or whiteboard challenges, developers now have a way to practice under realistic conditions—with feedback that adapts to their progress and stays entirely on their own machine.

With ongoing support for new problem sets and deeper integration with popular interview platforms, Structure is poised to become a trusted companion for engineers honing their technical interview skills.