With modern LLMs and AI-powered tools, getting AI assistance in coding interviews is easier than ever. This project explores how AI could seamlessly assist in real-time technical interviews—and what it means for hiring in the future.
With the rise of large language models (LLMs) and AI-powered tools, how could AI assist in real-time coding interviews?
Phantom-AI-Interview is a proof-of-concept that explores AI's ability to listen, read, and respond to technical questions in a virtual setting. This project aims to understand the boundaries of AI assistance and its implications in hiring and technical assessments.
✅ On-Screen Text Extraction – Using EasyOCR to capture and process text from your screen.
✅ AI-Assisted Response Automation – Simulating realistic typing for coding and text-based questions.
✅ Phantom Mode – Operates entirely with hotkey shortcuts, eliminating the need for a visible GUI.
✅ Active Listening Mode – AI stays alert for real-time interactions and responses.
✅ Clipboard Jacking – Silently captures copied text, sends it to an LLM, and retrieves a response without detection.
💡 Note: This project uses an open-source LLM on CPU. Performance and accuracy can be significantly improved with GPU acceleration, larger models, or by switching to GPT API with optimized prompts.
AI-powered tools like LLMs, automated assessments, and interview assistants are changing the way hiring works. Some key trends:
- Faster Screening: AI can analyze candidates and recommend top matches.
- Bias Concerns: AI-driven hiring tools risk reinforcing biases in decision-making.
- Real-Time Assistance: Tools like Phantom-AI-Interview show how AI could provide live support in technical interviews.
🔗 Read more: How AI is disrupting tech hiring
This project is not intended to be used for unethical purposes, including interview fraud. It is a conceptual study on how AI could integrate into virtual hiring processes and technical assessments.
🤖 All responses are played as audio output, which you can listen to through your connected headset 🎧. Notifications are enabled only for demonstration purposes; by default, they are turned off 💡.
🎧 Stereo Mode – AI Listens to Interviewer Directly
This mode allows PhantomAI to listen to the interviewer's voice directly from the system audio instead of the microphone. This ensures that the AI captures exactly what the interviewer is saying and generates responses accordingly.
OCR mode is helpful when you need to extract a question from the interviewer's chat, a coding screen, or an online test. PhantomAI reads the text displayed on the screen and processes it for AI-assisted responses.
Clipboard Jacking is the fastest way to get help from the AI without any visible UI activity. If an interviewer shares a coding question in chat or an online IDE, and you're stuck on syntax or logic, simply copy the part you need help with and press a hotkey. PhantomAI will silently send it to the LLM and provide a response.
Active Listening Mode
- Live Audio Processing: Uses Vosk for real-time speech recognition.
- File-Based Transcription: Uses Whisper for processing recorded audio files.
- Customizable Hotkeys: Modify key bindings via
config.inito trigger AI commands silently. - Screen Capture: Uses EasyOCR to read input from screen.
- Tested on: Windows 11
- Processor: Intel(R) Core(TM) i7-10510U CPU @ 1.80GHz 2.30GHz
- RAM: 32.0 GB (31.8 GB usable)
Your project directory should be structured as follows:
Phantom-AI-Interview/
│── whisper-bin-x64/ # Download [whisper-bin-x64](https://github.com/ggerganov/whisper.cpp/actions/runs/13716448084/) (Place at root)
│── Some whisper files
│── models
│── ggml-base.en.bin # Download ggml-base.en.bin [Whisper Models on Hugging Face](https://huggingface.co/ggerganov/whisper.cpp/tree/main)
│── vosk-model-en-in-0.5/ # Downloaded Vosk model (Place at root)
│── src/ # Source code
│── config # Config folder containing Configuration file for hotkeys
│── requirements.txt # Dependencies
│── run.py # Main script
Since some required files are large and cannot be pushed to GitHub, follow these steps to set up your environment:
-
Download Whisper whisper-bin-x64
Extract it to the root directory of the project
-
Download ggml-base.en.bin Whisper Models on Hugging Face
-
Download the Vosk Model Get the Vosk model (vosk-model-en-in-0.5)
Ensure you have Python 3.11.0 installed. You can download it from python.org.
py -m venv phantomAI_venv- Windows:
phantomAI_venv\Scripts\activate
pip install -r requirements.txtCheck config.ini for hotkey shortcuts. You can update them as per your preference.
py run.py- Uses
vosk-model-en-in-0.5for live transcription. - Find better models here: Vosk Models
- Uses
whisper-bin-x64andggml-base.en.binfor processing recorded audio. - Find the latest builds: Whisper.cpp Artifacts
- More models available here: Whisper Models on Hugging Face
- Docker setup:
docker-compose up -d
- Pull the model:
docker exec -it ollama ollama pull qwq - Find more models: Ollama Search
If you want to enhance PhantomAI for better performance and a more seamless experience, consider these improvements:
- Use GPU & Larger Models – For faster and more accurate responses, switch to a GPU-powered setup with a bigger local LLM.
- Switch to GPT API – If local inference is slow, using the GPT API provides better performance and higher accuracy.
- Better Prompt Engineering – Optimize response quality by designing improved prompt templates or dynamically selecting templates based on the question type.
- Move to Containers – This project was a quick Windows-based concept. For better portability and stability, consider switching to Docker/Linux-based deployment.
- Bundle into a Single Executable – Improve usability by packaging everything into a standalone .exe file for easy distribution.
This project is purely for educational purposes and ethical AI research. It highlights the potential risks associated with silent AI in virtual environments but is not intended to be used for unethical activities.
Feel free to use and modify as needed.
🔍 Disclaimer: This project is an experimental exploration of AI's capabilities in real-time assistance. It is meant to study AI-human interaction in technical settings and not to promote unfair practices in interviews or assessments.