GrillKit

Open-source AI technical interview trainer. Practice theory Q&A, live coding, or both in one session from curated YAML banks — with structured scoring, follow-ups, optional voice, and a local results history. Bring your own LLM (cloud or local).

Why GrillKit · Quick start · Changelog · Architecture

Why GrillKit (not just ChatGPT)

A general chat assistant is flexible, but it does not run an interview for you.

What you need	ChatGPT-style chat	GrillKit
Curated technical questions	You prompt each time	Built-in tracks (Python, Kafka, System Design, …), levels, and topics
Interview flow	Free-form thread	Fixed session: theory Q&A and/or coding tasks, up to 2 AI follow-ups per item, 1–5 scoring, session summary
Live coding practice	Paste code in chat	Monaco editor, Run against public tests, Submit for hidden tests + AI review (needs Judge0)
Practice history	Scattered chats	Dashboard with past sessions; open results and per-section review pages after completion
Skip what you already know	You repeat the same prompts	Known questions — mark bank items during practice; optionally exclude them when starting a new session
Time pressure	None	Optional per-round timer on theory and coding (expired round → 0, move on)
Voice practice	Depends on product	Offline Whisper dictation; optional Piper question audio; audio answers when your model supports it
Where data lives	Vendor cloud	Self-hosted: SQLite + data/ on your machine; use Ollama, vLLM, or any OpenAI-compatible API

Structured practice — You pick tracks, difficulty, and topics; GrillKit builds a question plan and keeps score across the whole session, not a single ad-hoc prompt.

Privacy and control — Run via Docker on your laptop or server. API keys and interview history stay under ./data (gitignored). No account or subscription required beyond your LLM provider (if you use a cloud model).

Screenshots & demo

Demo video — full flow from setup to scored feedback

Soon

Dashboard

Setup

Theory

Coding

Evaluation

Features

Session modes

Pick one mode on New interview (/setup):

Mode	What you practice
Theory only	Technical Q&A from data/questions/ — type, dictate, or record answers
Coding only	Programming tasks from data/coding/ — edit, Run, Submit
Theory then coding	Q&A first, then coding panel when theory finishes
Coding then theory	Coding first, then theory

Coding modes need a running Judge0 instance (see Coding sessions below).

Practice tools

• Theory — WebSocket Q&A, AI scoring 1–5, up to 2 follow-ups per question
• Coding — Monaco editor, Run (POST /coding/run) on public tests, Submit (WS /coding/ws) with hidden tests and AI feedback
• Question banks — Python, Database/SQL, System Design, Kafka, RabbitMQ, Docker, Kubernetes, Observability, Airflow, and more (junior / middle / senior where applicable)
• Timer — optional per-round limit on theory and coding; expired rounds score 0 and the session moves on
• Voice — offline Whisper dictation; optional Piper TTS to read theory questions aloud
• Audio answers — record a WAV theory answer when your model supports audio input and Whisper is ready
• Results hub — after you finish, /interview/{id}/results shows overall evaluation and links to theory and coding review pages with full chat/code history
• Known questions — mark theory or coding bank items as I know this during an interview or on review pages; optionally exclude them on New interview setup; manage the list at /known-questions/manage
• Dashboard — recent sessions on the home page (completed sessions link to results)
• Setup — model catalog on /config, interview locale, Whisper/Piper downloads from the UI
• Theme — light theme by default with a dark mode toggle (sun/moon) in the navbar; your choice is remembered and follows your system preference on first visit
• Deployment — Docker Compose on port 8000 with ./data volume for config, DB, and models

Quick start

Prerequisites

• Docker and Docker Compose
• API key for a cloud provider, or a local OpenAI-compatible server (Ollama, vLLM, …)

Run with Docker

git clone https://github.com/GrillKit/grillkit.git
cd grillkit
docker compose up --build

Open http://localhost:8000.

Optional question voice (Piper TTS, same app container):

1. Run docker compose up (or uv run uvicorn app.main:app for development).
2. Open /config, enable Read questions aloud, save.
3. On the Configuration page, use Download question voice when prompted (~60 MB per locale voice from Hugging Face).
4. Start an interview — questions can play aloud; WAV cache lives under data/tts-cache/v2/{locale}/.

./data on the host holds SQLite, config.json, llm_models.json, Whisper/Piper models, and TTS cache. Question banks, templates, and static files ship in the image.

If bind-mounted data/ is not writable (Linux UID mismatch):

PUID=$(id -u) PGID=$(id -g) docker compose up --build

Coding sessions (Monaco + code execution) require Judge0 CE. Start the optional coding profile:

docker compose --profile coding up --build

Judge0 listens on port 2358 inside the Compose network (JUDGE0_URL=http://judge0-server:2358 for the app service). For local development without Docker, run Judge0 separately and point JUDGE0_URL at http://localhost:2358.

On some Linux hosts Judge0 needs cgroup v1 (systemd.unified_cgroup_hierarchy=0 in GRUB). Set CODING_ENABLED=false to hide coding modes when Judge0 is unavailable.

First-time flow

1. Configuration (/config) — add one or more OpenAI-compatible models to the catalog, select an interview model, set interview locale; test connection, then save. Download Whisper (and optionally a Piper voice) from the same page if you want voice features.
2. New interview (/setup) — pick a session mode (theory only, coding only, or combined). Choose tracks, levels, topics, how many questions/tasks, optional per-round timers, and whether to exclude known questions. Coding modes require Judge0 (see Coding sessions above).
3. Practice (/interview/{id}) — answer theory questions in the chat (type, dictate, or record audio). On coding phases, use the editor: Run to check public tests, Submit when ready. Combined sessions switch panels automatically when a section ends (or use Continue to Coding). End the interview from the sidebar at any time.
4. Review (/interview/{id}/results) — after completion, read the overall evaluation, then open Theory or Coding review for full conversation history, scores, and feedback.

Without saved provider config, /setup redirects to /config.

Local development

For contributors: see CONTRIBUTING.md. Quick run:

uv sync --extra dev
uv run uvicorn app.main:app --reload

Same first-time flow at http://127.0.0.1:8000.

Configuration (essentials)

Any OpenAI-compatible HTTP API works:

Provider	Example base URL
OpenAI	https://api.openai.com/v1
Ollama	http://localhost:11434/v1
vLLM / others	your endpoint + /v1

On /config:

• Add model to catalog — display name, base URL, model name, optional API key (a stable catalog id is generated automatically from the display name); enable Accepts audio input only if the model supports multimodal audio (and download Whisper for transcription).
• Interview model — pick from the catalog, Test Connection, save.
• Locale — language for AI feedback and speech (stored in data/config.json, gitignored).
• Whisper — choose size (small, medium, large), download from the UI for dictation and audio answers.
• Read questions aloud — enable Piper, download a voice (~60 MB).

Do not commit data/config.json, data/llm_models.json, or API keys.

Optional environment variables (full list in ARCHITECTURE.md):

Variable	Purpose
DATABASE_URL	SQLAlchemy URL (default: SQLite under data/db/)
HF_TOKEN	Hugging Face token for faster Whisper/Piper downloads
WHISPER_DEVICE	cpu or cuda
WHISPER_COMPUTE_TYPE	int8 or float16
CODING_ENABLED	Enable coding session modes (default true; requires healthy Judge0)
JUDGE0_URL	Judge0 API base URL (default http://localhost:2358)
JUDGE0_AUTH_TOKEN	Optional Judge0 X-Auth-Token header
CODING_MAX_RUNS_PER_TASK	Max Run attempts per coding task (default 20)

Roadmap

Planned

• Session-wide time limit (total interview duration)
• More question banks and categories
• Custom question banks, PWA / standalone frontend

For developers

Document	Contents
ARCHITECTURE.md	Feature modules, routes, data flows, persistence, test layout
CONTRIBUTING.md	Dev setup, quality checks, question/coding YAML guidelines
CHANGELOG.md	Release history

Security

Report vulnerabilities as described in SECURITY.md. Do not open public issues for security problems.

License

Apache License 2.0 (see also NOTICE)