This course is a practical pre-training program designed to help you quickly complete the core technologies needed for **smart mirror** production in the form of "pre-missions." Rather than simple feature explanations, it aims to reach a level where you can immediately create an integrated demo (PoC) in the field.
The course requires completion of the following 4 components:
LLM API-based conversational response module
Including role separation (system/user), conversation history maintenance, retry/timeout, and log handling,
you'll learn patterns for creating short, card-style summary responses suitable for smart mirrors.
Face recognition + filter application with MediaPipe FaceMesh
Extract facial landmark coordinates and
implement a PoC that tracks visual filters like sunglasses/masks/stickers in real-time.
Raspberry Pi ↔ Arduino serial communication (Python)
Transmit Arduino sensor values via serial and
perform reception, parsing, and error handling on Raspberry Pi using Python (pyserial) to
first complete the system backbone of "sensor → Pi → (UI/storage)" flow.
Smart mirror frame/hardware 3D modeling
Considering display, Pi, power supply, cables/heat dissipation,
complete an assemblable frame (front/back, brackets/screw holes, etc.) as STEP/STL deliverables.
Additionally, optional sections allow expansion based on team capabilities, connecting step-by-step:
Weather data input on Pi,
Firebase/Supabase DB integration,
Responsive web UI (mobile/desktop),
AWS deployment basics.
As a result, upon completing the course, students will have not just "individual technology pieces," but an integrated portfolio (AI response + face recognition + sensor communication + hardware design) ready for immediate demonstration at makerthons.