Inspiration
As creators and photographers, we've all experienced the frustration of digging through complex camera menu systems — especially on feature-packed models like the Sony A7. We wanted to eliminate that friction and create an assistant that understands the camera like a pro, but speaks like a friend. That idea became Shashinka AI — a tool designed to help photographers stay in the creative flow without breaking it to decode settings.
What it does
Shashinka AI is a natural language assistant for mirrorless cameras, currently optimized for the Sony A7 series. Users can type questions like: “How do I enable 10-bit video?” or “What's the best profile for S-Log2?”
The system uses:
Vector similarity search to find the most relevant menu items
An LLM (via Vertex AI) to generate clear, friendly explanations
An optional dual engine mode (MongoDB Atlas or FAISS) to serve both low-latency and semantically rich results
All wrapped in a minimal, no-login GitLab Pages interface that loads instant results via iframe.
🛠️ How we built it Frontend: Pure static HTML served via GitLab Pages, with form-based inputs targeting Cloud Run endpoints
Backend (Cloud Run):
Python app using FastAPI or Flask
Dual-mode search: MongoDB Atlas vector search and local FAISS ANN index
LLM layer using Vertex AI or OpenAI’s text-davinci or gpt-4 for explanations
Security: Firebase Auth for user login, API Gateway + Cloud Armor for request throttling
Embeddings: Precomputed via local GPU and stored either in MongoDB or in a .faiss index bundled into the Docker image
Challenges we ran into
Managing concurrent LLM and embedding API calls while keeping latency low
Implementing rate limiting and abuse protection without hurting the UX
Designing a UI that’s useful for both beginner and expert users
Building dual backends (MongoDB + FAISS) in a way that felt seamless to users
🏆 Accomplishments that we're proud of Seamlessly switching between FAISS and MongoDB without changing the user experience
Generating clear, conversational explanations from raw camera documentation
Keeping the tool entirely stateless on the frontend with zero JS dependencies
Deploying both the ANN and LLM search pipelines on Google Cloud Run, fully serverless
What we learned
Deep dive into MongoDB Atlas vector search and hybrid ranking
How to manage concurrency and cost on GCP
The importance of tone and clarity in AI-generated answers for real-world users
That a lightweight frontend can feel powerful when paired with smart backends
What's next for Shashinka AI
Expand support to other mirrorless systems (My personal art powerhouse - Ricoh Gr 3x)
Add image-based search (e.g., upload menu screenshots)
Introduce multi-language support for international users
Offer workflow-based presets (e.g., “cinematic portrait setup”)
Release as a browser extension or mobile companion app for on-the-go use
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