# Firebase Functions Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/firebase-functions
> Markdown URL: https://aitinkerers.org/technologies/firebase-functions.md
> Technology record last updated: 2026-02-24T13:28:54Z
> Generated: 2026-09-22T08:35:50Z

Serverless backend logic: run event-driven Node.js or Python code on Google Cloud infrastructure without managing a single server.

Firebase Functions (Cloud Functions for Firebase) is a serverless framework: it executes your backend code automatically in response to events. Your code, written in Node.js or Python, runs in a secure, managed Google Cloud environment. Use it to implement custom server-side logic, like sending a push notification via FCM when a new document is written to Firestore, or running a scheduled job with Cloud Scheduler. It scales automatically from zero to planet-scale, eliminating infrastructure management. Deployment is handled efficiently via the Firebase CLI using the `firebase deploy` command.

- Official technology site: https://firebase.google.com/docs/functions
- Public AI Tinkerers demos and talks: 2
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Vibe✓ : Agentic Street-Level Stress Detection with Gemini and Vertex AI](https://nyc.aitinkerers.org/talks/rsvp_jmM5cVIBnT4)

This talk walks through the development of a real-time, cloud-native system for analyzing stress conditions across New York City using live CCTV footage. The system built using Angular and Google Cloud’s AI stack: Vertex AI, Gemini, BigQuery ML, together with Firebase app hosting, captures 940 concurrent camera streams and performs context-aware analysis through a Gemini-powered multimodal agent. Each camera is mapped to dynamically generated borough zones based on Voronoi tessellation, enabling location-specific metrics. The project spans five phases: 1. Multimodal vision analysis using Gemini for stress detection, with adaptive confidence thresholds. 2. A hybrid ML pipeline using Vertex AI and rule-based logic to classify high-risk conditions with 85%+ accuracy. 3. Time-series forecasting with BigQuery ML (ARIMA_PLUS) to detect seasonal patterns and enable pedestrian-aware predictive routing. 4. Event-driven, serverless infrastructure using Firebase Functions to support six-tier adaptive monitoring windows. 5. Live deployment with Firebase Hosting and GitHub Pages to ensure demo reliability and real-time updates. The demo includes a live analytics dashboard showing violation forecasts and real-time confidence scores for each camera zone. Each camera stream is sampled adaptively based on stress levels, and the backend is built for continuous training and edge-case reinforcement.

- Event context: Building AI Agents with Google Cloud AI — 2025-06-25 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_jmM5cVIBnT4

### [Plan Quest Demo](https://seattle.aitinkerers.org/talks/rsvp_Om-GA1Q2CG4)

Plan Quest helps with planning, tracking progress and retrospection. It uses AI to: - Turn an aspiration (ex: I want to run a marathon) into a detailed plan with a clear timeline and milestones. Each milestone is an objective with multiple key results. - Helps you identify new key results for an objective. For example, it would generate something like "Run 10 miles without stopping" - Helps you set clearer goals that are outcome base rather than activity based. For example, if your goal is "Run 10 miles without stopping", it would suggest something like "Complete 10-mile run in under 1 hour 30 minutes" - Automatically break down a goal (key result) into a task list, such as 'Run 5 miles twice this week,' 'Incorporate interval training,' and 'Increase distance by 1 mile each week.'

- Event context: AI Tinkerers Seattle - May 2024 — 2024-05-21 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_Om-GA1Q2CG4

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