Public Projects

clever-packages

by Neto Jaime Francisco Meque

mini 3 Correndo por segundosCorreu por 248 segundos summarize_auto Histórico de ações chevron_right Aqui estão as principais ações tomadas para o aplicativo: download Instalei 6 pacotes. edit Editei 8 arquivos. metadata.json check_circle src/lib/utils.ts check_circle src/types/game.ts check_circle src/types/countries.ts check_circle src/components/WorldMap.tsx check_circle src/components/EvolutionPanel.tsx check_circle src/App.tsx check_circle src/index.css check_circle build Construído

new-project-7e8f3b11

by Chance Hinson

girl playing with her tits

verdant-ai

by Nandini Pounikar

this is my topic for review paper can you tell me which AI help me to write the review paper in retail in within some second and with refrences and free Ai : AI-Assisted Forensic Technologies for Authentication and Preservation of Indigenous Knowledge Systems

moon-cms

by Minapara Siraz

Create cms builder using python

grand-hi

by jifar boy

Hi

todo app

spring-surveillance

by Janvi shah

1. Project Objective To design and develop a scalable, AI-powered hybrid (Edge + Cloud) surveillance system capable of: Monitoring 1,000+ cameras initially (scalable to 5,000–10,000+) Providing real-time intelligent detection Delivering automated voice response (AI agent-based intervention) Enabling centralized dashboard-based monitoring Reducing manual security oversight Supporting proactive threat prevention 2. System Architecture Overview 2.1 Hybrid Infrastructure Model As discussed in the meeting AI-Survilliance-System-Discussi… , the system will use: Edge Layer (On-site – Jetson or equivalent GPU devices): Real-time object detection Motion tracking Zone monitoring Immediate voice response trigger Cloud Layer (Server-side AI Processing): Advanced analytics LLM-based contextual reasoning Event classification Centralized dashboard Alert management Data storage & historical analysis This ensures: Fast response at edge Scalability and advanced intelligence via cloud Optimized infrastructure cost-performance balance 3. Core Functional Modules 3.1 Camera & Device Management Module Support for multi-camera units (7 cameras + IP speaker per unit) Device onboarding & provisioning Health monitoring of devices Remote firmware update capability Camera grouping by site 3.2 AI Detection & Event Recognition Engine The system will detect: Trespassing Detection Unauthorized entry detection Person tracking within defined restricted zones Real-time alert trigger Loitering Detection Time-based presence detection within a zone Threshold-based alert system Zone-Based Monitoring Virtual zone creation (e.g., doors, restricted areas) Rule-based triggers Door State Monitoring Detect door open/closed state Alert if door remains open beyond defined time Auto voice reminder to close door Custom Rule Engine Client-defined event configurations Future extensibility for additional use cases 3.3 AI Voice Response System (AI Agent Layer) Integration with IP speakers Dynamic AI-generated announcements Context-aware verbal warnings such as: “You are trespassing.” “Please leave the restricted area.” “Please ensure the door is locked.” LLM-based natural language generation (cloud-assisted) Event-specific scripted + dynamic responses 3.4 Notification & Alert System Real-time email notifications SMS integration (optional phase) Dashboard alert center Escalation workflow (e.g., notify security dispatch) Alert logs and audit trails 3.5 Central Monitoring Dashboard Web-based dashboard including: Live camera feed view Event timeline AI-detected events summary Multi-site overview User role management (Admin / Operator) Alert filtering and search Historical playback & analytics Enhanced UI/UX (competitive advantage over Spot AI as discussed AI-Survilliance-System-Discussi… ) 3.6 Scalability Framework Designed for 1,000 cameras (Phase 1) Infrastructure blueprint scalable to 5,000–10,000+ cameras Modular microservices architecture Horizontal scaling via cloud infrastructure 4. AI & Technical Stack (Proposed) Edge Layer NVIDIA Jetson (Or equivalent edge GPU) OpenCV / TensorRT optimization Lightweight object detection models (YOLO variant) Cloud Layer Scalable backend (Python/FastAPI or Node.js) LLM integration for contextual reasoning Event processing engine PostgreSQL / Time-series DB Cloud storage (AWS / GCP / Azure – TBD) Dockerized microservices 5. Phased Development Plan Phase 1 – Architecture & Infrastructure Design System architecture blueprint Edge-cloud workload split Data flow diagram Security architecture Phase 2 – Core AI Detection Module Person detection Trespassing logic Zone creation Door detection model Phase 3 – Voice AI Integration Speaker communication module Context-based announcement engine Response latency optimization Phase 4 – Dashboard Development Admin panel Live monitoring Alert management Analytics view Phase 5 – Pilot Deployment Limited site testing Performance benchmarking Model fine-tuning Phase 6 – Scale Optimization Load testing Multi-site rollout readiness Security hardening 6. Deliverables Complete hybrid AI surveillance system Web dashboard Edge AI module (deployable on Jetson) Voice response integration API documentation Deployment documentation Weekly progress reporting cadence

opal-website

by Archit Suthar

create me a website for buying and selling pet

Build a full-stack web application called "GlowTrack". Purpose: GlowTrack is a Smart Health + Skin Care Tracker web app where users can: - Register and login - Enter personal health and skin details - Log daily health data - Receive personalized skincare product suggestions - Receive simple diet plan recommendations - View health progress dashboard Tech Stack: Frontend: React (modern functional components) Backend: Node.js + Express Database: MongoDB Testing: Jest Charts: Lightweight React chart library Keep everything MVP level and clean. Use simple rule-based logic (not advanced AI). --------------------------------------------------- 1. SYSTEM ARCHITECTURE --------------------------------------------------- First: - Design system architecture - Define database schema - Define API endpoints - Explain recommendation logic structure Keep structure modular and production-ready. --------------------------------------------------- 2. DATABASE DESIGN (MongoDB Models) --------------------------------------------------- Create models: User: - name - email - password (hashed) - age - gender - skinType (dry, oily, acne-prone, combination) - weight - height - lifestyle (sedentary, active) - targetWaterIntake DailyLog: - userId (reference) - date - waterIntake - sleepHours - workoutMinutes - skinConditionRating (1-5) - notes Recommendation: - userId - skincareSuggestion - dietPlan - healthTips - createdAt --------------------------------------------------- 3. RECOMMENDATION ENGINE (Rule-Based Logic) --------------------------------------------------- Create backend logic function generateRecommendations(user, dailyLogs) Rules example: IF skinType = oily AND skinConditionRating <= 3: - Suggest oil-free cleanser - Suggest non-comedogenic moisturizer - Avoid fried and sugary food - Increase water intake to 3L IF skinType = dry: - Suggest hydrating cleanser - Suggest hyaluronic acid moisturizer - Add healthy fats to diet IF sleepHours < 6: - Add sleep improvement tip IF lifestyle = sedentary: - Suggest 20 min daily walk IF waterIntake < targetWaterIntake: - Suggest hydration reminder Return structured response: { skincareSuggestion: [], dietPlan: [], healthTips: [] } --------------------------------------------------- 4. API ROUTES (Express) --------------------------------------------------- Create routes: POST /api/register POST /api/login POST /api/daily-log GET /api/dashboard/:userId GET /api/recommendations/:userId Include: - Basic validation - Password hashing - Clean folder structure - Error handling middleware --------------------------------------------------- 5. FRONTEND (React) --------------------------------------------------- Create pages: 1. Register Page 2. Login Page 3. Dashboard Page 4. Daily Log Form Page 5. Recommendation Page Dashboard should show: - Water intake progress bar - Sleep hours - Workout minutes - Skin condition rating - 7-day water intake chart - 7-day skin rating chart - Button to generate recommendations Recommendation page should show: - Skincare suggestions in cards - Diet plan in bullet format - Health tips in separate section Use modern minimal UI. Keep design clean and simple. Use reusable components. --------------------------------------------------- 6. CHARTS --------------------------------------------------- Add: - Water intake chart (last 7 days) - Skin rating trend chart (last 7 days) --------------------------------------------------- 7. TESTING --------------------------------------------------- Generate Jest test cases for: - Recommendation logic - User registration - Daily log submission --------------------------------------------------- 8. DISCLAIMER --------------------------------------------------- Add disclaimer on recommendation page: "This app provides general wellness suggestions and is not a substitute for professional medical advice." --------------------------------------------------- Generate: - Backend folder structure - Frontend folder structure - All major files with code - Instructions to run locally - Environment variable example Keep code clean, readable, and beginner-friendly.

Build a full-stack web app called "GlowTrack". Purpose: Users can track daily health data and skin condition. Based on inputs, the system provides skincare product suggestions and a simple diet plan. Tech Stack: Frontend: React Backend: Node.js + Express Database: MongoDB First, design the system architecture: - Define database schema - Define collections/tables - Define API endpoints - Explain recommendation logic structure Keep it MVP level, simple rule-based logic.

Create portfolio website

clever-system

by Satyam Maravaniya

GenBI System – Build Specification 🎯 Objective I want to build a system where: User uploads a CSV file File is stored inside PostgreSQL (Docker-based PostgreSQL container) User interacts via chat AI analyzes data, gives insights, answers queries AI can modify data (add/remove/update rows or columns) All changes are tracked User can revert (undo) any action System maintains full audit trail This must be built as a production-ready architecture. 🧠 Core Functional Requirements 1️⃣ File Upload & Storage User uploads CSV Backend parses CSV Create table dynamically in PostgreSQL Store original dataset Maintain version history table Use Docker-based PostgreSQL image 2️⃣ Chat-Based Data Interaction User can: Ask questions: Ask for insights: Trends Aggregations Outliers Ask for modifications: "Remove duplicates" "Delete rows where amount < 0" "Add new column profit = revenue - cost" "Update all dates to 2024 format" System must: Generate SQL or Python code Execute safely Update database Return result 3️⃣ Data Versioning & Revert Every modification creates new version Store: Previous state SQL executed Timestamp User action User can: Revert to any previous version View change history Maintain audit log table 4️⃣ Agentic Architecture Use: LangGraph Tool-based architecture Google Gemini API key The AI must act as: Data Analyst + Data Engineer Agent Agent Flow: Understand user query Decide which tool to use Execute tool Validate output Return response 🛠 Required Tools (LangGraph Tools) Create tools: Schema Inspection Tool SQL Query Tool Data Cleaning Tool Aggregation Tool Data Update Tool Version Control Tool Revert Tool Insight Generation Tool Each tool: Executes structured logic Returns structured output Logs operation 🏗 Technical Stack Backend Python FastAPI LangGraph Google Gemini API (API Key) PostgreSQL SQLAlchemy Docker Frontend Node.js (or Next.js) Chat Interface File Upload UI Version History Panel Revert Button Data Table Preview 🐳 Infrastructure Docker Setup Create: Dockerfile (Backend) Dockerfile (Frontend) Dockerfile (PostgreSQL optional if needed) docker-compose.yml Docker Compose services: backend (FastAPI) frontend (Node.js) postgres redis (optional for state/session) 🔐 Security & Safety No raw SQL execution without validation Validate generated SQL Use parameterized queries Restrict dangerous operations (DROP DATABASE, etc.) Role-based DB access Session isolation 🔄 System Flow User uploads CSV CSV stored into PostgreSQL Schema extracted Metadata sent to Gemini Agent decides action Tool executes SQL/Python Result returned Version stored Audit log updated User can revert 📊 Output & Tracking System must show: Query result SQL executed Version number Change log Previous versions Revert option 📦 Deliverables Required Generate: Full system architecture Folder structure FastAPI backend skeleton LangGraph agent flow Gemini prompt template Tool implementations (basic structure) PostgreSQL schema design Versioning strategy Dockerfile (backend & frontend) docker-compose.yml Example API endpoints Sample agent execution flow