🔮 Foresight — AI-Powered Trend Intelligence

See trends before they break out. Detect emerging signals across 50+ platforms using multi-agent simulation.

---

🚀 What is Foresight?

Foresight is an AI-powered trend intelligence platform that detects emerging signals 48–72 hours before Google Trends using:

• Multi-Source NLP Pipeline — Monitors TikTok, Discord, Reddit, Telegram, GitHub, and 44+ more platforms
• MiroFish Behavioral Simulation — 1M+ agents predict exactly how trends spread through communities
• Predictive Forecasting — Prophet + time-series models forecast trend trajectories with 85%+ accuracy
• Action Prompts — Every signal comes with "the one thing to do right now"

---

🏗️ Tech Stack

Layer	Technology
Frontend	Next.js 15, React 19, TypeScript, Tailwind CSS 4, Framer Motion
Backend	FastAPI, Python 3.11, SQLAlchemy 2.x, Pydantic
Database	Supabase (PostgreSQL + pgvector)
Cache	Redis
Search	Elasticsearch
AI/ML	LangChain, spaCy, scikit-learn, Prophet
LLM	Groq (Mixtral/Llama)
Monitoring	Prometheus, Sentry, Grafana
CI/CD	GitHub Actions
Deployment	Railway (backend) + Vercel (frontend)

---

📂 Project Structure

Foresight.ai/
├── frontend/               # Next.js 15 + React 19 + TypeScript
│   ├── src/app/            # Pages (landing, auth, dashboard)
│   ├── src/lib/            # API client, utilities
│   ├── src/store/          # Zustand state management
│   └── src/test/           # Vitest test setup
│
├── backend/                # FastAPI + Python 3.11
│   ├── app/api/            # Route handlers (auth, signals, monitoring)
│   ├── app/db/             # Database connection (Supabase)
│   ├── app/models/         # SQLAlchemy ORM models
│   ├── app/services/       # Business logic (auth, detection)
│   ├── db/                 # SQL migrations
│   └── tests/              # pytest test suite
│
├── docs/                   # Documentation
│   ├── SUPABASE_SETUP.md   # Supabase configuration guide
│   └── DEPLOYMENT.md       # Railway + Vercel deployment guide
│
├── shared/                 # Cross-stack type contracts
├── tests/                  # E2E + integration tests
├── docker-compose.yml      # Redis + Elasticsearch (local dev)
├── .github/workflows/      # CI/CD pipeline
└── metrics.json            # Ralph Loop performance tracker

---

⚡ Quick Start

Prerequisites

• Node.js 20+
• Python 3.11+
• Docker (for Redis + Elasticsearch)

1. Clone & Install

git clone https://github.com/Shlok148Dev/Foresight.ai.git
cd Foresight.ai

# Backend
cd backend
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

# Frontend
cd ../frontend
npm install

2. Configure Environment

cp .env.example .env
# Edit .env with your Supabase credentials

3. Start Services

# Start Redis + Elasticsearch
docker compose up -d

# Backend (terminal 1)
cd backend
uvicorn app.main:app --reload

# Frontend (terminal 2)
cd frontend
npm run dev

4. Open

• Frontend: http://localhost:3000
• API Docs: http://localhost:8000/docs
• Health Check: http://localhost:8000/health

---

🧪 Testing

# Backend tests
cd backend && pytest tests/ -v --cov=app

# Frontend tests
cd frontend && npm run test

# E2E tests
cd frontend && npm run test:e2e

---

📊 Development Methodology

This project uses a 4-tool autonomous development cycle:

Tool	Purpose
GSD (Get Shit Done)	Weekly sprints with daily standups and clear checklists
Antigravity	Spec-driven code generation from Technical Bible
Ralph Loop	Weekly metrics tracking (coverage, latency, Lighthouse)
CodeRabbit	AI-powered code review on every PR

---

📄 License

MIT License — see LICENSE for details.

---

👤 Author

Shlok Tiwari — @Shlok148Dev