"Comment la réalité du terrain en 2023 a transformé mes compétences en optimisation algorithmique."
In 2023, I was a Rider. I lived the reality of the job: waiting in the rain, the uncertainty of the next order, and the inefficiency of blind navigation. I realized that effort wasn't enough; timing and positioning were everything.
Instead of enduring the inefficiency, I decided to deconstruct it. I wanted to understand the hidden patterns of urban demand.
Today, I have coded an upgrade version of the tool I've used when i was working as a deliver man . This is not just code; it is digitized experience. It transforms raw urban signals into actionable strategic advantages.
▪️ System Logic Flow
graph TD
A[Real-World Signals] -->|Weather, Time, Traffic| B(Strategy Engine)
B -->|Context Data| C{Gemini Inference}
C -->|Predictive Insight| B
B -->|Optimal Decision| D[User Interface]
style A fill:#f9f9f9,stroke:#333,stroke-width:2px
style B fill:#e1f5fe,stroke:#0277bd,stroke-width:2px
style C fill:#e8f5e9,stroke:#2e7d32,stroke-width:2px
style D fill:#fff3e0,stroke:#ff8f00,stroke-width:2px
Architected for Scalability
While this instance is configured for Last-Mile Delivery platforms (Uber Eats, Deliveroo), the core architecture uses a strict Strategy Pattern.
This engine is Platform Agnostic. The MarketTactics module can be swapped to optimize:
- 📦 Logistics: Amazon Flex, DHL, La Poste.
- 🚕 Ride-Hailing: Uber, Bolt, Heetch.
- 🛠️ Field Services: Technician dispatching.
The NeuralNetwork core remains unchanged, processing demand signals regardless of the specific industry vertical.
Quick Start (Windows)
Simply double-click the LAUNCH_APP.bat file.
Manual Start
- Backend
cd backend pip install -r requirements.txt python app.py - Frontend
cd frontend npm install npm run dev
To discuss architecture, scalability, or hiring opportunities:
✒️ Dylan ONDO