Operator-ready prompt for reuse, tuning, and workspace runs.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Develop a module that takes the tracked drone data (position, speed, classification) and dynamically assigns a threat priority score. Define at least three criteria for prioritization (e.g., proximity to critical assets, estimated intent, drone type). Implement a logic or a small ML model to calculate this score and determine a recommended engagement sequence. Visualize these priorities on your output video stream.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Build an AI-Driven Multi-Drone Threat Detection & Prioritization System
The proliferation of drones poses significant security challenges, necessitating advanced detection and neutralization systems. Inspired by the UK Royal Navy's adoption of drone-frying lasers, this challenge focuses on the critical 'sense and decide' component: an AI-driven system for real-time detection, tracking, classification, and prioritization of multiple drone threats from video feeds. Participants will develop a robust computer vision pipeline capable of identifying various drone types and assessing their threat level dynamically. This challenge involves building a Python-based application that processes simulated live video streams. The system must accurately detect and track multiple drones, classify them based on available visual features or metadata, and then prioritize them for potential engagement by a defensive system. Integration of a vector database like Qdrant will enhance drone identification and threat intelligence. A multi-agent simulation framework (CAMEL with WizardLM-2) will be used to generate diverse drone swarm scenarios for testing the system's resilience and decision-making capabilities.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.