Personalized Technical Intelligence Briefing for Edge AI in Defense

{
  "opening": "${bibleVerse}",
  "criticalIntelligence": [
    {
      "headline": "${headline1}",
      "source": "${sourceLink1}",
      "technicalSummary": "${technicalSummary1}",
      "relevanceScore": "${relevanceScore1}",
      "actionableInsight": "${actionableInsight1}"
    },
    {
      "headline": "${headline2}",
      "source": "${sourceLink2}",
      "technicalSummary": "${technicalSummary2}",
      "relevanceScore": "${relevanceScore2}",
      "actionableInsight": "${actionableInsight2}"
    },
    // Add up to 8 total items
  ],
  "technicalDeepDive": [
    {
      "breakthroughItem": "${breakthrough1}",
      "implementationDetails": "${implementationDetails1}"
    },
    {
      "breakthroughItem": "${breakthrough2}",
      "implementationDetails": "${implementationDetails2}"
    }
    // Add up to 3 items
  ],
  "priorityIntelligenceTargets": {
    "primary": [
      "False positive reduction methodologies",
      "Edge AI optimization for resource-constrained hardware",
      "Real-time inference benchmarks"
    ],
    "secondary": [
      "Defense procurement announcements",
      "SBIR/STTR opportunities",
      "Counter-UAS technologies"
    ],
    "tertiary": [
      "PyTorch/OpenCV updates",
      "Rust embedded frameworks",
      "Military robotics contracts"
    ]
  },
  "sourcesToPrioritize": [
    "arXiv (cs.CV, cs.RO, cs.LG)",
    "Breaking Defense",
    "The War Zone",
    "NVIDIA Developer Blog"
  ],
  "exclusions": [
    "Consumer tech unless directly applicable",
    "Theoretical papers without implementation paths",
    "Rehashed news",
    "General AI hype without substance"
  ],
  "enhancedFeatures": {
    "benchmarkComparisonTables": true,
    "reproducibleResearchLinks": true,
    "conferenceDeadlines": true,
    "defenseContractAwards": true,
    "weeklyTrendChart": true
  }
}

Run this prompt on a real model without leaving the page. Every run is saved to your history for this prompt.

How to use

1. 1Copy the prompt as-is — no variables required.
2. 2Paste it into ChatGPT, Claude, Gemini, or any capable model.
3. 3Iterate: follow up with corrections or extra context to refine the output.