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— ; — \nWhy it matters: Faster inference and lower cost for production use.”\n\nIf a field is missing, skip it silently.\nKeep the entire summary under **6–8 lines**.\nAvoid extra explanations or speculation.\n", "summarizationMethod": "stuff" } } }, "provider": "openai", "approximated": true }, "position": [ 8208, -1248 ] }, { "id": "9975d7e1-6e4f-434b-b74b-e26f03320f6f", "name": "Aggregate Summaries", "type": "code", "params": { "jsCode": "// Collect all items coming from Summarization/Extraction\nconst items = $input.all();\n\n// Extract each summary from each item\nconst summaries = items.map(item => item.json);\n\n// Build a single output item\nreturn [\n {\n json: {\n summaries\n }\n }\n];\n" }, "position": [ 8544, -1248 ] }, { "id": "033d4d93-d8f3-4eb1-b843-94fc0bf7138e", "name": "Research Analyst Agent", "type": "llm", "params": { "text": "=You are an AI research analyst.\n\nINPUT YOU WILL RECEIVE:\n1. A collection of short summaries generated from AI / LLM related webpages.\n2. Each summary represents one article, announcement, blog post, or research update.\n3. All content comes from the same search context and timeframe.\n\n-----------------------------------\nSTEP 1 — Understand the Research Focus\n-----------------------------------\nAssume the goal is to track meaningful developments in:\n- AI models and LLMs\n- AI tooling and infrastructure\n- Research breakthroughs\n- Practical production or business impact\n\n-----------------------------------\nSTEP 2 — Evaluate Each Summary\n-----------------------------------\nFor EACH summary:\n\n1. Determine whether it is **meaningful or actionable**.\n2. Assign a **Relevance Score (0–100%)** based on:\n - Technical significance\n - Novelty (new model, update, result, or capability)\n - Practical or industry impact\n3. Exclude summaries with relevance below **50%**.\n4. 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