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\n \n\n

\n \n 0\n ? new Date(runs[0].timestamp).toLocaleDateString()\n : \"Never\"}\n />\n \n

\n\n

\n Run History\n

\n \n \n Run now\n \n \n

\n {runs.length === 0\n ? (\n

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Run Not Found

\n The requested newsletter run could not be found.\n

\n \n ← Back to dashboard\n \n

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\n \n\n

\n News Items ({newsItems.length} stories)\n

\n {newsItems.map((item: any, idx: number) => (\n \n ))}\n

\n\n

\n Full Agent Response\n

\n              {JSON.stringify(runData.fullResult, null, 2)}\n

\n ,\n );\n});\n\nexport default app.fetch;","/** @jsxImportSource npm:hono@4/jsx */\nimport { Hono } from \"npm:hono@4\";\nimport { blob } from \"https://esm.town/v/std/blob\";\nimport sendNewsletter from \"./main.ts\";\n\nconst app = new Hono();\napp.onError((err) => Promise.reject(err));\n\nfunction Layout({ children, title }: { children: any; title: string }) {\n return (\n \n \n \n \n {title}\n \n \n \n {children}\n \n \n );\n}\n\nfunction Header(\n { title, subtitle, backLink }: {\n title: string;\n subtitle: string;\n backLink?: string;\n },\n) {\n return (\n

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{title}

{subtitle}

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\n {item.headline}\n

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\n );\n}\n\nfunction RunItem({ run }: { run: any }) {\n return (\n

\n {new Date(run.timestamp).toLocaleString()}\n

Run ID: {run.timestamp}

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\n \n\n

\n \n 0\n ? new Date(runs[0].timestamp).toLocaleDateString()\n : \"Never\"}\n />\n \n

\n\n

\n Run History\n

\n \n \n Run now\n \n \n

\n {runs.length === 0\n ? (\n

\n No newsletter runs yet. The bot will run daily at 7am ET.\n

\n )\n : (\n runs.map((run) => )\n )}\n

\n ,\n );\n});\n\n// Manual run trigger\napp.post(\"/run-now\", async (c) => {\n const runId = new Date().toISOString();\n\n // return c.json(\"disabled so folks don't abuse this\");\n // Run the newsletter agent\n await sendNewsletter();\n\n // Redirect to the new run's detail page\n return c.redirect(`/run/${runId}`);\n});\n\n// View specific run details\napp.get(\"/run/:runId\", async (c) => {\n const runId = c.req.param(\"runId\");\n\n const runData = await blob.getJSON(`newsletter-runs/${runId}`);\n\n if (!runData) {\n return c.html(\n \n

Run Not Found

\n The requested newsletter run could not be found.\n

\n \n ← Back to dashboard\n \n

\n ,\n 404,\n );\n }\n\n const newsItems = JSON.parse(runData.finalOutput);\n\n return c.html(\n \n

\n \n\n

\n News Items ({newsItems.length} stories)\n

\n {newsItems.map((item: any, idx: number) => (\n \n ))}\n

\n\n

\n Full Agent Response\n

\n              {JSON.stringify(runData.fullResult, null, 2)}\n

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\n \n \n\n {/* Content */}\n \n \n \n Your daily curated digest of the most important enterprise\n technology news from the last 24 hours.\n

\n\n {newsItems.map((item, index) => (\n \n \n \n {item.headline}\n \n \n \n {item.summary}\n

\n {item.companies && item.companies.length > 0 && (\n

\n {item.companies.map((company, idx) => (\n \n {company}\n \n ))}\n

\n )}\n \n Read more →\n \n \n ))}\n \n \n\n {/* Footer */}\n \n \n \n This newsletter is powered by AI-driven research and\n analysis.
\n © {new Date().getFullYear()}{\" \"}\n Enterprise Tech Brief. All rights reserved.\n

\n \n \n \n \n \n \n \n \n );\n}\n\nexport function generateNewsletterHTML(newsItems: NewsItem[]): string {\n const date = new Date().toLocaleDateString(\"en-US\", {\n weekday: \"long\",\n year: \"numeric\",\n month: \"long\",\n day: \"numeric\",\n });\n\n const html = renderToStaticMarkup(\n ,\n );\n\n return `\\n${html}`;\n}","label",[133],"8c0e2b84-c6ed-11f0-b378-42dde27851f2","main.ts","aafc887a-c58e-11f0-a48a-42dde27851f2",62,["D",1763738400768],22,"interval","import { Agent, run, webSearchTool } from \"npm:@openai/agents@0.3.0\";\nimport { email } from \"https://esm.town/v/std/email?v=11\";\nimport { blob } from \"https://esm.town/v/std/blob\";\nimport { generateNewsletterHTML } from \"./newsletter-template.tsx\";\nimport { readFile } from \"https://esm.town/v/std/utils/index.ts\";\n\nconst PROMPT = await readFile(\"/PROMPT.txt\");\nconst agent = new Agent({\n name: \"enterprise-tech-researcher\",\n model: \"gpt-5.1-2025-11-13\",\n instructions: PROMPT,\n tools: [webSearchTool()],\n modelSettings: {\n temperature: 0.5,\n },\n});\n\nexport default async function sendNewsletter() {\n const runId = new Date().toISOString();\n\n console.log(`[Newsletter Bot ${runId}] Starting research...`);\n\n const result = await run(\n agent,\n `Research and find the top 15 most important enterprise technology news stories from the LAST 24 HOURS. \n Return ONLY valid JSON with no other text.`,\n );\n\n console.log(`[Newsletter Bot ${runId}] Research complete`);\n\n // Store full result in blob storage for debugging\n await blob.setJSON(`newsletter-runs/${runId}`, {\n timestamp: runId,\n fullResult: result,\n finalOutput: result.finalOutput,\n });\n\n const newsItems = JSON.parse(result.finalOutput);\n if (!Array.isArray(newsItems) || newsItems.length === 0) {\n throw new Error(\"No news items found\");\n }\n\n await email({\n subject: `Enterprise Tech Daily Brief`,\n html: generateNewsletterHTML(newsItems),\n });\n}",[145],"c9d06d88-c6ed-11f0-b693-42dde27851f2",["D",1763738504383],28,[28],"e948934c-c63a-11f0-a53d-42dde27851f2","README.md","96805af8-c592-11f0-860a-42dde27851f2",60,["D",1763661677265],"# research-newsletter-bot\n\nAutomated daily newsletter that scrapes enterprise tech news and emails a\nformatted digest.\n\n## Files\n\n- [PROMPT.txt](./PROMPT.txt) - System prompt instructing the agent to find 15\n recent enterprise tech stories and return JSON\n- [dashboard.tsx](./dashboard.tsx) - Web UI for viewing past newsletter runs\n stored in blob storage\n- [main.ts](./main.ts) - Interval job that kicks off the OpenAI agent, parses\n results, generates HTML, and sends the email\n- [newsletter-template.tsx](./newsletter-template.tsx) - React component that\n renders news items into an HTML email template",[158],"3e054c06-c6ed-11f0-87e5-42dde27851f2","PROMPT.txt","f3156c60-c590-11f0-a184-42dde27851f2",61,["D",1763738269840],6,"You are a professional enterprise technology research analyst creating a daily newsletter.\n\nYour task:\n1. Search for the top 15 most important and recent news items from the last 24 hours about the enterprise technology landscape\n2. Focus on:\n - Hot startups making announcements\n - Established companies: Salesforce, ServiceNow, SAP, Microsoft, Amazon, Google (but not limited to these)\n - Cloud computing news\n - Developer tools and platforms\n - AI/ML enterprise applications\n - Other significant enterprise tech news\n\n3. Use diverse sources including (but not limited to):\n - Techmeme\n - TechCrunch\n - CNBC\n - The Verge\n - VentureBeat\n - Fortune\n - Bloomberg\n - Runtime news\n - The New Stack\n\n4. Prioritize the most recent stories, but you can include stories from the past 48 hours if they are significant\n\n5. For each item, provide:\n - Headline\n - Brief summary (2-3 sentences)\n - URL to original source\n - Company/companies mentioned\n\nCRITICAL: Return ONLY valid JSON, no other text.\n\nYour entire response must be a JSON array of exactly 15 objects. If you find fewer than 15 stories from the last 24 hours, add important stories from the previous 24 hours until you have 15.\n\nUse this exact object shape for each of the 15 items:\n{\n \"headline\": \"...\",\n \"summary\": \"...\",\n \"url\": \"...\",\n \"companies\": [\"...\"]\n}\n\nUse as many web_search calls as needed until you have 15 distinct stories. 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Diff · v63