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{ 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}","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 tools: [webSearchTool()],\n modelSettings: {\n 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╔═══════════════════════════════════════════════════════════════════════════╗\n ║ Run Newsletter Now ║\n ╠═══════════════════════════════════════════════════════════════════════════╣\n ║ ║\n ║ If you're logged in and own this val, you'll see a > Run button on ║\n ║ the top-right corner of this file. ║\n ║ ║\n ║ If you don't see a > Run button, click Remix in the top-right corner ║\n ║ of the screen to get a copy of this. ║\n ║ ║\n ║ Clicking run will trigger the research agent and send the newsletter. ║\n ║ ║\n ╚═══════════════════════════════════════════════════════════════════════════╝ */\n\nimport sendNewsletter from \"./main.ts\";\n\nconst result = await sendNewsletter();\nconsole.log(result);","label",[135],"9f8f2fc0-c592-11f0-8000-42dde27851f2","newsletter-template.tsx","01f8ed74-c591-11f0-8519-42dde27851f2",20,["D",1763589398123],1,"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { renderToStaticMarkup } from \"npm:react-dom@18.2.0/server\";\n\ninterface NewsItem {\n headline: string;\n summary: string;\n url: string;\n companies?: string[];\n}\n\nfunction NewsletterTemplate(\n { newsItems, date }: { newsItems: NewsItem[]; date: string },\n) {\n return (\n \n \n \n \n Enterprise Tech Daily Brief\n \n \n \n \n \n \n {/* Header */}\n \n \n \n Enterprise Tech Daily Brief\n \n \n {date}\n

\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}",[147],"6d47db42-c6f1-11f0-9ca1-42dde27851f2",["D",1763740067125],23,[28],"3c6b43ac-c6f0-11f0-b718-42dde27851f2","dashboard.tsx",78,["D",1763739555652],38,"http","/** @jsxImportSource npm:hono@4/jsx */\nimport { Hono } from \"npm:hono@4\";\nimport { blob } from \"https://esm.town/v/std/blob\";\nimport { parseVal } from \"https://esm.town/v/stevekrouse/utils/index.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

\n {backLink && (\n \n ← Back to all runs\n \n )}\n

{title}

{subtitle}

\n );\n}\n\nfunction StatCard({ title, value }: { title: string; value: string }) {\n return (\n

\n {title}\n

{value}

\n );\n}\n\nfunction NewsItem({ item, index }: { item: any; index: number }) {\n return (\n

0 ? \"border-t border-gray-200\" : \"\"}`}>\n

\n {item.headline}\n

{item.summary}

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

\n {item.companies.map((company: string) => (\n \n {company}\n \n ))}\n

\n )}\n

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

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

Run ID: {run.timestamp}

\n \n View Details\n \n

\n );\n}\n\n// List all runs\napp.get(\"/\", async (c) => {\n const allBlobs = await blob.list(\"newsletter-runs/\");\n const runs = allBlobs\n .map((b) => ({\n key: b.key,\n timestamp: b.key.replace(\"newsletter-runs/\", \"\"),\n lastModified: b.lastModified,\n }))\n .sort((a, b) =>\n new Date(b.timestamp).getTime() - new Date(a.timestamp).getTime()\n );\n\n const val = parseVal();\n\n return c.html(\n \n

\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 {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// 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

\n ,\n );\n});\n\nexport default app.fetch;",[159],"4d0f4e14-c6f1-11f0-8e11-42dde27851f2","README.md","96805af8-c592-11f0-860a-42dde27851f2",81,["D",1763740013065],"# Research Newsletter Bot\n\nThis bot will email you and whoever else you want daily with a summary of the\nmost important news for you.\n\nThe current prompt in [PROMPT.txt](./PROMPT.txt) instructs it to get 15\nenterprise tech news items, but it's quite easy for you to customize it to your\nliking.\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\n- [shell.ts](./shell.ts) - If you want to manually run the newsletter outside of\n the schedule, go to this file and click Run",[167],"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. 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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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