Luma Event Investor Finder

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\n \n Luma Event URL\n \n \n

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\n Scraping event and checking LinkedIn profiles... This may take a few minutes.\n

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Results

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Previously Found Investors

Loading saved investors...

\n\n \n\n",[],[185,192],[185],{"_105":201,"_96":202,"_155":203,"_157":-5,"_158":140,"_159":204,"_161":22,"_107":205,"_101":32,"_164":195,"_166":206,"_167":22,"_168":-5,"_169":207,"_99":208},"b041d63e-f0d4-11f0-9322-42dde27851f2","README.md","b041d6fc-f0d4-11f0-9322-42dde27851f2",5,["D",1768345672908],"# Luma Event Investor Finder\n\nThis tool scrapes Luma event pages to find attendees and checks their LinkedIn profiles to identify potential investors.\n\n## Features\n\n- **Event Scraping**: Extracts attendee information from Luma event pages\n- **LinkedIn Search**: Automatically searches for attendees' LinkedIn profiles\n- **Investor Detection**: Analyzes LinkedIn profiles for investor-related keywords\n- **Data Storage**: Saves found investors to SQLite database\n- **Web Interface**: Clean, responsive UI for easy interaction\n\n## How It Works\n\n1. **Input**: Provide a Luma event URL (e.g., `https://luma.com/zuwgv7lk?tk=UPLblL`)\n2. **Scraping**: The tool extracts attendee names from the event page\n3. **LinkedIn Search**: For each attendee, it searches Google for their LinkedIn profile\n4. **Investor Check**: Analyzes LinkedIn profiles for keywords like \"investor\", \"venture capital\", \"angel\", etc.\n5. **Storage**: Saves identified investors to the database\n6. **Display**: Shows results in the web interface\n\n## Investor Keywords Detected\n\nThe tool looks for these keywords in LinkedIn profiles:\n- investor, investment, venture capital, vc, angel investor\n- private equity, fund, capital, investing, portfolio\n- seed, series a, series b, startup investor, angel\n- managing partner, investment partner, venture partner\n\n## Technical Details\n\n### Backend (`/backend/index.ts`)\n- Built with Hono framework\n- SQLite database for storing results\n- Rate limiting to avoid being blocked\n- Error handling and logging\n\n### Frontend (`/frontend/index.html`)\n- Clean, responsive interface using TailwindCSS\n- Real-time progress updates\n- Display of current and historical results\n\n### Database Schema\n```sql\nCREATE TABLE event_investors_1 (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n name TEXT NOT NULL,\n linkedin_url TEXT,\n event_url TEXT,\n found_at DATETIME DEFAULT CURRENT_TIMESTAMP,\n UNIQUE(name, event_url)\n);\n```\n\n## Usage\n\n1. Open the web interface\n2. Enter a Luma event URL\n3. Click \"Find Investors\"\n4. Wait for the process to complete (may take several minutes)\n5. View results and previously found investors\n\n## Limitations\n\n- Rate limited to avoid being blocked by LinkedIn/Google\n- Processes first 20 attendees to manage execution time\n- Depends on publicly available information\n- May have false positives/negatives in investor detection\n\n## Privacy & Ethics\n\nThis tool only accesses publicly available information and respects rate limits to avoid overloading services. It's designed for legitimate networking and business development purposes.",[],[202],{"_105":210,"_96":100,"_155":211,"_157":-5,"_158":140,"_159":102,"_161":22,"_107":212,"_101":160,"_164":178,"_166":213,"_167":22,"_168":-5,"_169":214,"_99":215},"f512edec-f0d5-11f0-93e8-42dde27851f2","ea8c6e52-f0d5-11f0-a345-42dde27851f2",["D",1768346217876],"// Test LinkedIn search for a specific person\nasync function testLinkedInSearch() {\n const name = \"Daniel Kim\";\n \n try {\n console.log(`Testing LinkedIn search for: ${name}`);\n \n const searchQuery = `\"${name}\" site:linkedin.com/in`;\n const searchUrl = `https://www.google.com/search?q=${encodeURIComponent(searchQuery)}`;\n \n console.log(`Search URL: ${searchUrl}`);\n \n const response = await fetch(searchUrl, {\n headers: {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n }\n });\n \n console.log(`Response status: ${response.status}`);\n \n if (!response.ok) {\n return new Response('Search request failed', { status: 500 });\n }\n\n const html = await response.text();\n console.log(`HTML length: ${html.length}`);\n console.log(`HTML preview: ${html.substring(0, 500)}`);\n \n // Look for LinkedIn URLs\n const linkedinMatches = html.match(/https:\\/\\/[a-z]{2,3}\\.linkedin\\.com\\/in\\/[a-zA-Z0-9\\-]+/g);\n console.log(`LinkedIn matches found: ${linkedinMatches?.length || 0}`);\n \n const result = {\n name,\n searchUrl,\n responseStatus: response.status,\n htmlLength: html.length,\n linkedinMatches: linkedinMatches?.slice(0, 3) || [],\n htmlPreview: html.substring(0, 500)\n };\n \n return new Response(JSON.stringify(result, null, 2), {\n headers: { 'Content-Type': 'application/json' }\n });\n \n } catch (error) {\n console.error('Error:', error);\n return new Response(JSON.stringify({ error: error.message }), {\n status: 500,\n headers: { 'Content-Type': 'application/json' }\n });\n }\n}\n\nexport default testLinkedInSearch;",[],[100],"files",[152,171,183,190,200,209],"counts",{"_220":32,"_221":222,"_223":32,"_224":225},"pulls","branches",1,"forks","envVars",{"_226":32,"_227":32},"count","emptyCount","contents",[],{"_105":210,"_96":100,"_155":211,"_157":-5,"_158":140,"_159":102,"_161":22,"_107":231,"_101":160,"_164":178,"_166":213,"_167":22,"_168":-5,"_99":232},["D",1768346217876],[100],"upstreamProject","upstreamBranch","emptyEnvVars","isNew","flags",[],"routes/_app.x.$handle.$name.branch.$branch.version.$version.code.$",{"_94":95,"_96":97,"_98":44,"_101":102,"_99":100,"_103":241,"_150":246,"_216":265,"_195":266,"_228":268,"_269":-5,"_270":-5,"_271":-5,"_272":-5,"_273":274,"_224":-5},{"_105":106,"_96":97,"_107":242,"_109":-5,"_110":22,"_111":112,"_113":-5,"_114":-5,"_14":115,"_116":-5,"_117":22,"_118":-5,"_119":-5,"_120":32,"_16":121,"_122":123,"_124":123,"_125":126,"_127":126,"_128":126,"_129":123,"_130":123,"_23":243,"_134":244,"_137":245,"_149":22},["D",1768345479367],{"_94":95,"_132":133},{"_94":95,"_136":133,"_14":115},{"_101":139,"_96":44,"_105":140,"_141":106,"_142":-5,"_143":-5,"_144":-5,"_145":-5,"_146":-5,"_147":97,"_148":95},[247,253,259,262],{"_105":153,"_96":154,"_155":156,"_157":-5,"_158":140,"_159":160,"_161":162,"_107":163,"_101":32,"_164":165,"_166":-5,"_167":22,"_168":-5,"_169":248,"_99":252},[249],{"_105":172,"_96":173,"_155":174,"_157":156,"_158":140,"_159":175,"_161":22,"_107":176,"_101":177,"_164":178,"_166":179,"_167":22,"_168":-5,"_169":250,"_99":251},[],[154,173],[154],{"_105":184,"_96":185,"_155":186,"_157":-5,"_158":140,"_159":187,"_161":162,"_107":188,"_101":32,"_164":165,"_166":-5,"_167":22,"_168":-5,"_169":254,"_99":258},[255],{"_105":191,"_96":192,"_155":193,"_157":186,"_158":140,"_159":187,"_161":22,"_107":194,"_101":32,"_164":195,"_166":196,"_167":22,"_168":-5,"_169":256,"_99":257},[],[185,192],[185],{"_105":201,"_96":202,"_155":203,"_157":-5,"_158":140,"_159":204,"_161":22,"_107":205,"_101":32,"_164":195,"_166":206,"_167":22,"_168":-5,"_169":260,"_99":261},[],[202],{"_105":210,"_96":100,"_155":211,"_157":-5,"_158":140,"_159":102,"_161":22,"_107":212,"_101":160,"_164":178,"_166":213,"_167":22,"_168":-5,"_169":263,"_99":264},[],[100],[247,249,253,255,259,262],{"_105":210,"_96":100,"_155":211,"_157":-5,"_158":140,"_159":102,"_161":22,"_107":231,"_101":160,"_164":178,"_166":213,"_167":22,"_168":-5,"_99":267},[100],[],"upstream","pull","ahead","behind","templates",[],"actionData","errors"]

alpaca1712

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