Hi

\"\n});\n```\n\n## Val Town Utility Functions\n\nVal Town provides several utility functions to help with common project tasks.\n\n### Importing Utilities\n\nAlways import utilities with version pins to avoid breaking changes:\n\n```ts\nimport { parseProject, readFile, serveFile } from \"https://esm.town/v/std/utils@85-main/index.ts\";\n```\n\n### Available Utilities\n\n\n#### **serveFile** - Serve project files with proper content types\n\nFor example, in Hono:\n\n```ts\n// serve all files in frontend/ and shared/\napp.get(\"/frontend/*\", c => serveFile(c.req.path, import.meta.url));\napp.get(\"/shared/*\", c => serveFile(c.req.path, import.meta.url));\n```\n\n#### **readFile** - Read files from within the project:\n\n```ts\n// Read a file from the project\nconst fileContent = await readFile(\"/frontend/index.html\", import.meta.url);\n```\n\n#### **listFiles** - List all files in the project\n\n```ts\nconst files = await listFiles(import.meta.url);\n```\n\n#### **parseProject** - Extract information about the current project from import.meta.url\n\nThis is useful for including info for linking back to a val, ie in \"view source\" urls:\n\n```ts\nconst projectVal = parseProject(import.meta.url);\nconsole.log(projectVal.username); // Owner of the project\nconsole.log(projectVal.name); // Project name\nconsole.log(projectVal.version); // Version number\nconsole.log(projectVal.branch); // Branch name\nconsole.log(projectVal.links.self.project); // URL to the project page\n```\n\nHowever, it's *extremely importing* to note that `parseProject` and other Standard Library utilities ONLY RUN ON THE SERVER.\nIf you need access to this data on the client, run it in the server and pass it to the client by splicing it into the HTML page\nor by making an API request for it.\n\n## Val Town Platform Specifics\n\n- **Redirects:** Use `return new Response(null, { status: 302, headers: { Location: \"/place/to/redirect\" }})` instead of `Response.redirect` which is broken\n- **Images:** Avoid external images or base64 images. Use emojis, unicode symbols, or icon fonts/libraries instead\n- **AI Image:** To inline generate an AI image use: `

`\n- **Storage:** DO NOT use the Deno KV module for storage\n- **Browser APIs:** DO NOT use the `alert()`, `prompt()`, or `confirm()` methods\n- **Weather Data:** Use open-meteo for weather data (doesn't require API keys) unless otherwise specified\n- **View Source:** Add a view source link by importing & using `import.meta.url.replace(\"ems.sh\", \"val.town)\"` (or passing this data to the client) and include `target=\"_top\"` attribute\n- **Error Debugging:** Add `` to HTML to capture client-side errors\n- **Error Handling:** Only use try...catch when there's a clear local resolution; Avoid catches that merely log or return 500s. Let errors bubble up with full context\n- **Environment Variables:** Use `Deno.env.get('keyname')` when you need to, but generally prefer APIs that don't require keys\n- **Imports:** Use `https://esm.sh` for npm and Deno dependencies to ensure compatibility on server and browser\n- **Storage Strategy:** Only use backend storage if explicitly required; prefer simple static client-side sites\n- **React Configuration:** When using React libraries, pin versions with `?deps=react@18.2.0,react-dom@18.2.0` and start the file with `/** @jsxImportSource https://esm.sh/react@18.2.0 */`\n- Ensure all React dependencies and sub-dependencies are pinned to the same version\n- **Styling:** Default to using TailwindCSS via `` unless otherwise specified\n\n## Project Structure and Design Patterns\n\n### Recommended Directory Structure\n```\n├── backend/\n│ ├── database/\n│ │ ├── migrations.ts # Schema definitions\n│ │ ├── queries.ts # DB query functions\n│ │ └── README.md\n│ └── routes/ # Route modules\n│ ├── [route].ts\n│ └── static.ts # Static file serving\n│ ├── index.ts # Main entry point\n│ └── README.md\n├── frontend/\n│ ├── components/\n│ │ ├── App.tsx\n│ │ └── [Component].tsx\n│ ├── favicon.svg\n│ ├── index.html # Main HTML template\n│ ├── index.tsx # Frontend JS entry point\n│ ├── README.md\n│ └── style.css\n├── README.md\n└── shared/\n ├── README.md\n └── utils.ts # Shared types and functions\n```\n\n### Backend (Hono) Best Practices\n\n- Hono is the recommended API framework\n- Main entry point should be `backend/index.ts`\n- **Static asset serving:** Use the utility functions to read and serve project files:\n ```ts\n import { readFile, serveFile } from \"https://esm.town/v/std/utils@85-main/index.ts\";\n \n // serve all files in frontend/ and shared/\n app.get(\"/frontend/*\", c => serveFile(c.req.path, import.meta.url));\n app.get(\"/shared/*\", c => serveFile(c.req.path, import.meta.url));\n \n // For index.html, often you'll want to bootstrap with initial data\n app.get(\"/\", async c => {\n let html = await readFile(\"/frontend/index.html\", import.meta.url);\n \n // Inject data to avoid extra round-trips\n const initialData = await fetchInitialData();\n const dataScript = ``;\n \n html = html.replace(\"\", `${dataScript}`);\n return c.html(html);\n });\n ```\n- Create RESTful API routes for CRUD operations\n- Always include this snippet at the top-level Hono app to re-throwing errors to see full stack traces:\n ```ts\n // Unwrap Hono errors to see original error details\n app.onError((err, c) => {\n throw err;\n });\n ```\n\n### Database Patterns\n- Run migrations on startup or comment out for performance\n- Change table names when modifying schemas rather than altering\n- Export clear query functions with proper TypeScript typing\n\n## Common Gotchas and Solutions\n\n1. **Environment Limitations:** \n - Val Town runs on Deno in a serverless context, not Node.js\n - Code in `shared/` must work in both frontend and backend environments\n - Cannot use `Deno` keyword in shared code\n - Use `https://esm.sh` for imports that work in both environments\n\n2. **SQLite Peculiarities:**\n - Limited support for ALTER TABLE operations\n - Create new tables with updated schemas and copy data when needed\n - Always run table creation before querying\n\n3. **React Configuration:**\n - All React dependencies must be pinned to 18.2.0\n - Always include `@jsxImportSource https://esm.sh/react@18.2.0` at the top of React files\n - Rendering issues often come from mismatched React versions\n\n4. **File Handling:**\n - Val Town only supports text files, not binary\n - Use the provided utilities to read files across branches and forks\n - For files in the project, use `readFile` helpers\n\n5. **API Design:**\n - `fetch` handler is the entry point for HTTP vals\n - Run the Hono app with `export default app.fetch // This is the entry point for HTTP vals`\n\n",[],[508],{"_104":516,"_95":517,"_153":518,"_155":-5,"_156":138,"_157":519,"_159":465,"_106":520,"_100":32,"_161":492,"_163":521,"_164":22,"_165":-5,"_166":522,"_98":523},"cbd94602-81f8-11f0-976d-0224a6c84d84","chatCompletion.js","cbd946e8-81f8-11f0-976d-0224a6c84d84",41,["D",1756156602030],"// Minimal Groq Chat Completions client using fetch (for background checker)\nexport async function groqChatCompletion(apiKey, payload) {\n // console.log('>>> [groqChatCompletion] Payload:', payload);\n let response;\n try {\n response = await fetch('https://api.groq.com/openai/v1/chat/completions', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n 'Authorization': `Bearer ${apiKey}`,\n },\n body: JSON.stringify(payload),\n });\n } catch (err) {\n console.error('>>> [groqChatCompletion] Network error:', err && err.message ? err.message : String(err));\n throw err;\n }\n console.log('>>> [groqChatCompletion] Status:', response.status);\n if (!response.ok) {\n const 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}\n}\n",[],[535],{"_104":543,"_95":544,"_153":545,"_155":-5,"_156":138,"_157":546,"_159":22,"_106":547,"_100":32,"_161":474,"_163":548,"_164":22,"_165":-5,"_166":549,"_98":550},"cbfb3f32-81f8-11f0-b660-0224a6c84d84","index.html","cbfb404a-81f8-11f0-b660-0224a6c84d84",42,["D",1756156602253],"\n\n\n \n \n MailCheck\n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n\n\n

\n\n \n\n \n \n\n \n

\n \n

\n\n

\n\n \n

\n \n \n

\n \n

\n\n \n

\n \n \n

\n \n set\n

\n concurrency\n \n

\n\n \n

\n Upload CSV/JSON\n \n

\n System prompt (optional)\n \n

\n Paste emails or CSV/JSON\n \n

\n \n

\n emails detected: /\n field: \n source: \n

\n \n \n

\n Spot check a single email\n

\n \n \n

detail

\n\n \n

\n \n \n \n

progress: /

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

	Email	Status	Assessment
\n \n \n \n \n \n	\n \n \n \n \n \n \n \n \n	\n \n	\n \n \n \n \n \n \n \n \n \n detail \n \n \n \n \n \n \n \n \n \n \n \n \n
No items yet. Upload or paste emails to begin.

\n\n \n

\n\n

\n \n \n \n\n \n

\n \n \n \n github\n

\n \n \n\n \n\n\n",[],[544],{"_104":552,"_95":553,"_153":554,"_155":-5,"_156":138,"_157":555,"_159":22,"_106":556,"_100":32,"_161":492,"_163":557,"_164":22,"_165":96,"_166":558,"_98":559},"cc59d056-81f8-11f0-8f5e-0224a6c84d84","main.js","cc59d100-81f8-11f0-8f5e-0224a6c84d84",49,["D",1756156602863],"import { Hono } from 'https://deno.land/x/hono@v3.11.12/mod.ts';\nimport { serve } from 'https://deno.land/std@0.190.0/http/server.ts';\nimport { listFiles, readFile } from \"https://esm.town/v/std/utils/index.ts\";\nimport \"jsr:@std/dotenv/load\"; // needed for deno run; not req for smallweb or valtown\nimport { groqChatCompletion } from \"./chatCompletion.js\";\n\nconst app = new Hono();\nconst serverApiKey = null; // Deno.env.get(\"GROQ_API_KEY\");\n\n// Settings configuration (minimal)\nconst settings = {};\n\n// In-memory async job registry (ephemeral per process)\nconst jobRegistry = new Map(); // jobId -> { id, running, cancelled, total, completed, createdAt, updatedAt, items: [{ id, email, status, result?, error? }], systemPrompt }\n\nfunction generateId() {\n try { return crypto.randomUUID(); } catch (_) { return (Date.now().toString(36) + Math.random().toString(36).slice(2)); }\n}\n\nfunction nowTs() { return Date.now(); }\n\nfunction summarizeJob(job) {\n return {\n id: job.id,\n running: !!job.running,\n cancelled: !!job.cancelled,\n total: job.total | 0,\n completed: job.completed | 0,\n createdAt: job.createdAt,\n updatedAt: job.updatedAt,\n systemPrompt: job.systemPrompt || '',\n items: job.items.map((it) => ({ id: it.id, email: it.email, status: it.status, result: it.result || null, error: it.error || null }))\n };\n}\n\nasync function runAsyncJob(job, apiKey, concurrency) {\n if (!Array.isArray(job.items) || job.items.length === 0) { job.running = false; job.updatedAt = nowTs(); return; }\n job.running = true; job.cancelled = false; job.updatedAt = nowTs();\n const systemPrompt = job.systemPrompt || '';\n const indices = job.items.map((_, i) => i);\n job.total = indices.length; job.completed = 0;\n\n // Pre-mark pending as checking\n for (const idx of indices) {\n const it = job.items[idx];\n if (!it) continue;\n if (it.status === 'done' || it.status === 'error') continue;\n it.status = 'checking';\n }\n job.updatedAt = nowTs();\n\n const limit = Math.max(1, Number(concurrency || 8) || 8);\n let cursor = 0;\n const runNext = async () => {\n if (job.cancelled) return;\n if (cursor >= indices.length) return;\n const idx = indices[cursor++];\n const item = job.items[idx];\n if (!item) return runNext();\n if (item.status === 'done' || item.status === 'error') { job.completed++; return runNext(); }\n try {\n const res = await backgroundAssessEmail(apiKey, item.email, systemPrompt);\n item.result = res;\n item.status = 'done';\n } catch (err) {\n item.error = err && err.message ? err.message : String(err);\n item.status = 'error';\n } finally {\n job.completed++;\n job.updatedAt = nowTs();\n await runNext();\n }\n };\n const runners = [];\n for (let k = 0; k < limit; k++) runners.push(runNext());\n try { await Promise.all(runners); } catch (_) {}\n job.running = false; job.updatedAt = nowTs();\n}\n\n\n// Helper: Collect readable text from tool records for URL-grounded extraction\nfunction collectToolTextFromArray(toolArray) {\n if (!Array.isArray(toolArray)) return '';\n const parts = [];\n for (const t of toolArray) {\n try {\n const name = t?.name || t?.tool || t?.function?.name || t?.id || '';\n const args = t?.arguments || t?.input || t?.params || null;\n const output = t?.output || t?.result || t?.content || null;\n const searchResults = t?.search_results || t?.results || null;\n if (name) parts.push('name: ' + String(name));\n if (args) parts.push('arguments: ' + (typeof args === 'string' ? args : JSON.stringify(args)));\n if (output) parts.push('output: ' + (typeof output === 'string' ? output : JSON.stringify(output)));\n if (searchResults) parts.push('search_results: ' + (typeof searchResults === 'string' ? searchResults : JSON.stringify(searchResults)));\n } catch (_) {\n // ignore malformed tool objects\n }\n }\n return parts.join('\\n');\n}\n\n\n\n// Reusable background-check helpers\nfunction isEmailAddress(s) {\n const str = String(s || '').trim();\n if (!str) return false;\n return /^[^\\s@]+@[^\\s@]+\\.[^\\s@]{2,}$/.test(str);\n}\nfunction mergeSpamStatus(current, next) {\n if (!next) return current || undefined;\n if (!current) return next;\n const score = (s) => (s === 'likely_spam' ? 3 : s === 'suspected_spam' ? 2 : s === 'not_spam' ? 1 : 0);\n return score(next) >= score(current) ? next : current;\n}\nfunction mergeStatus(current, next) {\n if (!next) return current || undefined;\n if (!current) return next;\n const score = (s) => (s === 'spam' ? 4 : s === 'person_none' ? 3 : s === 'person_low' ? 2 : s === 'person_high' ? 1 : 0);\n return score(next) >= score(current) ? next : current;\n}\nfunction extractJsonBetweenTags(text) {\n try {\n if (!text) return null;\n const m = String(text).match(/[\\s\\S]*?<\\/json>/i);\n const slice = m ? m[0] : null;\n if (!slice) return null;\n const inner = slice.replace(/^/i, '').replace(/<\\/json>$/i, '');\n const trimmed = inner.trim();\n return JSON.parse(trimmed);\n } catch (err) { \n console.error('Failed to parse JSON between tags:', trimmed, 'Error:', err.message);\n return null; \n }\n}\nfunction logGroqCurl(tag, apiKey, payload) {\n try {\n const compact = JSON.stringify(payload);\n const esc = compact.replace(/'/g, `'\"'\"'`);\n const curl = [\n 'curl --request POST \\\\\\n' +\n ' --url https://api.groq.com/openai/v1/chat/completions \\\\\\n' +\n ` --header 'authorization: Bearer ${apiKey}' \\\\\\n` +\n \" --header 'content-type: application/json' \\\\\\n\" +\n ` --data '${esc}'`\n ].join('');\n console.log(`>> [${tag}] curl script:\\n${curl}`);\n } catch (_) {}\n}\nfunction coerceJsonFromText(text) {\n try {\n if (!text) return null;\n // 1) Try to find a raw JSON object substring\n const raw = String(text);\n const firstBrace = raw.indexOf('{');\n const lastBrace = raw.lastIndexOf('}');\n if (firstBrace !== -1 && lastBrace > firstBrace) {\n const candidate = raw.slice(firstBrace, lastBrace + 1);\n try { return JSON.parse(candidate); } catch (_) {}\n }\n // 2) Try loose key extraction\n const statusMatch = raw.match(/status\\s*[:=]\\s*[\"']?([a-zA-Z_]+)[\"']?/);\n const messageMatch = raw.match(/message\\s*[:=]\\s*[\"']([^\"']+)[\"']/);\n const explMatch = raw.match(/explanation_short\\s*[:=]\\s*[\"']([^\"']+)[\"']/);\n const out = {};\n if (statusMatch) out.status = statusMatch[1];\n if (messageMatch) out.message = messageMatch[1];\n if (explMatch) out.explanation_short = explMatch[1];\n return Object.keys(out).length > 0 ? out : null;\n } catch (_) { return null; }\n}\n\nfunction getEmailDomain(address) {\n try { return String(address || '').split('@')[1]?.toLowerCase() || ''; } catch (_) { return ''; }\n}\nfunction isGenericEmailDomain(domain) {\n const generic = new Set(['gmail.com','yahoo.com','outlook.com','hotmail.com','icloud.com','gmx.com','aol.com','proton.me','protonmail.com','mail.com','yandex.com']);\n return generic.has(String(domain || '').toLowerCase());\n}\nfunction dedupeStrings(arr) {\n const seen = new Set();\n const out = [];\n for (const s of (arr || [])) { const v = String(s || '').trim(); if (v && !seen.has(v)) { seen.add(v); out.push(v); } }\n return out;\n}\nfunction statusToLabel(status) {\n if (status === 'spam') return 'spam';\n if (status === 'person_none') return 'no evidence';\n if (status === 'person_low') return 'possible person';\n if (status === 'likely_spam') return 'spam';\n if (status === 'suspected_spam') return 'suspicious';\n if (status === 'not_spam' || status === 'person_high') return 'likely real person';\n return String(status || '');\n}\nfunction cleanDetailText(text) {\n try {\n if (!text) return '';\n let s = String(text);\n // Remove tool and output blocks often present in compound traces\n s = s.replace(/[\\s\\S]*?<\\/tool>/gi, '').replace(/[\\s\\S]*?<\\/output>/gi, '');\n // Drop embedded JSON blocks\n s = s.replace(/[\\s\\S]*?<\\/json>/gi, '');\n // Normalize whitespace\n s = s.replace(/[ \\t]+/g, ' ').replace(/\\n{3,}/g, '\\n\\n').trim();\n // Keep only 1-3 short paragraphs\n const paras = s.split(/\\n\\s*\\n/).filter(p => p && p.trim());\n const limited = paras.slice(0, 3).map(p => p.trim().slice(0, 500));\n const joined = limited.join('\\n\\n').trim();\n return joined.slice(0, 1400);\n } catch (_) { return ''; }\n}\n\n// Produce a concise one-sentence identity-first summary from an explanation\nfunction deriveIdentityShort(explanation) {\n try {\n if (!explanation) return '';\n let s = String(explanation).trim();\n // Take the first sentence\n try {\n const parts = s.split(/(?<=[.!?])\\s+/).filter(Boolean);\n if (parts.length > 0) s = parts[0];\n } catch (_) {}\n // Length guard\n if (s.length > 200) s = s.slice(0, 199).replace(/[,;:\\s]+$/, '').trim() + '…';\n return s;\n } catch (_) { return ''; }\n}\n\n// Convert a plain-text analysis (Status/Message/Explanation/Evidence sections) into strict JSON\n// Schema: { status: 'person_high|person_low|person_none|spam', message: string, explanation_short: string, evidence: string[] }\nasync function convertAnalysisToJson(apiKey, email, analysisText) {\n try {\n const convModel = 'openai/gpt-oss-20b';\n const convSys = [\n 'You convert analysis text into a strict JSON object. Do not browse or call tools.',\n 'Schema: { \"status\": \"person_high|person_low|person_none|spam\", \"message\": string, \"explanation_short\": string, \"evidence\": string[] }',\n 'Rules:',\n '- message: one short sentence.',\n '- explanation_short: 2-3 sentences (<= 400 chars). If analysis indicates nothing credible was found, include \"No public evidence found.\" If known, include person background (name, role/title, organization). If credible negative signals are present (e.g., StopForumSpam, Spamhaus, scam-reporting communities), mention that.',\n '- evidence: 0-5 short bullets (no links). Include a background bullet when available (e.g., \"Name — Title, Organization, City\"). Include negative bullets when applicable (e.g., \"Flagged on StopForumSpam\"). Use [] when no credible evidence.',\n '- Only output a JSON object (no markdown, no extra text).'\n ].join('\\n');\n const convMessages = [\n { role: 'system', content: convSys },\n { role: 'user', content: 'Email: ' + email + '\\nAnalysis to convert:\\n' + analysisText }\n ];\n const convPayload = { model: convModel, messages: convMessages, stream: false, temperature: 0, response_format: { type: 'json_object' }, tool_choice: 'none' };\n const convRes = await groqChatCompletion(apiKey, convPayload);\n const t2 = convRes?.choices?.[0]?.message?.content || '';\n try { return JSON.parse(t2); } catch (_) { return null; }\n } catch (_) {\n return null;\n }\n}\nasync function llmCompoundAssess(apiKey, email, systemPrompt) {\n const baseSystem = [\n 'You assess whether an email belongs to a real person. Perform targeted web checks. If you cannot browse, reason only from public patterns in the address and provider; do not fabricate.',\n 'Make an effort to look for identity signals on LinkedIn, company/staff pages, academic directories, GitHub, personal sites, press/newsroom/author pages, and social profiles.',\n 'When identity is indicated, do light background digging: capture the person\\'s name, role/title, organization, department/team, and location/city if available.',\n 'Also look for negative signals: credible anti-abuse/anti-spam sources (e.g., StopForumSpam, Spamhaus, PhishTank), scam-reporting communities, or reputable media coverage indicating misuse. If credible negative signals exist, prefer status: spam.',\n 'Return ONLY JSON wrapped in ... tags using this schema:',\n '{ \"status\": \"person_high|person_low|person_none|spam\", \"message\": string, \"explanation_short\": string, \"evidence\": string[] }',\n 'Guidelines:',\n '- person_high: strong identity signals clearly tie to the email/user (e.g., staff or author page, LinkedIn with matching name and employer, academic page listing the email).',\n '- person_low: weak or indirect signals; likely a person but unverified.',\n '- person_none: no public identity evidence.',\n '- spam: obvious spam/role-based/throwaway patterns, or credible anti-spam sources flag the address.',\n '- Leniency: If the domain appears to be a non-generic organization or an academic domain (e.g., .edu, .ac.*, or a known university/company), and there are weak but plausible signals (name match, role on org page without explicit email), lean toward person_low rather than person_none.',\n '- Escalation: When you find a plausible identity match (person name plus role/title and/or organization/institution) from credible sources and there are no credible negative signals, prefer status: person_high, even if the address uses a generic email provider (e.g., gmail).',\n '- message: one short sentence preferring an identity-first summary, e.g., \"Linked to Name — Title, Organization\" when available. explanation_short: 2-3 sentences (<= 400 chars), plain text; no tool logs, URLs, or headings. If known, include person background (name, role/title, organization).',\n '- evidence: 1-5 short bullets summarizing concrete findings (no links). Include at least one background bullet when available (e.g., \"Name — Title, Organization, City\"). Include negative bullets when applicable (e.g., \"Flagged on StopForumSpam\" or \"Reported in scam community\"). If nothing found, set evidence to [] and explicitly state \"No public evidence found.\" in explanation_short.',\n '- If the address indicates a role (e.g., info, noreply) or a notable role (e.g., reporter, journalist, professor, founder), include that in evidence.',\n '- Do not include any text outside the ... wrapper.'\n ].join('\\n');\n const messages = [\n { role: 'system', content: baseSystem + (systemPrompt ? ('\\nExtra instructions: ' + systemPrompt) : '') },\n { role: 'user', content: 'Email: ' + email + '\\nTask: quick legitimacy check.' }\n ];\n const model = 'compound-beta';\n const compoundPayload1 = { model, messages, stream: false, temperature: 0.2, tool_choice: 'none' };\n const r1 = await groqChatCompletion(apiKey, compoundPayload1);\n // logGroqCurl('compound r1', apiKey, compoundPayload1);\n // try { console.log('>> [compound] r1:', JSON.stringify(r1)); } catch (_) {}\n const t1 = r1?.choices?.[0]?.message?.content || '';\n const reasoning1 = r1?.choices?.[0]?.message?.reasoning || '';\n let parsed = extractJsonBetweenTags(t1);\n if (!parsed) {\n // In JSON mode, remove the wrapper instruction to avoid tool_use_failed\n const baseSystemJson = [\n 'You assess whether an email likely belongs to a real person based on quick web checks; do not fabricate.',\n 'Return ONLY a JSON object with this schema (no tags, no markdown):',\n '{ \"status\": \"person_high|person_low|person_none|spam\", \"message\": string, \"explanation_short\": string, \"evidence\": string[] }',\n 'Keep message concise; explanation_short 2-3 sentences (<= 400 chars).'\n ].join('\\n');\n const messagesJson = [\n { role: 'system', content: baseSystemJson + (systemPrompt ? ('\\nExtra instructions: ' + systemPrompt) : '') },\n { role: 'user', content: 'Email: ' + email + '\\nTask: quick legitimacy check.' }\n ];\n const payload2 = { model, messages: messagesJson, stream: false, temperature: 0.2, response_format: { type: 'json_object' }, tool_choice: 'none' };\n // logGroqCurl('compound r2', apiKey, payload2);\n const r2 = await groqChatCompletion(apiKey, payload2);\n try { console.log('>> [compound] r2:', JSON.stringify(r2)); } catch (_) {}\n const t2 = r2?.choices?.[0]?.message?.content || '';\n try { parsed = JSON.parse(t2); } catch (_) { parsed = null; }\n if (!parsed) {\n return {\n status: 'person_low',\n message: 'no compound signal',\n fields: { bg_compound_debug: (t1 || t2 || '').slice(0, 800), bg_llm_model: model }\n };\n }\n }\n const out = { fields: { bg_llm_model: model } };\n if (parsed) {\n if (parsed.status && (parsed.status === 'person_high' || parsed.status === 'person_low' || parsed.status === 'person_none' || parsed.status === 'spam')) out.status = parsed.status;\n if (typeof parsed.message === 'string' && parsed.message.trim()) {\n out.message = parsed.message.trim();\n } else if (out.status) {\n const label = statusToLabel(out.status);\n out.message = 'compound: ' + label;\n } else {\n out.message = 'compound: ' + statusToLabel('person_low');\n }\n // Add compact label + short/long messages\n const label = statusToLabel(out.status || (parsed.status || 'person_low'));\n out.fields.bg_compound_label = label;\n out.fields.bg_compound_short = out.message || label;\n // Prefer model-provided reasoning if available, else raw content stripped of JSON tags\n let detail = '';\n if (typeof parsed.explanation_short === 'string' && parsed.explanation_short.trim()) {\n detail = cleanDetailText(parsed.explanation_short);\n // further limit to ~2-3 sentences / 450 chars\n try {\n const parts = detail.split(/(?<=[.!?])\\s+/).filter(Boolean).slice(0, 3);\n detail = parts.join(' ');\n if (detail.length > 450) detail = detail.slice(0, 449).replace(/[,;:\\s]+$/, '').trim() + '…';\n } catch (_) {}\n }\n if (!detail && reasoning1 && typeof reasoning1 === 'string') {\n detail = cleanDetailText(reasoning1);\n }\n if (!detail && t1) {\n detail = cleanDetailText(String(t1));\n }\n if (detail) out.fields.bg_compound_detail = detail;\n // Prefer an identity-first short derived from explanation when available\n try {\n const shortPref = (typeof parsed.explanation_short === 'string' && parsed.explanation_short.trim()) ? deriveIdentityShort(parsed.explanation_short) : '';\n if (shortPref) out.fields.bg_compound_short = shortPref;\n } catch (_) {}\n // Evidence list\n try {\n let ev = parsed.evidence;\n if (Array.isArray(ev)) {\n ev = ev.map((s) => String(s || '').trim()).filter(Boolean).slice(0, 5);\n } else if (typeof ev === 'string' && ev.trim()) {\n ev = [ev.trim()];\n } else {\n ev = [];\n }\n if (ev.length > 0) out.fields.bg_compound_evidence = ev;\n } catch (_) {}\n }\n return out;\n}\n\n// Browser search assessor using Groq's browser_search tool\nasync function browserSearchAssess(apiKey, email, systemPrompt) {\n const baseSystem = [\n 'Use web browsing to gather public signals about whether this email belongs to a real person; do not fabricate.',\n 'Call browser_search with multiple targeted queries. Try: the full email in quotes; the local part and domain; site:linkedin.com with likely name/handle; site:github.com with the handle; university/staff directories; newsroom/author pages; personal sites; press pages.',\n 'Prefer authoritative sources: LinkedIn, staff/author/company pages, academic directories, GitHub, reputable media. Also check credible anti-abuse/anti-spam sources (StopForumSpam, Spamhaus, PhishTank) and scam-reporting communities. Avoid data brokers and spammy aggregators.',\n 'When identity is indicated, do light background digging: capture the person\\'s name, role/title, organization, department/team, and location/city if available.',\n 'Leniency rule: If the domain appears to be a non-generic organization or academic domain (e.g., .edu, .ac.*, or a recognized university/company), and you find weak but plausible signals (name on org page, directory listing without explicit email, matching role), prefer Status: person_low over person_none unless there are credible negative signals.',\n 'Escalation rule: If you find a plausible identity match (person name plus role/title and/or organization/institution) from credible sources and no credible negative signals, prefer Status: person_high, even when the address uses a generic provider (e.g., gmail).',\n 'Produce a concise plain-text analysis with the following sections only (do not include any JSON):',\n 'Status: one of person_high | person_low | person_none | spam',\n 'Message: one short sentence preferring an identity-first summary, e.g., \"Linked to Name — Title, Organization\" when available',\n 'Explanation: 2-3 sentences (<= 400 chars), reference evidence or say No public evidence found. If known, include person background (name, role/title, organization).',\n 'Evidence: 1-5 short bullet points summarizing concrete findings (no links). Include at least one bullet with person background when available (e.g., \"Name — Title, Organization, City\"). Include negative bullets such as \"Flagged on StopForumSpam\" or \"Mentioned in scam-reporting community\" when applicable. If nothing credible found, leave this section empty.',\n 'Also note recognizable roles when present (reporter, journalist, professor, founder, recruiter).'\n ].join('\\n');\n const messages = [\n { role: 'system', content: baseSystem + (systemPrompt ? ('\\nExtra instructions: ' + systemPrompt) : '') },\n { role: 'user', content: 'Email: ' + email + '\\nTask: web search legitimacy check.' }\n ];\n const model = 'openai/gpt-oss-20b';\n const payload = { model, messages, stream: false, temperature: 0.2, tools: [{ type: 'browser_search' }] };\n const r1 = await groqChatCompletion(apiKey, payload);\n // logGroqCurl('browser r1', apiKey, payload);\n // try { console.log('>> [browser] r1:', JSON.stringify(r1)); } catch (_) {}\n const t1 = r1?.choices?.[0]?.message?.content || '';\n const out = { fields: { bg_browser_model: model } };\n // Second pass: convert the plain-text analysis to strict JSON\n let parsed = await convertAnalysisToJson(apiKey, email, t1);\n\n if (!parsed) {\n // As a last resort, attempt to coerce\n parsed = coerceJsonFromText(t1);\n }\n\n if (parsed) {\n if (parsed.status && (parsed.status === 'person_high' || parsed.status === 'person_low' || parsed.status === 'person_none' || parsed.status === 'spam')) out.status = parsed.status;\n if (typeof parsed.message === 'string' && parsed.message.trim()) out.message = parsed.message.trim();\n const label = statusToLabel(out.status || (parsed.status || 'person_low'));\n out.fields.bg_browser_label = label;\n out.fields.bg_browser_short = out.message || label;\n if (typeof parsed.explanation_short === 'string' && parsed.explanation_short.trim()) {\n let detail = String(parsed.explanation_short).trim();\n if (detail.length > 450) detail = detail.slice(0, 449).replace(/[,;:\\s]+$/, '').trim() + '…';\n out.fields.bg_browser_detail = detail;\n // Prefer identity-first short if explanation is present\n try {\n const shortPref = deriveIdentityShort(parsed.explanation_short);\n if (shortPref) out.fields.bg_browser_short = shortPref;\n } catch (_) {}\n }\n try {\n let ev = parsed.evidence;\n if (Array.isArray(ev)) {\n ev = ev.map((s) => String(s || '').trim()).filter(Boolean).slice(0, 5);\n } else if (typeof ev === 'string' && ev.trim()) {\n ev = [ev.trim()];\n } else {\n ev = [];\n }\n if (ev.length > 0) out.fields.bg_browser_evidence = ev;\n } catch (_) {}\n } else {\n out.fields.bg_browser_debug = 'browser_search produced non-JSON analysis and conversion failed.';\n }\n\n return out;\n}\n\n// Final judge to consolidate all signals\nasync function finalJudgeAssess(apiKey, email, systemPrompt, evidence) {\n console.log('>> [final judge] evidence:', email, JSON.stringify(evidence));\n const model = 'openai/gpt-oss-120b';\n const schema = '{ \"status\": \"person_high|person_low|person_none|spam\", \"message\": string, \"explanation_short\": string, \"evidence\": string[] }';\n const sys = [\n 'You are a strict judge that consolidates signals from two assessors and heuristics (academic, role, domain) to decide if an email belongs to a real person.',\n 'Decide using severity: spam > person_none > person_low > person_high.',\n 'Return ONLY JSON per schema:', schema,\n 'Rules:',\n '- Compose evidence as a deduplicated list of concrete bullets merged from all inputs (no links).',\n '- Include at least one bullet with person background when available (name, role/title, organization, location).',\n '- If credible anti-spam signals exist (e.g., StopForumSpam, Spamhaus, scam-reporting communities), prefer status: spam and include a negative bullet.',\n '- Leniency: For non-generic organization or academic domains with weak but plausible identity signals, prefer person_low over person_none unless negative signals exist.',\n '- Escalation: If there is a plausible identity match (person name plus role/title and/or organization/institution) from credible sources and no credible negative signals, prefer person_high even if the address uses a generic provider (e.g., gmail).',\n '- If no credible public evidence is present, set evidence to [] and explicitly say \"No public evidence found.\" in explanation_short.',\n '- message must be one short, specific sentence preferring an identity-first summary like \"Linked to Name — Title, Organization\" when available. explanation_short must be 2-3 sentences (<= 400 chars) and reference key evidence or lack thereof. If known, weave in person background (name, role/title, organization).',\n '- Note recognizable roles when present (reporter, journalist, professor, founder, recruiter).'\n ].join('\\n');\n const user = [\n 'Email: ' + email,\n 'Evidence (JSON):',\n JSON.stringify(evidence)\n ].join('\\n');\n // Ask for json_object to avoid extra parsing\n const judgePayload = { model, messages: [ { role: 'system', content: sys + (systemPrompt ? ('\\nExtra: ' + systemPrompt) : '') }, { role: 'user', content: user } ], stream: false, temperature: 0.2, response_format: { type: 'json_object' } };\n // logGroqCurl('judge', apiKey, judgePayload);\n const r = await groqChatCompletion(apiKey, judgePayload);\n // try { console.log('>> [judge] r:', JSON.stringify(r)); } catch (_) {}\n let parsed = null;\n try { parsed = JSON.parse(r?.choices?.[0]?.message?.content || '{}'); } catch (_) { parsed = null; }\n if (!parsed) return {};\n const out = { fields: { bg_final_model: model } };\n if (parsed.status && (parsed.status === 'person_high' || parsed.status === 'person_low' || parsed.status === 'person_none' || parsed.status === 'spam')) out.status = parsed.status;\n if (typeof parsed.message === 'string' && parsed.message.trim()) out.message = parsed.message.trim();\n const label = statusToLabel(out.status || (parsed.status || 'person_low'));\n out.fields.bg_final_label = label;\n out.fields.bg_final_short = out.message || label;\n if (typeof parsed.explanation_short === 'string' && parsed.explanation_short.trim()) {\n let d = String(parsed.explanation_short).trim();\n if (d.length > 450) d = d.slice(0, 449).replace(/[,;:\\s]+$/, '').trim() + '…';\n out.fields.bg_final_detail = d;\n // Prefer identity-first one-liner for the short summary\n try {\n const shortPref = deriveIdentityShort(parsed.explanation_short);\n if (shortPref) out.fields.bg_final_short = shortPref;\n } catch (_) {}\n }\n try {\n let ev = parsed.evidence;\n if (Array.isArray(ev)) {\n ev = ev.map((s) => String(s || '').trim()).filter(Boolean).slice(0, 5);\n } else if (typeof ev === 'string' && ev.trim()) {\n ev = [ev.trim()];\n } else {\n ev = [];\n }\n if (ev.length > 0) out.fields.bg_final_evidence = ev;\n } catch (_) {}\n return out;\n}\nasync function academicAssess(email) {\n const out = { fields: {} };\n try {\n const { Verifier } = await import('npm:academic-email-verifier');\n let isAcademic = false; let institution = null;\n try { isAcademic = await Verifier.isAcademic(email); } catch (_) { isAcademic = false; }\n try { institution = await Verifier.getInstitutionName(email); } catch (_) { institution = null; }\n const inst = typeof institution === 'string' ? institution.trim() : (institution ? String(institution) : null);\n out.fields.bg_academic = !!isAcademic; if (inst) out.fields.bg_institution = inst;\n if (isAcademic) out.fields.bg_academic_msg = inst || 'Academic domain';\n } catch (_) {}\n return out;\n}\nfunction roleHeuristicAssess(email) {\n try {\n const local = String(email || '').split('@')[0].toLowerCase();\n const roleList = ['info', 'support', 'admin', 'sales', 'contact', 'hello', 'team', 'marketing', 'noreply', 'no-reply'];\n if (roleList.includes(local)) return { status: 'suspected_spam', message: 'Role-based address' };\n } catch (_) {}\n return {};\n}\nasync function backgroundAssessEmail(apiKey, email, systemPrompt) {\n let status = undefined; const messages = []; const fields = {};\n if (!isEmailAddress(email)) { status = mergeStatus(status, 'spam'); messages.push('Invalid email format'); }\n const acad = await academicAssess(email);\n if (acad && acad.fields) Object.assign(fields, acad.fields);\n const role = roleHeuristicAssess(email);\n if (role.status) status = mergeSpamStatus(status, role.status);\n if (role.message) messages.push(role.message);\n try {\n let llm = null; let browse = null;\n const [llmRes, browseRes] = await Promise.allSettled([\n llmCompoundAssess(apiKey, email, systemPrompt),\n browserSearchAssess(apiKey, email, systemPrompt)\n ]);\n if (llmRes.status === 'fulfilled') {\n llm = llmRes.value;\n if (llm) {\n if (llm.status) status = mergeSpamStatus(status, llm.status);\n if (llm.message) fields.bg_compound_msg = llm.message;\n if (llm.status) fields.bg_compound_label = statusToLabel(llm.status);\n if (!fields.bg_compound_short && llm.message) fields.bg_compound_short = llm.message;\n if (llm.fields) Object.assign(fields, llm.fields);\n }\n } else {\n try { console.error('>> [compound] error:', llmRes.reason && llmRes.reason.message ? llmRes.reason.message : String(llmRes.reason)); } catch (_) {}\n fields.bg_compound_debug = llmRes.reason && llmRes.reason.message ? llmRes.reason.message : String(llmRes.reason);\n }\n if (browseRes.status === 'fulfilled') {\n browse = browseRes.value;\n if (browse) {\n if (browse.status) status = mergeSpamStatus(status, browse.status);\n if (browse.message) fields.bg_browser_msg = browse.message;\n if (!fields.bg_browser_short && browse.message) fields.bg_browser_short = browse.message;\n if (browse.fields) Object.assign(fields, browse.fields);\n }\n } else {\n try { console.error('>> [browser] error:', browseRes.reason && browseRes.reason.message ? browseRes.reason.message : String(browseRes.reason)); } catch (_) {}\n fields.bg_browser_debug = browseRes.reason && browseRes.reason.message ? browseRes.reason.message : String(browseRes.reason);\n }\n // Final judge\n const d = getEmailDomain(email);\n const evidence = {\n academic: { academic: !!fields.bg_academic, institution: fields.bg_institution || null },\n role: { message: role.message || null, status: role.status || null },\n domain: { domain: d || null, generic_provider: d ? isGenericEmailDomain(d) : null },\n compound: { status: llm?.status || null, message: llm?.message || null, short: fields.bg_compound_short || null, label: fields.bg_compound_label || null, evidence: Array.isArray(fields.bg_compound_evidence) ? fields.bg_compound_evidence : [] },\n browser: { status: browse?.status || null, message: browse?.message || null, short: fields.bg_browser_short || null, label: fields.bg_browser_label || null, evidence: Array.isArray(fields.bg_browser_evidence) ? fields.bg_browser_evidence : [] }\n };\n console.log('>> [final judge] evidence:', JSON.stringify(evidence));\n const judge = await finalJudgeAssess(apiKey, email, systemPrompt, evidence);\n if (judge) {\n if (judge.status) status = judge.status; // override with judge decision\n if (judge.message && !fields.bg_final_short) fields.bg_final_short = judge.message;\n if (judge.fields) Object.assign(fields, judge.fields);\n }\n console.log('>> [final judge] judge:', JSON.stringify(judge));\n\n // Consolidate evidence bullets and detail for UI if missing\n const allEvidence = [];\n if (Array.isArray(fields.bg_final_evidence)) allEvidence.push(...fields.bg_final_evidence);\n if (Array.isArray(fields.bg_browser_evidence)) allEvidence.push(...fields.bg_browser_evidence);\n if (Array.isArray(fields.bg_compound_evidence)) allEvidence.push(...fields.bg_compound_evidence);\n // Heuristic evidence\n const domain = getEmailDomain(email);\n if (fields.bg_academic && fields.bg_institution) allEvidence.push(`Academic institution: ${fields.bg_institution}`);\n if (domain) {\n if (isGenericEmailDomain(domain)) allEvidence.push(`Generic email provider: ${domain}`); else allEvidence.push(`Organization domain detected: ${domain}`);\n }\n if (role && role.message) allEvidence.push(role.message);\n const deduped = dedupeStrings(allEvidence).slice(0, 8);\n if (!fields.bg_final_evidence) fields.bg_final_evidence = deduped.length > 0 ? deduped : ['No public evidence found'];\n if (!fields.bg_final_detail) {\n const parts = [];\n if (fields.bg_academic && fields.bg_institution) parts.push(`Academic institution: ${fields.bg_institution}.`);\n else if (domain) parts.push(isGenericEmailDomain(domain) ? `Generic provider: ${domain}.` : `Organization domain: ${domain}.`);\n if (judge && judge.message) parts.push(judge.message);\n const fallback = parts.join(' ').trim();\n fields.bg_final_detail = fallback || 'No public evidence found.';\n }\n } catch (err) {\n try { console.error('>> [compound] error:', err && err.message ? err.message : String(err)); } catch (_) {}\n fields.bg_compound_debug = err && err.message ? err.message : String(err);\n }\n if (!status) status = 'person_low';\n return { email, status, message: messages.join('; '), fields };\n}\n\n// Check if server has API key\napp.get('/api/check-key', (c) => {\n return c.json({ hasServerKey: !!serverApiKey });\n});\n\n// Environment-aware file reading function\nasync function readFileContent(filePath) {\n // Check if we're in Val Town environment\n if (typeof Deno !== \"undefined\" && Deno.env.get(\"valtown\")) {\n // Use Val Town's readFile\n return await readFile(filePath, import.meta.url);\n } else {\n // Use Deno's native readTextFile for local development\n return await Deno.readTextFile(filePath);\n }\n}\n\n// Serve root with HTML content\napp.get('/', async (c) => {\n try {\n const htmlContent = await readFileContent('index.html');\n return c.html(htmlContent);\n } catch (error) {\n console.error('Error reading HTML file:', error);\n return c.text('Error loading page', 500);\n }\n});\n\n// Serve frontend modules (no static middleware)\n// Removed legacy research frontend\n\napp.get('/frontend/init.js', async (c) => {\n try {\n const js = await readFileContent('frontend/init.js');\n return new Response(js, {\n headers: { 'Content-Type': 'application/javascript; charset=utf-8' },\n });\n } catch (error) {\n console.error('Error reading init.js:', error);\n return c.text('Not found', 404);\n }\n});\n\n// Removed legacy lorraine frontend\n\napp.get('/frontend/bgcheckApp.js', async (c) => {\n try {\n const js = await readFileContent('frontend/bgcheckApp.js');\n return new Response(js, {\n headers: { 'Content-Type': 'application/javascript; charset=utf-8' },\n });\n } catch (error) {\n console.error('Error reading bgcheckApp.js:', error);\n return c.text('Not found', 404);\n }\n});\n\n// Academic email check (server-side; uses npm:academic-email-verifier)\n// Removed standalone academic check endpoint; academic is part of /api/check/background\n\n// Background check endpoint: runs sequential checkers server-side\napp.post('/api/check/background', async (c) => {\n try {\n const body = await c.req.json().catch(() => ({}));\n const email = String(body?.email || '').trim();\n const systemPrompt = typeof body?.systemPrompt === 'string' ? body.systemPrompt : '';\n const apiKey = serverApiKey || body?.userApiKey || Deno.env.get(\"GROQ_API_KEY\");\n if (!apiKey) return c.json({ error: 'No Groq API key available.' }, 400);\n if (!email) return c.json({ error: 'email required' }, 400);\n const out = await backgroundAssessEmail(apiKey, email, systemPrompt);\n // try { console.log('>> [/api/check/background] result:', JSON.stringify(out)); } catch (_) {}\n return c.json(out);\n } catch (error) {\n console.error('background check error:', error);\n return c.json({ error: 'background check error: ' + error.message }, 500);\n }\n});\n\n// Async job APIs\n// Create a job with one or more items. Body: { items: string[] | { email }[], systemPrompt?, concurrency?, userApiKey? }\napp.post('/api/jobs', async (c) => {\n try {\n const body = await c.req.json().catch(() => ({}));\n let items = Array.isArray(body?.items) ? body.items : [];\n const systemPrompt = typeof body?.systemPrompt === 'string' ? body.systemPrompt : '';\n const concurrency = Math.max(1, Number(body?.concurrency || 8) || 8);\n const apiKey = serverApiKey || body?.userApiKey || Deno.env.get(\"GROQ_API_KEY\");\n if (!apiKey) return c.json({ error: 'No Groq API key available.' }, 400);\n // Normalize items to { id, email, status }\n items = items.map((v) => {\n const email = (v && typeof v === 'object') ? String(v.email || '').trim() : String(v || '').trim();\n return { id: generateId(), email, status: 'pending' };\n }).filter((it) => it.email);\n if (items.length === 0) return c.json({ error: 'no valid items' }, 400);\n const job = { id: generateId(), running: false, cancelled: false, total: 0, completed: 0, createdAt: nowTs(), updatedAt: nowTs(), items, systemPrompt };\n jobRegistry.set(job.id, job);\n // fire and forget\n runAsyncJob(job, apiKey, concurrency);\n return c.json(summarizeJob(job));\n } catch (error) {\n console.error('create job error:', error);\n return c.json({ error: 'create job error: ' + error.message }, 500);\n }\n});\n\n// Get job status by id\napp.get('/api/jobs/:id', (c) => {\n try {\n const id = c.req.param('id');\n const job = id && jobRegistry.get(id);\n if (!job) return c.json({ error: 'not found' }, 404);\n return c.json(summarizeJob(job));\n } catch (error) {\n console.error('get job error:', error);\n return c.json({ error: 'get job error: ' + error.message }, 500);\n }\n});\n\n// Cancel a job by id\napp.post('/api/jobs/:id/cancel', (c) => {\n try {\n const id = c.req.param('id');\n const job = id && jobRegistry.get(id);\n if (!job) return c.json({ error: 'not found' }, 404);\n job.cancelled = true;\n job.running = false;\n job.updatedAt = nowTs();\n return c.json(summarizeJob(job));\n } catch (error) {\n console.error('cancel job error:', error);\n return c.json({ error: 'cancel job error: ' + error.message }, 500);\n }\n});\n\n// LLM-based email legitimacy checker\n// Asks the model to return JSON wrapped in ... tags; retries with JSON modes if needed\n// Removed standalone llm-email endpoint; compound is part of /api/check/background\n\nexport default (typeof Deno !== \"undefined\" && Deno.env.get(\"valtown\")) ? app.fetch : app;",[],[553],{"_104":561,"_95":562,"_153":563,"_155":-5,"_156":138,"_157":101,"_159":465,"_106":564,"_100":32,"_161":474,"_163":565,"_164":22,"_165":-5,"_166":566,"_98":567},"cc68fd88-81f8-11f0-b660-0224a6c84d84","notes.md","cc68fe46-81f8-11f0-b660-0224a6c84d84",["D",1756156602938],"\n\n## random\n\n- [x] output questions to csv",[],[562],{"_104":569,"_95":570,"_153":571,"_155":-5,"_156":138,"_157":572,"_159":22,"_106":573,"_100":32,"_161":474,"_163":574,"_164":22,"_165":-5,"_166":575,"_98":576},"cc3b4c62-81f8-11f0-b51c-0224a6c84d84","readme.md","cc3b4d20-81f8-11f0-b51c-0224a6c84d84",47,["D",1756156602671],"### MailCheck — Real‑Person Email Verifier (Groq)\n\nVerify that email addresses belong to real people using lightweight web + LLM signals. Upload/paste lists, run concurrent checks, and export annotated results.\n\nWhat it does\n- Upload CSV/JSON or paste text; inline edit and delete rows\n- Jobs sidebar with autosave (localStorage)\n- Per‑email verification with concurrency and live row updates\n - Syntax check (invalid → spam)\n - Academic domain detection (institution annotation)\n - Role/alias heuristic (flags generic addresses)\n - Web + LLM assessments consolidated by a final judge into one decision\n- Export CSV/JSON (preserves original columns + `bg_*` fields)\n\nStatuses\n- person_high\n- person_low\n- person_none\n- spam\n\nRequirements\n- Deno 1.40+\n- Groq API key\n\nRun locally\n```sh\ndeno run -A main.js\n```\nOpen http://localhost:8000. If the server doesn’t have `GROQ_API_KEY`, set a key in the UI.\n\nEnvironment\n- `GROQ_API_KEY`: Groq key (server). 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