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FIXED section parser — was using \\n\\s*\\ anchors that never match after\n// stripHtml() collapses whitespace. Now finds \"Item N.\" markers directly.\n// 2. FIXED dedupe — was comparing full normalized contexts, but overlapping\n// context windows produce different strings. Now dedupes by keyword +\n// match position (within 400 chars = same passage).\n// 3. NEW 200KB hard cap — graceful trim if response exceeds Clay's limit.\n// 4. NEW note-section detection (e.g., \"Note 5 - Restructuring\").\n// ============================================================================\n\nconst HEADERS = {\n \"User-Agent\": \"AlexKrasiuk alex@myrunbook.com\",\n \"Accept-Encoding\": \"gzip, deflate\",\n};\n\nconst CONTEXT_CHARS = 800;\nconst MAX_HITS_RETURNED = 150;\nconst MAX_BYTES = 180_000; // Clay cell limit ~200KB, leave headroom\nconst DEDUPE_WINDOW = 400; // same-keyword hits within this distance = dupe\n\nconst KEYWORDS = [\n // AI / ML\n \"artificial intelligence\",\n \"AI agent\",\n \"AI agents\",\n \"generative AI\",\n \"gen AI\",\n \"GenAI\",\n \"machine learning\",\n \"large language model\",\n \"LLM\",\n \"AI-enabled\",\n \"AI-powered\",\n \"AI initiative\",\n \"AI technology\",\n \"AI tool\",\n \"neural network\",\n \"deep learning\",\n // Headcount / workforce\n \"headcount reduction\",\n \"headcount reductions\",\n \"workforce reduction\",\n \"reduction in force\",\n \"reduction-in-force\",\n \"workforce optimization\",\n \"restructuring\",\n \"severance\",\n \"severance charges\",\n \"layoff\",\n \"layoffs\",\n \"job cut\",\n \"overhead reduction\",\n \"workforce transformation\",\n \"reorganization\",\n \"reorganize\",\n // Back office / ops\n \"back office\",\n \"back-office\",\n \"shared services\",\n \"shared service\",\n \"shared service center\",\n \"administrative function\",\n \"administrative functions\",\n \"centralize\",\n \"centralization\",\n \"offshore\",\n \"offshoring\",\n \"outsourc\",\n // Automation\n \"process automation\",\n \"robotic process automation\",\n \"RPA\",\n \"automate\",\n \"automated\",\n \"automation\",\n \"intelligent automation\",\n \"workflow automation\",\n // Transformation\n \"digital transformation\",\n \"technology transformation\",\n \"digital initiative\",\n \"digital strategy\",\n \"modernize\",\n \"modernization\",\n \"technology investment\",\n \"technology upgrade\",\n // Efficiency\n \"process optimization\",\n \"process improvement\",\n \"operational efficiency\",\n \"operational excellence\",\n \"productivity improvement\",\n \"productivity gain\",\n \"cost savings\",\n \"cost reduction\",\n \"cost optimization\",\n \"margin expansion\",\n \"margin improvement\",\n \"efficiency initiative\",\n \"efficiency program\",\n // Manual work\n \"manual processes\",\n \"manual work\",\n \"manual task\",\n \"streamline\",\n \"streamlined\",\n \"streamlining\",\n // Field ops\n \"dispatch\",\n \"scheduling\",\n \"route optimization\",\n \"field service\",\n \"field operations\",\n \"field technician\",\n \"service technician\",\n \"work order\",\n \"call center\",\n \"contact center\",\n // Financial pressure\n \"margin pressure\",\n \"labor cost\",\n \"labor shortage\",\n];\n\n// Known 10-K item titles — used to produce clean section labels even when\n// the doc has weird formatting.\nconst ITEM_TITLES: Record = {\n \"1\": \"Business\",\n \"1A\": \"Risk Factors\",\n \"1B\": \"Unresolved Staff Comments\",\n \"1C\": \"Cybersecurity\",\n \"2\": \"Properties\",\n \"3\": \"Legal Proceedings\",\n \"4\": \"Mine Safety Disclosures\",\n \"5\": \"Market for Registrant's Common Equity\",\n \"6\": \"Selected Financial Data\",\n \"7\": \"Management's Discussion and Analysis\",\n \"7A\": \"Quantitative and Qualitative Disclosures About Market Risk\",\n \"8\": \"Financial Statements and Supplementary Data\",\n \"9\": \"Changes in and Disagreements with Accountants\",\n \"9A\": \"Controls and Procedures\",\n \"9B\": \"Other Information\",\n \"10\": \"Directors and Executive Officers\",\n \"11\": \"Executive Compensation\",\n \"12\": \"Security Ownership\",\n \"13\": \"Certain Relationships\",\n \"14\": \"Principal Accountant Fees\",\n \"15\": \"Exhibits\",\n};\n\n// ============================================================================\n// Ticker cache + fetch\n// ============================================================================\n\nlet tickerCache: { data: any; ts: number } | null = null;\nconst CACHE_TTL_MS = 24 * 60 * 60 * 1000;\n\nasync function secFetch(url: string, retries = 3): Promise {\n for (let i = 0; i < retries; i++) {\n const resp = await fetch(url, { headers: HEADERS });\n if (resp.ok) return resp.text();\n if (resp.status === 429) {\n await new Promise((r) => setTimeout(r, 1000 * (i + 1)));\n continue;\n }\n throw new Error(`SEC fetch failed: ${resp.status} for ${url}`);\n }\n throw new Error(`SEC fetch failed after ${retries} retries for ${url}`);\n}\n\nasync function getTickers(): Promise {\n if (tickerCache && Date.now() - tickerCache.ts < CACHE_TTL_MS) {\n return tickerCache.data;\n }\n const data = JSON.parse(\n await secFetch(\"https://www.sec.gov/files/company_tickers.json\"),\n );\n tickerCache = { data, ts: Date.now() };\n return data;\n}\n\nfunction stripHtml(html: string): string {\n let text = html.replace(/]*>.*?<\\/script>/gis, \" \");\n text = text.replace(/]*>.*?<\\/style>/gis, \" \");\n text = text.replace(/<\\/?ix:[^>]+>/gi, \" \");\n text = text.replace(/<[^>]+>/g, \" \");\n text = text\n .replace(/&/g, \"&\").replace(/</g, \"<\").replace(/>/g, \">\")\n .replace(/"/g, '\"').replace(/'/g, \"'\").replace(/ /g, \" \")\n .replace(/&#\\d+;/g, \" \").replace(/&[a-zA-Z]+;/g, \" \");\n text = text.replace(/\\s+/g, \" \").trim();\n return text;\n}\n\n// ============================================================================\n// FIXED Section detection\n// Strategy: find all \"Item N.\" markers in the text preceding the match.\n// The LAST one before matchIdx is the current section.\n// Also detects \"Note N.\" style markers for financial statement footnotes.\n// ============================================================================\n\ntype SectionMarker = {\n type: \"item\" | \"note\";\n code: string;\n title: string;\n idx: number;\n};\n\n// Pre-compute all section markers in a document once, then binary-search.\n// This is ~100x faster than re-scanning for each keyword hit.\n//\n// Design choice: rather than capture item titles from the text (fragile —\n// 10-Ks format them inconsistently), we match just the \"Item N.\" code and\n// look up the canonical title in ITEM_TITLES. Unknown codes are ignored so\n// we don't produce garbage like \"Item 99.1 - Press Release\".\nfunction indexSections(text: string): SectionMarker[] {\n const markers: SectionMarker[] = [];\n let m;\n\n // Item markers: \"Item 1.\", \"Item 1A.\", \"Item 7A.\" etc.\n // Require capital letter after the period so we don't match fractional\n // numbers or item cross-refs like \"Item 10.5a\" in exhibit lists.\n const itemRe = /\\bItem\\s+(\\d{1,2}[A-Z]?)\\.\\s+(?=[A-Z])/g;\n while ((m = itemRe.exec(text)) !== null) {\n const code = m[1];\n const title = ITEM_TITLES[code];\n if (!title) continue; // skip unknown codes\n markers.push({ type: \"item\", code, title, idx: m.index });\n }\n\n // Note markers in financial statements: \"5. RESTRUCTURING AND RELATED COSTS\".\n // Must be at sentence boundary, single/double digit, followed by ALL-CAPS title.\n const noteRe =\n /(?:\\.\\s+|\\s{2,})(\\d{1,2})\\s*\\.\\s+([A-Z][A-Z\\s&',\\-]{8,80}?)(?=\\s+[A-Z][a-z]|\\s+\\()/g;\n while ((m = noteRe.exec(text)) !== null) {\n const num = parseInt(m[1]);\n if (num < 1 || num > 25) continue;\n const title = m[2].trim().replace(/\\s+/g, \" \");\n if (title.length < 8) continue;\n markers.push({ type: \"note\", code: m[1], title, idx: m.index });\n }\n\n markers.sort((a, b) => a.idx - b.idx);\n return markers;\n}\n\nfunction findSection(markers: SectionMarker[], matchIdx: number): string {\n // Binary search for largest idx <= matchIdx\n if (markers.length === 0) return \"Unknown\";\n let lo = 0, hi = markers.length - 1, best = -1;\n while (lo <= hi) {\n const mid = (lo + hi) >> 1;\n if (markers[mid].idx <= matchIdx) {\n best = mid;\n lo = mid + 1;\n } else {\n hi = mid - 1;\n }\n }\n if (best < 0) return \"Unknown\";\n const m = markers[best];\n return m.type === \"item\"\n ? `Item ${m.code} - ${m.title}`\n : `Note ${m.code} - ${m.title}`;\n}\n\n// ============================================================================\n// Scoring (unchanged — informational, doesn't filter)\n// ============================================================================\n\nfunction scoreHit(context: string, section: string, _keyword: string): number {\n const lower = context.toLowerCase();\n let score = 0;\n\n const neighbors = [\n \"initiative\",\n \"invest\",\n \"deploy\",\n \"implement\",\n \"launch\",\n \"reduce\",\n \"reduction\",\n \"eliminate\",\n \"transform\",\n \"strategy\",\n \"pilot\",\n \"acquired\",\n \"acquisition\",\n \"partnership\",\n \"committed\",\n \"priority\",\n \"focus\",\n \"accelerate\",\n \"expand\",\n ];\n for (const w of neighbors) if (lower.includes(w)) score += 1;\n\n const s = section.toLowerCase();\n if (s.startsWith(\"item 7 \") || s.startsWith(\"item 7a\")) score += 3;\n if (s.startsWith(\"item 1a\")) score += 2;\n if (s.startsWith(\"note \") && /restructur|severance|impair/.test(s)) {\n score += 4;\n }\n\n const hasStreamline = /\\bstreamlin/i.test(lower);\n const hasHeadcount =\n /\\b(headcount|severance|workforce reduction|reduction in force|layoff)/i\n .test(lower);\n const hasDollar = /\\$\\s*\\d+(?:\\.\\d+)?\\s*(?:million|billion|M|B)\\b/i.test(\n context,\n );\n const hasBackOffice =\n /\\b(back[\\s-]?office|administrative function|shared service)/i.test(lower);\n\n if (hasStreamline && hasHeadcount) score += 8;\n if (hasStreamline && hasDollar) score += 4;\n if (hasHeadcount && hasDollar) score += 4;\n if (hasBackOffice) score += 3;\n\n return score;\n}\n\n// ============================================================================\n// FIXED position-based dedupe\n// Same keyword + match index within 400 chars = duplicate.\n// Keeps highest-scoring instance.\n// Different keywords at the same position are NOT deduped (valuable signal).\n// ============================================================================\n\ntype Hit = {\n keyword: string;\n context: string;\n section: string;\n score: number;\n filing_type: string;\n filing_date: string;\n _match_idx: number; // internal, stripped before return\n};\n\nfunction dedupeByPosition(hits: Hit[]): Hit[] {\n // Sort by score desc so highest-scoring wins when we collide\n const sorted = [...hits].sort((a, b) => b.score - a.score);\n // Track kept positions per (filing_date, keyword)\n const kept: Record = {};\n const out: Hit[] = [];\n for (const h of sorted) {\n const key = `${h.filing_date}|${h.keyword}`;\n const positions = kept[key] || [];\n const dupe = positions.some((p) =>\n Math.abs(p - h._match_idx) < DEDUPE_WINDOW\n );\n if (!dupe) {\n positions.push(h._match_idx);\n kept[key] = positions;\n out.push(h);\n }\n }\n // Re-sort by score for presentation\n return out.sort((a, b) => b.score - a.score);\n}\n\n// ============================================================================\n// Size cap — graceful trim to fit Clay's 200KB cell limit\n// ============================================================================\n\nfunction fitResponse(\n resp: T,\n): T {\n let json = JSON.stringify(resp);\n // Level 1: drop lowest-scored hits until under budget\n while (json.length > MAX_BYTES && resp.hits.length > 10) {\n resp.hits.pop();\n json = JSON.stringify(resp);\n }\n resp.total_hits_returned = resp.hits.length;\n // Level 2: shrink context windows\n if (json.length > MAX_BYTES) {\n for (const h of resp.hits) {\n if (h.context && h.context.length > 500) {\n h.context = h.context.slice(0, 500) + \"…\";\n }\n }\n }\n return resp;\n}\n\n// ============================================================================\n// Main handler\n// ============================================================================\n\nexport default async function handler(req: Request): Promise {\n const url = new URL(req.url);\n let ticker = (url.searchParams.get(\"ticker\") || \"\").toUpperCase().trim();\n\n if (!ticker && req.method === \"POST\") {\n try {\n const body = await req.json();\n ticker = (body.ticker || \"\").toUpperCase().trim();\n } catch { /* ignore */ }\n }\n\n if (!ticker) {\n return Response.json(\n { error: 'Pass ?ticker=AAPL or POST {\"ticker\": \"AAPL\"}' },\n { status: 400 },\n );\n }\n\n try {\n const tickersJson = await getTickers();\n let cik: string | null = null;\n let company: string | null = null;\n for (const entry of Object.values(tickersJson) as any[]) {\n if (entry.ticker.toUpperCase() === ticker) {\n cik = String(entry.cik_str).padStart(10, \"0\");\n company = entry.title;\n break;\n }\n }\n if (!cik) {\n return Response.json(\n { error: `Ticker ${ticker} not found in SEC EDGAR` },\n { status: 404 },\n );\n }\n\n const subs = JSON.parse(\n await secFetch(`https://data.sec.gov/submissions/CIK${cik}.json`),\n );\n const recent = subs.filings.recent;\n\n type FilingInfo = { url: string; date: string; type: string };\n const filings: FilingInfo[] = [];\n let found10K = false, found10Q = false;\n for (let i = 0; i < recent.form.length; i++) {\n const form = recent.form[i];\n if ((form === \"10-K\" && !found10K) || (form === \"10-Q\" && !found10Q)) {\n const acc = recent.accessionNumber[i].replace(/-/g, \"\");\n const doc = recent.primaryDocument[i];\n filings.push({\n url: `https://www.sec.gov/Archives/edgar/data/${\n parseInt(cik)\n }/${acc}/${doc}`,\n date: recent.filingDate[i],\n type: form,\n });\n if (form === \"10-K\") found10K = true;\n if (form === \"10-Q\") found10Q = true;\n if (found10K && found10Q) break;\n }\n }\n\n if (filings.length === 0) {\n return Response.json({ error: `No 10-K or 10-Q found for ${ticker}` }, {\n status: 404,\n });\n }\n\n const allHits: Hit[] = [];\n\n for (const filing of filings) {\n const html = await secFetch(filing.url);\n const text = stripHtml(html);\n const sectionMarkers = indexSections(text);\n\n for (const kw of KEYWORDS) {\n const pattern = new RegExp(\n \"\\\\b\" + kw.replace(/[.*+?^${}()|[\\]\\\\]/g, \"\\\\$&\") + \"\\\\b\",\n \"gi\",\n );\n let match;\n while ((match = pattern.exec(text)) !== null) {\n const charStart = Math.max(0, match.index - CONTEXT_CHARS);\n const charEnd = Math.min(\n text.length,\n match.index + kw.length + CONTEXT_CHARS,\n );\n const context = text.slice(charStart, charEnd).trim();\n const section = findSection(sectionMarkers, match.index);\n const score = scoreHit(context, section, kw);\n\n allHits.push({\n keyword: kw,\n context,\n section,\n score,\n filing_type: filing.type,\n filing_date: filing.date,\n _match_idx: match.index,\n });\n }\n }\n }\n\n const unique = dedupeByPosition(allHits);\n const returned = unique.slice(0, MAX_HITS_RETURNED).map((h) => {\n const { _match_idx, ...clean } = h;\n return clean;\n });\n\n const kwCounts: Record = {};\n for (const h of unique) {\n kwCounts[h.keyword] = (kwCounts[h.keyword] || 0) + 1;\n }\n const sectionCounts: Record = {};\n for (const h of unique) {\n sectionCounts[h.section] = (sectionCounts[h.section] || 0) + 1;\n }\n\n const response = {\n ticker,\n company,\n filings: filings.map((f) => ({ type: f.type, date: f.date, url: f.url })),\n total_hits_found: unique.length,\n total_hits_returned: returned.length,\n keyword_counts: kwCounts,\n section_counts: sectionCounts,\n hits: returned,\n };\n\n const trimmed = fitResponse(response);\n const finalJson = JSON.stringify(trimmed);\n return new Response(\n JSON.stringify({ ...trimmed, _response_size_bytes: finalJson.length }),\n { headers: { \"content-type\": \"application/json\" } },\n );\n } catch (e: any) {\n return Response.json({ error: e.message }, { status: 500 });\n }\n}",[142],"61195cdc-3f47-11f1-a505-42b51c65c3df","keywords.ts","61195db8-3f47-11f1-a505-42b51c65c3df",6,["D",1776971122935],"export const KEYWORDS = [\n // AI / ML\n \"artificial intelligence\", \"AI agent\", \"AI agents\",\n \"artificial intelligence initiative\",\n \"generative AI\", \"gen AI\", \"GenAI\",\n \"machine learning\", \"large language model\", \"LLM\",\n \"AI-enabled\", \"AI-powered\",\n\n // Workforce\n \"headcount reduction\", \"workforce reduction\", \"reduction in force\",\n \"workforce optimization\", \"do more with less\",\n\n // Operations / Automation\n \"back office\", \"back-office\",\n \"process automation\", \"robotic process automation\", \"RPA\",\n \"digital transformation\", \"technology transformation\",\n \"process optimization\", \"operational efficiency\",\n \"productivity improvement\",\n \"manual processes\", \"manual work\",\n \"automate\", \"automation\", \"streamline\",\n \"modernize\", \"modernization\",\n \"shared services\",\n\n // Cost\n \"cost savings\", \"cost reduction\",\n \"restructuring\",\n \"scale without\",\n];\n",[150],"64beae46-3f47-11f1-a505-42b51c65c3df","html.ts","64beaf0e-3f47-11f1-a505-42b51c65c3df",7,["D",1776971129053],"export function stripHtml(html: string): string {\n // Remove scripts and styles\n let text = html.replace(/]*>.*?<\\/script>/gis, \" \");\n text = text.replace(/]*>.*?<\\/style>/gis, \" \");\n // Strip XBRL inline tags (SEC filings have these)\n text = text.replace(/<\\/?ix:[^>]+>/gi, \" \");\n // Remove all tags\n text = text.replace(/<[^>]+>/g, \" \");\n // Unescape common HTML entities\n text = text.replace(/&/g, \"&\")\n .replace(/</g, \"<\")\n .replace(/>/g, \">\")\n .replace(/"/g, '\"')\n .replace(/'/g, \"'\")\n .replace(/ /g, \" \")\n .replace(/&#\\d+;/g, \" \")\n .replace(/&[a-zA-Z]+;/g, \" \");\n // Collapse whitespace\n text = text.replace(/\\s+/g, \" \").trim();\n return text;\n}\n",[157],"708b6160-3f47-11f1-829c-42b51c65c3df","analysis.ts","708b6200-3f47-11f1-829c-42b51c65c3df",["D",1776971148838],"// High-value neighbor words that indicate strategic intent (not boilerplate)\nconst HIGH_VALUE_NEIGHBORS = [\n \"initiative\", \"invest\", \"deploy\", \"implement\", \"launch\",\n \"reduce\", \"reduction\", \"eliminate\", \"transform\", \"strategy\",\n \"pilot\", \"roll out\", \"acquired\", \"acquisition\", \"partnership\",\n \"committed\", \"priority\", \"focus\", \"budget\", \"spend\",\n \"savings\", \"efficiency\", \"headcount\", \"workforce\",\n \"restructuring\", \"consolidat\", \"migrat\",\n];\n\n// Sections that indicate strategic commentary (higher signal)\nconst HIGH_VALUE_SECTIONS = [\n \"Item 7\", // MD&A\n \"Item 1A\", // Risk Factors\n \"Item 2\", // Properties (sometimes has ops strategy)\n];\n\nexport function scoreHit(context: string, section?: string): number {\n const lower = context.toLowerCase();\n let score = HIGH_VALUE_NEIGHBORS.filter(w => lower.includes(w)).length;\n\n // Bonus for high-signal sections\n if (section) {\n for (const s of HIGH_VALUE_SECTIONS) {\n if (section.startsWith(s)) {\n score += 2;\n break;\n }\n }\n }\n\n return score;\n}\n\nexport function normalize(s: string): string {\n return s.toLowerCase().replace(/[^a-z0-9\\s]/g, \"\").replace(/\\s+/g, \" \").trim();\n}\n\n// Find the nearest preceding \"Item X\" section header\nexport function findSection(text: string, matchIdx: number): string {\n const before = text.slice(Math.max(0, matchIdx - 10000), matchIdx);\n const sectionMatches = [...before.matchAll(/Item\\s+(\\d+[A-Z]?)\\.?\\s+([A-Z][A-Za-z\\s&']{3,60})/g)];\n if (sectionMatches.length === 0) return \"Unknown\";\n const last = sectionMatches[sectionMatches.length - 1];\n return `Item ${last[1]} - ${last[2].trim()}`;\n}\n\nexport function countByKeyword(hits: { keyword: string }[]): Record {\n const counts: Record = {};\n for (const h of hits) {\n counts[h.keyword] = (counts[h.keyword] || 0) + 1;\n }\n return counts;\n}\n",[164],"pulls",1,"forks","envVars",{"_176":52,"_178":52},"connections",{"_176":52,"_177":52},"count","unfilledCount","emptyCount",{"_20":163,"_13":164,"_120":165,"_122":-5,"_123":21,"_124":145,"_126":107,"_22":201,"_29":52,"_128":129,"_130":167,"_132":38,"_133":-5,"_186":202,"_88":203},{"_20":156,"_13":157,"_120":158,"_122":-5,"_123":21,"_124":159,"_126":107,"_22":198,"_29":52,"_128":129,"_130":161,"_132":38,"_133":-5,"_186":199,"_88":200},{"_20":149,"_13":150,"_120":151,"_122":-5,"_123":21,"_124":152,"_126":107,"_22":195,"_29":52,"_128":129,"_130":154,"_132":38,"_133":-5,"_186":196,"_88":197},{"_20":141,"_13":142,"_120":143,"_122":-5,"_123":21,"_124":30,"_126":107,"_22":192,"_29":145,"_128":146,"_130":147,"_132":38,"_133":-5,"_186":193,"_88":194},{"_20":135,"_13":136,"_120":137,"_122":-5,"_123":21,"_124":138,"_126":107,"_22":189,"_29":52,"_128":129,"_130":89,"_132":38,"_133":-5,"_186":190,"_88":191},{"_20":118,"_13":119,"_120":121,"_122":-5,"_123":21,"_124":125,"_126":107,"_22":185,"_29":52,"_128":129,"_130":131,"_132":38,"_133":-5,"_186":187,"_88":188},["D",1776971136950],"children",[],[119],["D",1776974890453],[],[136],["D",1776979764369],[],[142],["D",1776971122935],[],[150],["D",1776971129053],[],[157],["D",1776971148838],[],[164],["D",1776966215049],{"_11":12,"_86":82},{"_11":12,"_80":-5,"_81":82,"_45":46,"_83":84,"_85":38},{"_29":30,"_13":26,"_20":21,"_27":28,"_31":-5,"_32":-5,"_75":-5,"_76":-5,"_77":-5,"_78":14,"_79":12},"ctx",{"_68":-5,"_109":241,"_242":243,"_244":245,"_246":38,"_247":38,"_248":38,"_249":107,"_250":-5,"_251":-7,"_252":-7,"_253":52,"_254":38,"_255":38,"_256":-5,"_257":38,"_258":38,"_259":38,"_260":-5},"ENV",{"_212":213,"_214":215,"_216":26,"_217":218,"_219":220,"_221":222,"_223":224,"_225":226,"_227":228,"_229":230,"_231":232,"_233":234,"_235":236,"_237":238,"_239":240},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

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