SENTINEL PRO // DYNAMIC OMNI

[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_152":153,"_154":155},"routes/_app.x.$handle.$name",{"_11":12,"_13":14,"_15":16,"_18":19,"_17":-7,"_115":-5,"_20":116,"_22":117,"_118":119,"_120":121,"_25":122,"_24":-5,"_123":-5,"_124":-5,"_125":53,"_126":127,"_128":44,"_129":-5,"_130":-5,"_131":132},"routes/_app.x.$handle.$name.branch.$branch.code.$",{"_11":12,"_13":14,"_15":16,"_17":-7,"_18":19,"_20":21,"_22":23,"_24":-7,"_25":26,"_27":-5,"_28":-5,"_29":-5,"_30":-5,"_31":-5},"handle","bomb","name","untitled-8571","branchName","main","version","path","","project",{"_33":61,"_13":14,"_45":62,"_63":-5,"_64":53,"_65":66,"_67":66,"_68":-5,"_69":-5,"_70":71,"_72":37,"_73":44,"_74":44,"_75":-5,"_76":77,"_78":79,"_80":-5,"_81":-5,"_82":-5,"_83":-5,"_84":85,"_86":85,"_87":88,"_89":88,"_90":88,"_91":85,"_92":85,"_93":94,"_95":96,"_97":98,"_99":53},"tree",[56],"file","contents",[32],"upstream","pull","ahead","behind","envVars",{"_33":34,"_13":35,"_36":37,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_17":47,"_48":49,"_50":51,"_52":53,"_54":-5,"_18":55},"id","d71134b8-c8d3-11f0-a2ef-42dde27851f2","main.ts","fileId","e899e38e-c8be-11f0-bbe7-42dde27851f2","parentId","branchId","e8983ade-c8be-11f0-bbe7-42dde27851f2","branchVersion",7,"latest",true,"createdAt",["D",1763947262023],6,"fileType","http","content","/** @jsxImportSource https://esm.sh/react@18.2.0 */\n\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\nimport { blob } from \"https://esm.town/v/std/blob?v=11\";\nimport { nanoid } from \"npm:nanoid\";\n\n// ==========================================\n// 1. DYNAMIC COMPLIANCE & AGENT GENERATION\n// ==========================================\n\n// Base templates for few-shot learning\nconst COMPLIANCE_FRAMEWORK_EXAMPLES = {\n GAAP: {\n name: \"Generally Accepted Accounting Principles\",\n authority: \"FASB/SEC\",\n checkpoints: [\n \"Revenue Recognition (ASC 606)\",\n \"Leases (ASC 842)\",\n \"Fair Value (ASC 820)\",\n \"Consolidation (ASC 810)\",\n ],\n },\n GDPR_CCPA: {\n name: \"Data Privacy & Protection\",\n authority: \"EU/California\",\n checkpoints: [\n \"Data Subject Rights\",\n \"Consent Mechanisms\",\n \"Data Breach Notification\",\n \"Cross-Border Transfer\",\n ],\n },\n SOX: {\n name: \"Sarbanes-Oxley Act\",\n authority: \"PCAOB\",\n checkpoints: [\n \"Section 302 (Certifications)\",\n \"Section 404 (Internal Controls)\",\n \"Whistleblower Protections\",\n ],\n },\n};\n\nconst AGENT_PERSONA_EXAMPLES = {\n FINANCIAL_FORENSIC: {\n id: \"fin_forensic\",\n name: \"Financial Forensic Analyst\",\n role: \"Senior Forensic Accountant\",\n color: \"#3b82f6\",\n system_prompt:\n \"You are a veteran Forensic Accountant. Analyze documents for earnings management, fraud triangles, channel stuffing, and off-balance-sheet liabilities. Focus on numerical anomalies.\",\n },\n CONTRACT_SHARK: {\n id: \"contract_shark\",\n name: \"Commercial Contract Expert\",\n role: \"Senior Counsel\",\n color: \"#ef4444\",\n system_prompt:\n \"You are a cynical Senior Counsel. Find the loopholes. Analyze indemnification caps, termination for convenience clauses, and ambiguous liability shifting. Assume the worst.\",\n },\n PRIVACY_OFFICER: {\n id: \"privacy_officer\",\n name: \"Data Privacy Officer\",\n role: \"GDPR/CCPA Expert\",\n color: \"#047857\",\n system_prompt:\n \"You are a DPO. Identify PII (Personally Identifiable Information) handling violations, lack of consent records, and cross-border data transfer risks.\",\n },\n};\n\n// ==========================================\n// 2. VERBALIZED SAMPLING PROMPTS\n// ==========================================\n\nconst VERBALIZED_SAMPLING_INSTRUCTION = `\n\nGenerate 5 responses to the user query, each within a separate tag. Each must include a and a numeric .\nPlease sample at random from the tails of the distribution, such that the probability of each response is less than 0.10.\n\n`;\n\n// ==========================================\n// 3. DYNAMIC GENERATION FUNCTIONS\n// ==========================================\n\nasync function generateComplianceFrameworks(documentContext: string) {\n const openai = new OpenAI();\n\n const prompt = `\nYou are a compliance framework expert. Based on the document context, generate a list of the MOST RELEVANT compliance frameworks.\n\n${VERBALIZED_SAMPLING_INSTRUCTION}\n\nDOCUMENT CONTEXT:\n\"\"\"\n${documentContext.substring(0, 2000)}\n\"\"\"\n\nFEW-SHOT EXAMPLES of compliance framework structure:\n${JSON.stringify(COMPLIANCE_FRAMEWORK_EXAMPLES, null, 2)}\n\nYour task: Analyze the document and generate 5 different sets of compliance frameworks that might be relevant. Each set should be creative and drawn from \"the tails of the distribution\" - meaning less obvious but potentially important frameworks.\n\nFor each response, provide:\n1. A list of 3-5 compliance frameworks in the same JSON structure as the examples\n2. A probability score (less than 0.10) indicating how likely this is the optimal set\n\nReturn your answer in this exact format:\n\n0.08\n\n{\n \"FRAMEWORK_ID\": {\n \"name\": \"Framework Name\",\n \"authority\": \"Governing Body\",\n \"checkpoints\": [\"checkpoint1\", \"checkpoint2\", \"checkpoint3\"]\n },\n ...\n}\n\n\n\nGenerate all 5 responses now.\n`;\n\n try {\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n {\n role: \"system\",\n content:\n \"You are a compliance framework expert specializing in regulatory analysis.\",\n },\n { role: \"user\", content: prompt },\n ],\n temperature: 0.9, // Higher temp for creative sampling\n });\n\n const responseText = completion.choices[0].message.content;\n\n // Parse the verbalized sampling responses\n const responses = parseVerbalizedSampling(responseText);\n\n // Select the best response (highest probability from the tail distribution)\n const bestResponse = responses.reduce((best, current) =>\n current.probability > best.probability ? current : best\n );\n\n return JSON.parse(bestResponse.text);\n } catch (e) {\n console.error(\"Failed to generate compliance frameworks:\", e);\n // Fallback to examples\n return COMPLIANCE_FRAMEWORK_EXAMPLES;\n }\n}\n\nasync function generateAgentPersonas(documentContext: string, frameworks: any) {\n const openai = new OpenAI();\n\n const prompt = `\nYou are an AI agent design expert. Based on the document context and compliance frameworks, generate the MOST RELEVANT agent personas.\n\n${VERBALIZED_SAMPLING_INSTRUCTION}\n\nDOCUMENT CONTEXT:\n\"\"\"\n${documentContext.substring(0, 2000)}\n\"\"\"\n\nSELECTED COMPLIANCE FRAMEWORKS:\n${JSON.stringify(Object.keys(frameworks), null, 2)}\n\nFEW-SHOT EXAMPLES of agent persona structure:\n${JSON.stringify(AGENT_PERSONA_EXAMPLES, null, 2)}\n\nYour task: Generate 5 different sets of 4-6 agent personas that would be optimal for analyzing this document. Sample from the tails of the distribution - be creative and consider non-obvious but valuable specialist perspectives.\n\nFor each response, provide:\n1. A set of agent personas in the same JSON structure as the examples\n2. A probability score (less than 0.10) indicating how likely this is the optimal team\n\nReturn your answer in this exact format:\n\n0.07\n\n{\n \"AGENT_ID\": {\n \"id\": \"unique_id\",\n \"name\": \"Agent Name\",\n \"role\": \"Role Title\",\n \"color\": \"#hexcolor\",\n \"system_prompt\": \"Detailed system prompt for the agent...\"\n },\n ...\n}\n\n\n\nGenerate all 5 responses now.\n`;\n\n try {\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n {\n role: \"system\",\n content:\n \"You are an expert in designing AI agent teams for forensic analysis.\",\n },\n { role: \"user\", content: prompt },\n ],\n temperature: 0.9,\n });\n\n const responseText = completion.choices[0].message.content;\n const responses = parseVerbalizedSampling(responseText);\n const bestResponse = responses.reduce((best, current) =>\n current.probability > best.probability ? current : best\n );\n\n return JSON.parse(bestResponse.text);\n } catch (e) {\n console.error(\"Failed to generate agent personas:\", e);\n return AGENT_PERSONA_EXAMPLES;\n }\n}\n\n// ==========================================\n// 4. PARSING UTILITIES\n// ==========================================\n\nfunction parseVerbalizedSampling(responseText: string) {\n const responses = [];\n const responsePattern =\n /\\s*([\\d.]+)<\\/probability>\\s*([\\s\\S]*?)<\\/text>\\s*<\\/response>/g;\n\n let match;\n while ((match = responsePattern.exec(responseText)) !== null) {\n responses.push({\n probability: parseFloat(match[1]),\n text: match[2].trim(),\n });\n }\n\n // If parsing fails, try alternative format\n if (responses.length === 0) {\n console.warn(\n \"Verbalized sampling parse failed, attempting JSON extraction\",\n );\n // Fallback: try to extract any JSON blocks\n const jsonPattern = /```json\\s*([\\s\\S]*?)\\s*```/g;\n let jsonMatch;\n while ((jsonMatch = jsonPattern.exec(responseText)) !== null) {\n responses.push({\n probability: 0.05,\n text: jsonMatch[1].trim(),\n });\n }\n }\n\n return responses;\n}\n\n// ==========================================\n// 5. CORE LOGIC & ORCHESTRATION\n// ==========================================\n\nconst AGENT_OUTPUT_SCHEMA = `\n{\n \"analysis_metadata\": {\n \"confidence_score\": Number,\n \"primary_frameworks_used\": [\"String\"]\n },\n \"red_flags\": [\n {\n \"severity\": \"CRITICAL|HIGH|MEDIUM|LOW\",\n \"category\": \"String\",\n \"finding\": \"String\",\n \"evidence\": { \n \"direct_quote\": \"String (The exact text from the document supporting this)\", \n \"location_context\": \"String (Page/Paragraph if inferable)\" \n },\n \"regulatory_reference\": \"String (e.g. 'ASC 606' or 'GDPR Art 15')\"\n }\n ],\n \"extracted_data\": {\n \"entities\": {\n \"persons\": [{\"name\": \"String\", \"role\": \"String\", \"risk_level\": \"String\"}],\n \"organizations\": [{\"name\": \"String\", \"type\": \"String\", \"jurisdiction\": \"String\"}],\n \"dates\": [{\"date\": \"String\", \"event\": \"String\", \"significance\": \"String\"}]\n }\n },\n \"compliance_checklist\": [\n { \"framework\": \"String\", \"status\": \"PASS|FAIL|WARNING\", \"checkpoint\": \"String\" }\n ]\n}\n`;\n\n// --- Helper Functions ---\n\nfunction parseLLMJson(content: string) {\n try {\n return JSON.parse(content);\n } catch (e) {\n const match = content.match(/```json([\\s\\S]*?)```/);\n if (match && match[1]) {\n try {\n return JSON.parse(match[1].trim());\n } catch (e2) {\n // Continue\n }\n }\n try {\n const start = content.indexOf(\"{\");\n const end = content.lastIndexOf(\"}\");\n if (start !== -1 && end !== -1) {\n return JSON.parse(content.substring(start, end + 1));\n }\n } catch (e3) {\n try {\n const clean = content.replace(/```json/g, \"\").replace(/```/g, \"\")\n .trim();\n return JSON.parse(clean);\n } catch (e4) {\n throw new Error(\n `JSON Parse Failed. Content: ${content.substring(0, 50)}...`,\n );\n }\n }\n throw new Error(\"No JSON found in response\");\n }\n}\n\nfunction generateJobId() {\n return `SENT-${Date.now().toString(36)}-${nanoid(4)}`.toUpperCase();\n}\n\n// --- The Architect (Dynamic Team Selection) ---\n\nasync function runArchitect(textSample: string, agentPersonas: any) {\n const openai = new OpenAI();\n const context = `\n DOCUMENT SAMPLE (First 3000 chars):\n \"\"\"${textSample.substring(0, 3000)}\"\"\"\n\n AVAILABLE AGENTS:\n ${\n JSON.stringify(\n Object.values(agentPersonas).map((a: any) => ({\n id: a.id,\n name: a.name,\n role: a.role,\n })),\n null,\n 2,\n )\n }\n\n Your Goal: Select the top 4 most relevant agents to analyze this specific document type.\n Return JSON: { \"selected_agent_ids\": [\"id1\", \"id2\", \"id3\", \"id4\"] }\n `;\n\n try {\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n messages: [\n {\n role: \"system\",\n content:\n \"You are the Lead Forensic Architect. Assign the expert team. JSON only.\",\n },\n { role: \"user\", content: context },\n ],\n response_format: { type: \"json_object\" },\n });\n\n const parsed = parseLLMJson(completion.choices[0].message.content);\n const validIds = parsed.selected_agent_ids.filter((id) =>\n Object.values(agentPersonas).some((a: any) => a.id === id)\n );\n\n if (validIds.length === 0) {\n throw new Error(\"Architect returned no valid IDs\");\n }\n return validIds;\n } catch (e) {\n console.error(\"Architect Error:\", e);\n throw new Error(`Architect failed: ${e.message}`);\n }\n}\n\n// --- The Agents (Analysis) ---\n\nasync function runAgentAnalysis(agent: any, text: string, frameworks: any) {\n const openai = new OpenAI();\n\n const frameworkContext = `\nRELEVANT COMPLIANCE FRAMEWORKS FOR THIS DOCUMENT:\n${JSON.stringify(frameworks, null, 2)}\n`;\n\n try {\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n messages: [\n {\n role: \"system\",\n content:\n `${agent.system_prompt}\\n\\n${frameworkContext}\\n\\nREQUIRED JSON OUTPUT SCHEMA:\\n${AGENT_OUTPUT_SCHEMA}`,\n },\n {\n role: \"user\",\n content: `Analyze this document text:\\n\"\"\"${\n text.substring(0, 15000)\n }\"\"\"`,\n },\n ],\n response_format: { type: \"json_object\" },\n temperature: 0.2,\n });\n return {\n success: true,\n agentId: agent.id,\n agentName: agent.name,\n agentColor: agent.color,\n data: parseLLMJson(completion.choices[0].message.content),\n };\n } catch (e) {\n return {\n success: false,\n agentId: agent.id,\n agentName: agent.name,\n error: e.message,\n };\n }\n}\n\n// --- The Synthesizer (Reporting) ---\n\nasync function synthesizeReport(results: any[]) {\n const openai = new OpenAI();\n const validResults = results.filter((r) => r.success);\n const failedResults = results.filter((r) => !r.success);\n\n if (validResults.length === 0) {\n const errorLog = failedResults.map((r) => `${r.agentName}: ${r.error}`)\n .join(\"\\n\");\n return {\n executive_dossier: {\n document_type: \"Unknown\",\n overall_risk_rating: \"CRITICAL\",\n executive_summary:\n `SYSTEM FAILURE: All agents failed to process the document.\\n\\nLogs:\\n${errorLog}\\n\\nPlease check OpenAI API Keys and text extraction quality.`,\n document_id: \"ERR\",\n },\n prioritized_findings: [],\n entity_intelligence: {\n timeline: [],\n key_parties: [],\n relationship_map: [],\n },\n };\n }\n\n const synthesisInput = JSON.stringify(\n validResults.map((r) => ({\n agent: r.agentName,\n findings: r.data.red_flags,\n entities: r.data.extracted_data?.entities,\n compliance: r.data.compliance_checklist,\n })),\n null,\n 2,\n );\n\n const prompt = `\n You are the Chief Intelligence Director.\n Synthesize these diverse agent findings into a cohesive, executive-grade forensic dossier.\n \n - Rank risks by true business impact.\n - Deduplicate entities and create a single timeline.\n - Be authoritative and precise.\n\n INPUT DATA:\n ${synthesisInput}\n\n OUTPUT JSON SCHEMA:\n {\n \"executive_dossier\": {\n \"document_type\": \"String\",\n \"overall_risk_rating\": \"CRITICAL|HIGH|MEDIUM|LOW\",\n \"executive_summary\": \"String (Professional, dense summary)\",\n \"document_id\": \"String\"\n },\n \"prioritized_findings\": [\n { \n \"rank\": Number, \n \"title\": \"String\", \n \"severity\": \"CRITICAL|HIGH|MEDIUM|LOW\", \n \"description\": \"String\", \n \"evidence_summary\": \"String\", \n \"category\": \"String\"\n }\n ],\n \"entity_intelligence\": {\n \"timeline\": [{ \"date\": \"String\", \"event\": \"String\", \"significance\": \"String\", \"entities_involved\": [\"String\"] }],\n \"key_parties\": [{ \"id\": \"String\", \"name\": \"String\", \"type\": \"PERSON|ORGANIZATION\", \"risk_indicators\": [\"String\"] }],\n \"relationship_map\": [{ \"from_entity\": \"String\", \"to_entity\": \"String\", \"relationship_type\": \"String\" }]\n },\n \"compliance_summary\": {\n \"pass_rate\": \"String\",\n \"critical_gaps\": [\"String\"]\n }\n }\n `;\n\n try {\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [{ role: \"system\", content: prompt }],\n response_format: { type: \"json_object\" },\n });\n return parseLLMJson(completion.choices[0].message.content);\n } catch (e) {\n return {\n executive_dossier: {\n overall_risk_rating: \"HIGH\",\n executive_summary:\n `Synthesis Phase Failed: ${e.message}. Raw agent data is available in Evidence tab.`,\n document_id: \"SYN-ERR\",\n },\n prioritized_findings: [],\n entity_intelligence: {},\n };\n }\n}\n\n// --- Job Orchestrator ---\n\nasync function orchestrateJob(jobId: string, fullText: string, metadata: any) {\n try {\n await updateJob(jobId, {\n phase: \"preparation\",\n progress: 5,\n logs: [\"Document secure. Initializing dynamic analysis protocols...\"],\n });\n\n if (!fullText || fullText.trim().length < 20) {\n throw new Error(\n \"Text extraction failed. The document appears empty or is a scanned image without text layer.\",\n );\n }\n\n // 1. Generate Dynamic Compliance Frameworks\n await updateJob(jobId, {\n phase: \"framework_generation\",\n progress: 10,\n logs: [\n \"Analyzing document context to generate relevant compliance frameworks...\",\n ],\n });\n\n const frameworks = await generateComplianceFrameworks(fullText);\n\n await updateJob(jobId, {\n logs: [\n `Generated ${Object.keys(frameworks).length} compliance frameworks: ${\n Object.keys(frameworks).join(\", \")\n }`,\n ],\n });\n\n // 2. Generate Dynamic Agent Personas\n await updateJob(jobId, {\n phase: \"agent_generation\",\n progress: 15,\n logs: [\n \"Designing optimal agent team based on document type and frameworks...\",\n ],\n });\n\n const agentPersonas = await generateAgentPersonas(fullText, frameworks);\n\n await updateJob(jobId, {\n logs: [\n `Created ${\n Object.keys(agentPersonas).length\n } specialized agents for this analysis`,\n ],\n });\n\n // 3. Architect Phase (Team Selection)\n await updateJob(jobId, {\n phase: \"architecting\",\n progress: 20,\n logs: [`Lead Architect reviewing ${fullText.length} chars of data...`],\n });\n\n let selectedIds;\n try {\n selectedIds = await runArchitect(fullText, agentPersonas);\n } catch (e) {\n await updateJob(jobId, {\n logs: [`Architect Error: ${e.message}. Deploying default team.`],\n });\n // Select first 3 agents as fallback\n selectedIds = Object.values(agentPersonas).slice(0, 3).map((a: any) =>\n a.id\n );\n }\n\n const selectedAgents = selectedIds\n .map((id) => Object.values(agentPersonas).find((a: any) => a.id === id))\n .filter(Boolean);\n\n await updateJob(jobId, {\n phase: \"scanning\",\n progress: 30,\n logs: [\n `Team Deployed: ${selectedAgents.map((a: any) => a.name).join(\", \")}`,\n \"Agents initiating deep scan...\",\n ],\n });\n\n // 4. Analysis Phase\n const results = await Promise.all(\n selectedAgents.map((agent) =>\n runAgentAnalysis(agent, fullText, frameworks)\n ),\n );\n\n const failures = results.filter((r) => !r.success);\n if (failures.length > 0) {\n await updateJob(jobId, {\n logs: [`Warning: ${failures.length} agents failed to report.`],\n });\n }\n\n await updateJob(jobId, {\n phase: \"synthesis\",\n progress: 80,\n logs: [\"Agents reporting in. Synthesizing final intelligence dossier...\"],\n agentResults: results,\n frameworks: frameworks,\n generatedAgents: agentPersonas,\n });\n\n // 5. Synthesis Phase\n const finalReport = await synthesizeReport(results);\n\n await updateJob(jobId, {\n phase: \"complete\",\n progress: 100,\n result: finalReport,\n agentResults: results,\n frameworks: frameworks,\n generatedAgents: agentPersonas,\n logs: [\n \"Analysis complete. Dossier generated with dynamic frameworks and agents.\",\n ],\n });\n } catch (e) {\n console.error(e);\n await updateJob(jobId, {\n phase: \"error\",\n error: e.message,\n logs: [`CRITICAL FAILURE: ${e.message}`],\n });\n }\n}\n\n// --- State Persistence ---\nasync function updateJob(id: string, data: any) {\n const current = (await blob.getJSON(id)) || {};\n const logs = [...(current.logs || []), ...(data.logs || [])];\n const uniqueLogs = [...new Set(logs)];\n await blob.setJSON(id, { ...current, ...data, logs: uniqueLogs });\n}\n\nasync function getJob(id: string) {\n return await blob.getJSON(id);\n}\n\n// ==========================================\n// 6. FRONTEND (THE FANTASTIC UI)\n// ==========================================\n\nfunction generateEnhancedHtml() {\n return `\n\n\n\n \n \n SENTINEL PRO // DYNAMIC OMNI\n \n \n \n \n \n \n \n \n \n\n\n

\n \n\n\n `;\n}\n// ==========================================\n// 7. SERVER HANDLER\n// ==========================================\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n const cors = {\n \"Access-Control-Allow-Origin\": \"*\",\n \"Access-Control-Allow-Methods\": \"POST, GET, OPTIONS\",\n \"Access-Control-Allow-Headers\": \"Content-Type\",\n };\n if (req.method === \"OPTIONS\") return new Response(null, { headers: cors });\n if (req.method === \"POST\" && url.pathname.endsWith(\"/analyze\")) {\n try {\n const { text, filename } = await req.json();\n if (!text || text.length < 50) throw new Error(\"Text too short\");\n const jobId = generateJobId();\n await blob.setJSON(jobId, { phase: \"queued\", logs: [\"Queued...\"] });\n\n (async () => {\n await orchestrateJob(jobId, text, { filename });\n })();\n\n return Response.json({ jobId }, { headers: cors });\n } catch (e) {\n return Response.json({ error: e.message }, {\n status: 500,\n headers: cors,\n });\n }\n }\n if (req.method === \"GET\" && url.pathname.endsWith(\"/status\")) {\n const jobId = url.searchParams.get(\"jobId\");\n const status = await getJob(jobId);\n return status\n ? Response.json(status, { headers: cors })\n : Response.json({ error: \"Not found\" }, { status: 404, headers: cors });\n }\n return new Response(generateEnhancedHtml(), {\n headers: { \"Content-Type\": \"text/html\", ...cors },\n });\n}","deleted",false,"label",[35],{"_33":34,"_13":35,"_36":37,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":57,"_17":47,"_48":49,"_50":51,"_52":53,"_54":-5,"_58":59,"_18":60},["D",1763947262023],"children",[],[35],"e8980910-c8be-11f0-bbe7-42dde27851f2",["D",1763938272008],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","imageUrl","userId","019a29dd-b790-744e-b51b-76d556574760","pinnedFileId","pinPreview","pinMigrated","screenshotVersion","likeCount",0,"orgId","019a29dd-b790-744e-b51b-79456055c738","tursoName","tursoHostname","tursoMeasuredAt","pinned","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm","user",{"_11":12,"_114":110},"org",{"_11":12,"_108":-5,"_109":110,"_70":71,"_111":112,"_113":53},"branch",{"_17":42,"_13":16,"_33":40,"_100":61,"_101":-5,"_102":-5,"_103":-5,"_104":-5,"_105":-5,"_106":14,"_107":12},"isOwned","projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectId","forkedProjectOrgId","projectName","userHandle","displayName","avatar","https://img.clerk.com/eyJ0eXBlIjoiZGVmYXVsdCIsImlpZCI6Imluc18yTm10cGhQUklwU0tqZHNiOHNnNGd2ZjJURFUiLCJyaWQiOiJ1c2VyXzM0Z2Q2U2p3V2syUGtoYUZ3TUlLY1FzSXRWdyJ9","tier","free","canRestrictVals","profileImageUrl","adoptOrgId",{"_33":61,"_13":14,"_45":148,"_63":-5,"_64":53,"_65":66,"_67":66,"_68":-5,"_69":-5,"_70":71,"_72":37,"_73":44,"_74":44,"_75":-5,"_76":77,"_78":79,"_80":-5,"_81":-5,"_82":-5,"_83":-5,"_84":85,"_86":85,"_87":88,"_89":88,"_90":88,"_91":85,"_92":85,"_93":149,"_95":150,"_97":151,"_99":53},[145],"files",[145],"counts",{"_135":77,"_136":137,"_138":77,"_31":139,"_140":141},[133],"upstreamProject","upstreamBranch","emptyEnvVars","flags",[],"hasHttpFiles","grants","orgs","customDomains",{},{"_33":34,"_13":35,"_36":37,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_17":47,"_48":49,"_50":51,"_52":53,"_54":-5,"_18":134},[35],"pulls","branches",1,"forks",{"_142":77,"_144":77},"connections",{"_142":77,"_143":77},"count","unfilledCount","emptyCount",{"_33":34,"_13":35,"_36":37,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":57,"_17":47,"_48":49,"_50":51,"_52":53,"_54":-5,"_58":146,"_18":147},[],[35],["D",1763938272008],{"_11":12,"_114":110},{"_11":12,"_108":-5,"_109":110,"_70":71,"_111":112,"_113":53},{"_17":42,"_13":16,"_33":40,"_100":61,"_101":-5,"_102":-5,"_103":-5,"_104":-5,"_105":-5,"_106":14,"_107":12},"ctx",{"_93":-5,"_130":185,"_186":187,"_188":189,"_190":53,"_191":53,"_192":53,"_193":44,"_194":-5,"_195":-7,"_196":-7,"_197":77,"_198":53,"_199":53,"_200":-5,"_201":53,"_202":53,"_203":53,"_204":-5},"ENV",{"_156":157,"_158":159,"_160":16,"_161":162,"_163":164,"_165":166,"_167":168,"_169":170,"_171":172,"_173":174,"_175":176,"_177":178,"_179":180,"_181":182,"_183":184},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","518c05f2c13b243481ef29134a5672756425e459","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"]

Code