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Resume Screening System\n\nA machine learning-powered resume screening system that uses NLP to match resumes against job requirements.\n\n## Features\n\n- Upload and process resumes in text format\n- Define job requirements and skills\n- Automatically score and rank resumes based on relevance\n- View detailed matching scores for each resume\n\n## Technology Stack\n\n- **Backend**: TypeScript with Hono framework\n- **Frontend**: HTML, CSS (Tailwind), and TypeScript with React\n- **Database**: SQLite for storing resumes and job requirements\n- **NLP/ML**: OpenAI embeddings for semantic matching\n\n## Project Structure\n\n```\n├── backend/\n│ ├── database.ts # SQLite database operations\n│ ├── nlp.ts # NLP processing functions\n│ └── index.ts # Main API entry point\n├── frontend/\n│ ├── components/\n│ │ ├── App.tsx\n│ │ ├── JobForm.tsx\n│ │ ├── ResumeUpload.tsx\n│ │ └── Results.tsx\n│ ├── index.html # Main HTML template\n│ └── index.tsx # Frontend entry point\n└── shared/\n └── types.ts # Shared type definitions\n```\n\n## How It Works\n\n1. Users upload resumes and define job requirements\n2. The system processes resumes using NLP techniques\n3. Resumes are matched against job requirements using semantic similarity\n4. Results are displayed with matching scores and rankings\n\n## Getting Started\n\nAccess the application at the root URL. No setup required as it runs on Val Town.","label",[62],"a2f7da46-34cd-11f0-adef-9e149126039e",["D",1747671825549],[86],"bb661a52-34cd-11f0-ab63-9e149126039e",["D",1747671866536],[103],"pulls","branches","forks","envVars",{"_83":20,"_216":20},"connections",{"_83":20,"_215":20},"unfilledCount","emptyCount",{"_31":205,"_13":103,"_60":102,"_41":-5,"_33":34,"_194":90,"_195":196,"_35":251,"_37":20,"_63":87,"_198":-5,"_64":24,"_200":-5,"_225":252,"_168":253},{"_31":246,"_13":101,"_60":100,"_41":102,"_33":34,"_194":90,"_195":196,"_35":247,"_37":20,"_63":82,"_198":248,"_64":24,"_200":-5,"_225":249,"_168":250},{"_31":241,"_13":121,"_60":120,"_41":102,"_33":34,"_194":115,"_195":196,"_35":242,"_37":20,"_63":82,"_198":243,"_64":24,"_200":-5,"_225":244,"_168":245},{"_31":236,"_13":112,"_60":111,"_41":102,"_33":34,"_194":106,"_195":196,"_35":237,"_37":20,"_63":82,"_198":238,"_64":24,"_200":-5,"_225":239,"_168":240},{"_31":202,"_13":86,"_60":85,"_41":-5,"_33":34,"_194":70,"_195":196,"_35":233,"_37":20,"_63":87,"_198":-5,"_64":24,"_200":-5,"_225":234,"_168":235},{"_31":228,"_13":81,"_60":80,"_41":85,"_33":34,"_194":70,"_195":196,"_35":229,"_37":20,"_63":82,"_198":230,"_64":24,"_200":-5,"_225":231,"_168":232},{"_31":193,"_13":62,"_60":61,"_41":-5,"_33":34,"_194":38,"_195":196,"_35":224,"_37":20,"_63":51,"_198":199,"_64":24,"_200":-5,"_225":226,"_168":227},["D",1747671755663],"children",[],[62],"a2f7bbba-34cd-11f0-adef-9e149126039e",["D",1747671825549],"// Shared type definitions for the resume screening system\n\nexport interface Resume {\n id?: number;\n name: string;\n content: string;\n uploadDate: string;\n scores?: ResumeScore;\n}\n\nexport interface JobRequirement {\n id?: number;\n title: string;\n description: string;\n requiredSkills: string[];\n preferredSkills: string[];\n creationDate: string;\n}\n\nexport interface ResumeScore {\n overallScore: number;\n skillMatches: {\n skill: string;\n score: number;\n required: boolean;\n }[];\n experienceScore: number;\n educationScore: number;\n relevanceScore: number;\n}\n\nexport interface ScreeningResult {\n resumeId: number;\n resumeName: string;\n jobId: number;\n jobTitle: string;\n scores: ResumeScore;\n rank: number;\n}\n\nexport interface ApiResponse {\n success: boolean;\n data?: T;\n error?: string;\n}",[],[86,81],["D",1747671825549],[222],[86],"cb30c7ac-34cd-11f0-ac9b-9e149126039e",["D",1747671893035],"import { OpenAI } from \"https://esm.town/v/std/openai\";\nimport { Resume, JobRequirement, ResumeScore } from \"../shared/types.ts\";\n\n// Initialize OpenAI client\nconst openai = new OpenAI();\n\n/**\n * Extract structured information from resume text\n */\nexport async function analyzeResume(resumeText: string): Promise {\n const prompt = `\n Extract the following information from this resume:\n 1. Skills (as a comma-separated list)\n 2. Years of experience (total)\n 3. Education level (highest degree)\n 4. Previous job titles (comma-separated)\n \n Resume:\n ${resumeText}\n \n Format your response as JSON with the following structure:\n {\n \"skills\": [\"skill1\", \"skill2\", ...],\n \"yearsOfExperience\": number,\n \"educationLevel\": \"string\",\n \"previousJobs\": [\"title1\", \"title2\", ...]\n }\n `;\n\n const response = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n messages: [{ role: \"user\", content: prompt }],\n response_format: { type: \"json_object\" }\n });\n\n try {\n return JSON.parse(response.choices[0].message.content || \"{}\");\n } catch (error) {\n console.error(\"Error parsing resume analysis:\", error);\n return {\n skills: [],\n yearsOfExperience: 0,\n educationLevel: \"Unknown\",\n previousJobs: []\n };\n }\n}\n\n/**\n * Calculate similarity score between two texts using OpenAI embeddings\n */\nexport async function calculateSimilarity(text1: string, text2: string): Promise {\n const response = await openai.embeddings.create({\n model: \"text-embedding-3-small\",\n input: [text1, text2],\n });\n\n const embedding1 = response.data[0].embedding;\n const embedding2 = response.data[1].embedding;\n\n // Calculate cosine similarity\n let dotProduct = 0;\n let norm1 = 0;\n let norm2 = 0;\n\n for (let i = 0; i < embedding1.length; i++) {\n dotProduct += embedding1[i] * embedding2[i];\n norm1 += embedding1[i] * embedding1[i];\n norm2 += embedding2[i] * embedding2[i];\n }\n\n return dotProduct / (Math.sqrt(norm1) * Math.sqrt(norm2));\n}\n\n/**\n * Score a resume against job requirements\n */\nexport async function scoreResume(resume: Resume, job: JobRequirement): Promise {\n // Analyze resume to extract structured information\n const resumeAnalysis = await analyzeResume(resume.content);\n \n // Calculate skill matches\n const skillMatches = await calculateSkillMatches(\n resumeAnalysis.skills,\n job.requiredSkills,\n job.preferredSkills\n );\n \n // Calculate experience score (0-100)\n const experienceScore = calculateExperienceScore(resumeAnalysis.yearsOfExperience);\n \n // Calculate education score (0-100)\n const educationScore = calculateEducationScore(resumeAnalysis.educationLevel);\n \n // Calculate overall relevance score\n const relevanceScore = await calculateRelevanceScore(resume.content, job.description);\n \n // Calculate overall score (weighted average)\n const overallScore = (\n skillMatches.reduce((sum, match) => sum + match.score, 0) / skillMatches.length * 0.5 +\n experienceScore * 0.2 +\n educationScore * 0.1 +\n relevanceScore * 0.2\n );\n \n return {\n overallScore,\n skillMatches,\n experienceScore,\n educationScore,\n relevanceScore\n };\n}\n\n/**\n * Calculate skill match scores\n */\nasync function calculateSkillMatches(\n resumeSkills: string[],\n requiredSkills: string[],\n preferredSkills: string[]\n): Promise<{ skill: string; score: number; required: boolean }[]> {\n const allSkills = [...new Set([...requiredSkills, ...preferredSkills])];\n const results = [];\n \n for (const skill of allSkills) {\n const isRequired = requiredSkills.includes(skill);\n \n // Check for exact match\n if (resumeSkills.some(s => s.toLowerCase() === skill.toLowerCase())) {\n results.push({\n skill,\n score: 100,\n required: isRequired\n });\n continue;\n }\n \n // Check for semantic similarity\n let bestScore = 0;\n for (const resumeSkill of resumeSkills) {\n const similarity = await calculateSimilarity(skill, resumeSkill);\n bestScore = Math.max(bestScore, similarity * 100);\n }\n \n results.push({\n skill,\n score: bestScore,\n required: isRequired\n });\n }\n \n return results;\n}\n\n/**\n * Calculate experience score based on years of experience\n */\nfunction calculateExperienceScore(yearsOfExperience: number): number {\n // Simple scoring: 0-2 years (0-40), 3-5 years (40-70), 6+ years (70-100)\n if (yearsOfExperience <= 2) {\n return (yearsOfExperience / 2) * 40;\n } else if (yearsOfExperience <= 5) {\n return 40 + ((yearsOfExperience - 2) / 3) * 30;\n } else {\n return Math.min(70 + ((yearsOfExperience - 5) / 5) * 30, 100);\n }\n}\n\n/**\n * Calculate education score based on highest degree\n */\nfunction calculateEducationScore(educationLevel: string): number {\n const level = educationLevel.toLowerCase();\n \n if (level.includes(\"phd\") || level.includes(\"doctorate\")) {\n return 100;\n } else if (level.includes(\"master\")) {\n return 80;\n } else if (level.includes(\"bachelor\") || level.includes(\"bs\") || level.includes(\"ba\")) {\n return 60;\n } else if (level.includes(\"associate\") || level.includes(\"diploma\")) {\n return 40;\n } else if (level.includes(\"certificate\") || level.includes(\"certification\")) {\n return 30;\n } else {\n return 20; // High school or unknown\n }\n}\n\n/**\n * Calculate overall relevance score between resume and job description\n */\nasync function calculateRelevanceScore(resumeText: string, jobDescription: string): Promise {\n // Use semantic similarity between resume and job description\n const similarity = await calculateSimilarity(resumeText, jobDescription);\n return similarity * 100;\n}",[],[103,112],"dfd113c4-34cd-11f0-8dd3-9e149126039e",["D",1747671927639],"import { Hono } from \"https://esm.sh/hono@3.11.7\";\nimport { cors } from \"https://esm.sh/hono@3.11.7/middleware\";\nimport { readFile, serveFile } from \"https://esm.town/v/std/utils/index.ts\";\nimport { initDatabase, saveResume, getResume, getAllResumes, \n saveJobRequirement, getJobRequirement, getAllJobRequirements,\n saveScreeningResult, getScreeningResults } from \"./database.ts\";\nimport { scoreResume } from \"./nlp.ts\";\nimport { Resume, JobRequirement, ApiResponse } from \"../shared/types.ts\";\n\n// Initialize the app\nconst app = new Hono();\n\n// Unwrap Hono errors to see original error details\napp.onError((err, c) => {\n throw err;\n});\n\n// Add CORS middleware\napp.use(\"*\", cors());\n\n// Initialize database on startup\napp.use(\"*\", async (c, next) => {\n try {\n await initDatabase();\n } catch (error) {\n console.error(\"Database initialization error:\", error);\n }\n await next();\n});\n\n// Serve static files\napp.get(\"/frontend/*\", c => serveFile(c.req.path, import.meta.url));\napp.get(\"/shared/*\", c => serveFile(c.req.path, import.meta.url));\n\n// Serve the main HTML page with initial data\napp.get(\"/\", async c => {\n let html = await readFile(\"/frontend/index.html\", import.meta.url);\n \n try {\n // Get initial data for bootstrapping the app\n const resumes = await getAllResumes();\n const jobs = await getAllJobRequirements();\n \n // Inject data to avoid extra round-trips\n const dataScript = ``;\n \n html = html.replace(\"\", `${dataScript}`);\n } catch (error) {\n console.error(\"Error preparing initial data:\", error);\n }\n \n return c.html(html);\n});\n\n// API Routes\n\n// Resume endpoints\napp.post(\"/api/resumes\", async c => {\n try {\n const data = await c.req.json();\n \n if (!data.name || !data.content) {\n return c.json>({\n success: false,\n error: \"Resume name and content are required\"\n }, 400);\n }\n \n const resume: Resume = {\n name: data.name,\n content: data.content,\n uploadDate: new Date().toISOString()\n };\n \n const id = await saveResume(resume);\n \n return c.json>({\n success: true,\n data: { id }\n }, 201);\n } catch (error) {\n console.error(\"Error saving resume:\", error);\n return c.json>({\n success: false,\n error: \"Failed to save resume\"\n }, 500);\n }\n});\n\napp.get(\"/api/resumes\", async c => {\n try {\n const resumes = await getAllResumes();\n return c.json>({\n success: true,\n data: resumes\n });\n } catch (error) {\n console.error(\"Error fetching resumes:\", error);\n return c.json>({\n success: false,\n error: \"Failed to fetch resumes\"\n }, 500);\n }\n});\n\napp.get(\"/api/resumes/:id\", async c => {\n try {\n const id = parseInt(c.req.param(\"id\"));\n const resume = await getResume(id);\n \n if (!resume) {\n return c.json>({\n success: false,\n error: \"Resume not found\"\n }, 404);\n }\n \n return c.json>({\n success: true,\n data: resume\n });\n } catch (error) {\n console.error(\"Error fetching resume:\", error);\n return c.json>({\n success: false,\n error: \"Failed to fetch resume\"\n }, 500);\n }\n});\n\n// Job requirement endpoints\napp.post(\"/api/jobs\", async c => {\n try {\n const data = await c.req.json();\n \n if (!data.title || !data.description) {\n return c.json>({\n success: false,\n error: \"Job title and description are required\"\n }, 400);\n }\n \n const job: JobRequirement = {\n title: data.title,\n description: data.description,\n requiredSkills: data.requiredSkills || [],\n preferredSkills: data.preferredSkills || [],\n creationDate: new Date().toISOString()\n };\n \n const id = await saveJobRequirement(job);\n \n return c.json>({\n success: true,\n data: { id }\n }, 201);\n } catch (error) {\n console.error(\"Error saving job requirement:\", error);\n return c.json>({\n success: false,\n error: \"Failed to save job requirement\"\n }, 500);\n }\n});\n\napp.get(\"/api/jobs\", async c => {\n try {\n const jobs = await getAllJobRequirements();\n return c.json>({\n success: true,\n data: jobs\n });\n } catch (error) {\n console.error(\"Error fetching job requirements:\", error);\n return c.json>({\n success: false,\n error: \"Failed to fetch job requirements\"\n }, 500);\n }\n});\n\napp.get(\"/api/jobs/:id\", async c => {\n try {\n const id = parseInt(c.req.param(\"id\"));\n const job = await getJobRequirement(id);\n \n if (!job) {\n return c.json>({\n success: false,\n error: \"Job requirement not found\"\n }, 404);\n }\n \n return c.json>({\n success: true,\n data: job\n });\n } catch (error) {\n console.error(\"Error fetching job requirement:\", error);\n return c.json>({\n success: false,\n error: \"Failed to fetch job requirement\"\n }, 500);\n }\n});\n\n// Screening endpoints\napp.post(\"/api/screen\", async c => {\n try {\n const { resumeId, jobId } = await c.req.json<{ resumeId: number; jobId: number }>();\n \n if (!resumeId || !jobId) {\n return c.json>({\n success: false,\n error: \"Resume ID and Job ID are required\"\n }, 400);\n }\n \n const resume = await getResume(resumeId);\n const job = await getJobRequirement(jobId);\n \n if (!resume || !job) {\n return c.json>({\n success: false,\n error: \"Resume or job not found\"\n }, 404);\n }\n \n // Score the resume against the job requirements\n const scores = await scoreResume(resume, job);\n \n // Save the screening result\n await saveScreeningResult(resumeId, jobId, scores);\n \n return c.json>({\n success: true,\n data: { scores }\n });\n } catch (error) {\n console.error(\"Error screening resume:\", error);\n return c.json>({\n success: false,\n error: \"Failed to screen resume\"\n }, 500);\n }\n});\n\napp.post(\"/api/screen-batch\", async c => {\n try {\n const { resumeIds, jobId } = await c.req.json<{ resumeIds: number[]; jobId: number }>();\n \n if (!resumeIds || !resumeIds.length || !jobId) {\n return c.json>({\n success: false,\n error: \"Resume IDs and Job ID are required\"\n }, 400);\n }\n \n const job = await getJobRequirement(jobId);\n \n if (!job) {\n return c.json>({\n success: false,\n error: \"Job not found\"\n }, 404);\n }\n \n // Process each resume\n for (const resumeId of resumeIds) {\n const resume = await getResume(resumeId);\n \n if (resume) {\n const scores = await scoreResume(resume, job);\n await saveScreeningResult(resumeId, jobId, scores);\n }\n }\n \n // Get all screening results for this job\n const results = await getScreeningResults(jobId);\n \n return c.json>({\n success: true,\n data: { results }\n });\n } catch (error) {\n console.error(\"Error batch screening resumes:\", error);\n return c.json>({\n success: false,\n error: \"Failed to screen resumes\"\n }, 500);\n }\n});\n\napp.get(\"/api/results/:jobId\", async c => {\n try {\n const jobId = parseInt(c.req.param(\"jobId\"));\n const results = await getScreeningResults(jobId);\n \n return c.json>({\n success: true,\n data: results\n });\n } catch (error) {\n console.error(\"Error fetching screening results:\", error);\n return c.json>({\n success: false,\n error: \"Failed to fetch screening results\"\n }, 500);\n }\n});\n\n// Export the app for HTTP val\nexport default app.fetch;",[],[103,121],"bb65f6da-34cd-11f0-ab63-9e149126039e",["D",1747671866536],"import { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\nimport { Resume, JobRequirement, ResumeScore } from \"../shared/types.ts\";\n\n// Table names with versioning to allow for schema changes\nconst RESUMES_TABLE = \"resumes_v1\";\nconst JOB_REQUIREMENTS_TABLE = \"job_requirements_v1\";\nconst SCREENING_RESULTS_TABLE = \"screening_results_v1\";\n\n// Initialize database tables\nexport async function initDatabase() {\n // Create resumes table\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ${RESUMES_TABLE} (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n name TEXT NOT NULL,\n content TEXT NOT NULL,\n upload_date TEXT NOT NULL\n )\n `);\n\n // Create job requirements table\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ${JOB_REQUIREMENTS_TABLE} (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n title TEXT NOT NULL,\n description TEXT NOT NULL,\n required_skills TEXT NOT NULL,\n preferred_skills TEXT NOT NULL,\n creation_date TEXT NOT NULL\n )\n `);\n\n // Create screening results table\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ${SCREENING_RESULTS_TABLE} (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n resume_id INTEGER NOT NULL,\n job_id INTEGER NOT NULL,\n overall_score REAL NOT NULL,\n skill_matches TEXT NOT NULL,\n experience_score REAL NOT NULL,\n education_score REAL NOT NULL,\n relevance_score REAL NOT NULL,\n screening_date TEXT NOT NULL,\n FOREIGN KEY (resume_id) REFERENCES ${RESUMES_TABLE}(id),\n FOREIGN KEY (job_id) REFERENCES ${JOB_REQUIREMENTS_TABLE}(id)\n )\n `);\n}\n\n// Resume operations\nexport async function saveResume(resume: Resume): Promise {\n const result = await sqlite.execute(\n `INSERT INTO ${RESUMES_TABLE} (name, content, upload_date) VALUES (?, ?, ?)`,\n [resume.name, resume.content, resume.uploadDate]\n );\n return result.lastInsertId as number;\n}\n\nexport async function getResume(id: number): Promise {\n const result = await sqlite.execute(\n `SELECT * FROM ${RESUMES_TABLE} WHERE id = ?`,\n [id]\n );\n \n if (result.rows.length === 0) {\n return null;\n }\n \n const row = result.rows[0];\n return {\n id: row.id as number,\n name: row.name as string,\n content: row.content as string,\n uploadDate: row.upload_date as string\n };\n}\n\nexport async function getAllResumes(): Promise {\n const result = await sqlite.execute(`SELECT * FROM ${RESUMES_TABLE} ORDER BY upload_date DESC`);\n \n return result.rows.map(row => ({\n id: row.id as number,\n name: row.name as string,\n content: row.content as string,\n uploadDate: row.upload_date as string\n }));\n}\n\n// Job requirement operations\nexport async function saveJobRequirement(job: JobRequirement): Promise {\n const result = await sqlite.execute(\n `INSERT INTO ${JOB_REQUIREMENTS_TABLE} \n (title, description, required_skills, preferred_skills, creation_date) \n VALUES (?, ?, ?, ?, ?)`,\n [\n job.title, \n job.description, \n JSON.stringify(job.requiredSkills), \n JSON.stringify(job.preferredSkills), \n job.creationDate\n ]\n );\n return result.lastInsertId as number;\n}\n\nexport async function getJobRequirement(id: number): Promise {\n const result = await sqlite.execute(\n `SELECT * FROM ${JOB_REQUIREMENTS_TABLE} WHERE id = ?`,\n [id]\n );\n \n if (result.rows.length === 0) {\n return null;\n }\n \n const row = result.rows[0];\n return {\n id: row.id as number,\n title: row.title as string,\n description: row.description as string,\n requiredSkills: JSON.parse(row.required_skills as string),\n preferredSkills: JSON.parse(row.preferred_skills as string),\n creationDate: row.creation_date as string\n };\n}\n\nexport async function getAllJobRequirements(): Promise {\n const result = await sqlite.execute(\n `SELECT * FROM ${JOB_REQUIREMENTS_TABLE} ORDER BY creation_date DESC`\n );\n \n return result.rows.map(row => ({\n id: row.id as number,\n title: row.title as string,\n description: row.description as string,\n requiredSkills: JSON.parse(row.required_skills as string),\n preferredSkills: JSON.parse(row.preferred_skills as string),\n creationDate: row.creation_date as string\n }));\n}\n\n// Screening results operations\nexport async function saveScreeningResult(\n resumeId: number, \n jobId: number, \n scores: ResumeScore\n): Promise {\n const result = await sqlite.execute(\n `INSERT INTO ${SCREENING_RESULTS_TABLE} \n (resume_id, job_id, overall_score, skill_matches, experience_score, education_score, relevance_score, screening_date) \n VALUES (?, ?, ?, ?, ?, ?, ?, ?)`,\n [\n resumeId,\n jobId,\n scores.overallScore,\n JSON.stringify(scores.skillMatches),\n scores.experienceScore,\n scores.educationScore,\n scores.relevanceScore,\n new Date().toISOString()\n ]\n );\n return result.lastInsertId as number;\n}\n\nexport async function getScreeningResults(jobId: number): Promise {\n const result = await sqlite.execute(\n `SELECT sr.*, r.name as resume_name, j.title as job_title\n FROM ${SCREENING_RESULTS_TABLE} sr\n JOIN ${RESUMES_TABLE} r ON sr.resume_id = r.id\n JOIN ${JOB_REQUIREMENTS_TABLE} j ON sr.job_id = j.id\n WHERE sr.job_id = ?\n ORDER BY sr.overall_score DESC`,\n [jobId]\n );\n \n return result.rows.map((row, index) => ({\n resumeId: row.resume_id as number,\n resumeName: row.resume_name as string,\n jobId: row.job_id as number,\n jobTitle: row.job_title as string,\n scores: {\n overallScore: row.overall_score as number,\n skillMatches: JSON.parse(row.skill_matches as string),\n experienceScore: row.experience_score as number,\n educationScore: row.education_score as number,\n relevanceScore: row.relevance_score as number\n },\n rank: index + 1\n }));\n}",[],[103,101],["D",1747671866536],[218,219,220],[103],["D",1747671649644],{"_11":12,"_167":67},{"_11":12,"_163":-5,"_66":67,"_25":65,"_164":165,"_166":24},{"_37":115,"_13":16,"_31":34,"_155":122,"_156":-5,"_157":-5,"_158":-5,"_159":-5,"_160":-5,"_161":14,"_162":12},"ctx",{"_49":-5,"_187":291,"_292":293,"_294":295,"_296":24,"_297":24,"_298":24,"_299":196,"_300":-5,"_301":-7,"_302":-7,"_303":20,"_304":24,"_305":24,"_306":-5,"_307":24,"_308":24,"_309":24,"_310":-5},"ENV",{"_262":263,"_264":265,"_266":16,"_267":268,"_269":270,"_271":272,"_273":274,"_275":276,"_277":278,"_279":280,"_281":282,"_283":284,"_285":286,"_287":288,"_289":290},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","c73f21da490985bff19c279eb0e09db24d688e4f","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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