[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_191":192,"_193":194},"routes/_app.x.$handle.$name",{"_96":95,"_31":43,"_105":86,"_26":106,"_13":14,"_107":-5,"_11":108,"_109":110,"_111":112,"_113":114,"_115":116,"_20":-5,"_117":-5,"_118":-5,"_119":24,"_120":121,"_122":24,"_123":-5,"_124":-5,"_125":126},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_41":42,"_31":43,"_36":44,"_45":-5,"_46":24,"_47":48,"_49":48,"_50":51,"_52":-5,"_18":53,"_54":-5,"_55":24,"_56":-5,"_57":-5,"_58":59,"_60":61,"_62":63,"_64":65,"_66":67,"_68":-5,"_69":70,"_71":70,"_72":73,"_74":73,"_75":73,"_76":70,"_77":70,"_78":79,"_80":81,"_82":83,"_84":24},"version",4,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":-7,"_23":24,"_25":24,"_26":27,"_28":27},"file",{"_29":30,"_31":27,"_32":33,"_34":35,"_36":37,"_38":14,"_39":40,"_23":24},"previous","deleted",false,"deletedHere","path","analysis.ts","latestPath","fileId","b29bf888-fa75-11f0-85d4-42dde27851f2","name","fileType","script","content","import { readFile } from \"https://esm.town/v/std/utils/index.ts\";\nimport OpenAI from \"npm:openai\";\nimport {\n getAllResponses,\n getResponsesByDisappointment,\n storeInsights,\n updateResponseAnalysis,\n type SurveyResponse,\n} from \"./db.ts\";\n\nconst PROMPT = await readFile(\"/PROMPT.txt\", import.meta.url);\n\nconst openai = new OpenAI({ apiKey: Deno.env.get(\"OPENAI_API_KEY\") });\n\ninterface ResponseAnalysis {\n persona: string;\n themes: {\n benefits: string[];\n improvements: string[];\n };\n sentiment_notes: string;\n}\n\ninterface AggregateInsights {\n pmf_score: number;\n segmented_pmf_score: number;\n total_responses: number;\n very_disappointed_count: number;\n somewhat_disappointed_count: number;\n not_disappointed_count: number;\n hxc_profile: string;\n top_benefits: string[];\n top_improvements: string[];\n roadmap_double_down: string[];\n roadmap_address_gaps: string[];\n personas_in_segment: string[];\n}\n\nexport async function analyzeResponse(response: SurveyResponse): Promise {\n const prompt = `${PROMPT}\n\nAnalyze this single survey response:\n\n**Disappointment Level:** ${response.disappointment}\n**What type of people would benefit most?** ${response.user_type}\n**Main benefit received:** ${response.main_benefit}\n**How to improve?** ${response.improvement}\n\nReturn ONLY valid JSON with this exact structure:\n{\n \"persona\": \"string - 1-3 word professional role\",\n \"themes\": {\n \"benefits\": [\"array of key benefit themes mentioned\"],\n \"improvements\": [\"array of improvement requests\"]\n },\n \"sentiment_notes\": \"brief observation about user sentiment\"\n}`;\n\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n messages: [{ role: \"user\", content: prompt }],\n response_format: { type: \"json_object\" },\n max_tokens: 500,\n });\n\n const content = completion.choices[0].message.content || \"{}\";\n const analysis = JSON.parse(content) as ResponseAnalysis;\n\n // Update the response with persona and analysis\n await updateResponseAnalysis(\n response.id,\n analysis.persona,\n JSON.stringify(analysis),\n );\n\n return analysis;\n}\n\nexport async function generateAggregateInsights(): Promise {\n const allResponses = await getAllResponses();\n const veryDisappointed = await getResponsesByDisappointment(\"very\");\n const somewhatDisappointed = await getResponsesByDisappointment(\"somewhat\");\n const notDisappointed = await getResponsesByDisappointment(\"not\");\n\n const total = allResponses.length;\n const veryCount = veryDisappointed.length;\n const somewhatCount = somewhatDisappointed.length;\n const notCount = notDisappointed.length;\n\n // Basic PMF Score\n const pmfScore = total > 0 ? (veryCount / total) * 100 : 0;\n\n // Get personas from \"very disappointed\" group\n const vdPersonas = new Set();\n for (const r of veryDisappointed) {\n if (r.persona) vdPersonas.add(r.persona);\n }\n\n // Segmented PMF: filter to only personas that appear in \"very disappointed\"\n const segmentedResponses = allResponses.filter(\n (r) => r.persona && vdPersonas.has(r.persona),\n );\n const segmentedVD = segmentedResponses.filter((r) => r.disappointment === \"very\").length;\n const segmentedTotal = segmentedResponses.length;\n const segmentedPmfScore = segmentedTotal > 0 ? (segmentedVD / segmentedTotal) * 100 : 0;\n\n // Generate AI insights\n const insights = await generateAIInsights(\n veryDisappointed,\n somewhatDisappointed,\n Array.from(vdPersonas),\n );\n\n const aggregateInsights: AggregateInsights = {\n pmf_score: Math.round(pmfScore * 10) / 10,\n segmented_pmf_score: Math.round(segmentedPmfScore * 10) / 10,\n total_responses: total,\n very_disappointed_count: veryCount,\n somewhat_disappointed_count: somewhatCount,\n not_disappointed_count: notCount,\n hxc_profile: insights.hxc_profile,\n top_benefits: insights.top_benefits,\n top_improvements: insights.top_improvements,\n roadmap_double_down: insights.roadmap_double_down,\n roadmap_address_gaps: insights.roadmap_address_gaps,\n personas_in_segment: Array.from(vdPersonas),\n };\n\n // Store insights\n await storeInsights({\n ...aggregateInsights,\n top_benefits: JSON.stringify(aggregateInsights.top_benefits),\n top_improvements: JSON.stringify(aggregateInsights.top_improvements),\n roadmap_double_down: JSON.stringify(aggregateInsights.roadmap_double_down),\n roadmap_address_gaps: JSON.stringify(aggregateInsights.roadmap_address_gaps),\n personas_in_segment: JSON.stringify(aggregateInsights.personas_in_segment),\n });\n\n return aggregateInsights;\n}\n\nasync function generateAIInsights(\n veryDisappointed: SurveyResponse[],\n somewhatDisappointed: SurveyResponse[],\n vdPersonas: string[],\n): Promise<{\n hxc_profile: string;\n top_benefits: string[];\n top_improvements: string[];\n roadmap_double_down: string[];\n roadmap_address_gaps: string[];\n}> {\n if (veryDisappointed.length === 0) {\n return {\n hxc_profile: \"Not enough data yet. Need responses from users who would be 'very disappointed' without your product.\",\n top_benefits: [],\n top_improvements: [],\n roadmap_double_down: [],\n roadmap_address_gaps: [],\n };\n }\n\n // Collect benefits from \"very disappointed\" users\n const vdBenefits = veryDisappointed.map((r) => r.main_benefit).join(\"\\n- \");\n const vdUserTypes = veryDisappointed.map((r) => r.user_type).join(\"\\n- \");\n\n // Collect improvements from \"somewhat disappointed\" users\n const sdImprovements = somewhatDisappointed.map((r) => r.improvement).join(\"\\n- \");\n\n const prompt = `${PROMPT}\n\n## Current Survey Data\n\n### \"Very Disappointed\" Users (${veryDisappointed.length} responses)\n\n**Who they say would benefit most (Q2):**\n- ${vdUserTypes}\n\n**Main benefits they receive (Q3):**\n- ${vdBenefits}\n\n### \"Somewhat Disappointed\" Users (${somewhatDisappointed.length} responses)\n\n**How they want the product improved (Q4):**\n- ${sdImprovements || \"No responses yet\"}\n\n### Personas in \"Very Disappointed\" segment:\n${vdPersonas.join(\", \") || \"None identified yet\"}\n\n---\n\nBased on the Superhuman PMF framework, generate insights:\n\n1. **HXC Profile**: Create a detailed High-Expectation Customer profile ONLY from \"very disappointed\" Q2 answers. Write in third person, 2-3 paragraphs, describing their role, work patterns, pain points, and mindset. Be specific and vivid.\n\n2. **Top Benefits**: Extract the 3-5 most common benefit themes from \"very disappointed\" Q3 answers. These are what users love most.\n\n3. **Top Improvements**: Extract the 3-5 most requested improvements from \"somewhat disappointed\" Q4 answers. These are barriers to deeper adoption.\n\n4. **Roadmap - Double Down**: Suggest 3-5 specific features/improvements that enhance what users already love (based on benefits).\n\n5. **Roadmap - Address Gaps**: Suggest 3-5 specific features/improvements that remove barriers (based on improvement requests).\n\nReturn ONLY valid JSON:\n{\n \"hxc_profile\": \"detailed 2-3 paragraph profile\",\n \"top_benefits\": [\"benefit 1\", \"benefit 2\", ...],\n \"top_improvements\": [\"improvement 1\", \"improvement 2\", ...],\n \"roadmap_double_down\": [\"feature 1\", \"feature 2\", ...],\n \"roadmap_address_gaps\": [\"feature 1\", \"feature 2\", ...]\n}`;\n\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [{ role: \"user\", content: prompt }],\n response_format: { type: \"json_object\" },\n max_tokens: 2000,\n });\n\n const content = completion.choices[0].message.content || \"{}\";\n return JSON.parse(content);\n}\n\nexport async function processNewResponse(response: SurveyResponse): Promise {\n // Analyze the individual response\n await analyzeResponse(response);\n\n // Regenerate aggregate insights\n await generateAggregateInsights();\n}\n","createdAt",["D",1769404386309],"branchVersion","branchId","7d938fa2-fa75-11f0-aded-42dde27851f2","id","7d9310a4-fa75-11f0-aded-42dde27851f2","pmf-survey",["D",1769404297336],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","Superhuman PMF Framework - 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WHERE id = ?`,\n [persona, analysis, id],\n );\n}\n\nexport async function getAllResponses(): Promise {\n const result = await sqlite.execute(\n `SELECT * FROM ${RESPONSES_TABLE} ORDER BY timestamp DESC`,\n );\n return rowsToResponses(result);\n}\n\nexport async function getResponseById(id: number): Promise {\n const result = await sqlite.execute(\n `SELECT * FROM ${RESPONSES_TABLE} WHERE id = ?`,\n [id],\n );\n const responses = rowsToResponses(result);\n return responses[0] || null;\n}\n\nexport async function getResponsesByDisappointment(\n level: \"very\" | \"somewhat\" | \"not\",\n): Promise {\n const result = await sqlite.execute(\n `SELECT * FROM ${RESPONSES_TABLE} WHERE disappointment = ? ORDER BY timestamp DESC`,\n [level],\n );\n return rowsToResponses(result);\n}\n\nexport async function getResponseCounts(): Promise<{\n very: number;\n somewhat: number;\n not: number;\n total: number;\n}> {\n const result = await sqlite.execute(\n `SELECT disappointment, COUNT(*) as count FROM ${RESPONSES_TABLE} GROUP BY disappointment`,\n );\n\n const counts = { very: 0, somewhat: 0, not: 0, total: 0 };\n for (const row of result.rows as any[]) {\n const level = row[0] as \"very\" | \"somewhat\" | \"not\";\n const count = row[1] as number;\n counts[level] = count;\n counts.total += count;\n }\n return counts;\n}\n\nexport async function storeInsights(insights: Omit): Promise {\n await sqlite.execute(\n `INSERT INTO ${INSIGHTS_TABLE} \n (timestamp, pmf_score, segmented_pmf_score, total_responses, \n very_disappointed_count, somewhat_disappointed_count, not_disappointed_count,\n hxc_profile, top_benefits, top_improvements, roadmap_double_down, \n roadmap_address_gaps, personas_in_segment)\n VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)`,\n [\n Date.now(),\n insights.pmf_score,\n insights.segmented_pmf_score,\n insights.total_responses,\n insights.very_disappointed_count,\n insights.somewhat_disappointed_count,\n insights.not_disappointed_count,\n insights.hxc_profile,\n insights.top_benefits,\n insights.top_improvements,\n insights.roadmap_double_down,\n insights.roadmap_address_gaps,\n insights.personas_in_segment,\n ],\n );\n}\n\nexport async function getLatestInsights(): Promise {\n const result = await sqlite.execute(\n `SELECT * FROM ${INSIGHTS_TABLE} ORDER BY timestamp DESC LIMIT 1`,\n );\n const insights = rowsToInsights(result);\n return insights[0] || null;\n}\n\nexport async function getAllInsights(): Promise {\n const result = await sqlite.execute(\n `SELECT * FROM ${INSIGHTS_TABLE} ORDER BY timestamp DESC`,\n );\n return rowsToInsights(result);\n}\n\nfunction rowsToResponses(result: { columns: string[]; rows: unknown[] }): SurveyResponse[] {\n return result.rows.map((row: any) => {\n const obj: Record = {};\n result.columns.forEach((col, idx) => {\n obj[col] = row[idx];\n });\n return obj as SurveyResponse;\n });\n}\n\nfunction rowsToInsights(result: { columns: string[]; rows: unknown[] }): Insights[] {\n return result.rows.map((row: any) => {\n const obj: Record = {};\n result.columns.forEach((col, idx) => {\n obj[col] = row[idx];\n });\n return obj as Insights;\n });\n}\n\nasync function createTables() {\n await sqlite.execute(\n `CREATE TABLE IF NOT EXISTS ${RESPONSES_TABLE} (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n timestamp INTEGER NOT NULL,\n disappointment TEXT NOT NULL,\n user_type TEXT NOT NULL,\n main_benefit TEXT NOT NULL,\n improvement TEXT NOT NULL,\n email TEXT,\n persona TEXT,\n analysis TEXT\n )`,\n );\n\n await sqlite.execute(\n `CREATE TABLE IF NOT EXISTS ${INSIGHTS_TABLE} (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n timestamp INTEGER NOT NULL,\n pmf_score REAL NOT NULL,\n segmented_pmf_score REAL NOT NULL,\n total_responses INTEGER NOT NULL,\n very_disappointed_count INTEGER NOT NULL,\n somewhat_disappointed_count INTEGER NOT NULL,\n not_disappointed_count INTEGER NOT NULL,\n hxc_profile TEXT NOT NULL,\n top_benefits TEXT NOT NULL,\n top_improvements TEXT NOT NULL,\n roadmap_double_down TEXT NOT NULL,\n roadmap_address_gaps TEXT NOT NULL,\n personas_in_segment TEXT NOT NULL\n )`,\n );\n}\n","label",[132],"b29bf7de-fa75-11f0-85d4-42dde27851f2",["D",1769404386309],[27],"88f3af6c-fa75-11f0-831a-42dde27851f2","README.md","88f3b05c-fa75-11f0-831a-42dde27851f2",1,["D",1769404316408],"# PMF Survey\n\nMeasure and optimize your product-market fit using the Superhuman framework from [First Round Review](https://review.firstround.com/how-superhuman-built-an-engine-to-find-product-market-fit/).\n\n## What this does\n\n1. Collect survey responses with the 4 proven PMF questions\n2. AI automatically analyzes responses and assigns personas\n3. Calculate your PMF Score (benchmark: 40%)\n4. Generate actionable insights:\n - High-Expectation Customer (HXC) profile\n - What users love (from \"very disappointed\" responses)\n - What holds users back (from \"somewhat disappointed\" responses)\n - Roadmap recommendations (50% double down / 50% address gaps)\n\n## The Framework\n\nThe Superhuman PMF engine uses Sean Ellis's leading indicator: **% of users who would be \"very disappointed\"** without your product.\n\n**Benchmark:** Companies with strong traction exceed **40%**. Companies struggling to grow are below 40%.\n\n### The 4 Survey Questions\n\n1. **How would you feel if you could no longer use [product]?**\n - Very disappointed\n - Somewhat disappointed\n - Not disappointed\n\n2. **What type of people do you think would most benefit from [product]?**\n\n3. **What is the main benefit you receive from [product]?**\n\n4. **How can we improve [product] for you?**\n\n### The 4-Step Process\n\n1. **Segment** to find supporters and paint a picture of your high-expectation customers\n2. **Analyze** feedback to convert on-the-fence users into fanatics\n3. **Build roadmap** by doubling down on what users love AND addressing what holds others back\n4. **Repeat** and track your PMF score over time\n\n## Getting started\n\n1. Click [**Remix**](/?intent=remix)\n2. Store your `OPENAI_API_KEY` as an environment variable\n3. Customize [`PROMPT.txt`](./PROMPT.txt) with your product name and context\n4. Share the survey link with your users\n5. Watch insights populate in your dashboard\n\n## How it works\n\n**Survey**\n- Users complete the 4-question survey\n- Responses are stored with timestamp\n\n**AI Analysis**\n- Each response is analyzed to assign a persona\n- Personas are inferred from the \"type of people\" answer (Q2)\n- Main benefits and improvement requests are extracted\n\n**Dashboard Insights**\n- **PMF Score**: % of \"very disappointed\" responses\n- **Segmented PMF**: Score filtered to personas from the \"very disappointed\" group\n- **HXC Profile**: AI-generated High-Expectation Customer description\n- **Benefits Word Cloud**: What your happiest users love (from Q3)\n- **Improvements Word Cloud**: What holds \"somewhat disappointed\" users back (from Q4)\n- **Roadmap Recommendations**: Split 50/50 between doubling down and addressing gaps\n\n## Key Insights\n\nFrom the original Superhuman article:\n\n> \"If you only double down on what users love, your product-market fit score won't increase. If you only address what holds users back, your competition will likely overtake you.\"\n\nThe framework teaches you to:\n- **Ignore** \"not disappointed\" users entirely - they won't convert\n- **Focus on** \"very disappointed\" users to understand what to double down on\n- **Convert** \"somewhat disappointed\" users who value your main benefit\n",[145],"93ab5522-fa75-11f0-bd78-42dde27851f2","PROMPT.txt","93ab55ea-fa75-11f0-bd78-42dde27851f2",2,true,["D",1769404334403],"You are an expert product analyst specializing in the Superhuman PMF (Product-Market Fit) framework.\n\nYour job is to analyze survey responses and extract key insights for product-market fit optimization.\n\n## Your Product Context\n\nProduct Name: [YOUR PRODUCT NAME]\nProduct Description: [Brief description of what your product does]\n\n## Survey Response Analysis\n\nFor each survey response, you will:\n\n1. **Assign a Persona** based on the user's answer to \"What type of people would benefit most?\"\n - Extract the most relevant professional role or user type\n - Examples: \"Founder\", \"Manager\", \"Engineer\", \"Sales Rep\", \"Designer\", \"Executive\"\n - Keep personas concise (1-3 words)\n\n2. **Extract Key Themes** from the main benefit (Q3) and improvement requests (Q4)\n - Identify the core value propositions mentioned\n - Note specific features or capabilities valued\n - Flag concrete improvement requests\n\nReturn your analysis as JSON:\n\n{\n \"persona\": string,\n \"themes\": {\n \"benefits\": string[],\n \"improvements\": string[]\n },\n \"sentiment_notes\": string\n}\n\n## Aggregate Analysis\n\nWhen analyzing all responses together for insights, follow the Superhuman framework:\n\n### PMF Score Calculation\n- Count responses by disappointment level\n- PMF Score = (Very Disappointed / Total) × 100\n- Benchmark: 40% indicates strong product-market fit\n\n### Segmentation Strategy\n1. Identify all personas from \"very disappointed\" responses\n2. Filter to only include users with those personas\n3. Recalculate PMF Score for segmented group\n4. This reveals true fit within your ideal user base\n\n### High-Expectation Customer (HXC) Profile\nUsing ONLY \"very disappointed\" responses to Q2 (\"What type of people...\"):\n- Users describe themselves when answering this question\n- Synthesize into a rich, detailed persona description\n- Include: role, work patterns, pain points, goals, mindset\n\n### Benefits Analysis (What to Double Down On)\nFrom \"very disappointed\" users' Q3 answers:\n- Identify the most common benefits mentioned\n- These are your core value propositions\n- These should inform 50% of your roadmap\n\n### Improvements Analysis (What Holds Users Back)\nFrom \"somewhat disappointed\" users who value your main benefit:\n- First filter to those whose Q3 answer mentions your core benefits\n- Then analyze their Q4 answers\n- These are the barriers to deeper adoption\n- These should inform the other 50% of your roadmap\n\n### Roadmap Recommendations\nSplit evenly:\n- **Double Down (50%)**: Features/improvements that enhance what users already love\n- **Address Gaps (50%)**: Features that remove barriers for almost-fans\n\n### Important Notes\n- IGNORE \"not disappointed\" users entirely - they won't convert\n- The \"somewhat disappointed\" group matters only if they value your main benefit\n- Track PMF Score over time to measure progress\n",[152],"pulls","branches","forks","envVars",{"_166":59,"_168":59},"connections",{"_166":59,"_167":59},"count","unfilledCount","emptyCount",{"_41":151,"_31":152,"_29":153,"_134":-5,"_39":40,"_38":154,"_136":155,"_36":183,"_13":59,"_32":20,"_34":157,"_23":24,"_139":-5,"_174":184,"_26":185},{"_41":144,"_31":145,"_29":146,"_134":-5,"_39":40,"_38":147,"_136":24,"_36":180,"_13":59,"_32":20,"_34":149,"_23":24,"_139":-5,"_174":181,"_26":182},{"_41":141,"_31":27,"_29":30,"_134":-5,"_39":40,"_38":14,"_136":24,"_36":177,"_13":59,"_32":33,"_34":35,"_23":24,"_139":-5,"_174":178,"_26":179},{"_41":131,"_31":132,"_29":133,"_134":-5,"_39":40,"_38":135,"_136":24,"_36":173,"_13":59,"_32":33,"_34":138,"_23":24,"_139":-5,"_174":175,"_26":176},["D",1769404355241],"children",[],[132],["D",1769404386309],[],[27],["D",1769404316408],[],[145],["D",1769404334403],[],[152],["D",1769404297336],["D",1789446061244],{"_96":95,"_104":100},{"_96":95,"_97":98,"_99":100,"_18":53,"_101":102,"_103":24},{"_13":85,"_31":86,"_41":40,"_87":42,"_88":-5,"_89":-5,"_90":-5,"_91":-5,"_92":-5,"_93":43,"_94":95},"ctx",{"_78":-5,"_124":224,"_225":226,"_227":228,"_229":24,"_230":24,"_231":24,"_232":155,"_233":-5,"_234":-7,"_235":-7,"_236":59,"_237":24,"_238":24,"_239":-5,"_240":24,"_241":24,"_242":24,"_243":-5},"ENV",{"_195":196,"_197":198,"_199":86,"_200":201,"_202":203,"_204":205,"_206":207,"_208":209,"_210":211,"_212":213,"_214":215,"_216":217,"_218":219,"_220":221,"_222":223},"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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