Follow-up on pattern: ${pattern.pattern_type}

${response}

\n `;\n \n log.info(\"Pattern follow-up processed successfully\", { \n session_id,\n pattern_id\n });\n \n return {\n response,\n patterns: [pattern],\n html_response: htmlResponse\n };\n}\n\n/**\n * Format HTML response for the frontend\n */\nfunction formatHtmlResponse(analysisResult: any, patterns: db.Pattern[]): string {\n let html = `

Analysis Results:

`;\n \n if (patterns.length > 0) {\n patterns.forEach(pattern => {\n html += `\n

${pattern.pattern_type}

\n Confidence: ${Math.round(pattern.confidence * 100)}%\n

${pattern.explanation}

\n ${pattern.examples && pattern.examples.length > 0 ? \n `

Examples:

${ex.example_text}${ex.user_provided ? ' (user provided)' : ''}

` : ''\n }\n \n

\n `;\n });\n } else {\n html += `

No problematic patterns were identified in this text.

`;\n }\n \n html += `

Overall Assessment:

${analysisResult.overallAssessment}

`;\n \n if (analysisResult.suggestedResponses && analysisResult.suggestedResponses.length > 0) {\n html += `\n

Suggested Responses:

${response}

\n `;\n }\n \n return html;\n}\n\n/**\n * HTTP handler for the chat API\n */\nexport default async function(req: Request): Promise {\n // Handle OPTIONS request for CORS\n if (req.method === \"OPTIONS\") {\n return new Response(null, {\n headers: {\n \"Access-Control-Allow-Origin\": \"*\",\n \"Access-Control-Allow-Methods\": \"POST, OPTIONS\",\n \"Access-Control-Allow-Headers\": \"Content-Type\",\n },\n });\n }\n \n // Only accept POST requests\n if (req.method !== \"POST\") {\n log.warn(\"Method not allowed\", { method: req.method });\n return new Response(JSON.stringify({ \n success: false, \n error: \"Method not allowed\" \n }), {\n status: 405,\n headers: {\n \"Content-Type\": \"application/json\",\n \"Access-Control-Allow-Origin\": \"*\",\n },\n });\n }\n \n try {\n // Parse request body\n const body = await req.json() as ChatRequest;\n \n if (!body.action) {\n log.warn(\"Invalid request - missing action\");\n throw new ValidationError(\"Action is required\");\n }\n \n log.info(\"Processing chat API request\", { action: body.action });\n \n let response: ChatResponse;\n \n switch (body.action) {\n case \"create_session\":\n if (!body.title) {\n throw new ValidationError(\"Title is required\");\n }\n \n const session = await db.createSession(body.title);\n response = { success: true, data: session };\n break;\n \n case \"get_session\":\n if (!body.session_id) {\n throw new ValidationError(\"Session ID is required\");\n }\n \n const sessionData = await db.getSession(body.session_id);\n \n if (!sessionData) {\n throw new APIError(\"Session not found\", 404);\n }\n \n const messages = await db.getSessionMessages(body.session_id);\n const patterns = await db.getSessionPatternsWithExamples(body.session_id);\n \n response = { \n success: true, \n data: { \n session: sessionData, \n messages, \n patterns \n } \n };\n break;\n \n case \"get_all_sessions\":\n const sessions = await db.getAllSessions();\n response = { success: true, data: sessions };\n break;\n \n case \"send_message\":\n if (!body.session_id || !body.message) {\n throw new ValidationError(\"Session ID and message are required\");\n }\n \n const messageResponse = await processMessage(\n body.session_id, \n body.message,\n body.parent_id,\n body.pattern_id\n );\n \n response = { success: true, data: messageResponse };\n break;\n \n case \"add_example\":\n if (!body.pattern_id || !body.example) {\n throw new ValidationError(\"Pattern ID and example are required\");\n }\n \n const exampleId = await db.addExample(body.pattern_id, body.example, true);\n const updatedPattern = await db.getPatternWithExamples(body.pattern_id);\n \n response = { success: true, data: { example_id: exampleId, pattern: updatedPattern } };\n break;\n \n case \"get_pattern\":\n if (!body.pattern_id) {\n throw new ValidationError(\"Pattern ID is required\");\n }\n \n const pattern = await db.getPatternWithExamples(body.pattern_id);\n \n if (!pattern) {\n throw new APIError(\"Pattern not found\", 404);\n }\n \n response = { success: true, data: pattern };\n break;\n \n case \"search_patterns\":\n if (!body.session_id || !body.pattern_type) {\n throw new ValidationError(\"Session ID and pattern type are required\");\n }\n \n const matchingPatterns = await db.getPatternsByType(body.session_id, body.pattern_type);\n response = { success: true, data: matchingPatterns };\n break;\n \n case \"update_session_title\":\n if (!body.session_id || !body.title) {\n throw new ValidationError(\"Session ID and title are required\");\n }\n \n await db.updateSessionTitle(body.session_id, body.title);\n const updatedSession = await db.getSession(body.session_id);\n \n response = { success: true, data: updatedSession };\n break;\n \n case \"delete_session\":\n if (!body.session_id) {\n throw new ValidationError(\"Session ID is required\");\n }\n \n await db.deleteSession(body.session_id);\n response = { success: true };\n break;\n \n default:\n log.warn(\"Unknown action\", { action: body.action });\n throw new ValidationError(\"Unknown action\");\n }\n \n log.info(\"Request processed successfully\", { action: body.action });\n \n return new Response(JSON.stringify(response), {\n headers: {\n \"Content-Type\": \"application/json\",\n \"Access-Control-Allow-Origin\": \"*\",\n },\n });\n } catch (error) {\n log.error(\"Error processing request:\", error);\n return createErrorResponse(error, \"Failed to process request\");\n }\n}",[],[325,377,271],"a5b5dcd0-36bf-11f0-840e-9e149126039e",27,["D",1747885719524],[220,221,222],[325,377],"572da5fe-39b0-11f0-b9af-9e149126039e","8ec60838-36bf-11f0-8e76-9e149126039e","434e911f-c8cd-4f36-b84d-6885a1ac76d6",82,["D",1748208998806],9,"import logger from '../utils/logger.ts';\nimport { AnalysisError } from '../utils/error-handler.ts';\n\n// Create a logger for the analyzer\nconst log = logger.createLogger('Analyzer');\n\n// Initialize Cerebras client\nconst CEREBRAS_API_KEY = Deno.env.get('CEREBRAS_API_KEY');\nif (!CEREBRAS_API_KEY) {\n throw new Error('CEREBRAS_API_KEY environment variable is required');\n}\n\nconst CEREBRAS_API_URL = 'https://api.cerebras.ai/v1/chat/completions';\n\n// Types for our application\nexport interface AnalysisRequest {\n text: string;\n context?: string; // Optional context about the source\n isQuestion?: boolean; // Whether this is a question rather than text to analyze\n}\n\nexport interface AnalysisPattern {\n pattern: string;\n explanation: string;\n severity: \"subtle\" | \"moderate\" | \"overt\";\n confidence: number;\n examples: string[];\n}\n\nexport interface AnalysisResult {\n abusivePatterns: AnalysisPattern[];\n overallAssessment: string;\n suggestedResponses?: string[];\n}\n\n// Known examples of powerful figures and their problematic language patterns\nconst knownExamples: Record = {\n \"steve jobs\": {\n context: \"Steve Jobs was the co-founder and CEO of Apple Inc.\",\n examples: [\n \"Jobs was known for his 'reality distortion field' where he would convince employees that impossible deadlines were achievable.\",\n \"He would often tell engineers their work was 'garbage' even when it met industry standards.\",\n \"Jobs would sometimes publicly humiliate employees in meetings, calling their ideas 'stupid' or 'worthless'.\",\n \"He would take credit for others' ideas while dismissing their contributions.\",\n \"Jobs would use phrases like 'This is the worst thing I've ever seen' to manipulate employees into constant revisions.\"\n ]\n },\n \"ray dalio\": {\n context: \"Ray Dalio is the founder of Bridgewater Associates, the world's largest hedge fund.\",\n examples: [\n \"Dalio's 'radical transparency' approach involved employees constantly rating and criticizing each other.\",\n \"He would often publicly rank people's contributions in meetings with statements like 'That's a C- comment'.\",\n \"When employees expressed discomfort with his methods, he would say they were 'too weak' or their 'amygdala was hijacking their prefrontal cortex'.\",\n \"He created an environment where he could criticize anyone, but questioning his methods was labeled as 'not getting it'.\",\n \"Dalio would frame his controlling management style as necessary for 'personal growth' and 'evolution'.\"\n ]\n }\n};\n\n/**\n * Helper function to make requests to Cerebras API\n */\nasync function callCerebrasAPI(messages: Array<{role: string, content: string}>, temperature: number = 0.2): Promise {\n const response = await fetch(CEREBRAS_API_URL, {\n method: 'POST',\n headers: {\n 'Authorization': `Bearer ${CEREBRAS_API_KEY}`,\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({\n model: 'qwen-3-32b',\n messages: messages,\n temperature: temperature,\n max_tokens: 4000,\n }),\n });\n\n if (!response.ok) {\n const errorText = await response.text();\n log.error('Cerebras API error:', { status: response.status, error: errorText });\n throw new Error(`Cerebras API error: ${response.status} ${errorText}`);\n }\n\n const data = await response.json();\n return data.choices[0]?.message?.content || '';\n}\n\n/**\n * Analyzes text for subtle forms of abuse, manipulation, or problematic language\n * from powerful figures using Cerebras Qwen-3 32B model.\n */\nexport async function analyzeText(text: string, context?: string): Promise {\n log.info(\"Analyzing text\", { textLength: text.length, hasContext: !!context });\n \n const systemPrompt = `You are an expert in identifying subtle forms of abuse, manipulation, and problematic language from powerful people. \nYour task is to analyze the provided text and identify patterns that may indicate:\n\n1. Gaslighting or reality distortion - Manipulating someone into questioning their own reality, memory or perceptions\n2. Subtle threats or intimidation - Implied consequences without explicit threats\n3. Minimization of harm - Downplaying the negative impact of actions or decisions\n4. Shifting blame to victims - Making those harmed responsible for their own suffering\n5. Appeals to authority or expertise to silence criticism - Using position or credentials to avoid accountability\n6. False equivalencies - Comparing unrelated situations to normalize problematic behavior\n7. Dehumanizing language - Subtle ways of reducing people to objects, functions, or stereotypes\n8. Plausible deniability tactics - Crafting statements that can be defended as innocent if challenged\n9. Coded language that signals to specific groups - Using terms with special meaning to certain audiences\n10. Misuse of institutional power - Leveraging organizational structures to enable abuse\n11. Intellectual intimidation - Using complex frameworks or jargon to make others feel inferior\n12. Benevolent authoritarianism - Framing controlling behavior as being \"for your own good\"\n13. Selective meritocracy - Claiming decisions are based on merit when they actually favor those who conform\n14. Institutional shield - Using organizational policies or culture as a shield for personal abusive behavior\n\nFocus on SUBTLE and COVERT forms of these patterns that might not be immediately obvious.\nFor each identified pattern, provide:\n- A clear explanation of the pattern\n- The severity (subtle, moderate, or overt)\n- Your confidence level (0.0-1.0)\n- Specific examples from the text\n\nIf no problematic patterns are found, state so clearly.\nProvide an overall assessment of the text.\nIf appropriate, suggest constructive responses or ways to address the problematic language.\n\nFormat your response with clear headings for each pattern and section to make it easily parseable.`;\n\n const userPrompt = context \n ? `Text to analyze: \"${text}\"\\n\\nContext: ${context}`\n : `Text to analyze: \"${text}\"`;\n\n try {\n const analysisText = await callCerebrasAPI([\n { role: \"system\", content: systemPrompt },\n { role: \"user\", content: userPrompt }\n ], 0.2);\n \n log.debug(\"Received analysis response\", { responseLength: analysisText.length });\n \n // Process the AI response into our structured format\n return processAIResponse(analysisText);\n } catch (error) {\n log.error(\"Error analyzing text:\", error);\n throw new AnalysisError(\"Failed to analyze text. Please try again later.\", error instanceof Error ? error : undefined);\n }\n}\n\n/**\n * Handles questions about power abuse patterns or specific powerful figures\n */\nexport async function handleQuestion(question: string): Promise {\n log.info(\"Handling question\", { questionLength: question.length });\n \n // Check if the question is about a known figure\n const lowerQuestion = question.toLowerCase();\n let knownFigure: string | null = null;\n \n for (const figure in knownExamples) {\n if (lowerQuestion.includes(figure)) {\n knownFigure = figure;\n break;\n }\n }\n \n if (knownFigure) {\n log.info(`Question about known figure: ${knownFigure}`);\n const figureInfo = knownExamples[knownFigure];\n const exampleText = figureInfo.examples.join(\" \");\n return analyzeText(exampleText, figureInfo.context);\n }\n \n // For general questions about power abuse\n const systemPrompt = `You are an expert in identifying and explaining subtle forms of abuse, manipulation, and problematic language from powerful people.\nYour task is to answer questions about power dynamics, abuse patterns, and how to identify problematic behavior from people in positions of authority.\n\nFocus on being educational and informative. Provide concrete examples where appropriate.\nIf asked about specific powerful figures, provide balanced analysis based on well-documented behaviors.\n\nFormat your response with clear headings and sections to make it easily readable.`;\n\n try {\n const responseText = await callCerebrasAPI([\n { role: \"system\", content: systemPrompt },\n { role: \"user\", content: question }\n ], 0.3);\n\n log.debug(\"Received question response\", { responseLength: responseText.length });\n \n // Create a simplified result structure for questions\n return {\n abusivePatterns: [],\n overallAssessment: responseText\n };\n } catch (error) {\n log.error(\"Error handling question:\", error);\n throw new AnalysisError(\"Failed to process your question. Please try again later.\", error instanceof Error ? error : undefined);\n }\n}\n\n/**\n * Handles follow-up questions about specific patterns\n */\nexport async function handlePatternFollowUp(\n pattern: {\n pattern_type: string;\n explanation: string;\n severity: string;\n confidence: number;\n },\n examples: { example_text: string; user_provided: boolean }[],\n question: string\n): Promise {\n log.info(\"Handling pattern follow-up\", { \n pattern: pattern.pattern_type, \n questionLength: question.length,\n examplesCount: examples.length\n });\n \n // Create a prompt for the follow-up\n const examplesText = examples\n .map(ex => `- ${ex.example_text}${ex.user_provided ? \" (user provided)\" : \"\"}`)\n .join(\"\\n\");\n \n const systemPrompt = `You are an expert in identifying and explaining patterns of subtle abuse, manipulation, and problematic language from powerful people.\n \nA user has previously identified the following pattern in some text:\n\nPattern: ${pattern.pattern_type}\nExplanation: ${pattern.explanation}\nSeverity: ${pattern.severity}\nConfidence: ${pattern.confidence}\n\nExamples of this pattern:\n${examplesText}\n\nThe user is now asking a follow-up question or providing additional information about this pattern.\nIf they are providing a new example, acknowledge it and explain how it fits the pattern.\nIf they are asking a question, provide a detailed, educational response about this specific pattern.\nFocus on being helpful, informative, and sensitive to the nuances of power dynamics.`;\n\n try {\n // Generate a response using Cerebras API\n const response = await callCerebrasAPI([\n { role: \"system\", content: systemPrompt },\n { role: \"user\", content: question }\n ], 0.3);\n\n log.debug(\"Received pattern follow-up response\", { responseLength: response.length });\n \n return response || \"I'm sorry, I couldn't process your message.\";\n } catch (error) {\n log.error(\"Error handling pattern follow-up:\", error);\n throw new AnalysisError(\"Failed to process your follow-up question. Please try again later.\", error instanceof Error ? error : undefined);\n }\n}\n\n/**\n * Processes the raw AI response into a structured format\n */\nexport function processAIResponse(aiResponse: string): AnalysisResult {\n log.debug(\"Processing AI response\", { responseLength: aiResponse.length });\n \n // Extract patterns using regex or other parsing methods\n const patterns = extractPatterns(aiResponse);\n \n // Extract overall assessment\n const overallAssessment = extractOverallAssessment(aiResponse);\n \n // Extract suggested responses if any\n const suggestedResponses = extractSuggestedResponses(aiResponse);\n \n log.debug(\"Processed AI response\", { \n patternsCount: patterns.length, \n hasOverallAssessment: !!overallAssessment,\n suggestedResponsesCount: suggestedResponses.length\n });\n \n return {\n abusivePatterns: patterns,\n overallAssessment,\n suggestedResponses: suggestedResponses.length > 0 ? suggestedResponses : undefined,\n };\n}\n\n/**\n * Extracts pattern information from AI response\n */\nfunction extractPatterns(aiResponse: string): AnalysisPattern[] {\n const patterns: AnalysisPattern[] = [];\n \n // Look for pattern sections in the response\n // Updated regex to better capture multiline explanations and examples\n const patternRegex = /(?:Pattern|Type):\\s*([^\\n]+)(?:\\n|.)*?Explanation:\\s*((?:[^\\n]+(?:\\n(?!(?:Severity|Confidence|Examples?):)[^\\n]+)*))(?:\\n|.)*?Severity:\\s*([^\\n]+)(?:\\n|.)*?Confidence:\\s*([0-9.]+)(?:\\n|.)*?Examples?:\\s*((?:[^\\n]+(?:\\n(?!(?:Pattern|Type|Overall|Suggested):)[^\\n]+)*))/gi;\n \n let match;\n while ((match = patternRegex.exec(aiResponse)) !== null) {\n const [_, pattern, explanation, severityRaw, confidenceRaw, examplesRaw] = match;\n \n // Normalize severity\n let severity: \"subtle\" | \"moderate\" | \"overt\" = \"subtle\";\n if (severityRaw.toLowerCase().includes(\"moderate\")) {\n severity = \"moderate\";\n } else if (severityRaw.toLowerCase().includes(\"overt\")) {\n severity = \"overt\";\n }\n \n // Parse confidence\n const confidence = parseFloat(confidenceRaw) || 0.5;\n \n // Split examples\n const examples = examplesRaw\n .split(/\\n|;|(?:\\d+\\.)/)\n .map(ex => ex.trim())\n .filter(ex => ex.length > 0);\n \n patterns.push({\n pattern,\n explanation,\n severity,\n confidence,\n examples,\n });\n }\n \n // If no patterns were found but there's content, create a generic entry\n if (patterns.length === 0 && aiResponse.length > 50) {\n // Check if the response indicates no problematic patterns\n if (aiResponse.toLowerCase().includes(\"no problematic patterns\") || \n aiResponse.toLowerCase().includes(\"i did not identify\") ||\n aiResponse.toLowerCase().includes(\"did not find\")) {\n // Return empty array for no patterns\n return [];\n }\n \n // Otherwise create a generic entry with the full response\n return [{\n pattern: \"Analysis\",\n explanation: \"See overall assessment for details\",\n severity: \"subtle\",\n confidence: 0.5,\n examples: [aiResponse.substring(0, 100) + \"...\"]\n }];\n }\n \n return patterns;\n}\n\n/**\n * Extracts overall assessment from AI response\n */\nfunction extractOverallAssessment(aiResponse: string): string {\n // Look for overall assessment section\n const overallRegex = /(?:Overall Assessment|Overall|Summary):\\s*((?:[^\\n]+(?:\\n(?!(?:Pattern|Type|Suggested):)[^\\n]+)*))/i;\n const match = overallRegex.exec(aiResponse);\n \n if (match && match[1]) {\n return match[1].trim();\n }\n \n // If no specific overall assessment section, return a portion of the response\n return aiResponse.substring(0, Math.min(300, aiResponse.length)) + \n (aiResponse.length > 300 ? \"...\" : \"\");\n}\n\n/**\n * Extracts suggested responses from AI response\n */\nfunction extractSuggestedResponses(aiResponse: string): string[] {\n // Look for suggested responses section\n const responsesRegex = /(?:Suggested Responses|Responses|How to Respond):\\s*((?:[^\\n]+(?:\\n(?!(?:Pattern|Type|Overall):)[^\\n]+)*))/i;\n const match = responsesRegex.exec(aiResponse);\n \n if (match && match[1]) {\n return match[1]\n .split(/\\n|;|(?:\\d+\\.)/)\n .map(response => response.trim())\n .filter(response => response.length > 0);\n }\n \n return [];\n}\n\n/**\n * Helper function to determine if text is a question\n */\nexport function isQuestion(text: string): boolean {\n // Check if text ends with a question mark\n if (text.trim().endsWith('?')) return true;\n \n // Check for question words at the beginning\n const questionStarters = ['what', 'how', 'why', 'can you', 'could you', 'would you', 'will you', 'analyze', 'explain', 'tell me'];\n const lowerText = text.toLowerCase();\n return questionStarters.some(starter => lowerText.startsWith(starter));\n}",[],[325,406,317],"analyzer","8ec635ce-36bf-11f0-8e76-9e149126039e",25,["D",1747885681040],[218],[325,406],"35db68c6-36bf-11f0-82f4-9e149126039e",["D",1747885531865],[217,219,223,225,228,229],[325],"dbe3202e-38c7-11f0-913c-9e149126039e","index.html","30dc00ac-36be-11f0-8b91-9e149126039e","59b26f13-affe-4c0a-9cc5-be2e39167a17",71,["D",1748109148714],16,"\n\n\n \n \n Power Abuse Analyzer\n \n \n \n\n\n

Power Abuse Analyzer

Identifying subtle forms of abuse, manipulation, and problematic language from powerful figures

\n \n \n

\n 🔒 Privacy First: By default, nothing is saved. Choose your privacy level below.\n

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Sessions

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Privacy Mode:

\n \n \n 🔒 Don't save (temporary session)\n \n \n \n 💾 Save locally (private, browser only)\n \n \n \n ☁️ Save to server (shareable, persistent)\n \n

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Data Management:

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\n No sessions yet\n

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About This Tool

\n This analyzer identifies subtle forms of abuse from people in positions of power.\n

\n It looks for patterns such as gaslighting, reality distortion, subtle threats, blame-shifting, and other manipulative tactics.\n

\n \n

Privacy Modes:

🔒 Temporary: Nothing saved, maximum privacy
💾 Local: Saved in your browser only
☁️ Server: Full AI analysis, saved on server

\n \n

\n Current Status: Temporary mode - nothing saved\n

\n \n

\n Note: This tool provides analysis based on AI pattern recognition and should be used as one input among many when evaluating communication.\n

\n\n \n

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\n\n

Welcome to the Power Abuse Analyzer. I can help you identify subtle forms of abuse, manipulation, and problematic language from powerful figures.

You can:

Enter any text for analysis
Ask me to analyze statements from known figures like Steve Jobs or Ray Dalio
Request examples of specific abuse patterns
Add your own examples to identified patterns
Save and continue conversations about specific patterns

\n 🔒 Privacy: Currently in temporary mode - nothing is being saved.\n

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\n You can add examples or ask questions about this pattern\n

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Identified Patterns

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\n\n \n \n\n \n \n\n \n \n\n \n\n",[],[426,417],"frontend","c7f8c6bc-36bb-11f0-982e-9e149126039e",["D",1747884059014],[215],[426],["D",1747882898379],{"_11":12,"_154":73},{"_11":12,"_150":-5,"_72":73,"_31":71,"_151":152,"_153":30},{"_42":141,"_13":16,"_36":39,"_142":107,"_143":-5,"_144":-5,"_145":-5,"_146":-5,"_147":-5,"_148":14,"_149":12},"ctx",{"_55":-5,"_173":468,"_469":470,"_471":472,"_473":30,"_474":30,"_475":30,"_476":24,"_477":-5,"_478":-7,"_479":-7,"_480":26,"_481":30,"_482":30,"_483":-5,"_484":30,"_485":30,"_486":30,"_487":-5},"ENV",{"_439":440,"_441":442,"_443":16,"_444":445,"_446":447,"_448":449,"_450":451,"_452":453,"_454":455,"_456":457,"_458":459,"_460":461,"_462":463,"_464":465,"_466":467},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","811aa22498c9b0b5a47e8d778de579cdb990f332","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"]

Sessions

About This Tool

Identified Patterns

New Analysis Session

Rename Session

Delete Session

History · tests