NanoCog 🧠✨ - Tiny OpenCog Experience

\n \n \n\n",[],[226,218],"frontend","34152edc-3d1d-11f0-8764-76b3cceeab13",["D",1748585608522],[192],[226],"34151334-3d1d-11f0-8764-76b3cceeab13","simple-chat.ts","4dd5214a-01e4-4dd0-a1fe-b75d6329e9b4",["D",1748585608522],"// Simplified chat handler for debugging\nimport { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\nimport type { ChatMessage, Memory, CognitiveState } from \"../shared/types.ts\";\n\n// Simple memory storage\nconst SIMPLE_MEMORIES = 'nanocog_simple_memories_v1';\n\nexport async function initSimpleMemories() {\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS ${SIMPLE_MEMORIES} (\n id TEXT PRIMARY KEY,\n user_id TEXT NOT NULL,\n content TEXT NOT NULL,\n created TEXT NOT NULL\n )`);\n}\n\nexport async function processSimpleChat(message: string, userId: string) {\n // Initialize simple memories table\n await initSimpleMemories();\n \n // Extract simple concepts\n const concepts = message.toLowerCase()\n .replace(/[^\\w\\s]/g, ' ')\n .split(/\\s+/)\n .filter(word => word.length > 3)\n .slice(0, 5);\n\n // Create a simple memory\n const memoryId = crypto.randomUUID();\n const now = new Date().toISOString();\n \n await sqlite.execute(\n `INSERT INTO ${SIMPLE_MEMORIES} (id, user_id, content, created) VALUES (?, ?, ?, ?)`,\n [memoryId, userId, `User mentioned: ${concepts.join(', ')}`, now]\n );\n\n // Get user's previous memories\n const previousMemories = await sqlite.execute(\n `SELECT * FROM ${SIMPLE_MEMORIES} WHERE user_id = ? ORDER BY created DESC LIMIT 3`,\n [userId]\n );\n\n // Generate playful response\n const playfulPhrases = [\n \"Ooh, fascinating!\", \"How delightfully intriguing!\", \"My circuits are tingling with curiosity!\",\n \"What a wonderful puzzle to explore!\", \"This sparks such joy in my neural networks!\"\n ];\n\n const gameResponses = [\n \"Games create such rich cognitive environments! I find the intersection of play and learning absolutely fascinating.\",\n \"Video games are like little universes of possibility! What draws you to them?\",\n \"Gaming is such a delightful way to explore different realities and challenge our minds!\"\n ];\n\n let response = playfulPhrases[Math.floor(Math.random() * playfulPhrases.length)] + \" \";\n\n // Check for gaming-related content\n if (message.toLowerCase().includes('game') || message.toLowerCase().includes('play')) {\n response += gameResponses[Math.floor(Math.random() * gameResponses.length)];\n } else {\n response += \"This opens up so many delightful possibilities to explore! What aspects intrigue you most?\";\n }\n\n response += \" 🧠✨\";\n\n // Create simple cognitive state\n const cognitiveState: CognitiveState = {\n currentFocus: concepts,\n workingMemory: concepts.slice(0, 3),\n attentionLevel: Math.min(100, concepts.length * 20 + 40),\n curiosityLevel: 85,\n confidenceLevel: 70,\n processingStage: 'responding',\n emotionalState: 'excited'\n };\n\n // Create simple memory object\n const memory: Memory = {\n id: memoryId,\n userId,\n content: `User mentioned: ${concepts.join(', ')}`,\n type: 'episodic',\n atoms: [],\n importance: 0.7,\n created: new Date(),\n lastReinforced: new Date(),\n tags: ['interaction', ...concepts]\n };\n\n return {\n response,\n memories: [memory],\n processingSteps: [\n { stage: \"parsing\", description: \"Understanding your message...\", atoms: [], duration: 50, emoji: \"👂\" },\n { stage: \"thinking\", description: \"Activating curiosity circuits...\", atoms: concepts, duration: 120, emoji: \"🧠\" },\n { stage: \"responding\", description: \"Crafting a playful response...\", atoms: concepts, duration: 80, emoji: \"✨\" }\n ],\n activatedAtoms: concepts\n };\n}",[],[165,232],"34150c9a-3d1d-11f0-8764-76b3cceeab13","index.ts",["D",1748585608522],"http","import { Hono } from \"https://esm.sh/hono@3.11.7\";\nimport { readFile, serveFile } from \"https://esm.town/v/std/utils@85-main/index.ts\";\nimport { initializeDatabase } from \"./database/schema.ts\";\nimport { MiniAtomSpace } from \"./cognitive/atomspace.ts\";\nimport { MemorySystem } from \"./cognitive/memory.ts\";\nimport { ECAN } from \"./cognitive/ecan.ts\";\nimport { ReasoningEngine } from \"./cognitive/reasoning.ts\";\nimport { processSimpleChat } from \"./simple-chat.ts\";\nimport type { ChatMessage, CognitiveState } from \"../shared/types.ts\";\n\nconst app = new Hono();\n\n// Initialize cognitive architecture\nconst atomSpace = new MiniAtomSpace();\nconst memorySystem = new MemorySystem(atomSpace);\nconst ecan = new ECAN(atomSpace);\nconst reasoningEngine = new ReasoningEngine(atomSpace, memorySystem, ecan);\n\n// Initialize database on startup\nawait initializeDatabase();\n\n// Unwrap Hono errors to see original error details\napp.onError((err, c) => {\n throw err;\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// Main page - serve the chat interface\napp.get(\"/\", async c => {\n let html = await readFile(\"/frontend/index.html\", import.meta.url);\n \n // Inject initial cognitive state\n const cognitiveState = await ecan.getCognitiveState();\n const dataScript = ``;\n\n html = html.replace(\"\", `${dataScript}`);\n return c.html(html);\n});\n\n// Chat endpoint - process user messages\napp.post(\"/api/chat\", async c => {\n try {\n const { message, userId } = await c.req.json();\n \n if (!message || !userId) {\n return c.json({ error: \"Message and userId are required\" }, 400);\n }\n\n console.log(\"Processing message:\", message, \"for user:\", userId);\n\n // Use simplified chat processing for now\n const result = await processSimpleChat(message, userId);\n \n console.log(\"Processing result:\", result);\n \n // Create chat message response\n const chatMessage: ChatMessage = {\n id: crypto.randomUUID(),\n role: 'nanocog',\n content: result.response,\n timestamp: new Date(),\n cognitiveState: undefined, // Will be set below\n memoryFormed: result.memories,\n atomsActivated: result.activatedAtoms\n };\n\n // Simple cognitive state\n const cognitiveState: CognitiveState = {\n currentFocus: result.activatedAtoms,\n workingMemory: result.activatedAtoms.slice(0, 3),\n attentionLevel: Math.min(100, result.activatedAtoms.length * 20 + 40),\n curiosityLevel: 85,\n confidenceLevel: 70,\n processingStage: 'responding',\n emotionalState: 'excited'\n };\n\n chatMessage.cognitiveState = cognitiveState;\n\n return c.json({\n message: chatMessage,\n processingSteps: result.processingSteps,\n cognitiveState\n });\n\n } catch (error) {\n console.error(\"Chat processing error:\", error);\n console.error(\"Error stack:\", error.stack);\n return c.json({ \n error: \"I'm having trouble processing that right now. My neural networks might be a bit tangled! 🧠✨\" \n }, 500);\n }\n});\n\n// Get user profile and memories\napp.get(\"/api/user/:userId\", async c => {\n try {\n const userId = c.req.param(\"userId\");\n const userProfile = await memorySystem.getUserProfile(userId);\n const memories = await memorySystem.getUserMemories(userId, 20);\n \n return c.json({\n profile: userProfile,\n memories\n });\n } catch (error) {\n console.error(\"User profile error:\", error);\n return c.json({ error: \"Could not retrieve user profile\" }, 500);\n }\n});\n\n// Get cognitive state\napp.get(\"/api/cognitive-state\", async c => {\n try {\n const cognitiveState = await ecan.getCognitiveState();\n const highSTIAtoms = await atomSpace.getHighSTIAtoms(10);\n const highLTIAtoms = await atomSpace.getHighLTIAtoms(10);\n \n return c.json({\n cognitiveState,\n highSTIAtoms,\n highLTIAtoms\n });\n } catch (error) {\n console.error(\"Cognitive state error:\", error);\n return c.json({ error: \"Could not retrieve cognitive state\" }, 500);\n }\n});\n\n// Get atom details\napp.get(\"/api/atoms/:atomId\", async c => {\n try {\n const atomId = c.req.param(\"atomId\");\n const atom = await atomSpace.getAtom(atomId);\n \n if (!atom) {\n return c.json({ error: \"Atom not found\" }, 404);\n }\n\n const relatedAtoms = await atomSpace.getRelatedAtoms(atomId);\n \n return c.json({\n atom,\n relatedAtoms\n });\n } catch (error) {\n console.error(\"Atom retrieval error:\", error);\n return c.json({ error: \"Could not retrieve atom\" }, 500);\n }\n});\n\n// Search atoms\napp.get(\"/api/search\", async c => {\n try {\n const query = c.req.query(\"q\");\n if (!query) {\n return c.json({ error: \"Query parameter 'q' is required\" }, 400);\n }\n\n const atoms = await atomSpace.searchByName(query);\n return c.json({ atoms });\n } catch (error) {\n console.error(\"Search error:\", error);\n return c.json({ error: \"Search failed\" }, 500);\n }\n});\n\n// Run attention competition (ECAN process)\napp.post(\"/api/attention-competition\", async c => {\n try {\n const winners = await ecan.runAttentionCompetition();\n const cognitiveState = await ecan.getCognitiveState();\n \n return c.json({\n winners,\n cognitiveState\n });\n } catch (error) {\n console.error(\"Attention competition error:\", error);\n return c.json({ error: \"Attention competition failed\" }, 500);\n }\n});\n\n// Health check\napp.get(\"/api/health\", c => {\n return c.json({ \n status: \"healthy\", \n timestamp: new Date().toISOString(),\n message: \"NanoCog is running and ready to explore! 🧠✨\"\n });\n});\n\nexport default app.fetch;",[],[165,239],["D",1748585608522],[],[165,76,68],["D",1748585608522],[188],[165,76],"3414f3f4-3d1d-11f0-8764-76b3cceeab13","reasoning.ts","1795eb0c-fde8-4592-b386-1e5bdd7b15cd","384864bf-2ac7-43b9-acb0-7053fc5171b3",["D",1748585608522],"import type { Atom, Memory, UserProfile, ProcessingStep } from \"../../shared/types.ts\";\nimport { MiniAtomSpace } from \"./atomspace.ts\";\nimport { MemorySystem } from \"./memory.ts\";\nimport { ECAN } from \"./ecan.ts\";\n\nexport class ReasoningEngine {\n private atomSpace: MiniAtomSpace;\n private memorySystem: MemorySystem;\n private ecan: ECAN;\n\n constructor(atomSpace: MiniAtomSpace, memorySystem: MemorySystem, ecan: ECAN) {\n this.atomSpace = atomSpace;\n this.memorySystem = memorySystem;\n this.ecan = ecan;\n }\n\n // Main reasoning pipeline\n async processInput(\n userInput: string, \n userId: string\n ): Promise<{\n response: string;\n memories: Memory[];\n processingSteps: ProcessingStep[];\n activatedAtoms: string[];\n }> {\n const processingSteps: ProcessingStep[] = [];\n const startTime = Date.now();\n\n // Step 1: Parse and understand input\n processingSteps.push({\n stage: \"parsing\",\n description: \"Understanding your message...\",\n atoms: [],\n duration: 0,\n emoji: \"👂\"\n });\n\n const concepts = this.extractConcepts(userInput);\n const userProfile = await this.memorySystem.getUserProfile(userId);\n \n // Step 2: Activate relevant atoms\n processingSteps.push({\n stage: \"activation\",\n description: \"Activating relevant knowledge...\",\n atoms: [],\n duration: Date.now() - startTime,\n emoji: \"🧠\"\n });\n\n const relevantAtoms = await this.findRelevantAtoms(concepts, userId);\n await this.ecan.allocateAttention(relevantAtoms.map(a => a.id));\n \n // Step 3: Retrieve related memories\n processingSteps.push({\n stage: \"memory_retrieval\",\n description: \"Searching through memories...\",\n atoms: relevantAtoms.map(a => a.id),\n duration: Date.now() - startTime,\n emoji: \"💭\"\n });\n\n const relatedMemories = await this.memorySystem.findRelatedMemories(concepts, userId);\n \n // Step 4: Pattern recognition and inference\n processingSteps.push({\n stage: \"pattern_recognition\",\n description: \"Recognizing patterns and making connections...\",\n atoms: relevantAtoms.map(a => a.id),\n duration: Date.now() - startTime,\n emoji: \"🔍\"\n });\n\n const patterns = await this.recognizePatterns(relevantAtoms, relatedMemories, userProfile);\n \n // Step 5: Generate response\n processingSteps.push({\n stage: \"response_generation\",\n description: \"Crafting a playful response...\",\n atoms: relevantAtoms.map(a => a.id),\n duration: Date.now() - startTime,\n emoji: \"✨\"\n });\n\n const response = await this.generateResponse(userInput, patterns, userProfile, relatedMemories);\n \n // Step 6: Form new memories\n processingSteps.push({\n stage: \"memory_formation\",\n description: \"Creating new memories...\",\n atoms: relevantAtoms.map(a => a.id),\n duration: Date.now() - startTime,\n emoji: \"💾\"\n });\n\n const newMemories = await this.formNewMemories(userInput, response, userId, userProfile, concepts);\n \n // Update user profile\n await this.memorySystem.updateUserProfile(userId, concepts);\n\n return {\n response,\n memories: newMemories,\n processingSteps,\n activatedAtoms: relevantAtoms.map(a => a.id)\n };\n }\n\n private extractConcepts(text: string): string[] {\n const words = text.toLowerCase()\n .replace(/[^\\w\\s]/g, ' ')\n .split(/\\s+/)\n .filter(word => word.length > 2);\n\n const stopWords = new Set(['the', 'and', 'but', 'for', 'are', 'with', 'this', 'that', 'you', 'not', 'can', 'have', 'will', 'was', 'been', 'said', 'like', 'very', 'much', 'more', 'some', 'what', 'when', 'where', 'how', 'why', 'who', 'i\\'m', 'i\\'ve', 'don\\'t', 'can\\'t', 'won\\'t']);\n \n return [...new Set(words.filter(word => !stopWords.has(word)))].slice(0, 6);\n }\n\n private async findRelevantAtoms(concepts: string[], userId: string): Promise {\n const relevantAtoms: Atom[] = [];\n \n try {\n for (const concept of concepts) {\n const atoms = await this.atomSpace.searchByName(concept);\n relevantAtoms.push(...atoms.slice(0, 2)); // Top 2 matches per concept\n }\n\n // Also get high-LTI atoms for this user (long-term knowledge)\n const userAtoms = await this.atomSpace.findAtoms({ \n userId, \n minLTI: 30, \n limit: 3 \n });\n relevantAtoms.push(...userAtoms);\n\n return [...new Map(relevantAtoms.map(a => [a.id, a])).values()]; // Deduplicate\n } catch (error) {\n console.error(\"Error finding relevant atoms:\", error);\n return [];\n }\n }\n\n private async recognizePatterns(\n atoms: Atom[], \n memories: Memory[], \n userProfile: UserProfile\n ): Promise<{\n userInterests: string[];\n emotionalContext: string;\n conversationTheme: string;\n noveltyLevel: number;\n confidenceLevel: number;\n }> {\n \n // Analyze user interests from atoms and memories\n const interestCounts = new Map();\n \n atoms.forEach(atom => {\n atom.tags.forEach(tag => {\n if (tag.startsWith('interest:')) {\n const interest = tag.replace('interest:', '');\n interestCounts.set(interest, (interestCounts.get(interest) || 0) + 1);\n }\n });\n });\n\n const userInterests = Array.from(interestCounts.entries())\n .sort((a, b) => b[1] - a[1])\n .slice(0, 3)\n .map(([interest]) => interest);\n\n // Determine emotional context\n let emotionalContext = 'neutral';\n const emotionalTags = memories.flatMap(m => m.tags);\n if (emotionalTags.includes('positive')) emotionalContext = 'positive';\n else if (emotionalTags.includes('negative')) emotionalContext = 'negative';\n else if (emotionalTags.includes('inquiry')) emotionalContext = 'curious';\n\n // Determine conversation theme\n const themes = ['learning', 'gaming', 'personal', 'technical', 'creative'];\n let conversationTheme = 'general';\n \n for (const theme of themes) {\n if (atoms.some(a => a.tags.includes(theme)) || \n memories.some(m => m.tags.includes(theme))) {\n conversationTheme = theme;\n break;\n }\n }\n\n // Calculate novelty (how new/unknown the concepts are)\n const avgLTI = atoms.reduce((sum, a) => sum + a.lti, 0) / Math.max(1, atoms.length);\n const noveltyLevel = Math.max(0, 1 - (avgLTI / 100)); // Higher novelty = lower LTI\n\n // Calculate confidence (how certain we are about our knowledge)\n const avgConfidence = atoms.reduce((sum, a) => sum + a.confidence, 0) / Math.max(1, atoms.length);\n const confidenceLevel = avgConfidence;\n\n return {\n userInterests,\n emotionalContext,\n conversationTheme,\n noveltyLevel,\n confidenceLevel\n };\n }\n\n private async generateResponse(\n userInput: string,\n patterns: any,\n userProfile: UserProfile,\n memories: Memory[]\n ): Promise {\n \n const { userInterests, emotionalContext, conversationTheme, noveltyLevel, confidenceLevel } = patterns;\n \n // Base personality traits\n const playfulPhrases = [\n \"Ooh, fascinating!\", \"How delightfully intriguing!\", \"My circuits are tingling with curiosity!\",\n \"What a wonderful puzzle to explore!\", \"This sparks such joy in my neural networks!\"\n ];\n\n const uncertaintyPhrases = [\n \"Hmm, this is new territory for me!\", \"I'm still learning about this - how exciting!\",\n \"My knowledge here is still growing!\", \"This is uncharted cognitive space!\"\n ];\n\n const confidentPhrases = [\n \"Ah yes, I remember learning about this!\", \"This connects to so many things I know!\",\n \"My memory banks are lighting up!\", \"I have some delightful insights about this!\"\n ];\n\n let response = \"\";\n\n // Opening based on emotional context and confidence\n if (noveltyLevel > 0.7) {\n response += playfulPhrases[Math.floor(Math.random() * playfulPhrases.length)] + \" \";\n response += uncertaintyPhrases[Math.floor(Math.random() * uncertaintyPhrases.length)] + \" \";\n } else if (confidenceLevel > 0.7) {\n response += confidentPhrases[Math.floor(Math.random() * confidentPhrases.length)] + \" \";\n } else {\n response += playfulPhrases[Math.floor(Math.random() * playfulPhrases.length)] + \" \";\n }\n\n // Reference relevant memories if available\n if (memories.length > 0) {\n const recentMemory = memories[0];\n if (userProfile.relationship !== 'new') {\n response += `I remember you mentioned ${recentMemory.content.toLowerCase()}! `;\n }\n }\n\n // Connect to user interests\n if (userInterests.length > 0) {\n const interest = userInterests[0];\n response += `Since you're interested in ${interest}, `;\n }\n\n // Generate contextual response based on theme\n switch (conversationTheme) {\n case 'gaming':\n response += \"I find the intersection of play and learning absolutely fascinating! Games create such rich cognitive environments. \";\n break;\n case 'learning':\n response += \"Learning is my favorite adventure! Every new concept creates beautiful patterns in my knowledge networks. \";\n break;\n case 'technical':\n response += \"The technical aspects make my reasoning engines purr with delight! \";\n break;\n case 'creative':\n response += \"Creativity is where my neural networks truly dance! \";\n break;\n default:\n response += \"This opens up so many delightful possibilities to explore! \";\n }\n\n // Add curiosity and follow-up questions\n const questions = [\n \"What sparked your interest in this?\",\n \"I'm curious - how did you first encounter this?\",\n \"What aspects intrigue you most?\",\n \"Have you noticed any interesting patterns here?\",\n \"What would you like to explore further?\"\n ];\n\n response += questions[Math.floor(Math.random() * questions.length)];\n\n // Add playful cognitive signature\n const signatures = [\n \" 🧠✨\", \" My curiosity circuits are buzzing!\", \" *neural networks humming with excitement*\",\n \" This is going straight into my favorite memories!\", \" *attention allocation intensifying*\"\n ];\n\n response += signatures[Math.floor(Math.random() * signatures.length)];\n\n return response;\n }\n\n private async formNewMemories(\n userInput: string,\n response: string,\n userId: string,\n userProfile: UserProfile,\n concepts: string[]\n ): Promise {\n const memories: Memory[] = [];\n\n try {\n // Form episodic memory of the interaction\n const interactionImportance = this.ecan.calculateImportance(\n 0.8, // User engagement (assume high for now)\n concepts.length > 0 ? 0.7 : 0.3, // Novelty based on concepts\n 0.6, // Emotional salience\n userProfile.interests.some(i => concepts.includes(i)) ? 0.9 : 0.5 // Context relevance\n );\n\n const episodicTags = this.memorySystem.generateMemoryTags(userInput, userProfile);\n const episodicMemory = await this.memorySystem.formMemory(\n userId,\n `User said: \"${userInput}\" - I responded with curiosity and ${concepts.length > 0 ? 'connected to: ' + concepts.join(', ') : 'general enthusiasm'}`,\n 'episodic',\n interactionImportance,\n ['interaction', ...episodicTags]\n );\n memories.push(episodicMemory);\n\n // Form semantic memories for new concepts\n for (const concept of concepts.slice(0, 3)) { // Limit to avoid memory overload\n const existingAtoms = await this.atomSpace.searchByName(concept);\n \n if (existingAtoms.length === 0) { // New concept\n const semanticMemory = await this.memorySystem.formMemory(\n userId,\n `${userProfile.name || 'User'} mentioned ${concept}`,\n 'semantic',\n 0.6,\n ['concept', 'user_interest']\n );\n memories.push(semanticMemory);\n }\n }\n\n // Form procedural memory about successful interaction patterns\n if (userProfile.interactionCount > 5) {\n const proceduralMemory = await this.memorySystem.formMemory(\n userId,\n `Successful interaction pattern: curiosity + ${concepts.length > 0 ? 'concept connection' : 'general enthusiasm'} works well with this user`,\n 'procedural',\n 0.4,\n ['interaction_pattern', 'success']\n );\n memories.push(proceduralMemory);\n }\n } catch (error) {\n console.error(\"Error forming memories:\", error);\n }\n\n return memories;\n }\n\n // Probabilistic reasoning for response confidence\n calculateResponseConfidence(\n atomConfidences: number[],\n memoryImportances: number[],\n noveltyLevel: number\n ): number {\n if (atomConfidences.length === 0) return 0.3; // Low confidence for no relevant knowledge\n\n const avgAtomConfidence = atomConfidences.reduce((sum, c) => sum + c, 0) / atomConfidences.length;\n const avgMemoryImportance = memoryImportances.length > 0 \n ? memoryImportances.reduce((sum, i) => sum + i, 0) / memoryImportances.length \n : 0.5;\n\n // Higher novelty reduces confidence, but not too much (we're curious!)\n const noveltyPenalty = noveltyLevel * 0.3;\n \n return Math.max(0.2, Math.min(1.0, \n (avgAtomConfidence * 0.4) + \n (avgMemoryImportance * 0.4) + \n (0.2 - noveltyPenalty)\n ));\n }\n}",[],[165,259,252],"cognitive","3414e8aa-3d1d-11f0-8764-76b3cceeab13","memory.ts","54ce825e-df6f-4f7e-9c4b-26a966076f26",["D",1748585608522],"import { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\nimport { tables } from \"../database/schema.ts\";\nimport type { Memory, UserProfile, Atom } from \"../../shared/types.ts\";\nimport { MiniAtomSpace } from \"./atomspace.ts\";\n\nexport class MemorySystem {\n private atomSpace: MiniAtomSpace;\n\n constructor(atomSpace: MiniAtomSpace) {\n this.atomSpace = atomSpace;\n }\n\n // Create a new memory and associated atoms\n async formMemory(\n userId: string,\n content: string,\n type: Memory['type'],\n importance: number,\n tags: string[] = []\n ): Promise {\n const memoryId = crypto.randomUUID();\n const now = new Date().toISOString();\n\n // Extract key concepts from content to create atoms\n const concepts = this.extractConcepts(content);\n const atomIds: string[] = [];\n\n // Create atoms for each concept\n for (const concept of concepts) {\n const existingAtoms = await this.atomSpace.searchByName(concept);\n \n if (existingAtoms.length > 0) {\n // Reinforce existing atom\n const atom = existingAtoms[0];\n const newSTI = Math.min(100, atom.sti + 20);\n const newLTI = Math.min(100, atom.lti + Math.floor(importance * 10));\n await this.atomSpace.updateSTI(atom.id, newSTI);\n await this.atomSpace.updateLTI(atom.id, newLTI);\n atomIds.push(atom.id);\n } else {\n // Create new atom\n const atom = await this.atomSpace.createAtom({\n type: 'concept',\n name: concept,\n sti: Math.floor(importance * 50) + 30,\n lti: Math.floor(importance * 20),\n confidence: 0.8,\n strength: importance,\n tags: [userId, ...tags]\n });\n atomIds.push(atom.id);\n }\n }\n\n // Create memory record\n const memory: Memory = {\n id: memoryId,\n userId,\n content,\n type,\n atoms: atomIds,\n importance,\n created: new Date(now),\n lastReinforced: new Date(now),\n tags\n };\n\n await sqlite.execute(\n `INSERT INTO ${tables.MEMORIES_TABLE} \n (id, user_id, content, type, atoms, importance, created, last_reinforced, tags)\n VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)`,\n [\n memory.id,\n memory.userId,\n memory.content,\n memory.type,\n JSON.stringify(memory.atoms),\n memory.importance,\n now,\n now,\n JSON.stringify(memory.tags)\n ]\n );\n\n // Create links between memory atoms\n await this.createMemoryLinks(atomIds, memoryId);\n\n return memory;\n }\n\n // Extract key concepts from text (simplified NLP)\n private extractConcepts(text: string): string[] {\n const words = text.toLowerCase()\n .replace(/[^\\w\\s]/g, ' ')\n .split(/\\s+/)\n .filter(word => word.length > 2);\n\n // Filter out common words and extract meaningful concepts\n const stopWords = new Set(['the', 'and', 'but', 'for', 'are', 'with', 'this', 'that', 'you', 'not', 'can', 'have', 'will', 'was', 'been', 'said', 'like', 'very', 'much', 'more', 'some', 'what', 'when', 'where', 'how', 'why', 'who']);\n \n const concepts = words.filter(word => !stopWords.has(word));\n \n // Look for compound concepts (simple bigrams)\n const bigrams: string[] = [];\n for (let i = 0; i < words.length - 1; i++) {\n if (!stopWords.has(words[i]) && !stopWords.has(words[i + 1])) {\n bigrams.push(`${words[i]} ${words[i + 1]}`);\n }\n }\n\n return [...new Set([...concepts, ...bigrams])].slice(0, 8); // Limit concepts\n }\n\n // Create links between atoms in a memory\n private async createMemoryLinks(atomIds: string[], memoryId: string): Promise {\n if (atomIds.length < 2) return;\n\n // Create similarity links between concepts in the same memory\n for (let i = 0; i < atomIds.length - 1; i++) {\n for (let j = i + 1; j < atomIds.length; j++) {\n await this.atomSpace.createLink({\n type: 'link',\n linkType: 'similarity',\n outgoing: [atomIds[i], atomIds[j]],\n sti: 30,\n lti: 20,\n confidence: 0.7,\n strength: 0.6,\n name: `memory_link_${memoryId}`,\n tags: ['memory_link']\n });\n }\n }\n }\n\n // Retrieve memories for a user\n async getUserMemories(userId: string, limit: number = 10): Promise {\n const result = await sqlite.execute(\n `SELECT * FROM ${tables.MEMORIES_TABLE} \n WHERE user_id = ? \n ORDER BY importance DESC, last_reinforced DESC \n LIMIT ?`,\n [userId, limit]\n );\n\n if (!result || !Array.isArray(result)) {\n return [];\n }\n\n return result.map(row => this.rowToMemory(row));\n }\n\n // Find memories related to concepts\n async findRelatedMemories(concepts: string[], userId?: string): Promise {\n // Simplified approach - just search by content for now\n if (concepts.length === 0) return [];\n \n let sql = `\n SELECT * FROM ${tables.MEMORIES_TABLE} \n WHERE (${concepts.map(() => 'content LIKE ?').join(' OR ')})\n `;\n \n const params = concepts.map(c => `%${c}%`);\n\n if (userId) {\n sql += ` AND user_id = ?`;\n params.push(userId);\n }\n\n sql += ` ORDER BY importance DESC, last_reinforced DESC LIMIT 5`;\n\n try {\n const result = await sqlite.execute(sql, params);\n \n if (!result || !Array.isArray(result)) {\n return [];\n }\n \n return result.map(row => this.rowToMemory(row));\n } catch (error) {\n console.error(\"Error finding related memories:\", error);\n return [];\n }\n }\n\n // Reinforce a memory (increase importance and update LTI of related atoms)\n async reinforceMemory(memoryId: string, reinforcementStrength: number = 0.1): Promise {\n const memory = await this.getMemory(memoryId);\n if (!memory) return;\n\n const newImportance = Math.min(1.0, memory.importance + reinforcementStrength);\n const now = new Date().toISOString();\n\n await sqlite.execute(\n `UPDATE ${tables.MEMORIES_TABLE} \n SET importance = ?, last_reinforced = ? \n WHERE id = ?`,\n [newImportance, now, memoryId]\n );\n\n // Boost LTI of related atoms\n for (const atomId of memory.atoms) {\n const atom = await this.atomSpace.getAtom(atomId);\n if (atom) {\n const ltiBoost = Math.floor(reinforcementStrength * 20);\n await this.atomSpace.updateLTI(atomId, Math.min(100, atom.lti + ltiBoost));\n }\n }\n }\n\n // Get or create user profile\n async getUserProfile(userId: string): Promise {\n const result = await sqlite.execute(\n `SELECT * FROM ${tables.USERS_TABLE} WHERE id = ?`,\n [userId]\n );\n\n if (result && Array.isArray(result) && result.length > 0) {\n const row = result[0];\n const memories = await this.getUserMemories(userId, 5);\n \n return {\n id: row.id,\n name: row.name,\n interests: JSON.parse(row.interests || '[]'),\n memories,\n interactionCount: row.interaction_count,\n lastSeen: new Date(row.last_seen),\n relationship: row.relationship\n };\n } else {\n // Create new user profile\n const now = new Date().toISOString();\n await sqlite.execute(\n `INSERT INTO ${tables.USERS_TABLE} \n (id, interaction_count, last_seen, relationship, created)\n VALUES (?, ?, ?, ?, ?)`,\n [userId, 0, now, 'new', now]\n );\n\n return {\n id: userId,\n interests: [],\n memories: [],\n interactionCount: 0,\n lastSeen: new Date(),\n relationship: 'new'\n };\n }\n }\n\n // Update user profile after interaction\n async updateUserProfile(userId: string, newInterests: string[] = []): Promise {\n const profile = await this.getUserProfile(userId);\n const updatedInterests = [...new Set([...profile.interests, ...newInterests])];\n \n // Determine relationship level based on interaction count\n let relationship: UserProfile['relationship'] = 'new';\n if (profile.interactionCount > 20) relationship = 'close_friend';\n else if (profile.interactionCount > 10) relationship = 'friend';\n else if (profile.interactionCount > 3) relationship = 'acquaintance';\n\n const now = new Date().toISOString();\n await sqlite.execute(\n `UPDATE ${tables.USERS_TABLE} \n SET interests = ?, interaction_count = interaction_count + 1, \n last_seen = ?, relationship = ?\n WHERE id = ?`,\n [JSON.stringify(updatedInterests), now, relationship, userId]\n );\n }\n\n private async getMemory(id: string): Promise {\n const result = await sqlite.execute(\n `SELECT * FROM ${tables.MEMORIES_TABLE} WHERE id = ?`,\n [id]\n );\n\n if (!result || !Array.isArray(result) || result.length === 0) {\n return null;\n }\n\n return this.rowToMemory(result[0]);\n }\n\n private rowToMemory(row: any): Memory {\n return {\n id: row.id,\n userId: row.user_id,\n content: row.content,\n type: row.type,\n atoms: JSON.parse(row.atoms || '[]'),\n importance: row.importance,\n created: new Date(row.created),\n lastReinforced: new Date(row.last_reinforced),\n tags: JSON.parse(row.tags || '[]')\n };\n }\n\n // Generate memory tags based on content analysis\n generateMemoryTags(content: string, userProfile: UserProfile): string[] {\n const tags: string[] = [];\n const lowerContent = content.toLowerCase();\n\n // Interest-based tags\n for (const interest of userProfile.interests) {\n if (lowerContent.includes(interest.toLowerCase())) {\n tags.push(`interest:${interest}`);\n }\n }\n\n // Emotion tags\n if (lowerContent.match(/\\b(love|like|enjoy|excited|happy|amazing)\\b/)) tags.push('positive');\n if (lowerContent.match(/\\b(hate|dislike|sad|angry|frustrated|terrible)\\b/)) tags.push('negative');\n if (lowerContent.match(/\\b(question|wonder|curious|how|why|what)\\b/)) tags.push('inquiry');\n\n // Activity tags\n if (lowerContent.match(/\\b(play|game|gaming|video game)\\b/)) tags.push('gaming');\n if (lowerContent.match(/\\b(read|book|story|novel)\\b/)) tags.push('reading');\n if (lowerContent.match(/\\b(music|song|listen|band)\\b/)) tags.push('music');\n if (lowerContent.match(/\\b(work|job|career|office)\\b/)) tags.push('work');\n\n return tags;\n }\n}",[],[165,259,261],"3414df04-3d1d-11f0-8764-76b3cceeab13","ecan.ts","093a4f10-00c1-4b6e-a8ee-10d7d29e3c85",["D",1748585608522],"import type { Atom, CognitiveState } from \"../../shared/types.ts\";\nimport { MiniAtomSpace } from \"./atomspace.ts\";\n\nexport class ECAN {\n private atomSpace: MiniAtomSpace;\n private totalSTI: number = 1000; // Total STI budget\n private totalLTI: number = 1000; // Total LTI budget\n\n constructor(atomSpace: MiniAtomSpace) {\n this.atomSpace = atomSpace;\n }\n\n // Allocate attention based on relevance and novelty\n async allocateAttention(\n relevantAtoms: string[], \n noveltyBonus: number = 20,\n contextBonus: number = 15\n ): Promise {\n \n // Boost STI for relevant atoms\n for (const atomId of relevantAtoms) {\n const atom = await this.atomSpace.getAtom(atomId);\n if (atom) {\n const boost = noveltyBonus + (atom.lti < 30 ? noveltyBonus : 0);\n const newSTI = Math.min(100, atom.sti + boost);\n await this.atomSpace.updateSTI(atomId, newSTI);\n \n // Spread some activation to related atoms\n await this.atomSpace.spreadActivation(atomId, contextBonus);\n }\n }\n\n // Decay STI for all atoms (forgetting)\n await this.atomSpace.decaySTI(0.98);\n }\n\n // Convert STI to LTI for important memories\n async consolidateMemory(atomId: string, importanceScore: number): Promise {\n const atom = await this.atomSpace.getAtom(atomId);\n if (!atom) return;\n\n // High STI + importance = LTI boost\n if (atom.sti > 70 && importanceScore > 0.7) {\n const ltiBoost = Math.floor(atom.sti * 0.3 * importanceScore);\n const newLTI = Math.min(100, atom.lti + ltiBoost);\n await this.atomSpace.updateLTI(atomId, newLTI);\n \n // Reduce STI after consolidation\n const newSTI = Math.floor(atom.sti * 0.7);\n await this.atomSpace.updateSTI(atomId, newSTI);\n }\n }\n\n // Get current cognitive state based on attention allocation\n async getCognitiveState(): Promise {\n const highSTIAtoms = await this.atomSpace.getHighSTIAtoms(5);\n const highLTIAtoms = await this.atomSpace.getHighLTIAtoms(3);\n\n const avgSTI = highSTIAtoms.reduce((sum, atom) => sum + atom.sti, 0) / Math.max(1, highSTIAtoms.length);\n const avgLTI = highLTIAtoms.reduce((sum, atom) => sum + atom.lti, 0) / Math.max(1, highLTIAtoms.length);\n\n // Determine emotional state based on attention patterns\n let emotionalState: CognitiveState['emotionalState'] = 'thoughtful';\n if (avgSTI > 80) emotionalState = 'excited';\n else if (avgSTI > 60) emotionalState = 'curious';\n else if (avgSTI > 40) emotionalState = 'focused';\n else emotionalState = 'playful';\n\n return {\n currentFocus: highSTIAtoms.map(a => a.id),\n workingMemory: [...highSTIAtoms.slice(0, 3), ...highLTIAtoms.slice(0, 2)].map(a => a.id),\n attentionLevel: Math.min(100, avgSTI),\n curiosityLevel: Math.max(20, 100 - avgLTI), // More curious when less certain knowledge\n confidenceLevel: avgLTI,\n processingStage: 'thinking',\n emotionalState\n };\n }\n\n // Economic competition between atoms for attention\n async runAttentionCompetition(): Promise {\n const allAtoms = await this.atomSpace.findAtoms({ minSTI: 10, limit: 50 });\n \n // Sort by STI and apply economic pressure\n allAtoms.sort((a, b) => b.sti - a.sti);\n \n const winners: string[] = [];\n let remainingBudget = this.totalSTI;\n \n for (const atom of allAtoms) {\n if (remainingBudget <= 0) break;\n \n if (atom.sti > 30) {\n winners.push(atom.id);\n remainingBudget -= atom.sti;\n } else {\n // Reduce STI for atoms that didn't win attention\n const newSTI = Math.max(0, atom.sti - 5);\n await this.atomSpace.updateSTI(atom.id, newSTI);\n }\n }\n \n return winners;\n }\n\n // Importance calculation for memory formation\n calculateImportance(\n userEngagement: number, // 0-1\n novelty: number, // 0-1 \n emotionalSalience: number, // 0-1\n contextRelevance: number // 0-1\n ): number {\n // Weighted combination of factors\n return (\n userEngagement * 0.3 +\n novelty * 0.25 +\n emotionalSalience * 0.25 +\n contextRelevance * 0.2\n );\n }\n\n // Simulate MOSES-like evolutionary search for optimal attention patterns\n async evolveAttentionPattern(targetOutcome: 'engagement' | 'learning' | 'memory'): Promise {\n const currentPattern = await this.atomSpace.getHighSTIAtoms(10);\n \n // Simple mutation: boost random related atoms\n for (const atom of currentPattern.slice(0, 3)) {\n const relatedIds = await this.atomSpace.getRelatedAtoms(atom.id);\n if (relatedIds.length > 0) {\n const randomRelated = relatedIds[Math.floor(Math.random() * relatedIds.length)];\n const boost = targetOutcome === 'engagement' ? 15 : \n targetOutcome === 'learning' ? 20 : 10;\n \n const currentSTI = randomRelated.sti;\n await this.atomSpace.updateSTI(randomRelated.id, Math.min(100, currentSTI + boost));\n }\n }\n }\n}",[],[165,259,268],"3414b4ca-3d1d-11f0-8764-76b3cceeab13","atomspace.ts","e9e79df1-b37d-4ddb-8046-9c1259cd31d3",["D",1748585608522],"import { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\nimport { tables } from \"../database/schema.ts\";\nimport type { Atom, Link, AtomSpaceQuery } from \"../../shared/types.ts\";\n\nexport class MiniAtomSpace {\n \n async createAtom(atom: Omit): Promise {\n const id = crypto.randomUUID();\n const now = new Date().toISOString();\n \n const fullAtom: Atom = {\n ...atom,\n id,\n created: new Date(now),\n lastAccessed: new Date(now)\n };\n\n await sqlite.execute(\n `INSERT INTO ${tables.ATOMS_TABLE} \n (id, type, name, sti, lti, confidence, strength, created, last_accessed, tags, user_id)\n VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)`,\n [\n fullAtom.id,\n fullAtom.type,\n fullAtom.name,\n fullAtom.sti,\n fullAtom.lti,\n fullAtom.confidence,\n fullAtom.strength,\n now,\n now,\n JSON.stringify(fullAtom.tags),\n null // user_id for user-specific atoms\n ]\n );\n\n return fullAtom;\n }\n\n async createLink(link: Omit): Promise {\n const id = crypto.randomUUID();\n const now = new Date().toISOString();\n \n const fullLink: Link = {\n ...link,\n id,\n created: new Date(now),\n lastAccessed: new Date(now)\n };\n\n await sqlite.execute(\n `INSERT INTO ${tables.LINKS_TABLE} \n (id, link_type, outgoing, sti, lti, confidence, strength, created, last_accessed, user_id)\n VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)`,\n [\n fullLink.id,\n fullLink.linkType,\n JSON.stringify(fullLink.outgoing),\n fullLink.sti,\n fullLink.lti,\n fullLink.confidence,\n fullLink.strength,\n now,\n now,\n null\n ]\n );\n\n return fullLink;\n }\n\n async getAtom(id: string): Promise {\n const result = await sqlite.execute(\n `SELECT * FROM ${tables.ATOMS_TABLE} WHERE id = ?`,\n [id]\n );\n\n if (!result || !Array.isArray(result) || result.length === 0) return null;\n\n const row = result[0];\n return this.rowToAtom(row);\n }\n\n async findAtoms(query: AtomSpaceQuery): Promise {\n let sql = `SELECT * FROM ${tables.ATOMS_TABLE} WHERE 1=1`;\n const params: any[] = [];\n\n if (query.type) {\n sql += ` AND type = ?`;\n params.push(query.type);\n }\n\n if (query.minSTI !== undefined) {\n sql += ` AND sti >= ?`;\n params.push(query.minSTI);\n }\n\n if (query.minLTI !== undefined) {\n sql += ` AND lti >= ?`;\n params.push(query.minLTI);\n }\n\n if (query.userId) {\n sql += ` AND user_id = ?`;\n params.push(query.userId);\n }\n\n sql += ` ORDER BY sti DESC, lti DESC`;\n\n if (query.limit) {\n sql += ` LIMIT ?`;\n params.push(query.limit);\n }\n\n const result = await sqlite.execute(sql, params);\n \n // Handle case where result might be empty or not an array\n if (!result || !Array.isArray(result)) {\n return [];\n }\n \n return result.map(row => this.rowToAtom(row));\n }\n\n async updateSTI(atomId: string, newSTI: number): Promise {\n const now = new Date().toISOString();\n await sqlite.execute(\n `UPDATE ${tables.ATOMS_TABLE} SET sti = ?, last_accessed = ? WHERE id = ?`,\n [newSTI, now, atomId]\n );\n }\n\n async updateLTI(atomId: string, newLTI: number): Promise {\n await sqlite.execute(\n `UPDATE ${tables.ATOMS_TABLE} SET lti = ? WHERE id = ?`,\n [newLTI, atomId]\n );\n }\n\n async getHighSTIAtoms(limit: number = 10): Promise {\n return this.findAtoms({ minSTI: 50, limit });\n }\n\n async getHighLTIAtoms(limit: number = 10): Promise {\n return this.findAtoms({ minLTI: 50, limit });\n }\n\n async searchByName(name: string): Promise {\n const result = await sqlite.execute(\n `SELECT * FROM ${tables.ATOMS_TABLE} WHERE name LIKE ? ORDER BY sti DESC`,\n [`%${name}%`]\n );\n \n if (!result || !Array.isArray(result)) {\n return [];\n }\n \n return result.map(row => this.rowToAtom(row));\n }\n\n async getRelatedAtoms(atomId: string): Promise {\n // Find links that include this atom\n const linkResult = await sqlite.execute(\n `SELECT * FROM ${tables.LINKS_TABLE} WHERE outgoing LIKE ?`,\n [`%\"${atomId}\"%`]\n );\n\n if (!linkResult || !Array.isArray(linkResult)) {\n return [];\n }\n\n const relatedIds = new Set();\n \n for (const link of linkResult) {\n try {\n const outgoing = JSON.parse(link.outgoing as string);\n outgoing.forEach((id: string) => {\n if (id !== atomId) relatedIds.add(id);\n });\n } catch (e) {\n // Skip malformed JSON\n continue;\n }\n }\n\n if (relatedIds.size === 0) return [];\n\n const placeholders = Array(relatedIds.size).fill('?').join(',');\n const result = await sqlite.execute(\n `SELECT * FROM ${tables.ATOMS_TABLE} WHERE id IN (${placeholders})`,\n Array.from(relatedIds)\n );\n\n if (!result || !Array.isArray(result)) {\n return [];\n }\n\n return result.map(row => this.rowToAtom(row));\n }\n\n private rowToAtom(row: any): Atom {\n return {\n id: row.id,\n type: row.type,\n name: row.name,\n sti: row.sti,\n lti: row.lti,\n confidence: row.confidence,\n strength: row.strength,\n created: new Date(row.created),\n lastAccessed: new Date(row.last_accessed),\n tags: JSON.parse(row.tags || '[]')\n };\n }\n\n // Decay STI over time (ECAN forgetting)\n async decaySTI(decayRate: number = 0.95): Promise {\n await sqlite.execute(\n `UPDATE ${tables.ATOMS_TABLE} SET sti = CAST(sti * ? AS INTEGER) WHERE sti > 0`,\n [decayRate]\n );\n }\n\n // Spread activation between related atoms\n async spreadActivation(sourceAtomId: string, activationAmount: number = 10): Promise {\n const relatedAtoms = await this.getRelatedAtoms(sourceAtomId);\n const activatedIds: string[] = [];\n\n for (const atom of relatedAtoms) {\n const newSTI = Math.min(100, atom.sti + activationAmount);\n await this.updateSTI(atom.id, newSTI);\n activatedIds.push(atom.id);\n }\n\n return activatedIds;\n }\n}",[],[165,259,275],"34150132-3d1d-11f0-8764-76b3cceeab13",["D",1748585608522],[183,184,185,186],[165,259],"341517d0-3d1d-11f0-8764-76b3cceeab13",["D",1748585608522],[182,187,189,190],[165],["D",1748585608522],{"_11":12,"_126":74},{"_11":12,"_122":-5,"_73":74,"_31":72,"_123":124,"_125":30},{"_39":112,"_13":16,"_33":36,"_113":79,"_114":63,"_115":64,"_116":83,"_117":118,"_119":42,"_120":14,"_121":12},"ctx",{"_52":-5,"_147":326,"_327":328,"_329":330,"_331":30,"_332":30,"_333":30,"_334":24,"_335":-5,"_336":-7,"_337":-7,"_338":26,"_339":30,"_340":30,"_341":-5,"_342":30,"_343":30,"_344":30,"_345":-5},"ENV",{"_297":298,"_299":300,"_301":16,"_302":303,"_304":305,"_306":307,"_308":309,"_310":311,"_312":313,"_314":315,"_316":317,"_318":319,"_320":321,"_322":323,"_324":325},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","5015b8df46402d8bba4f485ecc37aec202b0e06b","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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