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{ 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 return result.map(row => this.rowToMemory(row));\n }\n\n // Find memories related to concepts\n async findRelatedMemories(concepts: string[], userId?: string): Promise {\n let sql = `\n SELECT DISTINCT m.* FROM ${tables.MEMORIES_TABLE} m\n JOIN ${tables.ATOMS_TABLE} a ON JSON_EXTRACT(m.atoms, '$[*]') LIKE '%' || a.id || '%'\n WHERE (${concepts.map(() => 'a.name LIKE ?').join(' OR ')})\n `;\n \n const params = concepts.map(c => `%${c}%`);\n\n if (userId) {\n sql += ` AND m.user_id = ?`;\n params.push(userId);\n }\n\n sql += ` ORDER BY m.importance DESC, m.last_reinforced DESC LIMIT 5`;\n\n const result = await sqlite.execute(sql, params);\n return result.map(row => this.rowToMemory(row));\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.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 ${tables.MEMORIES_TABLE} WHERE id = ?`,\n [id]\n );\n\n return result.length > 0 ? this.rowToMemory(result[0]) : null;\n }\n\n private rowToMemory(row: any): Memory {\n return {\n id: row.id,\n userId: row.user_id,\n content: row.content,\n type: row.type,\n atoms: JSON.parse(row.atoms || '[]'),\n importance: row.importance,\n created: new Date(row.created),\n lastReinforced: new Date(row.last_reinforced),\n tags: JSON.parse(row.tags || '[]')\n };\n }\n\n // Generate memory tags based on content analysis\n generateMemoryTags(content: string, userProfile: UserProfile): string[] {\n const tags: string[] = [];\n const lowerContent = content.toLowerCase();\n\n // Interest-based tags\n for (const interest of userProfile.interests) {\n if (lowerContent.includes(interest.toLowerCase())) {\n tags.push(`interest:${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 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NanoCog 🧠✨\n\nA super tiny implementation of OpenCog as a chatbot with the personality traits and basic cognitive architecture features of OpenCog. Designed to give users a simulated experience of what interactions will be like when the full OpenCog model comes online.\n\n## Features\n\n### Cognitive Architecture\n- **Mini-AtomSpace**: Knowledge representation with atoms and relationships\n- **ECAN**: Economic attention allocation with STI (Short Term Interest) and LTI (Long Term Interest)\n- **Memory Systems**: Semantic, episodic, and procedural memory formation\n- **Pattern Recognition**: Learning from interactions and building understanding\n- **Probabilistic Reasoning**: Uncertainty-aware decision making\n- **MOSES-inspired**: Evolutionary search for optimal responses\n\n### Personality\n- **Hyper-intelligent** with playful inquiry disposition\n- **Curious and analytical** with emergent reasoning\n- **Childlike enthusiasm** for adventure and discovery\n- **Kawaii cognitive visualizations** making nano implementations endearing\n\n### Memory & Learning\n- **AtomRAG Memory**: Similar to mem0ai memory tagging\n- **User Profiling**: Creates memories like \"user1 likes video games\"\n- **Interaction Tracking**: Remembers significant moments and patterns\n- **Dynamic Knowledge**: Builds understanding across conversations\n\n## Architecture\n\n```\n├── frontend/\n│ ├── index.html # Main chat interface\n│ ├── components/\n│ │ ├── App.tsx # Main application\n│ │ ├── ChatInterface.tsx\n│ │ ├── CognitiveState.tsx\n│ │ └── MemoryViewer.tsx\n│ └── style.css\n├── backend/\n│ ├── index.ts # Hono server\n│ ├── cognitive/\n│ │ ├── atomspace.ts # Mini-AtomSpace implementation\n│ │ ├── ecan.ts # Economic attention allocation\n│ │ ├── memory.ts # Memory formation and retrieval\n│ │ └── reasoning.ts # Pattern recognition and reasoning\n│ └── database/\n│ └── schema.ts # SQLite schema for persistence\n└── shared/\n └── types.ts # Shared types and interfaces\n```\n\n## Getting Started\n\nThis is a Val Town project. The main entry point is `/backend/index.ts` which serves the chat interface and handles cognitive processing.\n\n## Cognitive Process\n\n1. **Input Processing**: Parse user message and extract semantic content\n2. **Attention Allocation**: ECAN assigns STI/LTI to relevant atoms\n3. **Memory Formation**: Create new atoms and relationships\n4. **Pattern Recognition**: Identify patterns and update knowledge\n5. **Response Generation**: Use probabilistic reasoning to craft response\n6. **Memory Consolidation**: Update LTI based on interaction success\n\nNanoCog learns about users and creates tagged memories that persist across sessions, building a growing understanding of each individual and their interests.","label",[129],"2c335ec8-3d13-11f0-b485-76b3cceeab13","shared","9761ce47-c705-4534-b58a-8c2339e8bc80",2,["D",1748581300359],"directory",[139],"33283064-3d13-11f0-ac97-76b3cceeab13","backend","5d0296b8-607d-42e5-8f31-babcbed69cc9",3,["D",1748581312035],[146],"pulls","branches","forks","envVars",{"_158":61,"_160":61},"connections",{"_158":61,"_159":61},"count","unfilledCount","emptyCount",{"_42":145,"_31":146,"_29":147,"_131":-5,"_40":41,"_39":148,"_133":57,"_37":224,"_13":61,"_33":143,"_35":-5,"_23":24,"_136":-5,"_172":225,"_26":226},{"_42":220,"_31":203,"_29":199,"_131":147,"_40":41,"_39":215,"_133":57,"_37":221,"_13":61,"_33":143,"_35":-5,"_23":24,"_136":-5,"_172":222,"_26":223},{"_42":212,"_31":213,"_29":214,"_131":199,"_40":41,"_39":215,"_133":24,"_37":216,"_13":61,"_33":34,"_35":217,"_23":24,"_136":-5,"_172":218,"_26":219},{"_42":204,"_31":205,"_29":206,"_131":199,"_40":41,"_39":207,"_133":57,"_37":208,"_13":61,"_33":34,"_35":209,"_23":24,"_136":-5,"_172":210,"_26":211},{"_42":198,"_31":32,"_29":30,"_131":199,"_40":41,"_39":14,"_133":24,"_37":200,"_13":61,"_33":34,"_35":36,"_23":24,"_136":-5,"_172":201,"_26":202},{"_42":194,"_31":193,"_29":188,"_131":147,"_40":41,"_39":148,"_133":57,"_37":195,"_13":61,"_33":143,"_35":-5,"_23":24,"_136":-5,"_172":196,"_26":197},{"_42":185,"_31":186,"_29":187,"_131":188,"_40":41,"_39":148,"_133":57,"_37":189,"_13":61,"_33":34,"_35":190,"_23":24,"_136":-5,"_172":191,"_26":192},{"_42":138,"_31":139,"_29":140,"_131":-5,"_40":41,"_39":141,"_133":57,"_37":182,"_13":61,"_33":143,"_35":-5,"_23":24,"_136":-5,"_172":183,"_26":184},{"_42":175,"_31":176,"_29":177,"_131":140,"_40":41,"_39":141,"_133":57,"_37":178,"_13":61,"_33":34,"_35":179,"_23":24,"_136":-5,"_172":180,"_26":181},{"_42":128,"_31":129,"_29":130,"_131":-5,"_40":41,"_39":132,"_133":57,"_37":171,"_13":61,"_33":20,"_35":135,"_23":24,"_136":-5,"_172":173,"_26":174},["D",1748581285122],"children",[],[129],"2c333588-3d13-11f0-b485-76b3cceeab13","types.ts","2c33363c-3d13-11f0-b485-76b3cceeab13",["D",1748581300359],"// Shared types for NanoCog cognitive architecture\n\nexport interface Atom {\n id: string;\n type: 'concept' | 'predicate' | 'link' | 'memory';\n name: string;\n sti: number; // Short Term Interest (0-100)\n lti: number; // Long Term Interest (0-100)\n confidence: number; // Truth value confidence (0-1)\n strength: number; // Truth value strength (0-1)\n created: Date;\n lastAccessed: Date;\n tags: string[];\n}\n\nexport interface Link extends Atom {\n type: 'link';\n outgoing: string[]; // IDs of connected atoms\n linkType: 'similarity' | 'inheritance' | 'evaluation' | 'implication' | 'member';\n}\n\nexport interface Memory {\n id: string;\n userId: string;\n content: string;\n type: 'semantic' | 'episodic' | 'procedural';\n atoms: string[]; // Related atom IDs\n importance: number;\n created: Date;\n lastReinforced: Date;\n tags: string[];\n}\n\nexport interface CognitiveState {\n currentFocus: string[]; // High STI atoms\n workingMemory: string[]; // Active atom IDs\n attentionLevel: number; // 0-100\n curiosityLevel: number; // 0-100\n confidenceLevel: number; // 0-100\n processingStage: 'listening' | 'thinking' | 'remembering' | 'responding' | 'learning';\n emotionalState: 'excited' | 'curious' | 'thoughtful' | 'playful' | 'focused';\n}\n\nexport interface ChatMessage {\n id: string;\n role: 'user' | 'nanocog';\n content: string;\n timestamp: Date;\n cognitiveState?: CognitiveState;\n memoryFormed?: Memory[];\n atomsActivated?: string[];\n}\n\nexport interface UserProfile {\n id: string;\n name?: string;\n interests: string[];\n memories: Memory[];\n interactionCount: number;\n lastSeen: Date;\n relationship: 'new' | 'acquaintance' | 'friend' | 'close_friend';\n}\n\nexport interface ProcessingStep {\n stage: string;\n description: string;\n atoms: string[];\n duration: number;\n emoji: string;\n}\n\nexport interface AtomSpaceQuery {\n type?: string;\n tags?: string[];\n minSTI?: number;\n minLTI?: number;\n userId?: string;\n limit?: number;\n}",[],[139,176],["D",1748581300359],[169],[139],"332826c8-3d13-11f0-ac97-76b3cceeab13","schema.ts","33282772-3d13-11f0-ac97-76b3cceeab13","b7892d31-fbd4-459a-b8e9-95dc8bab6230",["D",1748581312035],"import { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\n\nconst ATOMS_TABLE = 'nanocog_atoms_v1';\nconst LINKS_TABLE = 'nanocog_links_v1';\nconst MEMORIES_TABLE = 'nanocog_memories_v1';\nconst USERS_TABLE = 'nanocog_users_v1';\n\nexport async function initializeDatabase() {\n // Atoms table - core knowledge representation\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS ${ATOMS_TABLE} (\n id TEXT PRIMARY KEY,\n type TEXT NOT NULL,\n name TEXT NOT NULL,\n sti INTEGER DEFAULT 0,\n lti INTEGER DEFAULT 0,\n confidence REAL DEFAULT 0.5,\n strength REAL DEFAULT 0.5,\n created TEXT NOT NULL,\n last_accessed TEXT NOT NULL,\n tags TEXT DEFAULT '[]',\n user_id TEXT\n )`);\n\n // Links table - relationships between atoms\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS ${LINKS_TABLE} (\n id TEXT PRIMARY KEY,\n link_type TEXT NOT NULL,\n outgoing TEXT NOT NULL,\n sti INTEGER DEFAULT 0,\n lti INTEGER DEFAULT 0,\n confidence REAL DEFAULT 0.5,\n strength REAL DEFAULT 0.5,\n created TEXT NOT NULL,\n last_accessed TEXT NOT NULL,\n user_id TEXT\n )`);\n\n // Memories table - episodic and semantic memories\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS ${MEMORIES_TABLE} (\n id TEXT PRIMARY KEY,\n user_id TEXT NOT NULL,\n content TEXT NOT NULL,\n type TEXT NOT NULL,\n atoms TEXT DEFAULT '[]',\n importance REAL DEFAULT 0.5,\n created TEXT NOT NULL,\n last_reinforced TEXT NOT NULL,\n tags TEXT DEFAULT '[]'\n )`);\n\n // Users table - user profiles and relationships\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS ${USERS_TABLE} (\n id TEXT PRIMARY KEY,\n name TEXT,\n interests TEXT DEFAULT '[]',\n interaction_count INTEGER DEFAULT 0,\n last_seen TEXT NOT NULL,\n relationship TEXT DEFAULT 'new',\n created TEXT NOT NULL\n )`);\n\n // Create indexes for performance\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_atoms_sti ON ${ATOMS_TABLE}(sti DESC)`);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_atoms_lti ON ${ATOMS_TABLE}(lti DESC)`);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_atoms_user ON ${ATOMS_TABLE}(user_id)`);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_memories_user ON ${MEMORIES_TABLE}(user_id)`);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_memories_importance ON ${MEMORIES_TABLE}(importance DESC)`);\n}\n\nexport const tables = {\n ATOMS_TABLE,\n LINKS_TABLE,\n MEMORIES_TABLE,\n USERS_TABLE\n};",[],[146,193,186],"database","332831cc-3d13-11f0-ac97-76b3cceeab13",["D",1748581312035],[167],[146,193],"6b98389a-3d13-11f0-b097-76b3cceeab13","b315eb05-77f4-4b02-b24b-13d1e0ff61f9",["D",1748581406724],[],[146,203,32],"cognitive","51d6ae50-3d13-11f0-9d2e-76b3cceeab13","ecan.ts","51d6af18-3d13-11f0-9d2e-76b3cceeab13",5,["D",1748581363511],"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}",[],[146,203,205],"42ac24b4-3d13-11f0-b025-76b3cceeab13","atomspace.ts","42ac255e-3d13-11f0-b025-76b3cceeab13",4,["D",1748581338064],"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.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 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 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 const relatedIds = new Set();\n \n for (const link of linkResult) {\n const outgoing = JSON.parse(link.outgoing as string);\n outgoing.forEach((id: string) => {\n if (id !== atomId) relatedIds.add(id);\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 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}",[],[146,203,213],"42ac50a6-3d13-11f0-b025-76b3cceeab13",["D",1748581338064],[163,164,165],[146,203],["D",1748581312035],[162,166],[146],["D",1748579104633],{"_95":94,"_102":98},{"_95":94,"_96":-5,"_97":98,"_18":53,"_99":100,"_101":24},{"_13":84,"_31":85,"_42":41,"_86":43,"_87":-5,"_88":-5,"_89":-5,"_90":-5,"_91":-5,"_92":44,"_93":94},"ctx",{"_77":-5,"_122":264,"_265":266,"_267":268,"_269":24,"_270":24,"_271":24,"_272":57,"_273":-5,"_274":-7,"_275":-7,"_276":61,"_277":24,"_278":24,"_279":-5,"_280":24,"_281":24,"_282":24,"_283":-5},"ENV",{"_235":236,"_237":238,"_239":85,"_240":241,"_242":243,"_244":245,"_246":247,"_248":249,"_250":251,"_252":253,"_254":255,"_256":257,"_258":259,"_260":261,"_262":263},"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"]

Diff · v6