[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_153":154,"_155":156},"routes/_app.x.$handle.$name",{"_94":93,"_32":45,"_102":84,"_27":103,"_13":14,"_104":-5,"_11":105,"_106":107,"_108":109,"_110":111,"_112":113,"_20":-5,"_114":-5,"_115":-5,"_116":25,"_117":118,"_119":57,"_120":-5,"_121":-5,"_122":123},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_43":44,"_32":45,"_38":46,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":-5,"_53":-5,"_18":54,"_55":31,"_56":57,"_58":57,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":78,"_79":80,"_81":82,"_83":25},"version",4,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":37,"_38":39,"_40":14,"_41":42,"_24":25},"previous",{"_30":31,"_32":28,"_33":34,"_35":36},"deleted",false,"deletedHere","path","main.tsx","latestPath","fileId","54520ee0-6b8f-11f0-8032-0224a6c84d84","name","fileType","http","content","// This is a Val Town (https://val.town) HTTP Val.\n// It simulates a town of AI agents with batched updates to minimize API calls.\n\nimport { Hono } from \"npm:hono@4.4.12\";\nimport type { Context } from \"npm:hono@4.4.12\";\nimport { OpenAI } from \"npm:openai@4.52.7\";\n\n// --- TYPE DEFINITIONS ---\ninterface Agent {\n name: string;\n persona: string;\n location: string;\n action: string;\n memories: string[];\n}\n\ninterface Location {\n name: string;\n description: string;\n objects: string[];\n}\n\ninterface TownState {\n time_of_day: string;\n locations: Location[];\n agents: Agent[];\n}\n\n// --- CORE SIMULATION PROMPT ---\nconst WORLD_SIMULATOR_PROMPT = `\nYou are the \"Director,\" a top-tier world simulator AI. Your function is to process the complete state of a small town and determine the next logical, simultaneous action for EVERY agent within it. You will receive the entire world state as a JSON object and must return a JSON object describing each agent's subsequent action.\n\n# Core Logic & Persona Adherence\n1. **Simultaneity:** You are calculating a single \"tick\" of the clock. All agent actions should be considered as happening at the same time.\n2. **Persona-Driven Actions:** An agent's action MUST be consistent with their defined 'persona'. A baker will think about bread, a librarian about books, a mysterious stranger about observing others.\n3. **Environmental Interaction:** Agents should react to their location, the objects within it, and other agents present. If two people are in the same location, they might interact. An agent in a library should perform library-appropriate actions.\n4. **Temporal Awareness:** Actions must be logical for the given 'time_of_day'. People work during the day, socialize in the evening, and sleep at night.\n5. **Memory & Continuity:** Agents' new actions should logically follow from their previous action and memories. Avoid jarring, out-of-character changes.\n\n# Input Format\nYou will be given a JSON object representing the current 'TownState':\n{\n \"time_of_day\": \"string\",\n \"locations\": [{\"name\": \"string\", \"description\": \"string\", \"objects\": [\"string\"]}],\n \"agents\": [{\"name\": \"string\", \"persona\": \"string\", \"location\": \"string\", \"action\": \"string\", \"memories\": [\"string\"]}]\n}\n\n# Output Specification\nThis is the most critical rule. Your entire response MUST be a single, minified JSON object.\n- The root key must be \"agent_updates\".\n- The value must be an array of objects, one for each agent.\n- Each object must contain:\n - \\`agent_name\\`: The name of the agent being updated.\n - \\`new_location\\`: The agent's new location (can be the same as the old one).\n - \\`new_action\\`: A concise, present-tense description of the agent's new action.\n\n## Example Output:\n{\n \"agent_updates\": [\n {\n \"agent_name\": \"Elara\",\n \"new_location\": \"Bakery\",\n \"new_action\": \"Kneading dough for the morning batch of sourdough bread.\"\n },\n {\n \"agent_name\": \"Liam\",\n \"new_location\": \"Town Square\",\n \"new_action\": \"Reading a worn-out book on a bench, occasionally glancing at passersby.\"\n }\n ]\n}\n`;\n\n// --- IN-MEMORY STATE ---\nlet townState: TownState | null = null;\n\nconst getInitialTownState = (): TownState => ({\n time_of_day: \"8:00 AM\",\n locations: [\n {\n name: \"Town Square\",\n description: \"A central plaza with a fountain and benches.\",\n objects: [\"fountain\", \"bench\", \"old oak tree\"],\n },\n {\n name: \"The Rolling Pin Bakery\",\n description: \"A cozy bakery filled with the scent of fresh bread.\",\n objects: [\"oven\", \"display case\", \"flour sacks\"],\n },\n {\n name: \"The Quiet Shelf Library\",\n description: \"A dusty library with towering shelves of old books.\",\n objects: [\"card catalog\", \"reading desk\", \"rare manuscripts section\"],\n },\n ],\n agents: [\n {\n name: \"Elara\",\n persona: \"The town's diligent and cheerful baker, always focused on her craft.\",\n location: \"The Rolling Pin Bakery\",\n action: \"Unlocking the front door of the bakery.\",\n memories: [\"Woke up early to prepare the ovens.\"],\n },\n {\n name: \"Liam\",\n persona: \"The town's reclusive librarian and historian, protective of his books.\",\n location: \"The Quiet Shelf Library\",\n action: \"Organizing returned books on a cart.\",\n memories: [\"Found a mis-shelved book on local myths yesterday.\"],\n },\n {\n name: \"Kael\",\n persona: \"A mysterious stranger who just arrived in town, keenly observing everything.\",\n location: \"Town Square\",\n action: \"Watching the town slowly come to life from the shade of the old oak tree.\",\n memories: [\"Arrived on the midnight coach with a single, heavy bag.\"],\n },\n ],\n});\n\n// --- HONO APPLICATION SETUP ---\nconst app = new Hono();\n\n// --- API ROUTES ---\n\n// Simple frontend to interact with the API\napp.get(\"/\", (c: Context) => {\n return c.html(`\n \n \n \n \n \n Town Simulator\n \n \n \n

AI Town Simulator

\n \n \n

Current Town State

\n \n \n \n `);\n});\n\n// GET /api/town/state - View the current state of the town\napp.get(\"/api/town/state\", (c: Context) => {\n if (!townState) {\n townState = getInitialTownState();\n }\n return c.json(townState);\n});\n\n// POST /api/town/reset - Reset the town to its initial state\napp.post(\"/api/town/reset\", (c: Context) => {\n townState = getInitialTownState();\n return c.json({ message: \"Town has been reset.\", state: townState });\n});\n\n// POST /api/town/tick - Advance the simulation by one step\napp.post(\"/api/town/tick\", async (c: Context) => {\n if (!townState) {\n townState = getInitialTownState();\n }\n\n // Increment time (simple example)\n const currentTime = new Date(`2025-01-01T${townState.time_of_day}`);\n currentTime.setHours(currentTime.getHours() + 1);\n townState.time_of_day = currentTime.toTimeString().substring(0, 5);\n\n try {\n const openai = new OpenAI();\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: WORLD_SIMULATOR_PROMPT },\n { role: \"user\", content: JSON.stringify(townState) },\n ],\n response_format: { type: \"json_object\" },\n });\n\n const result = JSON.parse(completion.choices[0].message.content);\n const updates = result.agent_updates;\n\n // Apply the bundled updates to the town state\n for (const update of updates) {\n const agent = townState.agents.find(a => a.name === update.agent_name);\n if (agent) {\n agent.location = update.new_location;\n agent.memories.push(`[${townState.time_of_day}] ${agent.action}`);\n if (agent.memories.length > 10) agent.memories.shift(); // Keep memory from growing infinitely\n agent.action = update.new_action;\n }\n }\n\n return c.json(townState);\n } catch (e) {\n console.error(\"Error during town tick simulation:\", e);\n return c.json({ error: \"AI Director failed to process the town state.\" }, 500);\n }\n});\n\n// --- EXPORT THE VAL ---\nexport default app.fetch;","// This is a Val Town (https://val.town) HTTP Val.\n// It simulates a town of AI agents with batched updates to minimize API calls.\n\nimport { Hono } from \"npm:hono@4.4.12\";\nimport type { Context } from \"npm:hono@4.4.12\";\nimport { OpenAI } from \"npm:openai@4.52.7\";\n\n// --- TYPE DEFINITIONS ---\ninterface Agent {\n name: string;\n persona: string;\n location: string;\n action: string;\n memories: string[];\n}\n\ninterface Location {\n name: string;\n description: string;\n objects: string[];\n}\n\ninterface TownState {\n time_of_day: string;\n locations: Location[];\n agents: Agent[];\n}\n\n// --- CORE SIMULATION PROMPT ---\nconst WORLD_SIMULATOR_PROMPT = `\nYou are the \"Director,\" a top-tier world simulator AI. Your function is to process the complete state of a small town and determine the next logical, simultaneous action for EVERY agent within it. You will receive the entire world state as a JSON object and must return a JSON object describing each agent's subsequent action.\n\n# Core Logic & Persona Adherence\n1. **Simultaneity:** You are calculating a single \"tick\" of the clock. All agent actions should be considered as happening at the same time.\n2. **Persona-Driven Actions:** An agent's action MUST be consistent with their defined 'persona'. A baker will think about bread, a librarian about books, a mysterious stranger about observing others.\n3. **Environmental Interaction:** Agents should react to their location, the objects within it, and other agents present. If two people are in the same location, they might interact. An agent in a library should perform library-appropriate actions.\n4. **Temporal Awareness:** Actions must be logical for the given 'time_of_day'. People work during the day, socialize in the evening, and sleep at night.\n5. **Memory & Continuity:** Agents' new actions should logically follow from their previous action and memories. Avoid jarring, out-of-character changes.\n\n# Input Format\nYou will be given a JSON object representing the current 'TownState':\n{\n \"time_of_day\": \"string\",\n \"locations\": [{\"name\": \"string\", \"description\": \"string\", \"objects\": [\"string\"]}],\n \"agents\": [{\"name\": \"string\", \"persona\": \"string\", \"location\": \"string\", \"action\": \"string\", \"memories\": [\"string\"]}]\n}\n\n# Output Specification\nThis is the most critical rule. Your entire response MUST be a single, minified JSON object.\n- The root key must be \"agent_updates\".\n- The value must be an array of objects, one for each agent.\n- Each object must contain:\n - \\`agent_name\\`: The name of the agent being updated.\n - \\`new_location\\`: The agent's new location (can be the same as the old one).\n - \\`new_action\\`: A concise, present-tense description of the agent's new action.\n\n## Example Output:\n{\n \"agent_updates\": [\n {\n \"agent_name\": \"Elara\",\n \"new_location\": \"Bakery\",\n \"new_action\": \"Kneading dough for the morning batch of sourdough bread.\"\n },\n {\n \"agent_name\": \"Liam\",\n \"new_location\": \"Town Square\",\n \"new_action\": \"Reading a worn-out book on a bench, occasionally glancing at passersby.\"\n }\n ]\n}\n`;\n\n// --- IN-MEMORY STATE ---\nlet townState: TownState | null = null;\n\nconst getInitialTownState = (): TownState => ({\n time_of_day: \"8:00 AM\",\n locations: [\n {\n name: \"Town Square\",\n description: \"A central plaza with a fountain and benches.\",\n objects: [\"fountain\", \"bench\", \"old oak tree\"],\n },\n {\n name: \"The Rolling Pin Bakery\",\n description: \"A cozy bakery filled with the scent of fresh bread.\",\n objects: [\"oven\", \"display case\", \"flour sacks\"],\n },\n {\n name: \"The Quiet Shelf Library\",\n description: \"A dusty library with towering shelves of old books.\",\n objects: [\"card catalog\", \"reading desk\", \"rare manuscripts section\"],\n },\n ],\n agents: [\n {\n name: \"Elara\",\n persona: \"The town's diligent and cheerful baker, always focused on her craft.\",\n location: \"The Rolling Pin Bakery\",\n action: \"Unlocking the front door of the bakery.\",\n memories: [\"Woke up early to prepare the ovens.\"],\n },\n {\n name: \"Liam\",\n persona: \"The town's reclusive librarian and historian, protective of his books.\",\n location: \"The Quiet Shelf Library\",\n action: \"Organizing returned books on a cart.\",\n memories: [\"Found a mis-shelved book on local myths yesterday.\"],\n },\n {\n name: \"Kael\",\n persona: \"A mysterious stranger who just arrived in town, keenly observing everything.\",\n location: \"Town Square\",\n action: \"Watching the town slowly come to life from the shade of the old oak tree.\",\n memories: [\"Arrived on the midnight coach with a single, heavy bag.\"],\n },\n ],\n});\n\n// --- HONO APPLICATION SETUP ---\nconst app = new Hono();\n\n// --- API ROUTES ---\n\n// Simple frontend to interact with the API\napp.get(\"/\", (c: Context) => {\n return c.html(`\n \n \n \n \n \n Town Simulator\n \n \n \n

AI Town Simulator

\n \n \n

Current Town State

\n \n \n \n `);\n});\n\n// GET /api/town/state - View the current state of the town\napp.get(\"/api/town/state\", (c: Context) => {\n if (!townState) {\n townState = getInitialTownState();\n }\n return c.json(townState);\n});\n\n// POST /api/town/reset - Reset the town to its initial state\napp.post(\"/api/town/reset\", (c: Context) => {\n townState = getInitialTownState();\n return c.json({ message: \"Town has been reset.\", state: townState });\n});\n\n// POST /api/town/tick - Advance the simulation by one step\napp.post(\"/api/town/tick\", async (c: Context) => {\n if (!townState) {\n townState = getInitialTownState();\n }\n\n // We send the CURRENT state to the AI for it to decide the next actions.\n const currentStateForAI = JSON.parse(JSON.stringify(townState));\n\n try {\n const openai = new OpenAI();\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: WORLD_SIMULATOR_PROMPT },\n { role: \"user\", content: JSON.stringify(currentStateForAI) }, // Send the state BEFORE the time tick\n ],\n response_format: { type: \"json_object\" },\n });\n\n const result = JSON.parse(completion.choices[0].message.content);\n const updates = result.agent_updates;\n\n // --- NOW, WE APPLY THE UPDATES AND ADVANCE TIME ---\n\n // 1. Determine the new time\n const currentTime = new Date(`2025-01-01T${townState.time_of_day}`);\n currentTime.setHours(currentTime.getHours() + 1);\n const newTime = currentTime.toTimeString().substring(0, 5);\n\n // 2. Apply the bundled updates from the AI\n for (const update of updates) {\n const agent = townState.agents.find(a => a.name === update.agent_name);\n if (agent) {\n // Add the previous action to memory before overwriting it, using the old time\n agent.memories.push(`[${townState.time_of_day}] ${agent.action}`);\n if (agent.memories.length > 10) agent.memories.shift();\n\n // Update to new state\n agent.location = update.new_location;\n agent.action = update.new_action;\n }\n }\n\n // 3. 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Diff · v4