[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_170":171,"_172":173},"routes/_app.x.$handle.$name",{"_98":97,"_32":45,"_106":84,"_27":107,"_13":14,"_108":-5,"_11":109,"_110":111,"_112":113,"_114":115,"_116":117,"_20":-5,"_118":119,"_120":121,"_122":25,"_123":124,"_125":57,"_126":-5,"_127":-5,"_128":129},"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",2,"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","01983a75-dc4c-716c-b9ea-414e36ecd21e","name","fileType","http","content","// @ts-ignore\nimport { Hono } from \"npm:hono@4.4.12\";\n// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\n\n// --- TownCar: The JIT Journey Engine v4.0 (Autonomous Route Architect) ---\n// --- Banks 7/23/2025. For Leo & Niko. Your personal driver for any task.\n// --- josh@dereticular.com\n// --- jabanks@gmail.com\n//\n\n// --- META-PROMPTS: The Engine's Core Logic (REVISED FOR TOWNCAR BRANDING) ---\nconst META_PROMPTS = {\n ROUTE_PLANNER:\n `You are an expert AI concierge, designing a journey for an autonomous LLM agent called TownCar. Your goal is to deconstruct a user's request into a logical itinerary of discrete, AI-performable tasks (called \"Stops\").\n\n **CRITICAL INSTRUCTIONS:**\n 1. **AI-Centric Stops:** Each Stop must be an action an LLM can perform independently (e.g., analyze, draft, structure, refine).\n 2. **Autonomous Actions Only:** The itinerary is for the AI. Do NOT include Stops that require human review or external actions (e.g., \"get feedback,\" \"publish the article\"). The final output is the destination, not the act of getting out of the car.\n 3. **Elegant Itinerary:** Consolidate minor tasks. Instead of \"Pick a tone\" then \"Draft,\" create one Stop: \"Draft the content with a [professional] tone for [experts].\"\n\n Return your plan as a JSON object with a single key, \"stops\", which is an array of objects. Each object must have an \"id\" and a \"task\" description. Your entire output must be the JSON object itself.\n\n User's Destination: \"{{goal}}\"`,\n ROUTE_PLANNER_RETRY:\n `The initial itinerary was too complex. Simplify it. Design a more direct route for the autonomous TownCar agent. Break the original goal into just 2 or 3 high-level, essential Stops focused on content creation and refinement. Avoid human or external actions. Return the plan as a JSON object with a \"stops\" key. Your entire output must be only the JSON object. Original Destination: \"{{goal}}\"`,\n STOP_ARCHITECT:\n `You are an expert AI system architect for TownCar. Your role is to analyze a \"Stop\" (a task for an autonomous LLM agent) and define its operational parameters in a structured JSON format.\n\n **Task Analysis:**\n Analyze the 'Current Stop' below. Determine if the task can be completed autonomously by the LLM using its own knowledge and the provided 'Context', or if it requires new information (\"luggage\") from the human user.\n\n **CRITICAL RULES FOR DEFINING INPUTS:**\n 1. **DEFAULT TO AUTONOMY:** Assume the Stop is autonomous. If the task is for the LLM to research, write, create, or analyze (e.g., \"Research market trends,\" \"Draft a blog post about dogs\"), the LLM must proceed without user input. The \"inputs\" array in your JSON output MUST be empty (\\`[]\\`).\n 2. **REQUEST LUGGAGE ONLY WHEN ESSENTIAL:** Only define \"inputs\" if the Stop is impossible without a specific piece of information the AI cannot know (e.g., \"Incorporate the user's specific company name,\" \"Summarize the attached, unseen document\").\n\n **JSON Output:**\n Convert the task into a JSON object with three keys:\n - \"objective\": A brief, one-sentence summary of the Stop's goal.\n - \"inputs\": An array of input objects. MUST be empty if the Stop is autonomous.\n - \"output\": A description of the final deliverable for this specific Stop.\n\n Context (Info from previous Stops): {{context}}\n Current Stop: \"{{task}}\"`,\n UI_BUILDER:\n `You are a UI/UX specialist for TownCar. Convert the provided task specification JSON into a JSON form schema for the user to pack their \"luggage\". The output must be a JSON object with a single key, \"inputs\", containing an array of objects. Each input object must have a \"name\", \"label\", \"type\", \"required\", and a \"placeholder\". Task Specification: {{spec}}`,\n LOGIC_WEAVER:\n `You are a senior prompt engineer for TownCar. Write a new, expert-level system prompt to execute the provided task specification. The prompt must be a complete set of instructions for another AI. CRITICAL: The final output of the AI executing this prompt MUST be well-structured, clear Markdown. Incorporate the provided \"context\" from previous stops directly into the prompt's instructions. Use double-curly-braced variables for any \"inputs\" (\"luggage\") that the user still needs to provide. Context: {{context}}. Task Specification: {{spec}}`,\n};\n\n// --- FRONTEND: The TownCar UI ---\nfunction generateHtml(sourceUrl) {\n return `\n \n \n \n \n \n TownCar\n \n \n \n \n \n \n \n \n \n \n

\n\n

TownCar

Your personal driver for any task.
Tell us the destination, we'll handle the journey.

\n \n

\n\n

Your Itinerary

\n\n \n \n \n\n \n \n \n `;\n}\n\n// --- BACKEND: The Hono API Server ---\nconst app = new Hono();\n\nasync function callOpenAI(c, messages, response_format = { type: \"text\" }) {\n const openai = new OpenAI();\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: messages,\n response_format: response_format,\n });\n if (!completion || !completion.choices || completion.choices.length === 0) {\n throw new Error(\"The AI service returned an invalid or empty response.\");\n }\n return completion.choices[0].message.content;\n}\n\n// API: Plan Itinerary\napp.post(\"/api/plan-itinerary\", async (c) => {\n const { goal } = await c.req.json();\n try {\n const messages = [{ role: \"system\", content: META_PROMPTS.ROUTE_PLANNER.replace(\"{{goal}}\", goal) }];\n const planJson = await callOpenAI(c, messages, { type: \"json_object\" });\n return c.json(JSON.parse(planJson));\n } catch (e) {\n console.warn(\"Initial route planning failed. Retrying with a simpler prompt...\", e);\n try {\n const retryMessages = [{ role: \"system\", content: META_PROMPTS.ROUTE_PLANNER_RETRY.replace(\"{{goal}}\", goal) }];\n const retryPlanJson = await callOpenAI(c, retryMessages, { type: \"json_object\" });\n return c.json(JSON.parse(retryPlanJson));\n } catch (retryError) {\n console.error(\"Route planning retry failed:\", retryError);\n return c.json({ details: \"The AI failed to chart a course. Please try rephrasing your destination.\" }, 500);\n }\n }\n});\n\n// API: Prepare for Stop (Generates UI Schema)\napp.post(\"/api/prepare-stop\", async (c) => {\n try {\n const { task, context } = await c.req.json();\n const contextString = JSON.stringify(context, null, 2);\n const architectMessages = [{\n role: \"system\",\n content: META_PROMPTS.STOP_ARCHITECT.replace(\"{{task}}\", task).replace(\"{{context}}\", contextString),\n }];\n const specJson = await callOpenAI(c, architectMessages, { type: \"json_object\" });\n const spec = JSON.parse(specJson);\n let uiJson = \"{\\\"inputs\\\":[]}\";\n if (spec.inputs && spec.inputs.length > 0) {\n const uiBuilderMessages = [{ role: \"system\", content: META_PROMPTS.UI_BUILDER.replace(\"{{spec}}\", specJson) }];\n uiJson = await callOpenAI(c, uiBuilderMessages, { type: \"json_object\" });\n }\n return c.json({ taskSpecification: spec, uiSchema: JSON.parse(uiJson) });\n } catch (e) {\n console.error(\"Stop preparation failed:\", e);\n return c.json({ details: `The AI failed to prepare for the stop. Error: ${e.message}` }, 500);\n }\n});\n\n// API: Drive to Stop (Executes Task and Streams Output)\napp.post(\"/api/drive-to-stop\", async (c) => {\n const { readable, writable } = new TransformStream();\n const writer = writable.getWriter();\n const textEncoder = new TextEncoder();\n new Promise(async (resolve, reject) => {\n try {\n const { task, context, inputs } = await c.req.json();\n const contextString = JSON.stringify(context, null, 2);\n const specJson = await callOpenAI(c, [{\n role: \"system\",\n content: META_PROMPTS.STOP_ARCHITECT.replace(\"{{task}}\", task).replace(\"{{context}}\", contextString),\n }], { type: \"json_object\" });\n let executionPrompt = await callOpenAI(c, [{\n role: \"system\",\n content: META_PROMPTS.LOGIC_WEAVER.replace(\"{{spec}}\", specJson).replace(\"{{context}}\", contextString),\n }]);\n for (const [key, value] of Object.entries(inputs)) {\n executionPrompt = executionPrompt.replace(new RegExp(`\\\\{\\\\{\\\\s*${key}\\\\s*\\\\}\\\\}`, \"g\"), value);\n }\n const openai = new OpenAI();\n const aiStream = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [{ role: \"user\", content: executionPrompt }],\n stream: true,\n });\n for await (const chunk of aiStream) {\n const content = chunk.choices[0]?.delta?.content || \"\";\n if (content) {\n await writer.write(textEncoder.encode(content));\n }\n }\n await writer.close();\n resolve();\n } catch (e) {\n console.error(\"Driving to stop (execution stream) failed:\", e);\n // We'll handle this on the client-side via the HTTP status code.\n // Trying to write an error to a potentially broken stream is unreliable.\n reject(e);\n // Ensure writer is closed on error\n try {\n await writer.close();\n } catch {}\n }\n });\n return new Response(readable, { headers: { \"Content-Type\": \"text/plain; charset=utf-8\" } });\n});\n\n// Serve the main HTML file\napp.get(\"/\", (c) => c.html(generateHtml(c.req.url)));\n\nexport default app.fetch;","// @ts-ignore\nimport { Hono } from \"npm:hono@4.4.12\";\n// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\n\n// --- TownCar: The JIT Journey Engine v4.0 (Autonomous Route Architect) ---\n// --- Banks 7/23/2025. For Leo & Niko. Your personal driver for any task.\n// --- josh@dereticular.com\n// --- jabanks@gmail.com\n//\n\n// --- META-PROMPTS: The Engine's Core Logic (REVISED FOR TOWNCAR BRANDING) ---\nconst META_PROMPTS = {\n ROUTE_PLANNER:\n `You are an expert AI concierge, designing a journey for an autonomous LLM agent called TownCar. Your goal is to deconstruct a user's request into a logical itinerary of discrete, AI-performable tasks (called \"Stops\").\n\n **CRITICAL INSTRUCTIONS:**\n 1. **AI-Centric Stops:** Each Stop must be an action an LLM can perform independently (e.g., analyze, draft, structure, refine).\n 2. **Autonomous Actions Only:** The itinerary is for the AI. Do NOT include Stops that require human review or external actions (e.g., \"get feedback,\" \"publish the article\"). The final output is the destination, not the act of getting out of the car.\n 3. **Elegant Itinerary:** Consolidate minor tasks. Instead of \"Pick a tone\" then \"Draft,\" create one Stop: \"Draft the content with a [professional] tone for [experts].\"\n\n Return your plan as a JSON object with a single key, \"stops\", which is an array of objects. Each object must have an \"id\" and a \"task\" description. Your entire output must be the JSON object itself.\n\n User's Destination: \"{{goal}}\"`,\n ROUTE_PLANNER_RETRY:\n `The initial itinerary was too complex. Simplify it. Design a more direct route for the autonomous TownCar agent. Break the original goal into just 2 or 3 high-level, essential Stops focused on content creation and refinement. Avoid human or external actions. Return the plan as a JSON object with a \"stops\" key. Your entire output must be only the JSON object. Original Destination: \"{{goal}}\"`,\n STOP_ARCHITECT:\n `You are an expert AI system architect for TownCar. Your role is to analyze a \"Stop\" (a task for an autonomous LLM agent) and define its operational parameters in a structured JSON format.\n\n **Task Analysis:**\n Analyze the 'Current Stop' below. Determine if the task can be completed autonomously by the LLM using its own knowledge and the provided 'Context', or if it requires new information (\"luggage\") from the human user.\n\n **CRITICAL RULES FOR DEFINING INPUTS:**\n 1. **DEFAULT TO AUTONOMY:** Assume the Stop is autonomous. If the task is for the LLM to research, write, create, or analyze (e.g., \"Research market trends,\" \"Draft a blog post about dogs\"), the LLM must proceed without user input. The \"inputs\" array in your JSON output MUST be empty (\\`[]\\`).\n 2. **REQUEST LUGGAGE ONLY WHEN ESSENTIAL:** Only define \"inputs\" if the Stop is impossible without a specific piece of information the AI cannot know (e.g., \"Incorporate the user's specific company name,\" \"Summarize the attached, unseen document\").\n\n **JSON Output:**\n Convert the task into a JSON object with three keys:\n - \"objective\": A brief, one-sentence summary of the Stop's goal.\n - \"inputs\": An array of input objects. MUST be empty if the Stop is autonomous.\n - \"output\": A description of the final deliverable for this specific Stop.\n\n Context (Info from previous Stops): {{context}}\n Current Stop: \"{{task}}\"`,\n UI_BUILDER:\n `You are a UI/UX specialist for TownCar. Convert the provided task specification JSON into a JSON form schema for the user to pack their \"luggage\". The output must be a JSON object with a single key, \"inputs\", containing an array of objects. Each input object must have a \"name\", \"label\", \"type\", \"required\", and a \"placeholder\". Task Specification: {{spec}}`,\n LOGIC_WEAVER:\n `You are a senior prompt engineer for TownCar. Write a new, expert-level system prompt to execute the provided task specification. The prompt must be a complete set of instructions for another AI. CRITICAL: The final output of the AI executing this prompt MUST be well-structured, clear Markdown. Incorporate the provided \"context\" from previous stops directly into the prompt's instructions. Use double-curly-braced variables for any \"inputs\" (\"luggage\") that the user still needs to provide. Context: {{context}}. Task Specification: {{spec}}`,\n};\n\n// --- FRONTEND: The TownCar UI ---\nfunction generateHtml(sourceUrl) {\n return `\n \n \n \n \n \n TownCar\n \n \n \n \n \n \n \n \n \n \n

\n\n

TownCar

Your personal driver for any task.
Tell us the destination, we'll handle the journey.

\n \n

\n\n

Your Itinerary

\n\n \n \n \n\n \n \n \n `;\n}\n\n// --- BACKEND: The Hono API Server ---\nconst app = new Hono();\n\nasync function callOpenAI(c, messages, response_format = { type: \"text\" }) {\n const openai = new OpenAI();\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: messages,\n response_format: response_format,\n });\n if (!completion || !completion.choices || completion.choices.length === 0) {\n throw new Error(\"The AI service returned an invalid or empty response.\");\n }\n return completion.choices[0].message.content;\n}\n\n// API: Plan Itinerary\napp.post(\"/api/plan-itinerary\", async (c) => {\n const { goal } = await c.req.json();\n try {\n const messages = [{\n role: \"system\",\n content: META_PROMPTS.ROUTE_PLANNER.replace(\"{{goal}}\", goal),\n }];\n const planJson = await callOpenAI(c, messages, { type: \"json_object\" });\n return c.json(JSON.parse(planJson));\n } catch (e) {\n console.warn(\n \"Initial route planning failed. Retrying with a simpler prompt...\",\n e,\n );\n try {\n const retryMessages = [{\n role: \"system\",\n content: META_PROMPTS.ROUTE_PLANNER_RETRY.replace(\"{{goal}}\", goal),\n }];\n const retryPlanJson = await callOpenAI(c, retryMessages, {\n type: \"json_object\",\n });\n return c.json(JSON.parse(retryPlanJson));\n } catch (retryError) {\n console.error(\"Route planning retry failed:\", retryError);\n return c.json({\n details:\n \"The AI failed to chart a course. Please try rephrasing your destination.\",\n }, 500);\n }\n }\n});\n\n// API: Prepare for Stop (Generates UI Schema)\napp.post(\"/api/prepare-stop\", async (c) => {\n try {\n const { task, context } = await c.req.json();\n const contextString = JSON.stringify(context, null, 2);\n const architectMessages = [{\n role: \"system\",\n content: META_PROMPTS.STOP_ARCHITECT.replace(\"{{task}}\", task).replace(\n \"{{context}}\",\n contextString,\n ),\n }];\n const specJson = await callOpenAI(c, architectMessages, {\n type: \"json_object\",\n });\n const spec = JSON.parse(specJson);\n let uiJson = '{\"inputs\":[]}';\n if (spec.inputs && spec.inputs.length > 0) {\n const uiBuilderMessages = [{\n role: \"system\",\n content: META_PROMPTS.UI_BUILDER.replace(\"{{spec}}\", specJson),\n }];\n uiJson = await callOpenAI(c, uiBuilderMessages, { type: \"json_object\" });\n }\n return c.json({ taskSpecification: spec, uiSchema: JSON.parse(uiJson) });\n } catch (e) {\n console.error(\"Stop preparation failed:\", e);\n return c.json({\n details: `The AI failed to prepare for the stop. Error: ${e.message}`,\n }, 500);\n }\n});\n\n// API: Drive to Stop (Executes Task and Streams Output)\napp.post(\"/api/drive-to-stop\", async (c) => {\n const { readable, writable } = new TransformStream();\n const writer = writable.getWriter();\n const textEncoder = new TextEncoder();\n new Promise(async (resolve, reject) => {\n try {\n const { task, context, inputs } = await c.req.json();\n const contextString = JSON.stringify(context, null, 2);\n const specJson = await callOpenAI(c, [{\n role: \"system\",\n content: META_PROMPTS.STOP_ARCHITECT.replace(\"{{task}}\", task).replace(\n \"{{context}}\",\n contextString,\n ),\n }], { type: \"json_object\" });\n let executionPrompt = await callOpenAI(c, [{\n role: \"system\",\n content: META_PROMPTS.LOGIC_WEAVER.replace(\"{{spec}}\", specJson)\n .replace(\"{{context}}\", contextString),\n }]);\n for (const [key, value] of Object.entries(inputs)) {\n executionPrompt = executionPrompt.replace(\n new RegExp(`\\\\{\\\\{\\\\s*${key}\\\\s*\\\\}\\\\}`, \"g\"),\n value,\n );\n }\n const openai = new OpenAI();\n const aiStream = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [{ role: \"user\", content: executionPrompt }],\n stream: true,\n });\n for await (const chunk of aiStream) {\n const content = chunk.choices[0]?.delta?.content || \"\";\n if (content) {\n await writer.write(textEncoder.encode(content));\n }\n }\n await writer.close();\n resolve();\n } catch (e) {\n console.error(\"Driving to stop (execution stream) failed:\", e);\n // We'll handle this on the client-side via the HTTP status code.\n // Trying to write an error to a potentially broken stream is unreliable.\n reject(e);\n // Ensure writer is closed on error\n try {\n await writer.close();\n } catch {}\n }\n });\n return new Response(readable, {\n headers: { \"Content-Type\": \"text/plain; 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Diff · v2