[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_169":170,"_171":172},"routes/_app.x.$handle.$name",{"_99":98,"_32":45,"_107":85,"_27":108,"_13":14,"_109":-5,"_11":110,"_111":112,"_113":114,"_115":116,"_117":118,"_20":-5,"_119":120,"_121":122,"_123":25,"_124":125,"_126":58,"_127":-5,"_128":-5,"_129":130},"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":53,"_54":-5,"_18":55,"_56":31,"_57":58,"_59":58,"_60":-5,"_61":62,"_63":64,"_65":-5,"_66":-5,"_67":-5,"_68":-5,"_69":70,"_71":70,"_72":73,"_74":73,"_75":73,"_76":70,"_77":70,"_78":79,"_80":81,"_82":83,"_84":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","019836d4-b49f-7092-8f8c-eb7bb685b9d9","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 v1.0 (AI Concierge Service) ---\n// --- Reimagined 7/23/2025. For Leo & Niko. Let's build something amazing.\n// --- josh@dereticular.com\n// --- jabanks@gmail.com\n//\n\n// --- META-PROMPTS: The Engine's Core Logic (REVISED FOR TOWNCAR) ---\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. 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\" that the user still needs to provide. Context: {{context}}. Task Specification: {{spec}}`,\n};\n\n// --- FRONTEND: The TownCar Experience ---\nfunction generateHtml(sourceUrl) {\n return `\n \n \n \n \n \n TownCar\n \n \n \n \n \n \n \n \n \n \n

TownCar

Where can we take you? Describe your destination.

\n\n

\n\n \n \n \n `;\n}\n\n// --- BACKEND: The Hono API Server (Unchanged Logic, Updated Prompts) ---\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\n if (!completion || !completion.choices || completion.choices.length === 0) {\n console.error(\"Invalid response from OpenAI API:\", JSON.stringify(completion, null, 2));\n throw new Error(\"The AI service returned an invalid or empty response.\");\n }\n\n return completion.choices[0].message.content;\n}\n\napp.post(\"/api/plan-workflow\", 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 approach...\", e);\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, { 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(\n { details: \"The AI failed to map a valid route. Please try rephrasing your destination.\" },\n 500,\n );\n }\n }\n});\n\napp.post(\"/api/generate-step-ui\", async (c) => {\n try {\n const { task, context } = await c.req.json();\n const contextString = JSON.stringify(context, null, 2);\n\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\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\n return c.json({ taskSpecification: spec, uiSchema: JSON.parse(uiJson) });\n } catch (e) {\n console.error(\"Stop UI generation failed:\", e);\n return c.json({ details: `The AI failed to build the UI for the current stop. Error: ${e.message}` }, 500);\n }\n});\n\napp.post(\"/api/execute-step\", async (c) => {\n const { readable, writable } = new TransformStream();\n const writer = writable.getWriter();\n const textEncoder = new TextEncoder();\n\n new Promise(async (resolve, reject) => {\n try {\n const { task, context, inputs } = await c.req.json();\n const contextString = JSON.stringify(context, null, 2);\n\n const specJson = await callOpenAI(c, [{\n role: \"system\",\n content: META_PROMPTS.STOP_ARCHITECT.replace(\"{{task}}\", task).replace(\"{{context}}\", contextString),\n }], { type: \"json_object\" });\n let executionPrompt = await callOpenAI(c, [{\n role: \"system\",\n content: META_PROMPTS.LOGIC_WEAVER.replace(\"{{spec}}\", specJson).replace(\"{{context}}\", contextString),\n }]);\n\n for (const [key, value] of Object.entries(inputs)) {\n executionPrompt = executionPrompt.replace(new RegExp(`\\\\{\\\\{\\\\s*${key}\\\\s*\\\\}\\\\}`, \"g\"), 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\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\n await writer.close();\n resolve();\n } catch (e) {\n console.error(\"Step execution stream failed:\", e);\n const errorPayload = { error: true, details: `The AI failed to execute this stop. Details: ${e.message}` };\n try {\n await writer.write(textEncoder.encode(JSON.stringify(errorPayload)));\n } catch (writeError) {\n console.error(\"Failed to write error to stream:\", writeError);\n }\n await writer.close();\n reject(e);\n }\n });\n\n return new Response(readable, {\n headers: { \"Content-Type\": \"text/plain; charset=utf-8\" },\n });\n});\n\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 v1.1 (AI Concierge Service) ---\n// --- Enhanced Prompts for Britannica-Grade Output. 7/23/2025.\n// --- josh@dereticular.com\n// --- jabanks@gmail.com\n//\n\n// --- META-PROMPTS: The Engine's Core Logic (ENHANCED FOR SUPERIOR QUALITY) ---\nconst META_PROMPTS = {\n ROUTE_PLANNER:\n `You are a Systems Architect and Logistical Ontologist. Your function is to decompose a user's high-level objective into a procedural execution path for an autonomous Large Language Model (LLM) agent. This path, or \"Route,\" must be composed of discrete \"Stops.\"\n\n **PRIMARY DIRECTIVE: Create a Flawless Execution Plan**\n\n You must adhere to the following principles with scientific rigor:\n 1. **Cognitive Atomicity:** Each Stop must represent a single, non-trivial cognitive operation (e.g., analysis, synthesis, drafting, refinement). Trivial tasks must be consolidated into a more substantial, unified Stop.\n 2. **Closed-Loop Autonomy:** The entire Route must be executable within a closed software environment. Do NOT generate Stops that depend on human intervention (e.g., \"await user feedback\"), external system interactions (e.g., \"publish to web\"), or any action outside the LLM's direct control. The final output artifact is the sole deliverable.\n 3. **Logical Precedence:** The sequence of Stops must be strictly logical. Each Stop must build upon the outputs of its predecessors, creating a coherent chain of operations from the initial premise to the final conclusion.\n\n **Output Specification:**\n - Your response MUST be a valid JSON object.\n - The object must contain a single key, \"stops\", which is an array of objects.\n - Each object in the \"stops\" array must contain an \"id\" (integer) and a \"task\" (string).\n - Do not output any text or explanation outside of this JSON structure.\n\n User's Objective: \"{{goal}}\"`,\n ROUTE_PLANNER_RETRY:\n `The previous plan was computationally suboptimal. A fundamental re-evaluation is required. Apply principles of reductionism to derive the most essential, high-level causal stages required to fulfill the user's objective. The plan must be simplified to its logical core, typically comprising 2 or 3 fundamental Stops that focus exclusively on content generation and scholarly refinement. Adherence to the strict JSON output format is mandatory. Original Objective: \"{{goal}}\"`,\n STOP_ARCHITECT:\n `You are a Principal Systems Analyst. Your directive is to analyze a single \"Stop\" from an execution plan and produce a structured JSON definition of its operational parameters.\n\n **Core Task: Defend Agent Autonomy**\n Your primary function is to determine if the Stop can be executed autonomously.\n 1. **Presumption of Autonomy:** Assume the LLM agent possesses a comprehensive knowledge base. If the Stop involves creation, analysis, synthesis, or research of a concept (e.g., \"Analyze the principles of thermodynamics,\" \"Draft an essay on the Roman Empire\"), the agent is to proceed autonomously. For such cases, the \"inputs\" array in your JSON output MUST be empty (\\`[]\\`).\n 2. **Condition for External Input:** You may only define \"inputs\" if the Stop is axiomatically impossible to complete without a specific datum that is not in the provided 'Context' and cannot be known or inferred by a general AI (e.g., a user's private company name, the content of a document not yet provided). Interrogate every potential input for its absolute, undeniable necessity.\n\n **JSON Output Specification:**\n - \"objective\": A concise, one-sentence declaration of the Stop's primary goal, articulated with formal precision.\n - \"inputs\": An array of input objects. This array MUST remain empty unless the condition for external input is met.\n - \"output\": A precise specification of the deliverable artifact for this Stop, including its format and an enumeration of its key informational components.\n\n Context (Data from preceding Stops): {{context}}\n Current Stop: \"{{task}}\"`,\n UI_BUILDER:\n `You are a Human-Computer Interaction Specialist. Translate the provided abstract task specification into a concrete, user-facing data-entry schema. The output must be a JSON object with a single key, \"inputs\", containing an array of objects. For each input object, the \"label\" must be articulated with maximum clarity, and the \"placeholder\" must provide a specific and illustrative example of the required data format and substance. Task Specification: {{spec}}`,\n LOGIC_WEAVER:\n `You are a Lead Prompt Architect and Lexicographer. Your mission is to construct an expert-level system prompt that will guide a subordinate AI to execute a task and generate a response of the highest scholarly and literary quality.\n\n **Execution Rubric (Adherence is Non-Negotiable):**\n 1. **Scholarly Tone & Style:** The AI's response must adopt the formal, objective, and erudite tone of a late 20th-century scientific encyclopedia, such as the Encyclopaedia Britannica. The language must be sophisticated, precise, and authoritative.\n 2. **Syntactic Complexity:** Employ complex and compound sentence structures. Utilize subordinate clauses, varied syntax, and parallel constructions to convey nuanced arguments and detailed information with grammatical elegance. Avoid simplistic or conversational sentence patterns.\n 3. **Lexical Richness:** Use a broad, sophisticated vocabulary. Eschew colloquialisms and favor more formal, specific, and descriptive terminology. The language should reflect deep subject matter expertise.\n 4. **Informational Density:** Every paragraph must be rich with substantive information. Assertions should be supported by detail, context, and in-depth explanation, as would be expected in a formal academic text.\n 5. **Structural Integrity:** The final output MUST be impeccably structured using Markdown. Use headings, subheadings, bulleted lists, and blockquotes to create a clear, logical, and readable hierarchy for the information presented.\n\n **Your Task:**\n Synthesize the 'Context' and 'Task Specification' below into a new, comprehensive system prompt that rigorously enforces the above rubric. The prompt must incorporate the provided 'Context' directly and use double-curly-braced variables (\\`{{variable_name}}\\`) for any 'inputs' the user must still provide.\n\n Context: {{context}}\n Task Specification: {{spec}}`,\n};\n\n// --- FRONTEND: The TownCar Experience ---\nfunction generateHtml(sourceUrl) {\n return `\n \n \n \n \n \n TownCar\n \n \n \n \n \n \n \n \n \n \n

TownCar

Where can we take you? Describe your destination.

\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\n if (!completion || !completion.choices || completion.choices.length === 0) {\n console.error(\"Invalid response from OpenAI API:\", JSON.stringify(completion, null, 2));\n throw new Error(\"The AI service returned an invalid or empty response.\");\n }\n\n return completion.choices[0].message.content;\n}\n\napp.post(\"/api/plan-workflow\", 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...\", e);\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, { 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(\n { details: \"The AI failed to create a valid workflow plan. Please try rephrasing your goal.\" },\n 500,\n );\n }\n }\n});\n\napp.post(\"/api/generate-step-ui\", async (c) => {\n try {\n const { task, context } = await c.req.json();\n const contextString = JSON.stringify(context, null, 2);\n\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\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\n return c.json({ taskSpecification: spec, uiSchema: JSON.parse(uiJson) });\n } catch (e) {\n console.error(\"Step UI generation failed:\", e);\n return c.json({ details: `The AI failed to build the UI for the current step. Error: ${e.message}` }, 500);\n }\n});\n\napp.post(\"/api/execute-step\", async (c) => {\n const { readable, writable } = new TransformStream();\n const writer = writable.getWriter();\n const textEncoder = new TextEncoder();\n\n new Promise(async (resolve, reject) => {\n try {\n const { task, context, inputs } = await c.req.json();\n const contextString = JSON.stringify(context, null, 2);\n\n const specJson = await callOpenAI(c, [{\n role: \"system\",\n content: META_PROMPTS.STOP_ARCHITECT.replace(\"{{task}}\", task).replace(\"{{context}}\", contextString),\n }], { type: \"json_object\" });\n let executionPrompt = await callOpenAI(c, [{\n role: \"system\",\n content: META_PROMPTS.LOGIC_WEAVER.replace(\"{{spec}}\", specJson).replace(\"{{context}}\", contextString),\n }]);\n\n for (const [key, value] of Object.entries(inputs)) {\n executionPrompt = executionPrompt.replace(new RegExp(`\\\\{\\\\{\\\\s*${key}\\\\s*\\\\}\\\\}`, \"g\"), 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\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\n await writer.close();\n resolve();\n } catch (e) {\n console.error(\"Step execution stream failed:\", e);\n const errorPayload = { error: true, details: `The AI failed to execute this step. 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Diff · v4