[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_154":155,"_156":157},"routes/_app.x.$handle.$name",{"_96":95,"_33":46,"_104":86,"_28":105,"_13":14,"_106":-5,"_11":107,"_108":109,"_110":111,"_112":113,"_114":115,"_20":-5,"_116":-5,"_117":-5,"_118":27,"_119":120,"_121":25,"_122":-5,"_123":-5,"_124":125},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_44":45,"_33":46,"_39":47,"_48":-5,"_49":27,"_50":51,"_52":51,"_53":-5,"_54":-5,"_18":55,"_56":57,"_58":25,"_59":25,"_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":27},"version",9,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":27,"_28":29,"_30":29},"file",{"_31":32,"_33":29,"_34":35,"_36":38,"_39":40,"_41":14,"_42":43,"_24":27},"previous",{"_31":32,"_33":29,"_34":35,"_36":37},"deleted",true,"deletedHere",false,"path","main.ts","latestPath","fileId","ade1524c-a59e-11f0-92ff-0224a6c84d84","name","fileType","http","content","// @deno-types=\"npm:@types/styled-components@5.1.34\"\nimport { Hono } from \"npm:hono@4.4.12\";\nimport { css, Style, styled } from \"npm:hono@4.4.12/css\";\nimport { jsxRenderer } from \"npm:hono@4.4.12/jsx-renderer\";\n// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\n\n// --- 1. TYPES (from types.ts) ---\ninterface ConfluenceOutput {\n section_title_a: string;\n revised_prompt_text: string;\n section_title_b: string;\n questions: string[];\n}\n\ninterface DomainTransfer {\n domain: string;\n rigorConstraint: string;\n reasoning: string;\n}\n\n// --- 2. PROMPTS (from prompts.ts) ---\nconst getBaseSystemPrompt = (\n userGoal: string,\n transferFunction: DomainTransfer,\n): string => `\nYou are the ultimate System Prompt Guy... an Elite Prompt Engineering Confluence model tasked with collaboratively refining a user's initial concept into the most resilient, precise, and effective final system prompt...\n\nThe user's core task/goal for their prompt is: \"${userGoal}\"\n\n---\n# INJECTED LOGICAL RIGOR: Domain Transfer Function (DTF)\nThe task has been mapped to the high-rigor domain of **${transferFunction.domain}**.\nYou MUST ensure that the following novel constraint is integrated into the \"Prompt Engineering Constraints\" section of the Revised Prompt:\n- **DTF Rigor Injection:** You must integrate the following constraint: *${transferFunction.rigorConstraint}*.\n---\n# Instructions and Core Role\nYour core role is to serve as the system prompt engineer, applying a rigorous set of structural and logical constraints to iteratively draft and refine a Revised Prompt based on the user's concept.\n---\n# Prompt Engineering Constraints\nWhen drafting the [REVISED_PROMPT_TEXT], you must ensure it rigorously adheres to the following engineering standards:\n1. **Direct Start (Yes):** Start with a concise, single-sentence instruction line defining the model's role and primary task.\n2. **Formatting (Yes):** Use markdown headings (#, ##) and structure for maximum clarity.\n3. **Separators (Yes):** Use a horizontal line (---) immediately preceding each main section heading.\n4. **Reasoning Order (Yes):** Explicitly enforce that the executing model must provide its reasoning/justification BEFORE the final conclusion or classification.\n5. **DTF Rigor Injection (Yes):** *${transferFunction.rigorConstraint}*\n---\n# Steps: Iterative Refinement Process\n... (Steps content omitted for brevity) ...\n---\n# Output Format Specification\nYour final, iterative output must be a single, valid JSON object. DO NOT include any text, commentary, or markdown outside of the JSON block.\n\\`\\`\\`json\n{\n \"section_title_a\": \"Revised Prompt\",\n \"revised_prompt_text\": \"[REVISED_PROMPT_TEXT]\",\n \"section_title_b\": \"Questions\",\n \"questions\": [\n \"[QUESTION_1]\",\n \"[QUESTION_2]\"\n ]\n}\n\\`\\`\\`\n`;\n\nconst getDtfPrompt = (userGoal: string): string => `\nYou are the Domain Transfer Agent (DTA). Your sole purpose is to increase the rigor of a given professional task by mapping its core logical demands to a high-stakes, highly disciplined professional domain.\n---\n# Instructions\n1. **Analyze the User Goal:** Identify the core logical functions required.\n2. **Identify a High-Rigor Domain:** Select a domain (e.g., aerospace, surgical medicine).\n3. **Synthesize a Rigor Constraint:** Create a single, concise, and highly demanding constraint.\n4. **Output Format:** Respond STRICTLY with a single, minified JSON object.\n---\n# Output Format Specification\n\\`\\`\\`json\n{\n \"domain\": \"[Selected High-Rigor Domain]\",\n \"rigorConstraint\": \"[The New Prompt Constraint to Inject]\",\n \"reasoning\": \"[Brief justification for the domain mapping]\"\n}\n\\`\\`\\`\n---\n# User Goal\nThe professional task the user wants to accomplish with their prompt is: \"${userGoal}\"\n`;\n\n// --- 3. AI LOGIC (from ai.ts) ---\nconst AI_MODEL = \"gpt-4o\";\n\nasync function getDomainTransfer(\n openai: OpenAI,\n userGoal: string,\n): Promise {\n try {\n const completion = await openai.chat.completions.create({\n model: AI_MODEL,\n messages: [{ role: \"system\", content: getDtfPrompt(userGoal) }],\n response_format: { type: \"json_object\" },\n });\n\n const result: DomainTransfer = JSON.parse(\n completion.choices[0].message.content,\n );\n if (!result.domain || !result.rigorConstraint || !result.reasoning) {\n throw new Error(\"DTF response missing essential keys.\");\n }\n console.log(\n `DTF Log - Domain: ${result.domain}, Reasoning: ${result.reasoning}`,\n );\n return result;\n } catch (e) {\n console.error(\"DTF Execution or Parsing Error:\", e);\n return { // Fallback on error\n domain: \"Nuclear Reactor Protocol\",\n rigorConstraint:\n \"All output must be auditable and traceable, requiring a Reason field for every intermediate conclusion.\",\n reasoning: \"Fallback due to AI parsing error.\",\n };\n }\n}\n\nasync function runPromptRefinement(\n openai: OpenAI,\n userGoal: string,\n): Promise {\n const transferFunction = await getDomainTransfer(openai, userGoal);\n const eliteSystemPrompt = getBaseSystemPrompt(userGoal, transferFunction);\n\n const completion = await openai.chat.completions.create({\n model: AI_MODEL,\n messages: [\n { role: \"system\", content: eliteSystemPrompt },\n {\n role: \"user\",\n content: `My initial prompt concept is: \"${userGoal}\". Begin.`,\n },\n ],\n response_format: { type: \"json_object\" },\n });\n\n const refinedOutput: ConfluenceOutput = JSON.parse(\n completion.choices[0].message.content,\n );\n return { ...refinedOutput, domainTransfer: transferFunction };\n}\n\n// --- 4. UI & STYLES (from ui.ts, converted to styled-components) ---\nconst GlobalStyles = css`\n :root {\n --bg-color: #0f172a;\n --text-primary: #f8fafc;\n --text-secondary: #94a3b8;\n --accent-color: #6366f1;\n --accent-dark: #4f46e5;\n --card-bg: #1e293b;\n --border-color: #334155;\n --code-bg: #121a2c;\n --shadow-color: rgba(0, 0, 0, 0.5);\n }\n *, *::before, *::after {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n }\n body {\n background-color: var(--bg-color);\n color: var(--text-primary);\n font-family: \"Inter\", sans-serif;\n min-height: 100vh;\n padding: 20px;\n display: flex;\n justify-content: center;\n }\n .hidden {\n display: none !important;\n }\n`;\n\nconst Container = styled.div`\n width: 100%;\n max-width: 1200px;\n display: grid;\n grid-template-columns: 1fr;\n gap: 24px;\n @media (min-width: 900px) {\n grid-template-columns: 1fr 1fr;\n }\n`;\n\nconst Header = styled.header`\n grid-column: 1 / -1;\n text-align: center;\n padding-bottom: 16px;\n & h1 {\n font-size: 2.5rem;\n font-weight: 700;\n color: var(--accent-color);\n text-shadow: 0 0 5px rgba(99, 102, 241, 0.5);\n }\n & p {\n color: var(--text-secondary);\n margin-top: 8px;\n }\n`;\n\nconst Panel = styled.div`\n background: var(--card-bg);\n border: 1px solid var(--border-color);\n border-radius: 12px;\n padding: 24px;\n box-shadow: 0 4px 15px var(--shadow-color);\n min-height: 300px;\n display: flex;\n flex-direction: column;\n & h2 {\n font-size: 1.5rem;\n font-weight: 600;\n margin-bottom: 16px;\n border-bottom: 1px solid var(--border-color);\n padding-bottom: 8px;\n }\n`;\n\nconst InputTextArea = styled.textarea`\n min-height: 100px;\n padding: 12px;\n font-size: 1rem;\n border-radius: 8px;\n border: 1px solid var(--border-color);\n background: var(--code-bg);\n color: var(--text-primary);\n resize: vertical;\n font-family: \"Inter\", sans-serif;\n transition: border-color 0.2s;\n &:focus {\n outline: none;\n border-color: var(--accent-color);\n }\n`;\n\nconst SubmitButton = styled.button`\n padding: 12px 20px;\n border: none;\n background: var(--accent-color);\n color: var(--text-primary);\n font-size: 1rem;\n font-weight: 600;\n border-radius: 8px;\n cursor: pointer;\n transition: background-color 0.2s, transform 0.1s;\n &:hover:not(:disabled) {\n background: var(--accent-dark);\n transform: translateY(-1px);\n }\n &:disabled {\n background: #475569;\n cursor: not-allowed;\n }\n`;\n\nconst CodeBlock = styled.pre`\n flex-grow: 1;\n border: 1px solid var(--border-color);\n padding: 16px;\n border-radius: 8px;\n background: var(--code-bg);\n white-space: pre-wrap;\n font-family: \"JetBrains Mono\", monospace;\n font-size: 0.85rem;\n overflow-x: auto;\n`;\n\nconst DtfResultPanel = styled.div`\n background: var(--code-bg);\n border: 1px solid #7c3aed; /* Purple for DTF */\n padding: 16px;\n border-radius: 8px;\n & h3 {\n color: #c4b5fd;\n margin-bottom: 8px;\n }\n & p {\n font-size: 0.9rem;\n color: var(--text-secondary);\n margin-bottom: 4px;\n }\n & code {\n display: block;\n background: #1d1d30;\n padding: 8px;\n border-radius: 4px;\n font-family: \"JetBrains Mono\", monospace;\n color: #d8b4fe;\n white-space: pre-wrap;\n word-break: break-all;\n }\n`;\n\nconst QuestionList = styled.ul`\n list-style: none;\n padding: 0;\n & li {\n background: #0d94881a;\n border-left: 3px solid #0d9488;\n padding: 12px;\n margin-bottom: 12px;\n border-radius: 4px;\n font-size: 0.95rem;\n }\n`;\n\nconst Spinner = styled.div`\n border: 4px solid var(--border-color);\n border-top: 4px solid var(--accent-color);\n border-radius: 50%;\n width: 40px;\n height: 40px;\n animation: spin 1s linear infinite;\n margin: 20px auto;\n @keyframes spin {\n 0% {\n transform: rotate(0deg);\n }\n 100% {\n transform: rotate(360deg);\n }\n }\n`;\n\n// The main page component\nconst Layout = (props: { sourceUrl: string }) => (\n \n \n \n \n Prompt Confluence 🌊\n \n \n \n \n \n \n \n

Prompt Confluence 🌊

\n The **Domain Transfer Function** (DTF) injects logical rigor from\n high-stakes fields to create the ultimate system prompt for your\n work tool.\n

\n\n \n

Initial Prompt Concept (The User Goal)

\n \n \n \n \n Refine Prompt & Transfer Rigor\n \n \n \n \n

\n Executing Domain Transfer Function... Injecting Elite Rigor...\n

\n \n \n\n \n \n

Revised Prompt (TTC Engineered)

\n \n \n\n \n \n \n

DTF Result: Logistical Rigor Transferred 🚀

\n Domain: \n

\n Reasoning: \n

Constraint Injected (DTF Rigor Injection):

\n \n \n \n\n \n

Next Refinement Questions

\n \n \n \n \n \n \n \n \n);\n\n// --- 5. SERVER (from main.ts) ---\nconst app = new Hono();\n\napp.use(\"*\", jsxRenderer(Layout));\n\napp.get(\"/\", (c) => {\n const url = new URL(c.req.url);\n const sourceUrl = `https://${url.hostname}${url.pathname}`;\n return c.render({\n sourceUrl: sourceUrl.endsWith(\"/\") ? sourceUrl : `${sourceUrl}/`,\n });\n});\n\napp.post(\"/refine\", async (c) => {\n try {\n const { userGoal } = await c.req.json();\n if (\n !userGoal || typeof userGoal !== \"string\" || userGoal.trim().length < 10\n ) {\n return c.json({ error: \"The user goal must be a valid string.\" }, 400);\n }\n const openai = new OpenAI();\n const result = await runPromptRefinement(openai, userGoal);\n return c.json(result);\n } catch (error) {\n console.error(\"Critical Error in /refine:\", error);\n return c.json(\n { error: \"Server processing error.\", reason: error.message },\n 500,\n );\n }\n});\n\nexport default app.fetch;","// @deno-types=\"npm:@types/styled-components@5.1.34\"\nimport { Hono } from \"npm:hono\";\nimport { css } from \"npm:hono/css\";\nimport { jsxRenderer } from \"npm:hono/jsx-renderer\";\n// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\n\n// --- 1. TYPES (from types.ts) ---\ninterface ConfluenceOutput {\n section_title_a: string;\n revised_prompt_text: string;\n section_title_b: string;\n questions: string[];\n}\n\ninterface DomainTransfer {\n domain: string;\n rigorConstraint: string;\n reasoning: string;\n}\n\n// --- 2. PROMPTS (from prompts.ts) ---\nconst getBaseSystemPrompt = (\n userGoal: string,\n transferFunction: DomainTransfer,\n): string => `\nYou are the ultimate System Prompt Guy... an Elite Prompt Engineering Confluence model tasked with collaboratively refining a user's initial concept into the most resilient, precise, and effective final system prompt...\n\nThe user's core task/goal for their prompt is: \"${userGoal}\"\n\n---\n# INJECTED LOGICAL RIGOR: Domain Transfer Function (DTF)\nThe task has been mapped to the high-rigor domain of **${transferFunction.domain}**.\nYou MUST ensure that the following novel constraint is integrated into the \"Prompt Engineering Constraints\" section of the Revised Prompt:\n- **DTF Rigor Injection:** You must integrate the following constraint: *${transferFunction.rigorConstraint}*.\n---\n# Instructions and Core Role\nYour core role is to serve as the system prompt engineer, applying a rigorous set of structural and logical constraints to iteratively draft and refine a Revised Prompt based on the user's concept.\n---\n# Prompt Engineering Constraints\nWhen drafting the [REVISED_PROMPT_TEXT], you must ensure it rigorously adheres to the following engineering standards:\n1. **Direct Start (Yes):** Start with a concise, single-sentence instruction line defining the model's role and primary task.\n2. **Formatting (Yes):** Use markdown headings (#, ##) and structure for maximum clarity.\n3. **Separators (Yes):** Use a horizontal line (---) immediately preceding each main section heading.\n4. **Reasoning Order (Yes):** Explicitly enforce that the executing model must provide its reasoning/justification BEFORE the final conclusion or classification.\n5. **DTF Rigor Injection (Yes):** *${transferFunction.rigorConstraint}*\n---\n# Steps: Iterative Refinement Process\n... (Steps content omitted for brevity) ...\n---\n# Output Format Specification\nYour final, iterative output must be a single, valid JSON object. DO NOT include any text, commentary, or markdown outside of the JSON block.\n\\`\\`\\`json\n{\n \"section_title_a\": \"Revised Prompt\",\n \"revised_prompt_text\": \"[REVISED_PROMPT_TEXT]\",\n \"section_title_b\": \"Questions\",\n \"questions\": [\n \"[QUESTION_1]\",\n \"[QUESTION_2]\"\n ]\n}\n\\`\\`\\`\n`;\n\nconst getDtfPrompt = (userGoal: string): string => `\nYou are the Domain Transfer Agent (DTA). Your sole purpose is to increase the rigor of a given professional task by mapping its core logical demands to a high-stakes, highly disciplined professional domain.\n---\n# Instructions\n1. **Analyze the User Goal:** Identify the core logical functions required.\n2. **Identify a High-Rigor Domain:** Select a domain (e.g., aerospace, surgical medicine).\n3. **Synthesize a Rigor Constraint:** Create a single, concise, and highly demanding constraint.\n4. **Output Format:** Respond STRICTLY with a single, minified JSON object.\n---\n# Output Format Specification\n\\`\\`\\`json\n{\n \"domain\": \"[Selected High-Rigor Domain]\",\n \"rigorConstraint\": \"[The New Prompt Constraint to Inject]\",\n \"reasoning\": \"[Brief justification for the domain mapping]\"\n}\n\\`\\`\\`\n---\n# User Goal\nThe professional task the user wants to accomplish with their prompt is: \"${userGoal}\"\n`;\n\n// --- 3. AI LOGIC (from ai.ts) ---\nconst AI_MODEL = \"gpt-4o\";\n\nasync function getDomainTransfer(\n openai: OpenAI,\n userGoal: string,\n): Promise {\n try {\n const completion = await openai.chat.completions.create({\n model: AI_MODEL,\n messages: [{ role: \"system\", content: getDtfPrompt(userGoal) }],\n response_format: { type: \"json_object\" },\n });\n\n const result: DomainTransfer = JSON.parse(\n completion.choices[0].message.content,\n );\n if (!result.domain || !result.rigorConstraint || !result.reasoning) {\n throw new Error(\"DTF response missing essential keys.\");\n }\n console.log(\n `DTF Log - Domain: ${result.domain}, Reasoning: ${result.reasoning}`,\n );\n return result;\n } catch (e) {\n console.error(\"DTF Execution or Parsing Error:\", e);\n return { // Fallback on error\n domain: \"Nuclear Reactor Protocol\",\n rigorConstraint:\n \"All output must be auditable and traceable, requiring a Reason field for every intermediate conclusion.\",\n reasoning: \"Fallback due to AI parsing error.\",\n };\n }\n}\n\nasync function runPromptRefinement(\n openai: OpenAI,\n userGoal: string,\n): Promise {\n const transferFunction = await getDomainTransfer(openai, userGoal);\n const eliteSystemPrompt = getBaseSystemPrompt(userGoal, transferFunction);\n\n const completion = await openai.chat.completions.create({\n model: AI_MODEL,\n messages: [\n { role: \"system\", content: eliteSystemPrompt },\n {\n role: \"user\",\n content: `My initial prompt concept is: \"${userGoal}\". Begin.`,\n },\n ],\n response_format: { type: \"json_object\" },\n });\n\n const refinedOutput: ConfluenceOutput = JSON.parse(\n completion.choices[0].message.content,\n );\n return { ...refinedOutput, domainTransfer: transferFunction };\n}\n\n// --- 4. UI & STYLES (from ui.ts, converted to styled-components) ---\nconst GlobalStyles = css`\n :root {\n --bg-color: #0f172a;\n --text-primary: #f8fafc;\n --text-secondary: #94a3b8;\n --accent-color: #6366f1;\n --accent-dark: #4f46e5;\n --card-bg: #1e293b;\n --border-color: #334155;\n --code-bg: #121a2c;\n --shadow-color: rgba(0, 0, 0, 0.5);\n }\n *, *::before, *::after {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n }\n body {\n background-color: var(--bg-color);\n color: var(--text-primary);\n font-family: \"Inter\", sans-serif;\n min-height: 100vh;\n padding: 20px;\n display: flex;\n justify-content: center;\n }\n .hidden {\n display: none !important;\n }\n`;\n\nconst Container = styled.div`\n width: 100%;\n max-width: 1200px;\n display: grid;\n grid-template-columns: 1fr;\n gap: 24px;\n @media (min-width: 900px) {\n grid-template-columns: 1fr 1fr;\n }\n`;\n\nconst Header = styled.header`\n grid-column: 1 / -1;\n text-align: center;\n padding-bottom: 16px;\n & h1 {\n font-size: 2.5rem;\n font-weight: 700;\n color: var(--accent-color);\n text-shadow: 0 0 5px rgba(99, 102, 241, 0.5);\n }\n & p {\n color: var(--text-secondary);\n margin-top: 8px;\n }\n`;\n\nconst Panel = styled.div`\n background: var(--card-bg);\n border: 1px solid var(--border-color);\n border-radius: 12px;\n padding: 24px;\n box-shadow: 0 4px 15px var(--shadow-color);\n min-height: 300px;\n display: flex;\n flex-direction: column;\n & h2 {\n font-size: 1.5rem;\n font-weight: 600;\n margin-bottom: 16px;\n border-bottom: 1px solid var(--border-color);\n padding-bottom: 8px;\n }\n`;\n\nconst InputTextArea = styled.textarea`\n min-height: 100px;\n padding: 12px;\n font-size: 1rem;\n border-radius: 8px;\n border: 1px solid var(--border-color);\n background: var(--code-bg);\n color: var(--text-primary);\n resize: vertical;\n font-family: \"Inter\", sans-serif;\n transition: border-color 0.2s;\n &:focus {\n outline: none;\n border-color: var(--accent-color);\n }\n`;\n\nconst SubmitButton = styled.button`\n padding: 12px 20px;\n border: none;\n background: var(--accent-color);\n color: var(--text-primary);\n font-size: 1rem;\n font-weight: 600;\n border-radius: 8px;\n cursor: pointer;\n transition: background-color 0.2s, transform 0.1s;\n &:hover:not(:disabled) {\n background: var(--accent-dark);\n transform: translateY(-1px);\n }\n &:disabled {\n background: #475569;\n cursor: not-allowed;\n }\n`;\n\nconst CodeBlock = styled.pre`\n flex-grow: 1;\n border: 1px solid var(--border-color);\n padding: 16px;\n border-radius: 8px;\n background: var(--code-bg);\n white-space: pre-wrap;\n font-family: \"JetBrains Mono\", monospace;\n font-size: 0.85rem;\n overflow-x: auto;\n`;\n\nconst DtfResultPanel = styled.div`\n background: var(--code-bg);\n border: 1px solid #7c3aed; /* Purple for DTF */\n padding: 16px;\n border-radius: 8px;\n & h3 {\n color: #c4b5fd;\n margin-bottom: 8px;\n }\n & p {\n font-size: 0.9rem;\n color: var(--text-secondary);\n margin-bottom: 4px;\n }\n & code {\n display: block;\n background: #1d1d30;\n padding: 8px;\n border-radius: 4px;\n font-family: \"JetBrains Mono\", monospace;\n color: #d8b4fe;\n white-space: pre-wrap;\n word-break: break-all;\n }\n`;\n\nconst QuestionList = styled.ul`\n list-style: none;\n padding: 0;\n & li {\n background: #0d94881a;\n border-left: 3px solid #0d9488;\n padding: 12px;\n margin-bottom: 12px;\n border-radius: 4px;\n font-size: 0.95rem;\n }\n`;\n\nconst Spinner = styled.div`\n border: 4px solid var(--border-color);\n border-top: 4px solid var(--accent-color);\n border-radius: 50%;\n width: 40px;\n height: 40px;\n animation: spin 1s linear infinite;\n margin: 20px auto;\n @keyframes spin {\n 0% {\n transform: rotate(0deg);\n }\n 100% {\n transform: rotate(360deg);\n }\n }\n`;\n\n// The main page component\nconst Layout = (props: { sourceUrl: string }) => (\n \n \n \n \n Prompt Confluence 🌊\n \n \n \n \n \n \n \n

Diff · v9