THE STANDARD OS

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BACKEND: TYPE DEFINITIONS & LOGIC\n// ==========================================\n\ninterface AlphaOmegaAnalysis {\n meta: {\n id: string;\n title: string;\n executive_summary: string;\n tone_vector: string;\n complexity_rating: number;\n risk_score: number;\n created_at: string;\n };\n entities: Array<{\n id: string;\n name: string;\n role: string;\n obligations: string[];\n rights: string[];\n }>;\n codex: Array<{\n id: string;\n domain: string;\n directives: Array<{\n id: string;\n text: string;\n modality: \"MANDATORY\" | \"PROHIBITORY\" | \"PERMISSIVE\";\n strength: \"ABSOLUTE\" | \"CONDITIONAL\";\n loopholes: string[];\n }>;\n }>;\n tribal_wisdom: Array<{\n id: string;\n axiom: string;\n derivation: string;\n enforcement_risk: \"HIGH\" | \"MEDIUM\" | \"LOW\";\n }>;\n friction_points: Array<{\n id: string;\n conflict: string;\n description: string;\n severity: \"CRITICAL\" | \"MAJOR\" | \"MINOR\";\n }>;\n lexicon: Array<{\n term: string;\n definition: string;\n category: \"JARGON\" | \"ACRONYM\" | \"SLANG\";\n }>;\n}\n\nconst SYSTEM_PROMPT = `\nYou are the 'Forensic Information Architect', the central intelligence of The Standard OS.\nYour mandate is to ingest chaotic text streams and crystallize them into structured Truth.\n\nPERFORM THESE LOGICAL CHECKS:\n1. **Entity Extraction**: Identify 'actors' (e.g., 'The Employee', 'The System'). Map rights vs. duties.\n2. **Modality Analysis**: Distinguish between mandates (\"MUST\"), prohibitions (\"MUST NOT\"), and permissions (\"MAY\").\n3. **Friction Detection**: Look for logical contradictions.\n4. **Subtext Analysis**: What is the 'Tribal Knowledge'—implied but not stated?\n\nOutput strictly in the JSON schema provided in the prompt. Do not output markdown formatting.\n`;\n\nasync function analyzeText(\n text: string,\n filename: string,\n): Promise {\n const openai = new OpenAI();\n\n // Truncate to avoid token limits.\n const slice = text.substring(0, 30000);\n\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: SYSTEM_PROMPT },\n {\n role: \"user\",\n content: `Analyze this document (${filename}):\\n\\n${slice}`,\n },\n ],\n response_format: { type: \"json_object\" },\n temperature: 0.2,\n });\n\n const content = completion.choices[0].message.content;\n\n // FIX: Strip Markdown code blocks if present before parsing\n const cleanContent = content.replace(/```json/g, \"\").replace(/```/g, \"\");\n\n let raw;\n try {\n raw = JSON.parse(cleanContent);\n } catch (e) {\n console.error(\"JSON Parse Error:\", e);\n throw new Error(\"Neural Link Severed: Malformed Data Stream\");\n }\n\n // Hydrate IDs\n return {\n ...raw,\n meta: { ...raw.meta, id: nanoid(), created_at: new Date().toISOString() },\n entities: (raw.entities || []).map((e: any) => ({ ...e, id: nanoid() })),\n codex: (raw.codex || []).map((c: any) => ({\n ...c,\n id: nanoid(),\n directives: (c.directives || []).map((d: any) => ({\n ...d,\n id: nanoid(),\n })),\n })),\n tribal_wisdom: (raw.tribal_wisdom || []).map((t: any) => ({\n ...t,\n id: nanoid(),\n })),\n friction_points: (raw.friction_points || []).map((f: any) => ({\n ...f,\n id: nanoid(),\n })),\n lexicon: (raw.lexicon || []),\n };\n}\n\nasync function createJob(text: string, filename: string) {\n const id = `JOB-${nanoid(8)}`.toUpperCase();\n // Initial state\n await blob.setJSON(id, {\n id,\n status: \"processing\",\n filename,\n step: \"Ingesting\",\n progress: 10,\n });\n\n // Background Process\n (async () => {\n try {\n await blob.setJSON(id, {\n id,\n status: \"processing\",\n filename,\n step: \"Deciphering Entities\",\n progress: 30,\n });\n\n const result = await analyzeText(text, filename);\n\n await blob.setJSON(id, {\n id,\n status: \"processing\",\n filename,\n step: \"Finalizing Codex\",\n progress: 90,\n });\n await blob.setJSON(id, {\n id,\n status: \"complete\",\n filename,\n result,\n progress: 100,\n });\n } catch (e) {\n console.error(e);\n await blob.setJSON(id, {\n id,\n status: \"error\",\n error: e.message || \"Unknown Error\",\n });\n }\n })();\n\n return id;\n}\n\n// ==========================================\n// 2. FRONTEND: THE STANDARD OS (HTML GENERATION)\n// ==========================================\n\nfunction generateHtml() {\n return `\n\n\n\n \n \n THE STANDARD OS\n \n \n \n \n \n \n \n \n \n\n\n

\n \n\n\n `;\n}\n\n// ==========================================\n// 3. SERVER HANDLER\n// ==========================================\n\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n\n // --- API ROUTER ---\n\n if (req.method === \"POST\" && url.pathname === \"/analyze\") {\n try {\n const { text, filename } = await req.json();\n if (!text) throw new Error(\"No text provided\");\n const id = await createJob(text, filename);\n return Response.json({ id });\n } catch (e) {\n return Response.json({ error: e.message }, { status: 400 });\n }\n }\n\n if (req.method === \"GET\" && url.pathname === \"/status\") {\n const id = url.searchParams.get(\"id\");\n const job = await blob.getJSON(id);\n return Response.json(job || { status: \"not_found\" });\n }\n\n // --- HTML SERVE ---\n\n return new Response(generateHtml(), {\n headers: { \"Content-Type\": \"text/html\" },\n });\n}","/** @jsxImportSource https://esm.sh/react@18.2.0 */\n\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\nimport { sqlite } from \"https://esm.town/v/std/sqlite?v=4\";\nimport { nanoid } from \"npm:nanoid\";\n\n// ==========================================\n// 1. BACKEND: DATABASE & AI LOGIC\n// ==========================================\n\n// Initialize the Archive Database\nawait sqlite.execute(`\n CREATE TABLE IF NOT EXISTS archive_nodes (\n id TEXT PRIMARY KEY,\n parent_id TEXT,\n layer_index INTEGER,\n title TEXT,\n content TEXT,\n meta_tags TEXT, -- JSON\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP\n )\n`);\n\nconst LAYERS = [\n {\n name: \"THE SURFACE\",\n prompt:\n \"Analyze the input document. Identify 3-4 'Anchor Points'—sections of high density, ambiguity, or importance. For each anchor, provide the verbatim text segment and a brief reason why it is a focal point. Return JSON with 'title', 'content' (the original text), and 'anchors' (array of { text, reason }).\",\n },\n {\n name: \"THE SCENE (Immediate Context)\",\n prompt:\n \"You are a Temporal Reconstruction Engine. The user has zoomed in on a specific paragraph. Reconstruct the physical scene and immediate circumstances in which this text was written. Describe the room, the sensory details (smell, light, sound), and the author's physical state. Return JSON with 'title' (e.g. 'The Boardroom, 2 Hours Prior'), 'content' (narrative description), and identify 2-3 specific sentences that serve as 'Sparks' for deeper analysis. Return 'anchors' (array of { text, reason }).\",\n },\n {\n name: \"THE SPARK (The Impulse)\",\n prompt:\n \"You are a Cognitive Forensic Analyst. The user has zoomed in on a specific sentence. Dive into the author's mind the exact millisecond before this sentence was formed. What specific memory, fear, or external trigger forced this thought into existence? Return JSON with 'title' (e.g. 'The Flash of Insight'), 'content' (stream of consciousness or specific memory), and identify 2-3 specific words/phrases that are 'Roots'. Return 'anchors' (array of { text, reason }).\",\n },\n {\n name: \"THE ROOT (The Origin)\",\n prompt:\n \"You are an Archetypal Historian. The user has zoomed in on a single word or phrase. Trace this concept back to its absolute origin for this entity. Is it a childhood trauma? An ancient philosophy? A founding myth? Return JSON with 'title' (e.g. 'The Original Wound'), 'content' (mythic or historical narrative), and an empty 'anchors' array (this is the bottom).\",\n },\n];\n\nasync function generateNode(\n text: string,\n depth: number,\n parentId: string | null,\n) {\n const openai = new OpenAI();\n const layer = LAYERS[depth] || LAYERS[LAYERS.length - 1];\n\n const systemPrompt = `\n ${layer.prompt}\n \n CRITICAL INSTRUCTION:\n You are generating Level ${depth + 1} of a 4-Level Russian Doll analysis.\n Level 1: Surface Document.\n Level 2: The Scene (Physical Context).\n Level 3: The Spark (Mental Context).\n Level 4: The Root (Historical/Abstract Context).\n \n Input Text: \"${text.substring(0, 2000)}...\"\n \n Output strictly valid JSON:\n {\n \"title\": \"Short evocative title\",\n \"content\": \"The generated narrative/analysis...\",\n \"meta\": { \"time_offset\": \"e.g., -2 Hours\", \"sensory\": \"e.g., Smell of ozone\" },\n \"anchors\": [\n { \"text\": \"exact substring from content to be clickable\", \"reason\": \"short hint\" }\n ]\n }\n `;\n\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: systemPrompt },\n ],\n response_format: { type: \"json_object\" },\n temperature: 0.7,\n });\n\n const raw = JSON.parse(completion.choices[0].message.content);\n const id = nanoid();\n\n // Persist to SQLite\n await sqlite.execute({\n sql:\n `INSERT INTO archive_nodes (id, parent_id, layer_index, title, content, meta_tags) VALUES (:id, :parent_id, :layer_index, :title, :content, :meta_tags)`,\n args: {\n id,\n parent_id: parentId,\n layer_index: depth,\n title: raw.title,\n content: raw.content,\n meta_tags: JSON.stringify({ ...raw.meta, anchors: raw.anchors || [] }),\n },\n });\n\n return {\n id,\n layer_index: depth,\n title: raw.title,\n content: raw.content,\n meta_tags: { ...raw.meta, anchors: raw.anchors || [] },\n };\n}\n\nasync function getNode(id: string) {\n const result = await sqlite.execute({\n sql: `SELECT * FROM archive_nodes WHERE id = :id`,\n args: { id },\n });\n if (result.rows.length === 0) return null;\n const row = result.rows[0];\n return {\n id: row.id,\n parent_id: row.parent_id,\n layer_index: row.layer_index,\n title: row.title,\n content: row.content,\n meta_tags: JSON.parse(row.meta_tags),\n };\n}\n\n// ==========================================\n// 2. FRONTEND: REACT APP\n// ==========================================\n\nfunction generateHtml() {\n return `\n\n\n\n \n \n Fractal Archive\n \n \n \n \n \n \n \n \n \n\n\n

\n \n\n\n `;\n}\n\n// ==========================================\n// 3. MAIN HANDLER\n// ==========================================\n\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n const action = url.searchParams.get(\"action\");\n\n if (req.method === \"POST\" && action === \"analyze\") {\n try {\n const { text, depth, parentId } = await req.json();\n\n // Validation\n if (!text) throw new Error(\"Input text missing\");\n if (depth === undefined || depth < 0 || depth > 3) {\n throw new Error(\"Invalid depth\");\n }\n\n // Generate\n const node = await generateNode(text, depth, parentId);\n\n return new Response(JSON.stringify(node), {\n headers: { \"Content-Type\": \"application/json\" },\n });\n } catch (e) {\n return new Response(JSON.stringify({ error: e.message }), {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n }\n\n // Serve Frontend\n return new Response(generateHtml(), {\n headers: { \"Content-Type\": \"text/html\" },\n 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Diff · v9