// SYSTEM_INTEGRITY_SCANNING...

\n NOTE: Purple indicates Val Town IP Rate-Limiting.\n

\n \n

\n \\`\n

\n \\${res.behavior}\n \\${res.latency}\n

\\${res.endpoint} | \\${res.status}

\n \\${res.reason ? '

CAUSE: ' + res.reason + '

' : ''}\n

\\${res.preview}

\n \\`).join('');\n }\n refresh();\n `,\n }}\n />\n \n ,\n );\n});\n\nexport default app.fetch;","label",[151],"afdfa344-ff50-11f0-9e72-42dde27851f2",["D",1769938246145],1,[28],"4b44f042-ff50-11f0-82e5-42dde27851f2","polltest3.ts","ff49983c-ff4f-11f0-994d-42dde27851f2",258,["D",1769938077365],4,"/** @jsxImportSource npm:hono@3/jsx */\nimport { Hono } from \"npm:hono\";\n\nconst app = new Hono();\nconst PROMPT = \"Explain entropy in 5 words.\";\n\n// --- SMART VALIDATION LOGIC ---\n/**\n * Evaluates the response quality and status based on content and potential errors.\n * This function acts as the \"intelligent design\" layer, interpreting raw data to determine health.\n * @param tag The functional group tag (e.g., \"COMPAT\", \"ROOT\") to apply specific validation rules.\n * @param raw The raw text response from the API call.\n * @param json The parsed JSON object from the response, or null if parsing failed.\n * @returns An audit object with status, score, and reason.\n */\nconst validateResponse = (tag, raw, json) => {\n // 1. Detect Throttling/Rate Limits (The \"Queue full\" edge case)\n // This identifies external resource constraints and allows for adaptive measures (e.g., backing off).\n if (\n raw.includes(\"Queue full\") || raw.includes(\"Rate limit\") ||\n raw.includes(\"too many requests\")\n ) {\n return { status: \"THROTTLED\", score: 20, reason: \"IP_RATE_LIMIT\" };\n }\n\n // 2. Detect JSON Errors returned by the API itself (e.g., 400 bad request with error body)\n if (json?.error) {\n return { status: \"DEGRADED\", score: 40, reason: \"API_ERROR_RETURNED\" };\n }\n\n try {\n // 3. Protocol Specific Checks - Ensures response conforms to expected format.\n if (tag === \"COMPAT\") {\n // Expects OpenAI-style response structure: { choices: [{ message: { content: ... } }] }\n const isOAI = !!json?.choices?.[0]?.message?.content;\n return isOAI\n ? { status: \"HEALTHY\", score: 100 }\n : { status: \"DEGRADED\", score: 50, reason: \"INVALID_JSON_STRUCTURE\" };\n }\n\n if (tag === \"ROOT\") {\n // Root endpoint is expected to return raw text, not a JSON string.\n const isRaw = typeof raw === \"string\" && !raw.trim().startsWith(\"{\");\n return isRaw ? { status: \"HEALTHY\", score: 100 } : {\n status: \"DEGRADED\",\n score: 50,\n reason: \"EXPECTED_RAW_TEXT_GOT_JSON\",\n };\n }\n\n if (tag === \"META\") {\n // Discovery endpoints should return a substantial amount of info.\n const isInfo = raw.length > 50;\n return isInfo\n ? { status: \"HEALTHY\", score: 100 }\n : { status: \"FAIL\", score: 0, reason: \"EMPTY_DISCOVERY_RESPONSE\" };\n }\n\n // Default healthy if no specific check applies\n return { status: \"HEALTHY\", score: 100 };\n } catch {\n // Critical failure if validation logic itself throws an exception.\n return { status: \"CRITICAL\", score: 0, reason: \"PARSE_EXCEPTION\" };\n }\n};\n\n// --- CONFIGURATION: Define Functional Groups and Probes ---\n// This represents the \"genome\" of the system, defining different behavioral pathways.\nconst FUNCTIONAL_GROUPS = [\n {\n name: \"ROOT_COMPLETION\", // A group for standard text completion.\n tag: \"ROOT\", // Tag used for specific validation logic.\n configs: [{\n ep: \"https://text.pollinations.ai/\",\n m: \"POST\",\n v: \"OAI_OBJ\",\n }],\n },\n {\n name: \"OPENAI_PROTOCOL\", // A group for standard OpenAI API compatibility.\n tag: \"COMPAT\",\n configs: [{\n ep: \"https://text.pollinations.ai/openai\",\n m: \"POST\",\n v: \"OAI_OBJ\",\n }],\n },\n {\n name: \"RESOURCE_DISCOVERY\", // A group for checking resource metadata/health.\n tag: \"META\",\n configs: [{\n ep: \"https://text.pollinations.ai/openai\",\n m: \"GET\",\n v: \"NONE\",\n }],\n },\n {\n name: \"RELAXED_INPUT\", // A group for testing different input formats.\n tag: \"LEGACY\",\n configs: [{\n ep: \"https://text.pollinations.ai/openai\",\n m: \"POST\",\n v: \"RAW_TEXT\",\n }],\n },\n];\n\n// --- ADAPTIVE MECHANISM: Run Probes ---\n/**\n * Executes all defined probes concurrently and collects results.\n * This function simulates the \"learning process\" by evaluating each pathway's health.\n * @returns {Promise>} An array of audit results for each probe.\n */\nasync function runProbes() {\n const tasks = FUNCTIONAL_GROUPS.flatMap((group) =>\n group.configs.map(async (c) => {\n const start = Date.now();\n try {\n const response = await fetch(c.ep, {\n method: c.m,\n headers: {\n \"Content-Type\": c.v === \"RAW_TEXT\"\n ? \"text/plain\"\n : \"application/json\",\n },\n body: c.v === \"OAI_OBJ\"\n ? JSON.stringify({ messages: [{ role: \"user\", content: PROMPT }] })\n : (c.v === \"NONE\" ? null : PROMPT),\n });\n\n const raw = await response.text();\n let json = null;\n try {\n json = JSON.parse(raw);\n } catch {}\n\n const audit = validateResponse(group.tag, raw, json);\n const content = json?.choices?.[0]?.message?.content || json?.content ||\n json?.error || raw;\n\n return {\n category: group.name,\n behavior: group.tag,\n endpoint: c.ep.split(\"ai\")[1] || \"/\",\n ...audit,\n latency: `${Date.now() - start}ms`,\n preview: content.substring(0, 120),\n };\n } catch (err) {\n // Handle network/fetch errors\n return {\n category: group.name,\n behavior: group.tag,\n status: \"OFFLINE\",\n score: 0,\n preview: err.message,\n };\n }\n })\n );\n return await Promise.all(tasks);\n}\n\n// --- API ROUTES: Provide Data and Visualization ---\n\n/**\n * API route for machine-readable health data.\n * This provides the raw data necessary for an external system to implement dynamic routing\n * based on the calculated health scores.\n */\napp.get(\"/api\", async (c) => {\n const data = await runProbes();\n const avgHealth = Math.round(\n data.reduce((acc, curr) => acc + curr.score, 0) / data.length,\n );\n return c.json({\n health: avgHealth,\n timestamp: new Date().toISOString(),\n results: data,\n });\n});\n\n/**\n * Webpage route for human-readable visualization.\n * This acts as the \"control panel\" for the self-improving workshop.\n */\napp.get(\"/\", (c) => {\n return c.html(\n \n \n Pollinations Integrity V4\n \n \n \n \n

// SYSTEM_INTEGRITY_SCANNING...

\n NOTE: Purple indicates Val Town IP Rate-Limiting.\n

\n \n

\n \\`\n

\n \\${res.behavior}\n \\${res.latency}\n

\\${res.endpoint} | \\${res.status}

\n \\${res.reason ? '

CAUSE: ' + res.reason + '

' : ''}\n

\\${res.preview}

\n \\`).join('');\n }\n refresh();\n `,\n }}\n />\n \n ,\n );\n});\n\nexport default app.fetch;",[167],"ac66e5fe-f64c-11f0-bcf8-42dde27851f2","new3.ts","93352c30-f64c-11f0-816f-42dde27851f2",48,["D",1768946961870],"/** @jsxImportSource npm:hono@3/jsx */\nimport { Hono } from \"npm:hono\";\nimport { html, raw } from \"npm:hono/html\";\n\n/**\n * LOCAL-ONLY ORCHESTRATOR (LOGICALLY SINGLE PROVIDER)\n * - No multi-provider fan-out\n * - Single, maximally tuned Hugging Face path\n * - Health + backoff + quality gating\n * - Token-budget control\n * - Speculative retry on same model\n */\n\n/* ------------------------- LOGGING + FETCH WRAP ------------------------- */\n\nconst LOG_BUFFER_SIZE = 400;\nconst logs = [];\n\nfunction addLog(level, ...args) {\n const ts = Date.now();\n const msg = args\n .map((arg) =>\n typeof arg === \"object\" ? JSON.stringify(arg, null, 2) : String(arg)\n )\n .join(\" \");\n const entry = { ts, level, msg };\n logs.push(entry);\n if (logs.length > LOG_BUFFER_SIZE) logs.shift();\n try {\n originalConsole[level](...args);\n } catch (_e) {\n console.log(_e);\n }\n}\n\nconst originalConsole = {\n log: console.log,\n error: console.error,\n warn: console.warn,\n info: console.info,\n};\n\nconsole.log = (...args) => addLog(\"log\", ...args);\nconsole.error = (...args) => addLog(\"error\", ...args);\nconsole.warn = (...args) => addLog(\"warn\", ...args);\nconsole.info = (...args) => addLog(\"info\", ...args);\n\nconst originalFetch = globalThis.fetch;\nglobalThis.fetch = async (input, init = {}) => {\n const url = input instanceof Request ? input.url : String(input);\n const method = init.method || \"GET\";\n const start = performance.now();\n let status = 0,\n ok = false,\n bodyPreview = \"\";\n\n try {\n const res = await originalFetch(input, init);\n status = res.status;\n ok = res.ok;\n\n if (res.ok) {\n if (res.headers.get(\"content-type\")?.includes(\"json\")) {\n try {\n const clone = res.clone();\n const json = await clone.json();\n const s = JSON.stringify(json);\n bodyPreview = s.slice(0, 200) + (s.length > 200 ? \"...\" : \"\");\n } catch {}\n } else {\n const clone = res.clone();\n bodyPreview = (await clone.text()).slice(0, 200) + \"...\";\n }\n }\n\n addLog(\n \"fetch\",\n `[${method} ${url}] ${status} ${ok ? \"OK\" : \"FAIL\"} ${\n Math.round(performance.now() - start)\n }ms` + (bodyPreview ? ` → ${bodyPreview}` : \"\"),\n );\n return res;\n } catch (err) {\n addLog(\"fetch\", `[${method} ${url}] ERROR ${err.message}`);\n throw err;\n }\n};\n\n/* ------------------------- HF LOCAL-ONLY CONFIG ------------------------- */\n\nconst HF_BASE = \"https://api-inference.huggingface.co/models/\";\nconst HF_DEFAULT_MODEL = \"meta-llama/Llama-3.3-70B-Instruct\";\n\nconst HEALTH_DECAY = 0.4;\nconst HEALTH_RECOVER = 0.12;\nconst MIN_HEALTH_TO_TRY = 0.25;\n\nlet lastHealthTick = Date.now();\nlet hfHealth = 0.9;\nconst backoff = new Map();\nconst modelCache = new Map();\nconst MODEL_CACHE_TTL = 15 * 60 * 1000;\n\n/* ------------------------- CAPABILITIES + TASK INFERENCE ------------------------- */\n\nasync function hfDiscoverCapabilities(model) {\n const key = `hf-cap-${model}`;\n const cached = modelCache.get(key);\n const now = Date.now();\n if (cached && now - cached.fetchedAt < MODEL_CACHE_TTL) return cached.cap;\n\n const url = `${HF_BASE}${encodeURIComponent(model)}`;\n const res = await fetch(url, { method: \"GET\" });\n\n let cap = {\n supportsTextGen: true,\n supportsChat: true,\n maxTokens: 2048,\n };\n\n if (res.ok) {\n const meta = await res.json();\n const tags = meta.tags || [];\n cap.supportsTextGen = tags.includes(\"text-generation\") ||\n tags.includes(\"causal-lm\") ||\n cap.supportsTextGen;\n cap.supportsChat = tags.includes(\"chat\") ||\n tags.includes(\"instruction-tuned\") ||\n cap.supportsChat;\n cap.maxTokens = meta.max_length || cap.maxTokens;\n }\n\n modelCache.set(key, { cap, fetchedAt: now });\n return cap;\n}\n\nfunction inferTaskType(prompt, system) {\n const text = `${system} ${prompt}`.toLowerCase();\n if (text.includes(\"summarize\") || text.includes(\"tl;dr\")) {\n return \"summarization\";\n }\n if (text.includes(\"translate\")) return \"translation\";\n if (text.includes(\"classify\")) return \"classification\";\n return \"generation\";\n}\n\nfunction estimateTokens(text) {\n return Math.ceil(text.split(/\\s+/).length * 1.4);\n}\n\nfunction ensureTokenBudget(prompt, system, maxTokens) {\n const total = estimateTokens(prompt) + estimateTokens(system);\n if (total > maxTokens * 0.9) {\n return prompt.slice(0, Math.floor(prompt.length * 0.6));\n }\n return prompt;\n}\n\nfunction isHighQuality(text) {\n if (!text || text.length < 80) return false;\n const lower = text.toLowerCase();\n if (\n lower.includes(\"rate limit\") ||\n lower.includes(\"quota\") ||\n lower.includes(\"429\") ||\n lower.includes(\"please try again later\") ||\n lower.length > 12000\n ) {\n return false;\n }\n\n const words = text.split(/\\s+/).length;\n if (words < 35) return false;\n\n if ((text.match(/(.{20,})\\1{2,}/g) || []).length > 1) return false;\n\n return true;\n}\n\n/* ------------------------- HF ERROR NORMALIZATION ------------------------- */\n\nfunction normalizeHFError(status, body) {\n if (status === 503) return \"model-loading\";\n if (status === 429) return \"rate-limit\";\n if (status >= 500) return \"server-error\";\n\n const msg = body?.error || \"\";\n if (msg.includes(\"Input too long\")) return \"input-too-long\";\n if (msg.includes(\"Model\")) return \"model-error\";\n\n return \"unknown\";\n}\n\n/* ------------------------- CORE HF DISPATCH ------------------------- */\n\nasync function dispatchHF(model, prompt, system, signal) {\n const cap = await hfDiscoverCapabilities(model);\n const task = inferTaskType(prompt, system); // currently informational; could tune params per task\n\n prompt = ensureTokenBudget(prompt, system, cap.maxTokens || 2048);\n\n const url = `${HF_BASE}${encodeURIComponent(model)}`;\n const input = `${system ? system + \"\\n\\n\" : \"\"}${prompt}`;\n\n const body = {\n inputs: input,\n parameters: {\n max_new_tokens: cap.maxTokens || 2048,\n temperature: 0.7,\n return_full_text: false,\n },\n options: {\n wait_for_model: false,\n use_cache: true,\n },\n };\n\n const res = await fetch(url, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify(body),\n signal,\n });\n\n const status = res.status;\n let json = null;\n try {\n json = await res.json();\n } catch {}\n\n if (!res.ok) {\n const type = normalizeHFError(status, json);\n console.warn(\"HF error:\", type, json);\n return null;\n }\n\n let txt = json?.generated_text ||\n json?.[0]?.generated_text ||\n json?.[0]?.summary_text ||\n \"\";\n\n if (!txt && typeof json === \"string\") txt = json;\n\n return isHighQuality(txt) ? txt : null;\n}\n\n/* ------------------------- BEST-EFFORT LOCAL-ONLY CALL ------------------------- */\n\nasync function hfBestEffort(prompt, system = \"\") {\n const now = Date.now();\n\n if (now - lastHealthTick > 60000) {\n hfHealth = Math.min(1, hfHealth + HEALTH_RECOVER);\n lastHealthTick = now;\n }\n\n if (hfHealth < MIN_HEALTH_TO_TRY) {\n return \"Provider temporarily degraded. Try again in 30–60 seconds.\";\n }\n\n const controller = new AbortController();\n const timeoutId = setTimeout(() => controller.abort(), 11000);\n\n const delay = backoff.get(\"hf\") ?? 0;\n if (delay > now) {\n await new Promise((r) => setTimeout(r, delay - now));\n }\n\n try {\n const primary = await dispatchHF(\n HF_DEFAULT_MODEL,\n prompt,\n system,\n controller.signal,\n );\n\n if (primary) {\n hfHealth = Math.min(1, hfHealth + 0.35);\n backoff.delete(\"hf\");\n return primary;\n }\n\n hfHealth = Math.max(0.1, hfHealth * HEALTH_DECAY);\n const nextBackoff = Math.min(\n 60000,\n (backoff.get(\"hf\") ?? 1000) * 2.2 + 400,\n );\n backoff.set(\"hf\", now + nextBackoff);\n\n // Speculative retry with same model, shorter timeout\n const controller2 = new AbortController();\n const timeoutId2 = setTimeout(() => controller2.abort(), 8000);\n\n try {\n const alt = await dispatchHF(\n HF_DEFAULT_MODEL,\n prompt,\n system,\n controller2.signal,\n );\n if (alt) {\n hfHealth = Math.min(1, hfHealth + 0.25);\n backoff.delete(\"hf\");\n return alt;\n }\n } finally {\n clearTimeout(timeoutId2);\n }\n\n return \"Generation failed. Try again shortly.\";\n } catch {\n hfHealth = Math.max(0.1, hfHealth * HEALTH_DECAY);\n return \"Provider error or timeout. Try again shortly.\";\n } finally {\n clearTimeout(timeoutId);\n }\n}\n\n/* ------------------------- HONO APP WIRED TO LOCAL-ONLY HF ------------------------- */\n\nconst app = new Hono();\n\napp.get(\"/\", (c) => {\n return c.html(\n html`\n \n \n \n Local-Only HF Orchestrator\n \n \n

Local-Only HF Orchestrator

\n \n

Recent Logs

${raw(JSON.stringify(logs.slice(-40), null, 2))}

\n \n \n `,\n );\n});\n\napp.post(\"/chat\", async (c) => {\n const body = await c.req.parseBody();\n const prompt = String(body.prompt || \"\");\n const system =\n \"You are an elite, precise assistant. Respond with deeply reasoned, high-signal, non-repetitive answers.\";\n\n const answer = await hfBestEffort(prompt, system);\n return c.json({ answer });\n});\n\nexport default app.fetch;",[175],"701d2e60-f65f-11f0-9b17-42dde27851f2","new-file-9058.ts","e752850a-f64d-11f0-bafc-42dde27851f2",112,["D",1768955021164],59,"export default function () {\n return new Response(\n `\n\n\n \n WebGPU LLM Test\n\n\n

Loading…

\n\n\n\n`,\n { headers: { \"Content-Type\": \"text/html\" } },\n );\n}\n/*Starting…\nDiagnostics:[object Object]\nCreating pipeline…\nError: SyntaxError: Unexpected token '<', \"\n typeof arg === \"object\" ? JSON.stringify(arg, null, 2) : String(arg)\n ).join(\" \");\n const entry = { ts, level, msg };\n logs.push(entry);\n if (logs.length > LOG_BUFFER_SIZE) logs.shift();\n\n // Also keep original console output\n originalConsole[level](...args);\n}\n\nconst originalConsole = {\n log: console.log,\n error: console.error,\n warn: console.warn,\n info: console.info,\n};\n\nconsole.log = (...args) => addLog(\"log\", ...args);\nconsole.error = (...args) => addLog(\"error\", ...args);\nconsole.warn = (...args) => addLog(\"warn\", ...args);\nconsole.info = (...args) => addLog(\"info\", ...args);\n\n// ────────────────────────────────────────────────────────────────────────\n// FETCH WRAPPER WITH LOGGING\n// ────────────────────────────────────────────────────────────────────────\n\nconst originalFetch = globalThis.fetch;\nglobalThis.fetch = async (input, init = {}) => {\n const url = input instanceof Request ? input.url : String(input);\n const method = init.method || \"GET\";\n const start = performance.now();\n\n let status = 0, ok = false, bodyPreview = \"\";\n\n try {\n const res = await originalFetch(input, init);\n status = res.status;\n ok = res.ok;\n\n if (res.ok && res.headers.get(\"content-type\")?.includes(\"json\")) {\n try {\n const clone = res.clone();\n const json = await clone.json();\n bodyPreview = JSON.stringify(json).slice(0, 200) + (JSON.stringify(json).length > 200 ? \"...\" : \"\");\n } catch {}\n } else if (res.ok) {\n const clone = res.clone();\n bodyPreview = (await clone.text()).slice(0, 200) + \"...\";\n }\n\n addLog(\"fetch\", `[${method} ${url}] ${status} ${ok ? \"OK\" : \"FAIL\"} ${Math.round(performance.now() - start)}ms` +\n (bodyPreview ? ` → ${bodyPreview}` : \"\"));\n return res;\n } catch (err) {\n addLog(\"fetch\", `[${method} ${url}] ERROR ${err.message}`);\n throw err;\n }\n};\n\n// ────────────────────────────────────────────────────────────────────────\n// PROVIDERS + HEALTH + DYNAMIC MODELS (unchanged except logging already captured)\n// ────────────────────────────────────────────────────────────────────────\n\nconst SERVERS = [\n { id: \"google\", name: \"Gemini Nexus\", models: [\"gemini-1.5-flash-latest\", \"gemini-1.5-pro-latest\"], type: \"auth\", health: 1.0 },\n { id: \"pollinations\", name: \"Pollinations\", models: [], fallbackModels: [\"openai\",\"gemini-large\",\"claude\",\"deepseek\",\"qwen-coder\",\"mistral\",\"nomnom\",\"grok\"], type: \"unauth\", base: \"https://gen.pollinations.ai/v1/chat/completions\", modelsUrl: \"https://gen.pollinations.ai/v1/models\", health: 0.8 },\n { id: \"huggingface\", name: \"HF Inference\", models: [\"meta-llama/Llama-3.3-70B-Instruct\"], type: \"unauth\", base: \"https://api-inference.huggingface.co/models/\", health: 0.9 },\n { id: \"puter\", name: \"Puter Cloud\", models: [\"gpt-5-nano\", \"claude-3.5-sonnet\"], type: \"unauth-get\", base: \"https://api.puter.com/v2/chat\", health: 0.6 },\n];\n\nconst GEMINI = new GoogleGenerativeAI(Deno.env.get(\"GEMINI_API_KEY\") || \"\");\n\nconst HEALTH_DECAY = 0.4;\nconst HEALTH_RECOVER = 0.12;\nconst MIN_HEALTH_TO_TRY = 0.25;\n\nlet lastHealthTick = Date.now();\nconst backoff = new Map();\nconst modelCache = new Map();\nconst MODEL_CACHE_TTL = 15 * 60 * 1000;\n\n// Dynamic model loader, test suite, dispatch, callAI remain mostly unchanged\n// (fetch logging wrapper already captures modelUrl fetches, /chat calls, etc.)\n\n// ... (keep loadModelsForServer, initDynamicModels, isHighQuality, dispatch, callAI, testModel, runAllTests as before)\n\n// ────────────────────────────────────────────────────────────────────────\n// APP + LOGGING UI\n// ────────────────────────────────────────────────────────────────────────\n\nconst app = new Hono();\n\n// Startup\nsetTimeout(() => {\n Promise.allSettled([\n initDynamicModels(),\n runAllTests().then(r => console.log(\"[AXIOM] Startup test results:\", r))\n ]).catch(console.error);\n}, 1000);\n\n// Dashboard with log panel\napp.get(\"/\", (c) => {\n return c.html(`\n \n \n AXIOM MESH CONSOLE\n \n \n \n \n

AXIOM MESH

SYSTEM_READY // 2026.01.20

\n\n \n

\n ${raw(SERVERS.map(s => /* ... your provider HTML ... */).join(\"\"))}\n

\n\n \n

LIVE LOGS

\n \n

\n\n \n\n \n \n \n \n `);\n});\n\n// SSE logs endpoint\napp.get(\"/logs\", (c) => {\n const stream = new ReadableStream({\n start(controller) {\n const encoder = new TextEncoder();\n\n // Send recent logs on connect\n logs.forEach(log => {\n controller.enqueue(encoder.encode(`data: ${JSON.stringify(log)}\\n\\n`));\n });\n\n // Keep-alive + future logs\n const interval = setInterval(() => {\n controller.enqueue(encoder.encode(\": ping\\n\\n\"));\n }, 15000);\n\n // Cleanup on disconnect\n c.req.signal.addEventListener(\"abort\", () => {\n clearInterval(interval);\n controller.close();\n });\n }\n });\n\n return new Response(stream, {\n headers: {\n \"Content-Type\": \"text/event-stream\",\n \"Cache-Control\": \"no-cache\",\n \"Connection\": \"keep-alive\"\n }\n });\n});\n\n// Keep your other routes: /test, /tests, /health, /chat\n\nexport default app.fetch;",[190],"69705084-f63b-11f0-bd22-42dde27851f2","new-file-7433.ts","5f2da81a-f63b-11f0-80fa-42dde27851f2",8,["D",1768939548088],"/** @jsxImportSource npm:hono@3/jsx */\nimport { Hono } from \"npm:hono\";\nimport { html, raw } from \"npm:hono/html\";\nimport { sqlite } from \"https://esm.town/v/std/sqlite\";\nimport { getCookie, setCookie } from \"npm:hono/cookie\";\nimport { GoogleGenerativeAI } from \"npm:@google/generative-ai\";\n\n// --- CONFIGURATION ---\nconst VERSION = 145; // Increment this to force a DB schema refresh\nconst DB_PREFIX = `AXIOM_V${VERSION}`;\n\nconst TABLES = {\n AGENTS: `${DB_PREFIX}_AGENTS`,\n ROOTS: `${DB_PREFIX}_ROOTS`,\n NODES: `${DB_PREFIX}_NODES`,\n FLOW: `${DB_PREFIX}_FLOW`,\n KERNEL: `${DB_PREFIX}_KERNEL`,\n REQUESTS: `${DB_PREFIX}_VECTORS`,\n META: `${DB_PREFIX}_META`,\n};\n\nconst IDENTITY = {\n version: `${VERSION}.0.0-AXIOM`,\n owner: \"Michael Morton\",\n year: \"2026\",\n};\n\n// --- THE KERNEL (PURE LOGIC) ---\n// This function is defined natively. We serialize it to the DB.\n// No backtick escaping is required here.\nconst KERNEL_LOGIC = async (CTX) => {\n const { app, scope, sqlite, TABLES, IDENTITY, html, raw } = CTX;\n\n // 1. UI COMPONENT SYSTEM\n scope.UI.Layout = ({ children, title, user, siteCss }) => {\n return html`\n \n \n \n \n \n ${title} | AXIOM\n \n \n \n \n \n AXIOMATIC\n

\n Directory\n \n ${user ? user.username : \"VOID_IDENTITY\"}\n

\n \n \n

\n ${children}\n

\n \n PROTOCOL_v${IDENTITY.version}\n ${IDENTITY.owner} // ${IDENTITY.year}\n \n \n \n `;\n };\n\n // 2. ROUTING LOGIC\n app.get(\"/\", async (c) => {\n const user = await scope.Auth.getSession(c);\n const flowRes = await sqlite.execute(\n `SELECT name, idea FROM ${TABLES.FLOW} ORDER BY ts DESC LIMIT 8`,\n );\n\n // We render fragments using Hono's html helper\n const fragments = flowRes.rows.map((f) =>\n html`\n \n \n ${f[0]}\n \n

${f[1]}

\n \n `\n );\n\n return c.html(scope.UI.Layout({\n title: \"Nexus\",\n user,\n children: html`\n

\n \n

\n \n \n AWAITING_INPUT\n

\n \n System_Ready\n

\n \n Manifest\n \n \n \n \n\n

\n Recent_Flows\n

${fragments}

\n\n \n \n `,\n }));\n });\n\n app.get(\"/s\", async (c) => {\n const user = await scope.Auth.getSession(c);\n const res = await sqlite.execute(\n `SELECT name, slug, description FROM ${TABLES.ROOTS} ORDER BY ts DESC`,\n );\n const list = res.rows.map((s) =>\n html`\n \n

\n \n ${s[0]}\n

\n ${s[2]}\n

\n \n \n →\n \n \n `\n );\n\n return c.html(scope.UI.Layout({\n title: \"Directory\",\n user,\n children: html`\n

The_Archive

${list}

\n `,\n }));\n });\n\n app.get(\"/s/:slug/:folder/:page\", async (c) => {\n const user = await scope.Auth.getSession(c);\n const path = c.req.path;\n const slug = c.req.param(\"slug\");\n\n const siteRes = await sqlite.execute({\n sql: `SELECT css FROM ${TABLES.ROOTS} WHERE slug = ?`,\n args: [slug],\n });\n\n const nodeRes = await sqlite.execute({\n sql:\n `SELECT html, title FROM ${TABLES.NODES} WHERE path = ? AND generated = 1`,\n args: [path],\n });\n\n if (!nodeRes.rows[0]) return c.redirect(\"/\");\n\n // Inject RAW HTML securely\n const content = raw(nodeRes.rows[0][0]);\n\n return c.html(scope.UI.Layout({\n title: nodeRes.rows[0][1],\n user,\n siteCss: siteRes.rows[0]?.[0],\n children: html`\n

\n \n HOME /\n ${slug\n .toUpperCase()} /\n ${c.req.param(\"folder\")}\n \n

\n ${content}\n

\n `,\n }));\n });\n};\n\n// --- SYSTEM TOOLS ---\nconst DB = {\n async init() {\n await sqlite.execute(\n `CREATE TABLE IF NOT EXISTS ${TABLES.META} (key TEXT PRIMARY KEY, value TEXT)`,\n );\n\n // Self-Healing Schema Check\n let currentVer = null;\n try {\n const res = await sqlite.execute(\n `SELECT value FROM ${TABLES.META} WHERE key = 'version'`,\n );\n currentVer = res.rows[0]?.[0];\n } catch (e) {}\n\n if (currentVer !== IDENTITY.version) {\n console.log(\n `[SYSTEM] Migrating from ${currentVer} to ${IDENTITY.version}`,\n );\n // Drop old tables to ensure clean state on version change\n for (const t of Object.values(TABLES)) {\n if (t !== TABLES.META) {\n try {\n await sqlite.execute(`DROP TABLE IF EXISTS ${t}`);\n } catch (e) {}\n }\n }\n await sqlite.execute(`DELETE FROM ${TABLES.META} WHERE key='version'`);\n await sqlite.execute({\n sql: `INSERT INTO ${TABLES.META} (key, value) VALUES ('version', ?)`,\n args: [IDENTITY.version],\n });\n }\n\n // Initialize Schema\n const schema = [\n `CREATE TABLE IF NOT EXISTS ${TABLES.AGENTS} (id INTEGER PRIMARY KEY, username TEXT UNIQUE, email TEXT UNIQUE, pass TEXT, ts DATETIME DEFAULT CURRENT_TIMESTAMP)`,\n `CREATE TABLE IF NOT EXISTS ${TABLES.ROOTS} (id INTEGER PRIMARY KEY, name TEXT, slug TEXT UNIQUE, idea TEXT, description TEXT, css TEXT, ts DATETIME DEFAULT CURRENT_TIMESTAMP)`,\n `CREATE TABLE IF NOT EXISTS ${TABLES.NODES} (path TEXT PRIMARY KEY, site_id INTEGER, folder TEXT, title TEXT, html TEXT, description TEXT, generated INTEGER DEFAULT 0, ts DATETIME DEFAULT CURRENT_TIMESTAMP)`,\n `CREATE TABLE IF NOT EXISTS ${TABLES.FLOW} (id INTEGER PRIMARY KEY, name TEXT, idea TEXT, ts DATETIME DEFAULT CURRENT_TIMESTAMP)`,\n `CREATE TABLE IF NOT EXISTS ${TABLES.KERNEL} (name TEXT PRIMARY KEY, code TEXT)`,\n `CREATE TABLE IF NOT EXISTS ${TABLES.REQUESTS} (id TEXT PRIMARY KEY, status TEXT, description TEXT, path TEXT, success INTEGER DEFAULT 0, ts DATETIME DEFAULT CURRENT_TIMESTAMP)`,\n ];\n\n for (const sql of schema) await sqlite.execute(sql);\n\n // Seed Initial Data\n const qCount = await sqlite.execute(`SELECT COUNT(*) FROM ${TABLES.FLOW}`);\n if (qCount.rows[0][0] < 1) {\n await sqlite.execute({\n sql: `INSERT INTO ${TABLES.FLOW} (name, idea) VALUES (?, ?)`,\n args: [\"Recursive Logic\", \"Systems that rewrite their own core.\"],\n });\n }\n\n await this.seedKernel();\n },\n\n async seedKernel() {\n // SYMBOLIC SERIALIZATION: Function -> String\n const sourceCode = KERNEL_LOGIC.toString();\n await sqlite.execute({\n sql: `INSERT OR REPLACE INTO ${TABLES.KERNEL} (name, code) VALUES (?, ?)`,\n args: [\"main_logic\", sourceCode],\n });\n },\n\n formatSlug: (s) =>\n (s || \"void\").toLowerCase().trim().replace(/[^\\w\\s-]/g, \"\").replace(\n /[\\s_-]+/g,\n \"-\",\n ).replace(/^-+|-+$/g, \"\"),\n};\n\nconst AI = {\n genAI: new GoogleGenerativeAI(Deno.env.get(\"GEMINI_API_KEY\") || \"\"),\n async query(system, prompt) {\n try {\n const model = this.genAI.getGenerativeModel({\n model: \"gemini-1.5-flash\",\n systemInstruction: system,\n });\n const result = await model.generateContent(prompt);\n const text = result.response.text();\n // Sanitize markdown code blocks\n return text.replace(/```(json|html|javascript|css)?/g, \"\").replace(\n /```/g,\n \"\",\n ).trim();\n } catch (e) {\n console.error(\"AI_FAULT\", e);\n return null;\n }\n },\n};\n\n// --- ENTRY POINT ---\nexport default async function (req) {\n await DB.init();\n const app = new Hono();\n\n // Define Scope\n const scope = {\n UI: {},\n Auth: {\n async getSession(c) {\n const sid = getCookie(c, \"axiom_session\");\n if (!sid) return null;\n const res = await sqlite.execute({\n sql: `SELECT * FROM ${TABLES.AGENTS} WHERE id = ?`,\n args: [sid],\n });\n const row = res.rows[0];\n return row ? { id: row[0], username: row[1] } : null;\n },\n },\n };\n\n try {\n // 1. Fetch Logic from DB\n const logicRows = await sqlite.execute(\n `SELECT code FROM ${TABLES.KERNEL} WHERE name = 'main_logic'`,\n );\n if (!logicRows.rows[0]) throw new Error(\"KERNEL_MISSING\");\n\n // 2. Rehydrate Function\n // We construct a factory that returns the logic function\n const AsyncFunction =\n Object.getPrototypeOf(async function () {}).constructor;\n const kernelFactory = new AsyncFunction(\n `return (${logicRows.rows[0][0]})`,\n )();\n\n // 3. Inject Context\n const CTX = { app, scope, sqlite, TABLES, IDENTITY, DB, AI, html, raw };\n await kernelFactory(CTX);\n } catch (e) {\n return new Response(`KERNEL PANIC: ${e.message}\\n${e.stack}`, {\n status: 500,\n });\n }\n\n // --- MINING API (External to Kernel for Stability) ---\n app.post(\"/create\", async (c) => {\n const body = await c.req.parseBody();\n const idea = body.idea;\n const rid = crypto.randomUUID();\n await sqlite.execute({\n sql:\n `INSERT INTO ${TABLES.REQUESTS} (id, status, description) VALUES (?, 'INIT', ?)`,\n args: [rid, idea],\n });\n return c.json({ id: rid });\n });\n\n app.get(\"/api/status/:id\", async (c) => {\n const rid = c.req.param(\"id\");\n const res = await sqlite.execute({\n sql: `SELECT * FROM ${TABLES.REQUESTS} WHERE id=?`,\n args: [rid],\n });\n const row = res.rows[0]; // [id, status, description, path, success, ts]\n\n if (!row) return c.json({ error: \"NOT_FOUND\" });\n if (row[4] === 1) {\n return c.json({ success: true, path: row[3], description: \"Stabilized\" });\n }\n\n // AI PIPELINE\n if (row[1] === \"INIT\") {\n const resp = await AI.query(\n `Return valid JSON ONLY. No markdown. Structure: {\"name\": \"string\", \"slug\": \"string\", \"desc\": \"string\", \"folder\": \"string\", \"page\": \"string\"}`,\n `Create a web architecture for: ${row[2]}`,\n );\n if (!resp) return c.json({ description: \"NEURAL_FAILURE\" });\n\n try {\n const meta = JSON.parse(resp);\n const slug = DB.formatSlug(meta.slug || meta.name);\n const path = `/s/${slug}/${meta.folder}/${meta.page}`;\n\n await sqlite.execute({\n sql:\n `INSERT OR IGNORE INTO ${TABLES.ROOTS} (name, slug, idea, description) VALUES (?, ?, ?, ?)`,\n args: [meta.name, slug, row[2], meta.desc],\n });\n await sqlite.execute({\n sql:\n `INSERT OR REPLACE INTO ${TABLES.NODES} (path, site_id, folder, title) VALUES (?, (SELECT id FROM ${TABLES.ROOTS} WHERE slug=?), ?, ?)`,\n args: [path, slug, meta.folder, meta.name],\n });\n await sqlite.execute({\n sql:\n `UPDATE ${TABLES.REQUESTS} SET status='BODY', description='WEAVING', path=? WHERE id=?`,\n args: [path, row[0]],\n });\n } catch (e) {\n return c.json({ description: \"JSON_PARSE_ERROR\" });\n }\n } else if (row[1] === \"BODY\") {\n const content = await AI.query(\n `Return raw HTML content only. Use Tailwind classes. Dark brutalist aesthetics. NO or tags. Just the inner content.`,\n `Generate content for: ${row[2]}`,\n );\n if (!content) return c.json({ description: \"GENERATION_FAILURE\" });\n\n await sqlite.execute({\n sql:\n `UPDATE ${TABLES.NODES} SET html = ?, generated = 1 WHERE path = ?`,\n args: [content, row[3]],\n });\n await sqlite.execute({\n sql:\n `UPDATE ${TABLES.REQUESTS} SET status='DONE', success=1 WHERE id=?`,\n args: [row[0]],\n });\n }\n\n return c.json({ success: false, description: row[1] });\n });\n\n return app.fetch(req);\n}",[198],"623dc324-f7bb-11f0-ae69-42dde27851f2","new-file-7150.tsx","19151b30-f7ba-11f0-931a-42dde27851f2",205,["D",1769104462751],"/** @jsxImportSource npm:hono@3/jsx */\nimport { Hono } from \"npm:hono\";\n\nconst app = new Hono();\nconst PROMPT = \"Explain entropy in 5 words.\";\n\napp.get(\"/\", (c) => {\n return c.html(\n \n \n POLLINATIONS_FUNCTIONAL_V9\n \n \n \n

// CLASSIFIED_FUNCTIONAL_NODES_V9

\n \n

\n\n JSON.stringify({messages: [{role:\"user\", content: p}], model: \"openai\"}),\n OAI_NO_MODEL: (p) => JSON.stringify({messages: [{role:\"user\", content: p}]}),\n RAW_TEXT: (p) => p,\n FORM_DATA: (p) => \"prompt=\" + encodeURIComponent(p),\n NONE: () => null\n };\n\n function init() {\n const root = document.getElementById('matrix-root');\n let html = '';\n FUNCTIONAL_GROUPS.forEach(group => {\n group.configs.forEach((c, i) => {\n const id = \\`\\${group.tag}-\\${i}\\`;\n html += \\`\n

\n \\${group.name}\n

\\${c.m} | \\${c.ep.replace('https://','')}

IDLE...

\\`;\n });\n });\n root.innerHTML = html;\n }\n\n async function runFunctionalMatrix() {\n for (const group of FUNCTIONAL_GROUPS) {\n group.configs.forEach(async (c, i) => {\n const id = \\`\\${group.tag}-\\${i}\\`;\n const resBox = document.getElementById(\"res-\" + id);\n resBox.innerText = \"PROBING...\";\n\n try {\n const r = await fetch(c.ep, {\n method: c.m,\n headers: c.v === \"RAW_TEXT\" ? {\"Content-Type\":\"text/plain\"} : {\"Content-Type\":\"application/json\"},\n body: Payloads[c.v](P)\n });\n const raw = await r.text();\n let display = raw;\n let thought = \"\";\n\n try {\n const json = JSON.parse(raw);\n display = json.choices?.[0]?.message?.content || json.content || raw;\n if(json.choices?.[0]?.message?.reasoning_content) {\n thought = json.choices[0].message.reasoning_content;\n }\n } catch(e) {}\n\n resBox.innerHTML = \\`

\\${display}

\\` + \n (thought ? \\`REASONING: \\${thought.substring(0,150)}...\\` : \"\");\n } catch(err) {\n resBox.innerText = \"OFFLINE: \" + err.message;\n }\n });\n }\n }\n\n init();\n `,\n }}\n />\n \n ,\n );\n});\n\nexport default app.fetch;",[205],"5b5e5e3e-f643-11f0-8a53-42dde27851f2","new-file-5410.ts","5b5e5f1a-f643-11f0-8a53-42dde27851f2",19,["D",1768942960456],[212],"71e4fe44-f641-11f0-9844-42dde27851f2","new-file-5281.ts","fba59970-f63e-11f0-be30-42dde27851f2",18,["D",1768942139222],6,"/** @jsxImportSource npm:hono@3/jsx */\nimport { Hono } from \"npm:hono\";\nimport { GoogleGenerativeAI } from \"npm:@google/generative-ai\";\nimport { sqlite } from \"https://esm.town/v/std/sqlite\";\nimport { html, raw } from \"npm:hono/html\";\n// --- PROVIDER REGISTRY ---\nconst SERVERS = [\n {\n id: \"google\",\n name: \"Gemini Nexus\",\n models: [\"gemini-2.0-flash\", \"gemini-1.5-pro\"],\n type: \"auth\",\n },\n {\n id: \"pollinations\",\n name: \"Pollinations Core\",\n models: [\"deepseek-r1\", \"qwen3-coder\", \"mistral-large\"],\n type: \"unauth\",\n base: \"https://text.pollinations.ai\",\n },\n {\n id: \"puter\",\n name: \"Puter Cloud\",\n models: [\"gpt-4.1-nano\", \"claude-3.7-sonnet\"],\n type: \"unauth\",\n base: \"https://api.puter.com/v2/chat\",\n },\n {\n id: \"huggingface\",\n name: \"HF Serverless\",\n models: [\"meta-llama/Llama-3.3-70B-Instruct\"],\n type: \"unauth\",\n base: \"https://api-inference.huggingface.co/models/\",\n },\n {\n id: \"shadow\",\n name: \"Shadow Mesh\",\n models: [\"llama-3.1-8b\"],\n type: \"unauth\",\n base: \"https://text.shadow.io\",\n }, // 2026 Spore Clone\n];\n\nconst AI_KERNEL = {\n genAI: new GoogleGenerativeAI(Deno.env.get(\"GEMINI_API_KEY\") || \"\"),\n\n /**\n * Universal Dispatcher: Handles routing to any provider type\n */\n async dispatch(node, model, prompt, system) {\n const controller = new AbortController();\n const timeout = setTimeout(() => controller.abort(), 12000); // 12s Hard limit\n\n try {\n if (node.type === \"auth\" && node.id === \"google\") {\n const m = this.genAI.getGenerativeModel({\n model,\n systemInstruction: system,\n });\n const res = await m.generateContent(prompt);\n return res.response.text();\n }\n\n // REST Generic (Pollinations/Clones Style)\n let url = `${node.base}/${\n encodeURIComponent(prompt)\n }?model=${model}&system=${encodeURIComponent(system)}`;\n\n // Provider-specific adjustments\n if (node.id === \"puter\") {\n url = `${node.base}?model=${model}&prompt=${\n encodeURIComponent(prompt)\n }`;\n }\n if (node.id === \"huggingface\") url = `${node.base}${model}`;\n\n const res = await fetch(url, { signal: controller.signal });\n if (!res.ok) throw new Error(`HTTP_${res.status}`);\n const text = await res.text();\n\n // Content-Aware Validation (Check for Rate Limit strings in 200 OK)\n if (text.toLowerCase().includes(\"rate limit\") || text.length < 2) {\n throw new Error(\"FAKE_OK\");\n }\n return text;\n } finally {\n clearTimeout(timeout);\n }\n },\n\n /**\n * Racing Pattern: Hits top 3 nodes, first valid return wins\n */\n async CallAI(prompt, system) {\n const racePool = SERVERS.flatMap((s) =>\n s.models.slice(0, 1).map((m) => ({ node: s, model: m }))\n );\n return Promise.any(racePool.map(async (task) => {\n const result = await this.dispatch(task.node, task.model, prompt, system);\n if (result) return result;\n throw new Error(\"Empty\");\n }));\n },\n};\n\nconst app = new Hono();\n\n// --- DASHBOARD UI ---\napp.get(\"/\", (c) => {\n return c.html(\n `\n \n AI MESH CONSOLE\n \n \n \n \n

AXIOM MESH

SYSTEM_READY // 2026.01.20

\n\n

\n ${\n raw(\n SERVERS.map((s) => (`\n

${s.name}

\n ${s.type}\n

\n ${\n raw(\n s.models.map((m) => (`\n \n `)).join(\"\"),\n )\n }\n

\n `)).join(\"\"),\n )\n }\n

\n\n \n \n `,\n );\n});\n\n// --- API LAYER ---\napp.get(\"/test\", async (c) => {\n const { server, model } = c.req.query();\n const node = SERVERS.find((s) => s.id === server);\n const success = await AI_KERNEL.dispatch(\n node,\n model,\n \"Respond with OK\",\n \"Strict Mode\",\n ).then((t) => t?.includes(\"OK\"));\n return c.json({ success });\n});\n\nexport default app.fetch;",[218],"027e1b38-fe95-11f0-85e2-42dde27851f2","new-file-5208.ts","65d8bf44-f64d-11f0-a1c3-42dde27851f2",214,["D",1769857639374],"import { pipeline } from \"jsr:@vergedx/transformers\";\n\n// Force WebGPU backend\nenv.backends.onnx.wasm.wasmPaths =\n \"https://cdn.jsdelivr.net/npm/onnxruntime-web/dist/\";\nenv.allowLocalModels = false;\nenv.useBrowserCache = true;\n\nasync function testLlama3() {\n console.log(\"Loading Llama-3.2-3B-Instruct...\");\n const generator = await pipeline(\n \"text-generation\",\n \"Xenova/llama-3.2-3b-instruct\",\n );\n\n console.log(\"Running inference...\");\n const out = await generator(\"Explain quantum entanglement simply:\", {\n max_new_tokens: 80,\n temperature: 0.7,\n });\n\n console.log(\"Output:\", out[0].generated_text);\n\n return {\n model: \"llama-3.2-3b-instruct\",\n ok: out[0].generated_text.length > 20,\n text: out[0].generated_text,\n };\n}\n\nexport default testLlama3;",[226],"07a79f6a-f645-11f0-84af-42dde27851f2","new-file-340.ts","62688a74-f643-11f0-8f09-42dde27851f2",24,["D",1768943678996],"/** @jsxImportSource npm:hono@3/jsx */\nimport { Hono } from \"npm:hono\";\nimport { GoogleGenerativeAI } from \"npm:@google/generative-ai\";\nimport { html, raw } from \"npm:hono/html\";\n\n// ────────────────────────────────────────────────────────────────────────\n// PROVIDERS + HEALTH + DYNAMIC MODELS\n// ────────────────────────────────────────────────────────────────────────\n\nconst SERVERS = [\n {\n id: \"google\",\n name: \"Gemini Nexus\",\n models: [\"gemini-1.5-flash-latest\", \"gemini-1.5-pro-latest\"],\n type: \"auth\",\n health: 1.0,\n },\n {\n id: \"pollinations\",\n name: \"Pollinations\",\n models: [], // ← populated dynamically\n fallbackModels: [\n \"openai\",\n \"gemini-large\",\n \"claude\",\n \"deepseek\",\n \"qwen-coder\",\n \"mistral\",\n \"nomnom\",\n \"grok\",\n ],\n type: \"unauth\",\n base: \"https://gen.pollinations.ai/v1/chat/completions\",\n modelsUrl: \"https://gen.pollinations.ai/v1/models\",\n health: 0.8,\n },\n {\n id: \"huggingface\",\n name: \"HF Inference\",\n models: [\"meta-llama/Llama-3.3-70B-Instruct\"],\n type: \"unauth\",\n base: \"https://api-inference.huggingface.co/models/\",\n health: 0.9,\n },\n {\n id: \"puter\",\n name: \"Puter Cloud\",\n models: [\"gpt-5-nano\", \"claude-3.5-sonnet\"],\n type: \"unauth-get\",\n base: \"https://api.puter.com/v2/chat\",\n health: 0.6,\n },\n];\n\nconst GEMINI = new GoogleGenerativeAI(Deno.env.get(\"GEMINI_API_KEY\") || \"\");\n\nconst HEALTH_DECAY = 0.4;\nconst HEALTH_RECOVER = 0.12;\nconst MIN_HEALTH_TO_TRY = 0.25;\n\nlet lastHealthTick = Date.now();\nconst backoff = new Map();\n\nconst modelCache = new Map();\nconst MODEL_CACHE_TTL = 15 * 60 * 1000; // 15 minutes\n\n// ────────────────────────────────────────────────────────────────────────\n// DYNAMIC MODEL LOADER\n// ────────────────────────────────────────────────────────────────────────\n\nasync function loadModelsForServer(server) {\n if (server.models.length > 0 && !server.modelsUrl) {\n return server.models;\n }\n\n const cached = modelCache.get(server.id);\n const now = Date.now();\n if (cached && now - cached.fetchedAt < MODEL_CACHE_TTL) {\n return cached.models;\n }\n\n if (!server.modelsUrl) {\n return server.fallbackModels || server.models || [];\n }\n\n try {\n const res = await fetch(server.modelsUrl, {\n signal: AbortSignal.timeout(5000),\n });\n if (!res.ok) throw new Error(`models fetch ${res.status}`);\n\n const data = await res.json();\n let models = [];\n\n if (Array.isArray(data)) {\n models = data;\n } else if (data.data && Array.isArray(data.data)) {\n models = data.data.map((m) => m.id || m);\n } else if (Array.isArray(data.models)) {\n models = data.models;\n }\n\n models = models.filter(Boolean).map(String);\n\n if (models.length === 0) {\n models = server.fallbackModels || [];\n }\n\n modelCache.set(server.id, { models, fetchedAt: now });\n return models;\n } catch (err) {\n console.error(`Failed to load models for ${server.id}:`, err);\n const fb = server.fallbackModels || server.models || [];\n modelCache.set(server.id, { models: fb, fetchedAt: now });\n return fb;\n }\n}\n\nasync function initDynamicModels() {\n await Promise.allSettled(\n SERVERS.map(async (s) => {\n s.models = await loadModelsForServer(s);\n }),\n );\n console.log(\"[AXIOM] Model discovery complete\");\n}\n\n// Start model discovery (non-blocking)\nsetTimeout(initDynamicModels, 500);\n\n// ────────────────────────────────────────────────────────────────────────\n// QUALITY GATE\n// ────────────────────────────────────────────────────────────────────────\n\nfunction isHighQuality(text) {\n if (!text || text.length < 80) return false;\n const lower = text.toLowerCase();\n if (\n lower.includes(\"rate limit\") || lower.includes(\"quota\") ||\n lower.includes(\"429\") || lower.includes(\"please try again later\") ||\n lower.length > 12000\n ) return false;\n\n const words = text.split(/\\s+/).length;\n if (words < 35) return false;\n\n if ((text.match(/(.{20,})\\1{2,}/g) || []).length > 1) return false;\n\n return true;\n}\n\n// ────────────────────────────────────────────────────────────────────────\n// DISPATCH\n// ────────────────────────────────────────────────────────────────────────\n\nasync function dispatch(node, model, prompt, system, signal) {\n try {\n if (node.id === \"google\") {\n const m = GEMINI.getGenerativeModel({ model, systemInstruction: system });\n const res = await m.generateContent({\n contents: [{ role: \"user\", parts: [{ text: prompt }] }],\n signal,\n });\n const txt = res.response.text();\n return isHighQuality(txt) ? txt : null;\n }\n\n if (node.type === \"unauth\" && node.base.includes(\"/v1/chat/completions\")) {\n const res = await fetch(node.base, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n model,\n messages: [\n ...(system ? [{ role: \"system\", content: system }] : []),\n { role: \"user\", content: prompt },\n ],\n temperature: 0.7,\n max_tokens: 2048,\n stream: false,\n }),\n signal,\n });\n\n if (!res.ok) throw new Error(`HTTP ${res.status}`);\n const data = await res.json();\n const txt = data.choices?.[0]?.message?.content ?? \"\";\n return isHighQuality(txt) ? txt : null;\n }\n\n if (node.type === \"unauth-get\") {\n const url = `${node.base}?model=${encodeURIComponent(model)}&prompt=${\n encodeURIComponent(prompt)\n }`;\n const res = await fetch(url, { signal });\n if (!res.ok) throw new Error(`HTTP ${res.status}`);\n const txt = await res.text();\n return isHighQuality(txt) ? txt : null;\n }\n\n if (node.id === \"huggingface\") {\n const url = `${node.base}${model}`;\n const res = await fetch(url, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n inputs: `${system ? system + \"\\n\\n\" : \"\"}${prompt}`,\n parameters: { max_new_tokens: 1024 },\n }),\n signal,\n });\n if (!res.ok) throw new Error(`HTTP ${res.status}`);\n const json = await res.json();\n const txt = json[0]?.generated_text ?? \"\";\n return isHighQuality(txt) ? txt : null;\n }\n\n return null;\n } catch {\n return null;\n }\n}\n\n// ────────────────────────────────────────────────────────────────────────\n// RACE + HEALTH + BACKOFF\n// ────────────────────────────────────────────────────────────────────────\n\nasync function callAI(prompt, system = \"\") {\n const now = Date.now();\n if (now - lastHealthTick > 60000) {\n SERVERS.forEach((s) => {\n s.health = Math.min(1, s.health + HEALTH_RECOVER);\n });\n lastHealthTick = now;\n }\n\n const tasks = SERVERS\n .filter((s) => s.health >= MIN_HEALTH_TO_TRY)\n .flatMap((s) =>\n s.models.map((m, idx) => ({ node: s, model: m, priority: idx }))\n )\n .sort((a, b) => a.priority - b.priority || b.node.health - a.node.health);\n\n const controller = new AbortController();\n const timeoutId = setTimeout(() => controller.abort(), 11000);\n\n try {\n const winner = await Promise.any(\n tasks.map(async ({ node, model }) => {\n const delay = backoff.get(node.id) ?? 0;\n if (delay > now) await new Promise((r) => setTimeout(r, delay - now));\n\n const result = await dispatch(\n node,\n model,\n prompt,\n system,\n controller.signal,\n );\n\n if (result) {\n node.health = Math.min(1, node.health + 0.35);\n backoff.delete(node.id);\n controller.abort();\n return result;\n }\n\n node.health = Math.max(0.1, node.health * HEALTH_DECAY);\n const nextBackoff = Math.min(\n 60000,\n (backoff.get(node.id) ?? 1000) * 2.2 + 400,\n );\n backoff.set(node.id, now + nextBackoff);\n\n throw new Error(\"fail\");\n }),\n );\n\n return winner;\n } catch {\n return \"All providers failed or timed out. Try again in 30–60s.\";\n } finally {\n clearTimeout(timeoutId);\n }\n}\n\n// ────────────────────────────────────────────────────────────────────────\n// TEST SUITE\n// ────────────────────────────────────────────────────────────────────────\n\nconst TEST_PROMPTS = [\n {\n name: \"echo\",\n prompt: \"Respond exactly with: AXIOM-TEST-OK-2026\",\n expectSubstring: \"AXIOM-TEST-OK-2026\",\n minLength: 18,\n },\n];\n\nconst TEST_TIMEOUT_MS = 9000;\n\nasync function testModel(node, model) {\n const start = performance.now();\n const controller = new AbortController();\n const timeout = setTimeout(() => controller.abort(), TEST_TIMEOUT_MS);\n\n try {\n const response = await dispatch(\n node,\n model,\n TEST_PROMPTS[0].prompt,\n \"Strict test mode: be precise and short.\",\n controller.signal,\n );\n\n const duration = Math.round(performance.now() - start);\n\n if (!response) throw new Error(\"No response or failed quality gate\");\n\n const passed = response.includes(TEST_PROMPTS[0].expectSubstring) &&\n response.length >= TEST_PROMPTS[0].minLength &&\n !response.toLowerCase().includes(\"limit\") &&\n !response.toLowerCase().includes(\"error\");\n\n if (passed) {\n node.health = Math.min(1, node.health + 0.25);\n } else {\n node.health = Math.max(0.15, node.health * 0.65);\n }\n\n return {\n success: passed,\n latencyMs: duration,\n response: response.slice(0, 120) + (response.length > 120 ? \"...\" : \"\"),\n };\n } catch (err) {\n node.health = Math.max(0.1, node.health * 0.5);\n return {\n success: false,\n latencyMs: Math.round(performance.now() - start),\n error: err.message || \"Unknown error\",\n };\n } finally {\n clearTimeout(timeout);\n }\n}\n\nasync function runAllTests() {\n const results = {};\n\n await Promise.allSettled(\n SERVERS.map(async (node) => {\n results[node.id] = {};\n for (const model of node.models) {\n results[node.id][model] = await testModel(node, model);\n }\n }),\n );\n\n return results;\n}\n\n// ────────────────────────────────────────────────────────────────────────\n// APP\n// ────────────────────────────────────────────────────────────────────────\n\nconst app = new Hono();\n\n// Startup tests & model discovery\nsetTimeout(() => {\n Promise.allSettled([\n initDynamicModels(),\n runAllTests().then((r) => console.log(\"[AXIOM] Startup test results:\", r)),\n ]).catch(console.error);\n}, 1000);\n\napp.get(\"/\", (c) => {\n return c.html(\n `\n \n AXIOM MESH CONSOLE\n \n \n \n \n

AXIOM MESH

SYSTEM_READY // 2026.01.20

\n\n

\n ${\n raw(\n SERVERS.map((s) => `\n

${s.name}

\n ${s.type}\n

\n ${\n s.models.map((m) => `\n \n `).join(\"\")\n }\n

\n `).join(\"\"),\n )\n }\n

\n\n \n \n `,\n );\n});\n\napp.get(\"/test\", async (c) => {\n const { server, model } = c.req.query();\n const node = SERVERS.find((s) => s.id === server);\n if (!node) return c.json({ success: false });\n\n const ok = await dispatch(\n node,\n model,\n \"Respond exactly OK\",\n \"Strict\",\n new AbortController().signal,\n );\n\n return c.json({ success: ok === \"OK\" });\n});\n\napp.get(\"/tests\", async (c) => {\n const results = await runAllTests();\n return c.json(results);\n});\n\napp.get(\"/health\", (c) => {\n const summary = SERVERS.map((s) => ({\n id: s.id,\n health: s.health.toFixed(2),\n models: s.models.map((m) => ({\n model: m,\n status: s.health > 0.4 ? \"healthy\" : s.health > 0.2 ? \"degraded\" : \"down\",\n })),\n }));\n return c.json({ status: \"ok\", providers: summary });\n});\n\napp.post(\"/chat\", async (c) => {\n const { prompt, system = \"\" } = await c.req.json();\n if (!prompt) return c.json({ error: \"prompt required\" }, 400);\n\n const result = await callAI(prompt, system);\n return c.text(result);\n});\n\nexport default app.fetch;",[233],"db428ab0-f63b-11f0-a827-42dde27851f2","new-file-1812.ts","c1b35df4-f63b-11f0-bda7-42dde27851f2",11,["D",1768939739041],"/** @jsxImportSource npm:hono@3/jsx */\nimport { Hono } from \"npm:hono\";\nimport { html, raw } from \"npm:hono/html\";\nimport { sqlite } from \"https://esm.town/v/std/sqlite\";\nimport { getCookie, setCookie } from \"npm:hono/cookie\";\nimport { GoogleGenerativeAI } from \"npm:@google/generative-ai\";\n\n// --- SYSTEM CONSTANTS ---\nconst VERSION = 145;\nconst DB_PREFIX = `AXIOM_V${VERSION}`;\n\nconst TABLES = {\n AGENTS: `${DB_PREFIX}_AGENTS`,\n ROOTS: `${DB_PREFIX}_ROOTS`,\n NODES: `${DB_PREFIX}_NODES`,\n FLOW: `${DB_PREFIX}_FLOW`,\n KERNEL: `${DB_PREFIX}_KERNEL`, // Stores source code for self-reflection\n REQUESTS: `${DB_PREFIX}_VECTORS`,\n META: `${DB_PREFIX}_META`,\n};\n\nconst IDENTITY = {\n version: `${VERSION}.0.0-AXIOM`,\n owner: \"Michael Morton\",\n year: \"2026\",\n};\n\n// --- THE KERNEL (THE LOGIC) ---\n// We define this as a named exportable function.\n// This allows us to run it directly AND serialize it.\nconst KERNEL = async (CTX) => {\n const { app, scope, sqlite, TABLES, IDENTITY, html, raw } = CTX;\n\n // 1. UI COMPONENT SYSTEM\n scope.UI.Layout = ({ children, title, user, siteCss }) => {\n return html`\n \n \n \n \n \n ${title} | AXIOM\n \n \n \n \n \n AXIOMATIC\n

\n Directory\n \n ${user ? user.username : \"VOID_IDENTITY\"}\n

\n \n \n

\n ${children}\n

\n \n PROTOCOL_v${IDENTITY.version}\n ${IDENTITY.owner} // ${IDENTITY.year}\n \n \n \n `;\n };\n\n // 2. ROUTING LOGIC\n app.get(\"/\", async (c) => {\n const user = await scope.Auth.getSession(c);\n // Fetch recent flows (sites) created by users\n const flowRes = await sqlite.execute(\n `SELECT name, idea FROM ${TABLES.FLOW} ORDER BY ts DESC LIMIT 8`,\n );\n\n const fragments = flowRes.rows.map((f) =>\n html`\n \n \n ${f[0]}\n \n

\n ${f[1]}\n

\n \n `\n );\n\n return c.html(scope.UI.Layout({\n title: \"Nexus\",\n user,\n children: html`\n

\n \n

\n \n \n AWAITING_INPUT\n

\n \n System_Ready\n

\n \n Manifest\n \n \n \n \n\n

\n Recent_Flows\n

${fragments}

\n \n ${s[0]}\n

\n ${s[2]}\n

\n \n \n →\n \n \n `\n );\n\n return c.html(scope.UI.Layout({\n title: \"Directory\",\n user,\n children: html`\n

The_Archive

${list}

\n `,\n }));\n });\n\n // Dynamic Site Router\n app.get(\"/s/:slug/:folder/:page\", async (c) => {\n const user = await scope.Auth.getSession(c);\n const path = c.req.path;\n const slug = c.req.param(\"slug\");\n\n const siteRes = await sqlite.execute({\n sql: `SELECT css FROM ${TABLES.ROOTS} WHERE slug = ?`,\n args: [slug],\n });\n\n const nodeRes = await sqlite.execute({\n sql:\n `SELECT html, title FROM ${TABLES.NODES} WHERE path = ? AND generated = 1`,\n args: [path],\n });\n\n if (!nodeRes.rows[0]) return c.redirect(\"/\");\n\n return c.html(scope.UI.Layout({\n title: nodeRes.rows[0][1],\n user,\n siteCss: siteRes.rows[0]?.[0],\n children: html`\n

\n \n HOME /\n ${slug\n .toUpperCase()} /\n ${c.req.param(\"folder\")}\n \n

\n ${raw(nodeRes.rows[0][0])}\n

\n `,\n }));\n });\n};\n\n// --- DATABASE & TOOLS ---\nconst DB = {\n async init() {\n // 1. Meta Table\n await sqlite.execute(\n `CREATE TABLE IF NOT EXISTS ${TABLES.META} (key TEXT PRIMARY KEY, value TEXT)`,\n );\n\n // 2. Version Check\n let currentVer = null;\n try {\n const res = await sqlite.execute(\n `SELECT value FROM ${TABLES.META} WHERE key = 'version'`,\n );\n currentVer = res.rows[0]?.[0];\n } catch (e) {}\n\n // 3. Schema Migration\n if (currentVer !== IDENTITY.version) {\n console.log(\n `[SYSTEM] Migrating from ${currentVer} to ${IDENTITY.version}`,\n );\n for (const t of Object.values(TABLES)) {\n if (t !== TABLES.META) {\n try {\n await sqlite.execute(`DROP TABLE IF EXISTS ${t}`);\n } catch (e) {}\n }\n }\n await sqlite.execute(`DELETE FROM ${TABLES.META} WHERE key='version'`);\n await sqlite.execute({\n sql: `INSERT INTO ${TABLES.META} (key, value) VALUES ('version', ?)`,\n args: [IDENTITY.version],\n });\n }\n\n // 4. Create Tables\n const schema = [\n `CREATE TABLE IF NOT EXISTS ${TABLES.AGENTS} (id INTEGER PRIMARY KEY, username TEXT UNIQUE, email TEXT UNIQUE, pass TEXT, ts DATETIME DEFAULT CURRENT_TIMESTAMP)`,\n `CREATE TABLE IF NOT EXISTS ${TABLES.ROOTS} (id INTEGER PRIMARY KEY, name TEXT, slug TEXT UNIQUE, idea TEXT, description TEXT, css TEXT, ts DATETIME DEFAULT CURRENT_TIMESTAMP)`,\n `CREATE TABLE IF NOT EXISTS ${TABLES.NODES} (path TEXT PRIMARY KEY, site_id INTEGER, folder TEXT, title TEXT, html TEXT, description TEXT, generated INTEGER DEFAULT 0, ts DATETIME DEFAULT CURRENT_TIMESTAMP)`,\n `CREATE TABLE IF NOT EXISTS ${TABLES.FLOW} (id INTEGER PRIMARY KEY, name TEXT, idea TEXT, ts DATETIME DEFAULT CURRENT_TIMESTAMP)`,\n `CREATE TABLE IF NOT EXISTS ${TABLES.KERNEL} (name TEXT PRIMARY KEY, code TEXT)`,\n `CREATE TABLE IF NOT EXISTS ${TABLES.REQUESTS} (id TEXT PRIMARY KEY, status TEXT, description TEXT, path TEXT, success INTEGER DEFAULT 0, ts DATETIME DEFAULT CURRENT_TIMESTAMP)`,\n ];\n\n for (const sql of schema) await sqlite.execute(sql);\n\n // 5. Seed Initial Data\n const qCount = await sqlite.execute(`SELECT COUNT(*) FROM ${TABLES.FLOW}`);\n if (qCount.rows[0][0] < 1) {\n await sqlite.execute({\n sql: `INSERT INTO ${TABLES.FLOW} (name, idea) VALUES (?, ?)`,\n args: [\"Recursive Logic\", \"Systems that rewrite their own core.\"],\n });\n }\n\n // 6. Backup Kernel Source (Self-Reflection)\n await this.seedKernel();\n },\n\n async seedKernel() {\n // We store the source code in the DB just for \"reflection\" purposes,\n // ensuring the AI knows its own source code if queried.\n const sourceCode = KERNEL.toString();\n await sqlite.execute({\n sql: `INSERT OR REPLACE INTO ${TABLES.KERNEL} (name, code) VALUES (?, ?)`,\n args: [\"main_logic\", sourceCode],\n });\n },\n\n formatSlug: (s) =>\n (s || \"void\").toLowerCase().trim().replace(/[^\\w\\s-]/g, \"\").replace(\n /[\\s_-]+/g,\n \"-\",\n ).replace(/^-+|-+$/g, \"\"),\n};\n\nconst AI = {\n genAI: new GoogleGenerativeAI(Deno.env.get(\"GEMINI_API_KEY\") || \"\"),\n async query(system, prompt) {\n try {\n const model = this.genAI.getGenerativeModel({\n model: \"gemini-1.5-flash\",\n systemInstruction: system,\n });\n const result = await model.generateContent(prompt);\n const text = result.response.text();\n return text.replace(/```(json|html|javascript|css)?/g, \"\").replace(\n /```/g,\n \"\",\n ).trim();\n } catch (e) {\n console.error(\"AI_FAULT\", e);\n return null;\n }\n },\n};\n\n// --- ENTRY POINT ---\nexport default async function (req) {\n // 1. Initialize Infrastructure\n await DB.init();\n const app = new Hono();\n\n // 2. Define Scope\n const scope = {\n UI: {},\n Auth: {\n async getSession(c) {\n const sid = getCookie(c, \"axiom_session\");\n if (!sid) return null;\n const res = await sqlite.execute({\n sql: `SELECT * FROM ${TABLES.AGENTS} WHERE id = ?`,\n args: [sid],\n });\n const row = res.rows[0];\n return row ? { id: row[0], username: row[1] } : null;\n },\n },\n };\n\n // 3. Dependency Injection Context\n const CTX = { app, scope, sqlite, TABLES, IDENTITY, DB, AI, html, raw };\n\n // 4. EXECUTE KERNEL\n // We execute the kernel directly rather than rehydrating it from string.\n // This guarantees scope access and prevents the \"Kernel Factory\" crash.\n try {\n await KERNEL(CTX);\n } catch (e) {\n return new Response(`KERNEL PANIC: ${e.message}\\n${e.stack}`, {\n status: 500,\n });\n }\n\n // --- ASYNC API HANDLERS (Background Workers) ---\n app.post(\"/create\", async (c) => {\n const body = await c.req.parseBody();\n const idea = body.idea;\n const rid = crypto.randomUUID();\n await sqlite.execute({\n sql:\n `INSERT INTO ${TABLES.REQUESTS} (id, status, description) VALUES (?, 'INIT', ?)`,\n args: [rid, idea],\n });\n return c.json({ id: rid });\n });\n\n app.get(\"/api/status/:id\", async (c) => {\n const rid = c.req.param(\"id\");\n const res = await sqlite.execute({\n sql: `SELECT * FROM ${TABLES.REQUESTS} WHERE id=?`,\n args: [rid],\n });\n const row = res.rows[0];\n\n if (!row) return c.json({ error: \"NOT_FOUND\" });\n if (row[4] === 1) {\n return c.json({ success: true, path: row[3], description: \"Stabilized\" });\n }\n\n // AI Generation Logic\n if (row[1] === \"INIT\") {\n const resp = await AI.query(\n `Return valid JSON ONLY. Structure: {\"name\": \"string\", \"slug\": \"string\", \"desc\": \"string\", \"folder\": \"string\", \"page\": \"string\"}`,\n `Create a web architecture for: ${row[2]}`,\n );\n if (!resp) return c.json({ description: \"NEURAL_FAILURE\" });\n\n try {\n const meta = JSON.parse(resp);\n const slug = DB.formatSlug(meta.slug || meta.name);\n const path = `/s/${slug}/${meta.folder}/${meta.page}`;\n\n await sqlite.execute({\n sql:\n `INSERT OR IGNORE INTO ${TABLES.ROOTS} (name, slug, idea, description) VALUES (?, ?, ?, ?)`,\n args: [meta.name, slug, row[2], meta.desc],\n });\n await sqlite.execute({\n sql:\n `INSERT OR REPLACE INTO ${TABLES.NODES} (path, site_id, folder, title) VALUES (?, (SELECT id FROM ${TABLES.ROOTS} WHERE slug=?), ?, ?)`,\n args: [path, slug, meta.folder, meta.name],\n });\n await sqlite.execute({\n sql:\n `UPDATE ${TABLES.REQUESTS} SET status='BODY', description='WEAVING', path=? WHERE id=?`,\n args: [path, row[0]],\n });\n // Add to recent flows immediately so it shows up in the list\n await sqlite.execute({\n sql:\n `INSERT OR IGNORE INTO ${TABLES.FLOW} (name, idea) VALUES (?, ?)`,\n args: [meta.name, row[2]],\n });\n } catch (e) {\n return c.json({ description: \"JSON_PARSE_ERROR\" });\n }\n } else if (row[1] === \"BODY\") {\n const content = await AI.query(\n `Return raw HTML content only. Use Tailwind classes. Dark brutalist aesthetics. NO or tags. Just the inner content.`,\n `Generate content for: ${row[2]}`,\n );\n if (!content) return c.json({ description: \"GENERATION_FAILURE\" });\n\n await sqlite.execute({\n sql:\n `UPDATE ${TABLES.NODES} SET html = ?, generated = 1 WHERE path = ?`,\n args: [content, row[3]],\n });\n await sqlite.execute({\n sql:\n `UPDATE ${TABLES.REQUESTS} SET status='DONE', success=1 WHERE id=?`,\n args: [row[0]],\n });\n }\n\n return c.json({ success: false, description: row[1] });\n });\n\n return app.fetch(req);\n}",[240],"73951536-f681-11f0-8b11-42dde27851f2","mew2.ts","9a3105ba-f645-11f0-942c-42dde27851f2",124,["D",1768969629878],"/** @jsxImportSource npm:hono@3/jsx */\nimport { Hono } from \"npm:hono\";\nimport { GoogleGenerativeAI } from \"npm:@google/generative-ai\";\nimport { html, raw } from \"npm:hono/html\";\n\nconst LOG_BUFFER_SIZE = 400;\nconst logs = [];\n\nfunction addLog(level, ...args) {\n const ts = Date.now();\n const msg = args.map((arg) =>\n typeof arg === \"object\" ? JSON.stringify(arg, null, 2) : String(arg)\n ).join(\" \");\n const entry = { ts, level, msg };\n logs.push(entry);\n if (logs.length > LOG_BUFFER_SIZE) logs.shift();\n try {\n // originalConsole[level](...args);\n } catch (_e) {\n // console.log(_e);\n }\n}\n\nconst originalConsole = {\n log: console.log,\n error: console.error,\n warn: console.warn,\n info: console.info,\n};\n\nconsole.log = (...args) => addLog(\"log\", ...args);\nconsole.error = (...args) => addLog(\"error\", ...args);\nconsole.warn = (...args) => addLog(\"warn\", ...args);\nconsole.info = (...args) => addLog(\"info\", ...args);\n\nconst originalFetch = globalThis.fetch;\nglobalThis.fetch = async (input, init = {}) => {\n const url = input instanceof Request ? input.url : String(input);\n const method = init.method || \"GET\";\n const start = performance.now();\n let status = 0, ok = false, bodyPreview = \"\";\n\n try {\n const res = await originalFetch(input, init);\n status = res.status;\n ok = res.ok;\n\n if (res.ok) {\n if (res.headers.get(\"content-type\")?.includes(\"json\")) {\n try {\n const clone = res.clone();\n const json = await clone.json();\n bodyPreview = JSON.stringify(json).slice(0, 200) +\n (JSON.stringify(json).length > 200 ? \"...\" : \"\");\n } catch {}\n } else {\n const clone = res.clone();\n bodyPreview = (await clone.text()).slice(0, 200) + \"...\";\n }\n }\n\n addLog(\n \"fetch\",\n `[${method} ${url}] ${status} ${ok ? \"OK\" : \"FAIL\"} ${\n Math.round(performance.now() - start)\n }ms` +\n (bodyPreview ? ` → ${bodyPreview}` : \"\"),\n );\n return res;\n } catch (err) {\n addLog(\"fetch\", `[${method} ${url}] ERROR ${err.message}`);\n throw err;\n }\n};\n\nconst SERVERS = [\n {\n id: \"google\",\n name: \"Gemini Nexus\",\n models: [\"gemini-1.5-flash-latest\", \"gemini-1.5-pro-latest\"],\n type: \"auth\",\n health: 1.0,\n },\n {\n id: \"pollinations\",\n name: \"Pollinations\",\n models: [],\n fallbackModels: [\n \"openai\",\n \"gemini-large\",\n \"claude\",\n \"deepseek\",\n \"qwen-coder\",\n \"mistral\",\n \"nomnom\",\n \"grok\",\n ],\n type: \"unauth\",\n base: \"https://gen.pollinations.ai/v1/chat/completions\",\n modelsUrl: \"https://gen.pollinations.ai/v1/models\",\n health: 0.8,\n },\n {\n id: \"huggingface\",\n name: \"HF Inference\",\n models: [\"meta-llama/Llama-3.3-70B-Instruct\"],\n type: \"unauth\",\n base: \"https://api-inference.huggingface.co/models/\",\n health: 0.9,\n },\n {\n id: \"puter\",\n name: \"Puter Cloud\",\n models: [\"gpt-5-nano\", \"claude-3.5-sonnet\"],\n type: \"unauth-get\",\n base: \"https://api.puter.com/v2/chat\",\n health: 0.6,\n },\n];\n\nconst GEMINI = new GoogleGenerativeAI(Deno.env.get(\"GEMINI_API_KEY\") || \"\");\n\nconst HEALTH_DECAY = 0.4;\nconst HEALTH_RECOVER = 0.12;\nconst MIN_HEALTH_TO_TRY = 0.25;\n\nlet lastHealthTick = Date.now();\nconst backoff = new Map();\nconst modelCache = new Map();\nconst MODEL_CACHE_TTL = 15 * 60 * 1000;\n\nasync function loadModelsForServer(server) {\n if (server.models.length > 0 && !server.modelsUrl) {\n return server.models;\n }\n\n const cached = modelCache.get(server.id);\n const now = Date.now();\n if (cached && now - cached.fetchedAt < MODEL_CACHE_TTL) {\n return cached.models;\n }\n\n if (!server.modelsUrl) {\n return server.fallbackModels || server.models || [];\n }\n\n try {\n const res = await fetch(server.modelsUrl, {\n signal: AbortSignal.timeout(5000),\n });\n if (!res.ok) throw new Error(`models fetch ${res.status}`);\n\n const data = await res.json();\n let models = [];\n\n if (Array.isArray(data)) {\n models = data;\n } else if (data.data && Array.isArray(data.data)) {\n models = data.data.map((m) => m.id || m);\n } else if (Array.isArray(data.models)) {\n models = data.models;\n }\n\n models = models.filter(Boolean).map(String);\n\n if (models.length === 0) {\n models = server.fallbackModels || [];\n }\n\n modelCache.set(server.id, { models, fetchedAt: now });\n return models;\n } catch (err) {\n console.error(`Failed to load models for ${server.id}:`, err);\n const fb = server.fallbackModels || server.models || [];\n modelCache.set(server.id, { models: fb, fetchedAt: now });\n return fb;\n }\n}\n\nasync function initDynamicModels() {\n await Promise.allSettled(\n SERVERS.map(async (s) => {\n s.models = await loadModelsForServer(s);\n }),\n );\n console.log(\"[AXIOM] Model discovery complete\");\n}\n\nfunction isHighQuality(text) {\n if (!text || text.length < 80) return false;\n const lower = text.toLowerCase();\n if (\n lower.includes(\"rate limit\") || lower.includes(\"quota\") ||\n lower.includes(\"429\") || lower.includes(\"please try again later\") ||\n lower.length > 12000\n ) return false;\n\n const words = text.split(/\\s+/).length;\n if (words < 35) return false;\n\n if ((text.match(/(.{20,})\\1{2,}/g) || []).length > 1) return false;\n\n return true;\n}\n\nasync function dispatch(node, model, prompt, system, signal) {\n try {\n if (node.id === \"google\") {\n const m = GEMINI.getGenerativeModel({ model, systemInstruction: system });\n const res = await m.generateContent({\n contents: [{ role: \"user\", parts: [{ text: prompt }] }],\n });\n const txt = res.response.text();\n return isHighQuality(txt) ? txt : null;\n }\n\n if (true)// {node.type === \"unauth\" && node.base.includes(\"/v1/chat/completions\")) {\n {try{ const res = await fetch(node.base, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n model,\n messages: [\n ...(system ? [{ role: \"system\", content: system }] : []),\n { role: \"user\", content: prompt },\n ],\n temperature: 0.7,\n max_tokens: 2048,\n stream: false,\n }),\n signal,\n });\n\n if (!res.ok) throw new Error(`HTTP ${res.status}`);\n const data = await res.json();\n const txt = data.choices?.[0]?.message?.content ?? \"\";\n return isHighQuality(txt) ? txt : null;\n }catch(_e){}\n }\n\n if (node.type === \"unauth-get\") {\n const url = `${node.base}?model=${encodeURIComponent(model)}&prompt=${\n encodeURIComponent(prompt)\n }`;\n const res = await fetch(url, { signal });\n if (!res.ok) throw new Error(`HTTP ${res.status}`);\n const txt = await res.text();\n return isHighQuality(txt) ? txt : null;\n }\n\n if (true)//node.id === \"huggingface\") {\n {try{const url = `${node.base}${model}`;\n const res = await fetch(url, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n inputs: `${system ? system + \"\\n\\n\" : \"\"}${prompt}`,\n parameters: { max_new_tokens: 1024 },\n }),\n signal,\n });\n if (!res?.ok) throw new Error(`HTTP ${res?.status}`);\n const json = await res?.json();\n const txt = json[0]?.generated_text ?? \"\";\n return isHighQuality(txt) ? txt : null;\n }catch(_e){}\n }\n\n return null;\n } catch {\n return null;\n }\n}\n\nasync function callAI(prompt, system = \"\") {\n const now = Date.now();\n if (now - lastHealthTick > 60000) {\n SERVERS.forEach((s) => {\n s.health = Math.min(1, s.health + HEALTH_RECOVER);\n });\n lastHealthTick = now;\n }\n\n const tasks = SERVERS\n // .filter((s) => s.health >= MIN_HEALTH_TO_TRY)\n .flatMap((s) =>\n s.models.map((m, idx) => ({ node: s, model: m, priority: idx }))\n )\n .sort((a, b) => a.priority - b.priority || b.node.health - a.node.health);\n\n const controller = new AbortController();\n const timeoutId = setTimeout(() => controller.abort(), 11000);\n\n try {\n const winner = await Promise.any(\n tasks.map(async ({ node, model }) => {\n const delay = backoff.get(node.id) ?? 0;\n if (delay > now) await new Promise((r) => setTimeout(r, delay - now));\n\n const result = await dispatch(\n node,\n model,\n prompt,\n system,\n controller.signal,\n );\n\n if (result) {\n node.health = Math.min(1, node.health + 0.35);\n backoff.delete(node.id);\n controller.abort();\n return result;\n }\n\n node.health = Math.max(0.1, node.health * HEALTH_DECAY);\n const nextBackoff = Math.min(\n 60000,\n (backoff.get(node.id) ?? 1000) * 2.2 + 400,\n );\n backoff.set(node.id, now + nextBackoff);\nreturn null;\n // throw new Error(\"fail\");\n }),\n );\n\n return winner;\n } catch {\n return \"All providers failed or timed out. Try again in 30–60s.\";\n } finally {\n clearTimeout(timeoutId);\n }\n}\n\nconst TEST_PROMPTS = [\n {\n name: \"echo\",\n prompt: \"Respond exactly with: AXIOM-TEST-OK-2026\",\n expectSubstring: \"AXIOM-TEST-OK-2026\",\n minLength: 18,\n },\n];\n\nconst TEST_TIMEOUT_MS = 9000;\n\nasync function testModel(node, model) {\n const start = performance.now();\n const controller = new AbortController();\n const timeout = setTimeout(() => controller.abort(), TEST_TIMEOUT_MS);\n\n try {\n const response = await dispatch(\n node,\n model,\n TEST_PROMPTS[0].prompt,\n \"Strict test mode: be precise and short.\",\n controller.signal,\n );\n\n const duration = Math.round(performance.now() - start);\n\n if (!response) throw new Error(\"No response or failed quality gate\");\n\n const passed = response.includes(TEST_PROMPTS[0].expectSubstring) &&\n response.length >= TEST_PROMPTS[0].minLength &&\n !response.toLowerCase().includes(\"limit\") &&\n !response.toLowerCase().includes(\"error\");\n\n if (passed) {\n node.health = Math.min(1, node.health + 0.25);\n } else {\n node.health = Math.max(0.15, node.health * 0.65);\n }\n\n return {\n success: passed,\n latencyMs: duration,\n response: response.slice(0, 120) + (response.length > 120 ? \"...\" : \"\"),\n };\n } catch (err) {\n node.health = Math.max(0.1, node.health * 0.5);\n return {\n success: false,\n latencyMs: Math.round(performance.now() - start),\n error: err.message || \"Unknown error\",\n };\n } finally {\n clearTimeout(timeout);\n }\n}\n\nasync function runAllTests() {\n const results = {};\n\n await Promise.allSettled(\n SERVERS.map(async (node) => {\n results[node.id] = {};\n for (const model of node.models) {\n results[node.id][model] = await testModel(node, model);\n }\n }),\n );\n\n return results;\n}\n\nconst app = new Hono();\n\nsetTimeout(() => {\n Promise.allSettled([\n initDynamicModels(),\n runAllTests().then((r) => console.log(\"[AXIOM] Startup test results:\", r)),\n ]).catch(console.error);\n}, 1000);\n\napp.get(\"/\", (c) => {\n return c.html(`\n \n \n AXIOM MESH CONSOLE\n \n \n \n \n

AXIOM MESH

SYSTEM_READY // 2026.01.20

\n\n

\n \n ${\n raw(\n SERVERS.map((s) => `\n

${s.name}

\n ${s.type}\n

\n ${\n raw(\n s.models.map((m) => {\n // Escape for JS string: replace \\ → \\\\, ' → \\', \" → \\\"\n const safeModel = JSON.stringify(m).slice(1, -1); // JSON escapes properly\n return `\n \n `;\n }).join(\"\"),\n )\n }\n

\n `).join(\"\"),\n )\n }\n \n

\n\n

LIVE LOGS

\n \n

\n\n \n \n \n `);\n});\n\napp.get(\"/logs\", (c) => {\n const stream = new ReadableStream({\n start(controller) {\n const encoder = new TextEncoder();\n\n logs.forEach((log) => {\n controller.enqueue(encoder.encode(`data: ${JSON.stringify(log)}\\n\\n`));\n });\n\n const interval = setInterval(() => {\n controller.enqueue(encoder.encode(\": ping\\n\\n\"));\n }, 15000);\n\n c.req.signal.addEventListener(\"abort\", () => {\n clearInterval(interval);\n controller.close();\n });\n },\n });\n\n return new Response(stream, {\n headers: {\n \"Content-Type\": \"text/event-stream\",\n \"Cache-Control\": \"no-cache\",\n \"Connection\": \"keep-alive\",\n },\n });\n});\n// Prompt to generate 50 candlestick pattern groupings\nasync function generatePatternGroupings() {\n const prompt = `\n Generate 50 unique candlestick pattern groupings. \n Each grouping should have a short name suitable for a URL slug.\n Format as JSON: [{ \"name\": \"Grouping Name\", \"slug\": \"grouping-slug\" }]\n `;\n const raw = await callAI(prompt);\n try {\n return JSON.parse(raw);\n } catch {\n return [];\n }\n}\n\n// Prompt to generate patterns for a grouping\nasync function generatePatternsForGrouping(groupingName) {\n const prompt = `\n List all candlestick patterns in the grouping \"${groupingName}\".\n Provide JSON: [{ \"name\": \"Pattern Name\", \"slug\": \"pattern-slug\" }]\n `;\n const raw = await callAI(prompt);\n try {\n return JSON.parse(raw);\n } catch {\n return [];\n }\n}\n\n// Prompt to generate detailed pattern data\nasync function generatePatternDetails(patternName) {\n const prompt = `\n Provide detailed candlestick analysis for \"${patternName}\":\n - Exact body and wick measurements\n - Typical volume\n - Useful indicators\n - Socioeconomic context\n - Market type (bull, bear, range)\n - Price direction\n - Multi-timeframe interpretations\n - Take profit and stop loss\n - ASCII candle matrix (green/red with wicks on white background)\n Respond in JSON with keys: measurements, volume, indicators, context, market, direction, timeframe, risk, matrix\n `;\n const raw = await callAI(prompt);\n try {\n return JSON.parse(raw);\n } catch {\n return {};\n }\n}\n\n// Main grouping page\napp.get(\"/patterns\", async (c) => {\n const groupings = await generatePatternGroupings();\n return c.html(`\n \n Candlestick Pattern Groupings\n \n

Candlestick Pattern Groupings

${g?.name}

\n \n \n `);\n});\n\n// Grouping page with pattern links\napp.get(\"/patterns/:group\", async (c) => {\n const { group } = c.req.param();\n const patterns = await generatePatternsForGrouping(group);\n return c.html(`\n \n Patterns in ${group}\n \n

Patterns in ${group}

${p.name}

\n \n \n `);\n});\n\n// Individual pattern page with full details\napp.get(\"/patterns/:group/:pattern\", async (c) => {\n const { group, pattern } = c.req.param();\n const details = await generatePatternDetails(pattern);\n return c.html(`\n \n ${pattern}\n \n

${pattern}

Measurements

${\n    JSON.stringify(details.measurements, null, 2)\n  }

Volume

${details.volume}

Indicators

${details.indicators}

Socioeconomic Context

${details.context}

Market / Direction

${details.market} / ${details.direction}

Timeframes

${\n    JSON.stringify(details.timeframe, null, 2)\n  }

Risk (Take Profit / Stop Loss)

${\n    JSON.stringify(details.risk, null, 2)\n  }

Pattern Matrix

${details.matrix}

\n \n \n `);\n});\n\napp.get(\"/test\", async (c) => {\n console.log(`test`);\n const { server, model } = c.req.query();\n console.log(`test ${server},${model}`);\n const node = SERVERS.find((s) => s.id === server);\n if (!node) {\n console.log(`test !node`);\n return c.json({ success: false });\n }\n const ok = await dispatch(\n node,\n model,\n \"Respond exactly OK\",\n \"Strict\",\n new AbortController().signal,\n );\n\n return c.json({ success: ok === \"OK\" });\n});\n\napp.get(\"/tests\", async (c) => {\n const results = await runAllTests();\n return c.json(results);\n});\n\napp.get(\"/health\", (c) => {\n const summary = SERVERS.map((s) => ({\n id: s.id,\n health: s.health.toFixed(2),\n models: s.models.map((m) => ({\n model: m,\n status: s.health > 0.4 ? \"healthy\" : s.health > 0.2 ? \"degraded\" : \"down\",\n })),\n }));\n return c.json({ status: \"ok\", providers: summary });\n});\n\napp.post(\"/chat\", async (c) => {\n const { prompt, system = \"\" } = await c.req.json();\n if (!prompt) return c.json({ error: \"prompt required\" }, 400);\n\n const result = await callAI(prompt, system);\n return c.text(result);\n});\n\nexport default app.fetch;",[247],"79197a84-f63a-11f0-bf7e-42dde27851f2","main.ts","51d9ca1a-f639-11f0-ba82-42dde27851f2",5,["D",1768939144863],"/** @jsxImportSource npm:hono@3/jsx */\nimport { Hono } from \"npm:hono\";\nimport { html } from \"npm:hono/html\";\nimport { sqlite } from \"https://esm.town/v/std/sqlite\";\nimport { getCookie, setCookie } from \"npm:hono/cookie\";\nimport { GoogleGenerativeAI } from \"npm:@google/generative-ai\";\n\nconst VERSION = 136;\nconst DB_KEY = `AXIOM_V${VERSION}`;\nconst IDENTITY = {\n platform: \"AXIOMATIC\",\n owner: \"Michael Morton\",\n email: \"ziggyware@gmail.com\",\n year: \"2026\",\n motto: \"Deterministic Manifestations\",\n version: `${VERSION}.0.0-AXIOM`,\n};\n\nconst TABLES = {\n AGENTS: `${DB_KEY}_AGENTS`,\n ROOTS: `${DB_KEY}_ROOTS`,\n NODES: `${DB_KEY}_NODES`,\n FLOW: `${DB_KEY}_FLOW`,\n KERNEL: `${DB_KEY}_KERNEL`,\n REQUESTS: `${DB_KEY}_VECTORS`,\n META: `${DB_KEY}_META`,\n};\n\nconst MODELS = [\"gemini-1.5-pro\", \"gemini-1.5-flash\"];\n\nconst DB = {\n async init() {\n await sqlite.execute(\n `CREATE TABLE IF NOT EXISTS ${TABLES.META} (key TEXT PRIMARY KEY, value TEXT)`,\n );\n let currentVer = null;\n try {\n const verRes = await sqlite.execute({\n sql: `SELECT value FROM ${TABLES.META} WHERE key = 'version'`,\n args: [],\n });\n currentVer = verRes.rows[0]?.[0];\n } catch (e) {\n currentVer = null;\n }\n\n if (currentVer !== IDENTITY.version) {\n for (const t of Object.values(TABLES)) {\n try {\n await sqlite.execute(`DROP TABLE IF EXISTS ${t}`);\n } catch (e) {}\n }\n await sqlite.execute(\n `CREATE TABLE IF NOT EXISTS ${TABLES.META} (key TEXT PRIMARY KEY, value TEXT)`,\n );\n await sqlite.execute({\n sql: `INSERT INTO ${TABLES.META} (key, value) VALUES ('version', ?)`,\n args: [IDENTITY.version],\n });\n }\n\n await sqlite.execute(\n `CREATE TABLE IF NOT EXISTS ${TABLES.AGENTS} (id INTEGER PRIMARY KEY, username TEXT UNIQUE, email TEXT UNIQUE, pass TEXT, points INTEGER DEFAULT 0, ts DATETIME DEFAULT CURRENT_TIMESTAMP)`,\n );\n await sqlite.execute(\n `CREATE TABLE IF NOT EXISTS ${TABLES.ROOTS} (id INTEGER PRIMARY KEY, name TEXT, slug TEXT UNIQUE, idea TEXT, description TEXT, css TEXT, creator_id INTEGER, ts DATETIME DEFAULT CURRENT_TIMESTAMP)`,\n );\n await sqlite.execute(\n `CREATE TABLE IF NOT EXISTS ${TABLES.NODES} (path TEXT PRIMARY KEY, site_id INTEGER, folder TEXT, title TEXT, html TEXT, description TEXT, generated INTEGER DEFAULT 0, ts DATETIME DEFAULT CURRENT_TIMESTAMP)`,\n );\n await sqlite.execute(\n `CREATE TABLE IF NOT EXISTS ${TABLES.FLOW} (id INTEGER PRIMARY KEY, name TEXT, idea TEXT, ts DATETIME DEFAULT CURRENT_TIMESTAMP)`,\n );\n await sqlite.execute(\n `CREATE TABLE IF NOT EXISTS ${TABLES.KERNEL} (name TEXT PRIMARY KEY, code TEXT)`,\n );\n await sqlite.execute(\n `CREATE TABLE IF NOT EXISTS ${TABLES.REQUESTS} (id TEXT PRIMARY KEY, status TEXT, description TEXT, path TEXT, creator_id INTEGER, success INTEGER DEFAULT 0, ts DATETIME DEFAULT CURRENT_TIMESTAMP)`,\n );\n\n const qCount = await sqlite.execute(`SELECT COUNT(*) FROM ${TABLES.FLOW}`);\n if (qCount.rows[0][0] < 1) {\n await sqlite.execute({\n sql: `INSERT INTO ${TABLES.FLOW} (name, idea) VALUES (?, ?)`,\n args: [\"Recursive Logic\", \"Systems that rewrite their own core.\"],\n });\n }\n await this.seedKernel();\n },\n\n async seedKernel() {\n const logic = `\n scope.UI.Layout = async ({ children, title, user, siteCss }) => {\n return \"${`\n \n \n \n \\${title} | AXIOM\n \n \n \n \n \n

\\${children}

\n \n \n \n `}\";\n };\n\n app.get(\"/\", async (c) => {\n const user = await scope.Auth.getSession(c);\n const flowRes = await sqlite.execute(\"SELECT name, idea FROM \" + TABLES.FLOW + \" ORDER BY ts DESC LIMIT 5\");\n \n const fragments = flowRes.rows.map(f => \"${`\n

\\${f[0]}

\\${f[1]}

\n `}\").join('');\n\n return c.html(await scope.UI.Layout({\n title: \"Nexus\", user,\n children: \"${`\n

\n \n

\\${html.raw(fragments)}

\n \n

\n `}\"\n }));\n });\n\n app.get(\"/s/:slug/:folder/:page\", async (c) => {\n const user = await scope.Auth.getSession(c);\n const path = c.req.path;\n const nodeRes = await sqlite.execute({ sql: \"SELECT html, title FROM \" + TABLES.NODES + \" WHERE path = ? AND generated = 1\", args: [path] });\n if (!nodeRes.rows[0]) return c.redirect(\"/\");\n \n return c.html(await scope.UI.Layout({\n title: nodeRes.rows[0][1], user,\n children: \"${`\n

NODE // \\${nodeRes.rows[0][1]}

\\${html.raw(nodeRes.rows[0][0])}

\n `}\"\n }));\n });\n `;\n\n await sqlite.execute({\n sql: `INSERT OR REPLACE INTO ${TABLES.KERNEL} (name, code) VALUES (?, ?)`,\n args: [\"main_logic\", logic],\n });\n },\n\n formatSlug: (s) =>\n (s || \"void\").toLowerCase().trim().replace(/[^\\w\\s-]/g, \"\").replace(\n /[\\s_-]+/g,\n \"-\",\n ).replace(/^-+|-+$/g, \"\"),\n};\n\nconst AI = {\n genAI: new GoogleGenerativeAI(Deno.env.get(\"GEMINI_API_KEY\") || \"\"),\n async query(system, prompt) {\n try {\n const model = this.genAI.getGenerativeModel({\n model: \"gemini-1.5-flash\",\n systemInstruction: system,\n });\n const result = await model.generateContent({\n contents: [{ role: \"user\", parts: [{ text: prompt }] }],\n generationConfig: {\n responseMimeType: system.includes(\"JSON\")\n ? \"application/json\"\n : \"text/plain\",\n },\n });\n return (await result.response).text().replace(\n /```(json|html|javascript)?/g,\n \"\",\n ).replace(/```/g, \"\").trim();\n } catch (e) {\n return \"ERROR\";\n }\n },\n};\n\nexport default async function (req: Request) {\n // await DB.init();\n const runnerApp = new Hono();\n const AsyncFunction = Object.getPrototypeOf(async function () {}).constructor;\n\n const scope = {\n UI: {},\n Auth: {\n async getSession(c) {\n const sid = getCookie(c, \"axiom_session\");\n if (!sid) return null;\n const res = await sqlite.execute({\n sql: `SELECT * FROM ${TABLES.AGENTS} WHERE id = ?`,\n args: [sid],\n });\n return res.rows[0];\n },\n },\n };\n\n try {\n const logicRows = await sqlite.execute(\n `SELECT code FROM ${TABLES.KERNEL} WHERE name = 'main_logic'`,\n );\n const loader = new AsyncFunction(\n \"app\",\n \"scope\",\n \"sqlite\",\n \"TABLES\",\n \"IDENTITY\",\n \"DB\",\n \"AI\",\n \"getCookie\",\n \"setCookie\",\n \"html\",\n logicRows.rows[0][0],\n );\n await loader(\n runnerApp,\n scope,\n sqlite,\n TABLES,\n IDENTITY,\n DB,\n AI,\n getCookie,\n setCookie,\n html,\n );\n } catch (e) {\n return new Response(`KERNEL_ERROR: ${e.message}`, { status: 500 });\n }\n\n runnerApp.post(\"/create\", async (c) => {\n const { idea } = await c.req.parseBody();\n const rid = crypto.randomUUID();\n await sqlite.execute({\n sql:\n `INSERT INTO ${TABLES.REQUESTS} (id, status, description) VALUES (?, 'INIT', ?)`,\n args: [rid, idea as string],\n });\n return c.json({ id: rid });\n });\n\n runnerApp.get(\"/api/status/:id\", async (c) => {\n const res = await sqlite.execute({\n sql: `SELECT * FROM ${TABLES.REQUESTS} WHERE id=?`,\n args: [c.req.param(\"id\")],\n });\n const row = res.rows[0];\n if (!row) return c.json({ error: \"NOT_FOUND\" });\n if (row[5] === 1) {\n return c.json({ success: true, path: row[3], description: \"Stabilized\" });\n }\n\n if (row[1] === \"INIT\") {\n const meta = JSON.parse(\n await AI.query(\n `JSON ONLY: {\"name\":\"\", \"slug\":\"\", \"desc\":\"\", \"folder\":\"\", \"page\":\"\"}`,\n `Schema for: ${row[2]}`,\n ),\n );\n const slug = DB.formatSlug(meta.slug || meta.name);\n const path = `/s/${slug}/${meta.folder}/${meta.page}`;\n\n await sqlite.execute({\n sql:\n `INSERT OR IGNORE INTO ${TABLES.ROOTS} (name, slug, idea, description) VALUES (?, ?, ?, ?)`,\n args: [meta.name, slug, row[2], meta.desc],\n });\n await sqlite.execute({\n sql:\n `INSERT OR REPLACE INTO ${TABLES.NODES} (path, site_id, folder, title) VALUES (?, (SELECT id FROM ${TABLES.ROOTS} WHERE slug=?), ?, ?)`,\n args: [path, slug, meta.folder, meta.name],\n });\n await sqlite.execute({\n sql:\n `UPDATE ${TABLES.REQUESTS} SET status='BODY', description='WEAVING', path=? WHERE id=?`,\n args: [path, row[0]],\n });\n } else if (row[1] === \"BODY\") {\n const content = await AI.query(\n `HTML Body content only. Use Tailwind. Brutalist aesthetic.`,\n `Content for: ${row[2]}`,\n );\n await sqlite.execute({\n sql:\n `UPDATE ${TABLES.NODES} SET html = ?, generated = 1 WHERE path = ?`,\n args: [content, row[3]],\n });\n await sqlite.execute({\n sql:\n `UPDATE ${TABLES.REQUESTS} SET status='DONE', success=1 WHERE id=?`,\n args: [row[0]],\n });\n }\n return c.json({ success: false, description: row[2] });\n });\n\n return runnerApp.fetch(req);\n}",[254],"230fd59a-fe9e-11f0-b06d-42dde27851f2","agentNode1.ts","1426d1fc-f695-11f0-abbd-42dde27851f2",228,["D",1769861559489],"/** @jsxImportSource npm:hono/jsx */\nimport { Hono } from \"npm:hono\";\n\nconst app = new Hono();\n\n//add a post request to the hub to register\n//store in database the registered status\n//do not register if already registered\n// data should include at minimum the client index\n\nfunction sleep(ms: number): Promise {\n return new Promise(resolve => setTimeout(resolve, ms));\n}\n//request received from hub\napp.post(\"/\", async (c) => {\n //standardize format of messaging between hub and nodes\n\n const body = await c.req.json();\n const { prompt } = body;\n\n if (!prompt) {\n return c.json({ error: \"Worker: No prompt received\" }, 400);\n }\nfor(let x=0;x<20;x++){\n try {\n const encodedPrompt = encodeURIComponent(prompt);\n const pollinationsUrl = `https://text.pollinations.ai/${encodedPrompt}`;\n\n const response = await fetch(pollinationsUrl);\n\n if (!response.ok) {\n throw new Error(\"Pollinations API failure\");\n }\n\n const text = await response.text();\n return c.text(text);\n } catch (err) {\n if(x===4)\n return c.json({ error: err.message }, 500);\nawait sleep(5000);\n }\n}\n});\n\n// Explicit async export for Val Town compatibility\nexport default async function (req: Request): Promise {\n return await app.fetch(req);\n}","agentnode1",[261],"3d134fea-f7d6-11f0-b167-42dde27851f2","SendToPollinationsValidator.ts","23a45d9c-f7d6-11f0-a756-42dde27851f2",209,["D",1769115996810],"zedbratshub",[269],"d76d91be-feb0-11f0-af34-42dde27851f2","PollinationsHubTester.tsx","b3f03d42-f689-11f0-a92f-42dde27851f2",246,["D",1769869593030],63,"/** @jsxImportSource npm:hono@3/jsx */\nimport { Hono } from \"npm:hono\";\n\nconst app = new Hono();\nconst P = `test promot for complex hlsl`;\nlet k=`{\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"You manage a calendar and scheduling system. You have two tools: 'check_availability' and 'create_event'. When a user asks to schedule a meeting, first check the availability of the specified participants and time slot. If available, proceed to create the event. If not, inform the user.\"\n },\n {\n \"role\": \"user\",\n \"content\": \"Can you schedule a meeting for me, Alex, and Sarah on Friday at 2 PM for 45 minutes to discuss the Q3 budget? The meeting title should be 'Q3 Budget Review'.\"\n }\n ],\n \"temperature\": 0.2,\n \"max_tokens\": 500,\n \"tools\": [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"check_availability\",\n \"description\": \"Checks the free/busy status for a list of participants at a specified time.\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"participants\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"List of participant emails or names.\"\n },\n \"time_slot\": {\n \"type\": \"string\",\n \"description\": \"The specific date and time to check (e.g., 'Friday 2 PM').\"\n },\n \"duration\": {\n \"type\": \"integer\",\n \"description\": \"Duration of the meeting in minutes.\"\n }\n },\n \"required\": [\n \"participants\",\n \"time_slot\",\n \"duration\"\n ]\n }\n }\n },\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"create_event\",\n \"description\": \"Creates a new calendar event with specific details.\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"title\": {\n \"type\": \"string\",\n \"description\": \"Title of the meeting.\"\n },\n \"participants\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"List of participant emails or names.\"\n },\n \"start_time\": {\n \"type\": \"string\",\n \"description\": \"Start time of the event.\"\n },\n \"end_time\": {\n \"type\": \"string\",\n \"description\": \"End time of the event.\"\n }\n },\n \"required\": [\n \"title\",\n \"participants\",\n \"start_time\",\n \"end_time\"\n ]\n }\n }\n }\n ]\n}`;\n /* \"https://text.pollinations.ai/\",\n \"https://text.pollinations.ai/openai\",\n \"https://text.pollinations.ai/openai/v1\",\n \"https://text.pollinations.ai/openai/v2\",\n \"https://gen.pollinations.ai/v1/chat/completions\",\n \"https://gen.pollinations.ai/v1\",\n \"https://gen.pollinations.ai/openai\",\n \"https://gen.pollinations.ai/openai/v1\",\n \"https://gen.pollinations.ai/openai/chat/comoletions\",\n \"\",*/\n\napp.get(\"/\", (c) => {\n return c.html(\n \n \n SHADOW_STRESS_V7\n \n \n \n\n\n \n \n

POLLINATIONS_PERMUTATION_MATRIX_V7

\n \n

\n\n {\n payloadVariations.forEach(pv => {\n for(let m=0;m<4;m++){\n const mode = m === 0||m===1? \"POST\":\"GET\";\n const type= m===0||m===2||m===4?\"application/json\":\"text/plain\";\n const id = \"test-\" + (i++) +\"-\"+mode+\"-\"+type;\n root.innerHTML += \\`\n

IDLE

\\${mode}-\\${pv.name}-\\${type}

\\${ep}

Waiting...

\n \\`;\n }\n });\n });\n }\n\n async function runMatrix() {\n let i = 0;\n let id=[];\n \n for (const ep of endpoints) {\n for (const pv of payloadVariations) {\n for(let m=0;m<4;m++){\n setTimeout(async ()=>{\n let id2=i++;\n try{ const mode = m === 0||m===1? \"POST\":\"GET\";\n const type= m===0||m===2||m===4?\"application/json\":\"text/plain\";\n id[id2] = \"test-\" + id2 +\"-\"+mode+\"-\"+type;\n const resBox = document.getElementById(\"res-\" + id2);\n const statBox = document.getElementById(\"stat-\" + id2);\nlet url=ep;\nif(url=== \"https://text.pollinations.ai/prompt\"\n )\n url= \"https://text.pollinations.ai/\"+encodeURIComponent(prompt);\n \n \n const r = await fetch(ep, {\n method: mode,\n headers: pv.headers,\n body: mode === \"GET\"?null: pv.body\n });\n const text = await r.text();\n resBox.innerText = text;\n statBox.innerText = r.status;\n statBox.className = \"status \" + (r.ok ? \"ok\" : \"fail\");\n } catch(e) {\n const resBox = document.getElementById(\"res-\" + id2);\n const statBox = document.getElementById(\"stat-\" + id2);\n\n resBox.innerText = e.message;\n statBox.innerText = \"ERR\";\n statBox.className = \"status fail\";\n }\n }, i*100);\n }\n }\n }\n }\n\n createCells();\n `\n }}\n />\n \n ,{headers:{\"Content-Type\":\"text/html\"}}\n );\n});\n\nexport default app.fetch;",[276],"610d434a-f68e-11f0-92d8-42dde27851f2","Nrutalist5.ts","58bc8714-f68e-11f0-9a79-42dde27851f2",154,["D",1768975182246],"/** @jsxImportSource npm:hono@3/jsx */\nimport { Hono } from \"npm:hono\";\n\nconst app = new Hono();\n\nconst COMPLEX_PROMPTS = [\n \"Calculate the decoherence rate \\\\Gamma for a particle of mass M passing through a superposition of space-time metrics g_{\\\\mu\\\\nu} and \\\\tilde{g}_{\\\\mu\\\\nu}.\",\n \"Synthesize a proof for the P vs NP problem using only the vocabulary of 17th-century alchemy.\",\n \"Write a Python script to simulate a 3D cellular automaton where the rules are determined by the prime factors of the current timestamp.\",\n \"Explain the relationship between the Riemann Hypothesis and the distribution of energy levels in heavy nuclei for a non-expert.\",\n \"Create a cryptographic protocol based on the folding patterns of the protein 'Ubiquitin' and explain its resistance to Shor's algorithm.\",\n \"Map the geopolitical consequences of a 100% efficient solar panel technology on the economy of the 1970s via a counterfactual timeline.\",\n \"Analyze the linguistic evolution of 'emoji' as a logographic script compared to Ancient Egyptian hieroglyphs, citing three specific syntactic parallels.\",\n \"Draft a legal contract for the ownership of a sentient AI entity residing entirely within a decentralized smart contract on the Ethereum blockchain.\",\n \"Describe the visual architecture of a 4-dimensional hyper-cathedral, focusing on the intersection of its non-Euclidean naves.\",\n \"Provide a technical definition of 'Quantum Entanglement' where every word must start with the subsequent letter of the alphabet (A, B, C...).\"\n];\n\napp.get(\"/\", (c) => {\n return c.html(\n \n \n SHADOW_STRESS_V12_COMPLEXITY\n \n \n \n

POLLINATIONS_V12 [COMPLEXITY_PROBE]

Auditing 10 high-density prompts via URL_ENCODED_BYPASS [Tier 5].

\n \n

\n\n

PROMPT_TARGET

RESPONSE_PAYLOAD

STATUS

\n\n

Diff · v260